VesselDetection_train.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Install YOLOv8 and Comet from pip"
      ],
      "metadata": {
        "id": "HL6CQAZzSyaQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "we use Comet to visialize the process and result of this project."
      ],
      "metadata": {
        "id": "T6xW9VEqZUoE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SAlh6w7K78-y",
        "outputId": "2a2563df-51e2-4e73-94b2-3f3dcb515d74"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: everett, wurlitzer, websocket-client, simplejson, sentry-sdk, semantic-version, python-box, dulwich, configobj, requests-toolbelt, comet_ml, ultralytics\n",
            "  Attempting uninstall: websocket-client\n",
            "    Found existing installation: websocket-client 1.6.3\n",
            "    Uninstalling websocket-client-1.6.3:\n",
            "      Successfully uninstalled websocket-client-1.6.3\n",
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            "    Found existing installation: python-box 7.1.1\n",
            "    Uninstalling python-box-7.1.1:\n",
            "      Successfully uninstalled python-box-7.1.1\n",
            "Successfully installed comet_ml-3.33.11 configobj-5.0.8 dulwich-0.21.6 everett-3.1.0 python-box-6.1.0 requests-toolbelt-1.0.0 semantic-version-2.10.0 sentry-sdk-1.31.0 simplejson-3.19.1 ultralytics-8.0.134 websocket-client-1.3.3 wurlitzer-3.0.3\n"
          ]
        }
      ],
      "source": [
        "!pip install ultralytics==8.0.134 comet_ml"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Initialize Comet with Credientials"
      ],
      "metadata": {
        "id": "cGlXgsxqTy8h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import comet_ml\n",
        "comet_ml.init(project_name='vesselDetection')"
      ],
      "metadata": {
        "id": "qTVmfBS38XR-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download some data from roboflow"
      ],
      "metadata": {
        "id": "0vr70Iuxu_0u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "install roboflow"
      ],
      "metadata": {
        "id": "9xr-xmB4WpeK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install roboflow"
      ],
      "metadata": {
        "id": "Q2oRoUb2Wo6x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "On the roboflow website you could find ways to export dataset. Here we are using the download code to get data from roboflow (replace with your own api key):"
      ],
      "metadata": {
        "id": "kLWF2SRMWvmu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from roboflow import Roboflow\n",
        "rf = Roboflow(api_key=\"your_apikey\")\n",
        "project = rf.workspace(\"cuize\").project(\"vesseldetection-dhmgv\")\n",
        "dataset = project.version(17).download(\"yolov8\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "i6GCR-HI92pX",
        "outputId": "dc10c439-bf6d-4a80-b055-73cf5cf54db8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "certifi",
                  "chardet",
                  "cycler",
                  "idna",
                  "pyparsing"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n",
            "Exporting format yolov8 in progress : 85.0%\n",
            "Version export complete for yolov8 format\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading Dataset Version Zip in vesselDetection-17 to yolov8:: 100%|██████████| 249911/249911 [00:03<00:00, 75563.55it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "Extracting Dataset Version Zip to vesselDetection-17 in yolov8:: 100%|██████████| 5291/5291 [00:00<00:00, 5661.70it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train YOLOv8 on the dowloaded dataset"
      ],
      "metadata": {
        "id": "YrSAafcmUYi6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!yolo task=detect \\\n",
        "mode=train \\\n",
        "model=yolov8m.pt \\\n",
        "data= /content/vesselDetection_LaJolla-3/data.yaml \\\n",
        "patience =20 \\\n",
        "imgsz=640"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4uL5kBrc8lzp",
        "outputId": "716ccc73-94ce-41af-bd96-4bc26c9a036c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "New https://pypi.org/project/ultralytics/8.0.190 available 😃 Update with 'pip install -U ultralytics'\n",
            "Ultralytics YOLOv8.0.134 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (NVIDIA A100-SXM4-40GB, 40514MiB)\n",
            "\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8m.pt, data=/content/vesselDetection_LaJolla-3/data.yaml, epochs=100, patience=20, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/train5\n",
            "Overriding model.yaml nc=80 with nc=1\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1      1392  ultralytics.nn.modules.conv.Conv             [3, 48, 3, 2]                 \n",
            "  1                  -1  1     41664  ultralytics.nn.modules.conv.Conv             [48, 96, 3, 2]                \n",
            "  2                  -1  2    111360  ultralytics.nn.modules.block.C2f             [96, 96, 2, True]             \n",
            "  3                  -1  1    166272  ultralytics.nn.modules.conv.Conv             [96, 192, 3, 2]               \n",
            "  4                  -1  4    813312  ultralytics.nn.modules.block.C2f             [192, 192, 4, True]           \n",
            "  5                  -1  1    664320  ultralytics.nn.modules.conv.Conv             [192, 384, 3, 2]              \n",
            "  6                  -1  4   3248640  ultralytics.nn.modules.block.C2f             [384, 384, 4, True]           \n",
            "  7                  -1  1   1991808  ultralytics.nn.modules.conv.Conv             [384, 576, 3, 2]              \n",
            "  8                  -1  2   3985920  ultralytics.nn.modules.block.C2f             [576, 576, 2, True]           \n",
            "  9                  -1  1    831168  ultralytics.nn.modules.block.SPPF            [576, 576, 5]                 \n",
            " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 12                  -1  2   1993728  ultralytics.nn.modules.block.C2f             [960, 384, 2]                 \n",
            " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 15                  -1  2    517632  ultralytics.nn.modules.block.C2f             [576, 192, 2]                 \n",
            " 16                  -1  1    332160  ultralytics.nn.modules.conv.Conv             [192, 192, 3, 2]              \n",
            " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 18                  -1  2   1846272  ultralytics.nn.modules.block.C2f             [576, 384, 2]                 \n",
            " 19                  -1  1   1327872  ultralytics.nn.modules.conv.Conv             [384, 384, 3, 2]              \n",
            " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 21                  -1  2   4207104  ultralytics.nn.modules.block.C2f             [960, 576, 2]                 \n",
            " 22        [15, 18, 21]  1   3776275  ultralytics.nn.modules.head.Detect           [1, [192, 384, 576]]          \n",
            "Model summary: 295 layers, 25856899 parameters, 25856883 gradients\n",
            "\n",
            "Transferred 469/475 items from pretrained weights\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m To get all data logged automatically, import comet_ml before the following modules: torch.\n",
            "\u001b[1;38;5;196mCOMET ERROR:\u001b[0m Failed to set run cmd args\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in '/content' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/tsuize/yolov8-on-1080/fcb763a877704f118766e723117134bd\u001b[0m\n",
            "\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train5', view at http://localhost:6006/\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/vesselDetection_LaJolla-3/train/labels.cache... 111 images, 22 backgrounds, 0 corrupt: 100%|██████████| 111/111 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/vesselDetection_LaJolla-3/valid/labels.cache... 38 images, 11 backgrounds, 0 corrupt: 100%|██████████| 38/38 [00:00<?, ?it/s]\n",
            "Plotting labels to runs/detect/train5/labels.jpg... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.002, momentum=0.9) with parameter groups 77 weight(decay=0.0), 84 weight(decay=0.0005), 83 bias(decay=0.0)\n",
            "Image sizes 640 train, 640 val\n",
            "Using 8 dataloader workers\n",
            "Logging results to \u001b[1mruns/detect/train5\u001b[0m\n",
            "Starting training for 100 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      1/100      6.49G      1.433      4.193     0.9461         41        640: 100%|██████████| 7/7 [00:02<00:00,  2.34it/s]\n",
            "/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
            "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  3.55it/s]\n",
            "                   all         38         30      0.166      0.267      0.096     0.0498\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      2/100      6.78G      1.439      2.671     0.9675         45        640: 100%|██████████| 7/7 [00:00<00:00,  7.28it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  9.65it/s]\n",
            "                   all         38         30     0.0196      0.667     0.0175    0.00939\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      3/100      6.98G       1.39      1.164     0.9682         26        640: 100%|██████████| 7/7 [00:00<00:00,  7.73it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  9.99it/s]\n",
            "                   all         38         30     0.0099        0.8    0.00911    0.00533\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      4/100      6.98G      1.375      1.034     0.9768         56        640: 100%|██████████| 7/7 [00:00<00:00,  8.30it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  9.24it/s]\n",
            "                   all         38         30      0.774        0.7      0.769      0.501\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      5/100      6.99G      1.338      1.097     0.9822         24        640: 100%|██████████| 7/7 [00:00<00:00,  7.86it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  9.90it/s]\n",
            "                   all         38         30     0.0347      0.733     0.0302     0.0159\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      6/100      6.98G      1.396       1.02      1.008         27        640: 100%|██████████| 7/7 [00:00<00:00,  8.35it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 10.69it/s]\n",
            "                   all         38         30    0.00289      0.367    0.00146   0.000742\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      7/100         7G      1.392     0.8325      1.032         30        640: 100%|██████████| 7/7 [00:00<00:00,  8.06it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 10.14it/s]\n",
            "                   all         38         30     0.0498      0.567     0.0361     0.0161\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      8/100      6.98G      1.585     0.9097      1.014         27        640: 100%|██████████| 7/7 [00:00<00:00,  8.42it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  9.78it/s]\n",
            "                   all         38         30   0.000838      0.267   0.000541   0.000273\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      9/100         7G      1.411     0.8173     0.9711         39        640: 100%|██████████| 7/7 [00:00<00:00,  8.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  9.87it/s]\n",
            "                   all         38         30   0.000838      0.267   0.000541   0.000273\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     10/100      6.98G      1.508      0.903      1.001         46        640: 100%|██████████| 7/7 [00:00<00:00,  8.64it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 11.79it/s]\n",
            "                   all         38         30   0.000838      0.267   0.000541   0.000273\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     11/100      6.99G      1.506     0.8441     0.9894         27        640: 100%|██████████| 7/7 [00:00<00:00,  8.56it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 11.02it/s]\n",
            "                   all         38         30   0.000838      0.267   0.000541   0.000273\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     12/100         7G      1.515      0.975      1.055         42        640: 100%|██████████| 7/7 [00:00<00:00,  8.41it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 11.24it/s]\n",
            "                   all         38         30   0.000193     0.0667   0.000111    1.7e-05\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     13/100      6.97G      1.372      1.008     0.9915         30        640: 100%|██████████| 7/7 [00:00<00:00,  8.96it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 10.56it/s]\n",
            "                   all         38         30   0.000193     0.0667   0.000111    1.7e-05\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     14/100      6.99G      1.579     0.9011      1.045         47        640: 100%|██████████| 7/7 [00:00<00:00,  8.72it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 11.55it/s]\n",
            "                   all         38         30   0.000412     0.0333   0.000173   8.67e-05\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     15/100      7.01G      1.459     0.9029      1.026         48        640: 100%|██████████| 7/7 [00:00<00:00,  8.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 11.64it/s]\n",
            "                   all         38         30   0.000627        0.1   0.000291   0.000172\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     16/100         7G      1.557     0.9435      1.044         50        640: 100%|██████████| 7/7 [00:00<00:00,  8.57it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 11.51it/s]\n",
            "                   all         38         30   8.77e-05     0.0333   5.35e-05   5.35e-06\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     17/100      6.99G      1.313     0.7253     0.9733         52        640: 100%|██████████| 7/7 [00:00<00:00,  8.70it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 10.60it/s]\n",
            "                   all         38         30   8.77e-05     0.0333   5.32e-05   5.32e-06\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     18/100         7G      1.557     0.9102       1.01         28        640: 100%|██████████| 7/7 [00:00<00:00,  8.46it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  9.38it/s]\n",
            "                   all         38         30   8.77e-05     0.0333   5.32e-05   5.32e-06\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     19/100         7G      1.614      0.913      1.042         63        640: 100%|██████████| 7/7 [00:00<00:00,  8.34it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  8.24it/s]\n",
            "                   all         38         30   8.77e-05     0.0333   5.32e-05   5.32e-06\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     20/100         7G      1.549     0.8502      1.029         46        640: 100%|██████████| 7/7 [00:00<00:00,  8.63it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 10.90it/s]\n",
            "                   all         38         30    0.00516      0.533    0.00367    0.00187\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     21/100      6.97G      1.469     0.8096     0.9793         32        640: 100%|██████████| 7/7 [00:00<00:00,  8.68it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 11.04it/s]\n",
            "                   all         38         30     0.0017     0.0667   0.000869   0.000603\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     22/100      6.99G      1.589     0.8697      1.034         29        640: 100%|██████████| 7/7 [00:00<00:00,  8.77it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 11.63it/s]\n",
            "                   all         38         30     0.0017     0.0667   0.000869   0.000603\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     23/100      7.01G      1.569      0.912      1.039         40        640: 100%|██████████| 7/7 [00:00<00:00,  8.62it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 10.23it/s]\n",
            "                   all         38         30     0.0207      0.467      0.013    0.00756\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     24/100      7.01G      1.499     0.7969     0.9966         37        640: 100%|██████████| 7/7 [00:00<00:00,  8.66it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  6.52it/s]\n",
            "                   all         38         30     0.0115        0.5    0.00799    0.00439\n",
            "Stopping training early as no improvement observed in last 20 epochs. Best results observed at epoch 4, best model saved as best.pt.\n",
            "To update EarlyStopping(patience=20) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.\n",
            "\n",
            "24 epochs completed in 0.018 hours.\n",
            "Optimizer stripped from runs/detect/train5/weights/last.pt, 52.0MB\n",
            "Optimizer stripped from runs/detect/train5/weights/best.pt, 52.0MB\n",
            "\n",
            "Validating runs/detect/train5/weights/best.pt...\n",
            "Ultralytics YOLOv8.0.134 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (NVIDIA A100-SXM4-40GB, 40514MiB)\n",
            "Model summary (fused): 218 layers, 25840339 parameters, 0 gradients\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  7.50it/s]\n",
            "                   all         38         30      0.777        0.7      0.772      0.502\n",
            "Speed: 1.0ms preprocess, 1.0ms inference, 0.0ms loss, 0.6ms postprocess per image\n",
            "Results saved to \u001b[1mruns/detect/train5\u001b[0m\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/tsuize/yolov8-on-1080/fcb763a877704f118766e723117134bd\u001b[0m\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr/pg0 [25]               : (0.00012000000000000002, 0.0017228)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr/pg1 [25]               : (0.00012000000000000002, 0.0017228)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr/pg2 [25]               : (0.00012000000000000002, 0.0017228)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP50(B) [50]     : (5e-05, 0.7722827088828825)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP50-95(B) [50]  : (1e-05, 0.5019272673976204)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision(B) [50] : (9e-05, 0.7766953461397905)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall(B) [50]    : (0.03333, 0.8)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model/GFLOPs              : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model/parameters          : 25856899\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model/speed_PyTorch(ms)   : 9.641\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [192]      : (1.21369, 1.86142)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [192]      : (0.68879, 4.41809)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/dfl_loss [192]      : (0.91471, 1.1769)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [48]         : (0.00641, 5.09349)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [48]         : (1.22378, inf)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/dfl_loss [48]         : (0.05417, 1743.1626)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_batch_logging_interval  : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     log_confusion_matrix_on_eval : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     log_image_predictions        : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     max_image_predictions        : 100\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     agnostic_nms    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     amp             : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     augment         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch           : 16\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box             : 7.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boxes           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cfg             : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     classes         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     close_mosaic    : 10\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls             : 0.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conf            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste      : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     data            : /content/vesselDetection_LaJolla-3/data.yaml\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees         : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     deterministic   : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device          : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dfl             : 1.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dnn             : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dropout         : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dynamic         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epochs          : 100\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr          : 0.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud          : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     format          : torchscript\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fraction        : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     half            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h           : 0.015\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s           : 0.7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v           : 0.4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz           : 640\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     int8            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou             : 0.7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     keras           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     kobj            : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     line_width      : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0             : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf             : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mask_ratio      : 4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     max_det         : 300\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup           : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mode            : train\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model           : yolov8m.pt\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum        : 0.937\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic          : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nbs             : 64\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nms             : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     opset           : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimize        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer       : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     overlap_mask    : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience        : 20\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective     : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     plots           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pose            : 12.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pretrained      : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     profile         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     retina_masks    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save            : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_conf       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_crop       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir        : runs/detect/train5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_hybrid     : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_json       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period     : -1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_txt        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale           : 0.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed            : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear           : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     show            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     show_conf       : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     show_labels     : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     simplify        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     source          : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     split           : val\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     task            : detect\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tracker         : botsort.yaml\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate       : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     v5loader        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val             : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     verbose         : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     vid_stride      : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     visualize       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr  : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs   : 3.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum : 0.8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay    : 0.0005\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers         : 8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workspace       : 4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix    : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     filename            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images              : 10\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages  : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model-element       : 1 (49.59 MB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     source_code         : 2 (12.96 KB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
            "\u001b[1;38;5;196mCOMET ERROR:\u001b[0m Error sending a notification, make sure you have opted-in for notifications\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m To get all data logged automatically, import comet_ml before the following modules: torch.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for metadata to finish uploading (timeout is 3600 seconds)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Uploading 1 metrics, params and output messages\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still uploading 1 file(s), remaining 26.34 MB/51.57 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!yolo task=detect \\\n",
        "mode=train \\\n",
        "model=yolov8m.pt \\\n",
        "data= /content/vesselDetection_LaJolla-4/data.yaml \\\n",
        "patience =20 \\\n",
        "imgsz=640"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uUGovU28Cy7U",
        "outputId": "0c8fe372-afdc-4d54-ab46-83a4f57f3e80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "New https://pypi.org/project/ultralytics/8.0.190 available 😃 Update with 'pip install -U ultralytics'\n",
            "Ultralytics YOLOv8.0.134 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (NVIDIA A100-SXM4-40GB, 40514MiB)\n",
            "\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8m.pt, data=/content/vesselDetection_LaJolla-4/data.yaml, epochs=100, patience=20, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/train6\n",
            "Overriding model.yaml nc=80 with nc=1\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1      1392  ultralytics.nn.modules.conv.Conv             [3, 48, 3, 2]                 \n",
            "  1                  -1  1     41664  ultralytics.nn.modules.conv.Conv             [48, 96, 3, 2]                \n",
            "  2                  -1  2    111360  ultralytics.nn.modules.block.C2f             [96, 96, 2, True]             \n",
            "  3                  -1  1    166272  ultralytics.nn.modules.conv.Conv             [96, 192, 3, 2]               \n",
            "  4                  -1  4    813312  ultralytics.nn.modules.block.C2f             [192, 192, 4, True]           \n",
            "  5                  -1  1    664320  ultralytics.nn.modules.conv.Conv             [192, 384, 3, 2]              \n",
            "  6                  -1  4   3248640  ultralytics.nn.modules.block.C2f             [384, 384, 4, True]           \n",
            "  7                  -1  1   1991808  ultralytics.nn.modules.conv.Conv             [384, 576, 3, 2]              \n",
            "  8                  -1  2   3985920  ultralytics.nn.modules.block.C2f             [576, 576, 2, True]           \n",
            "  9                  -1  1    831168  ultralytics.nn.modules.block.SPPF            [576, 576, 5]                 \n",
            " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 12                  -1  2   1993728  ultralytics.nn.modules.block.C2f             [960, 384, 2]                 \n",
            " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 15                  -1  2    517632  ultralytics.nn.modules.block.C2f             [576, 192, 2]                 \n",
            " 16                  -1  1    332160  ultralytics.nn.modules.conv.Conv             [192, 192, 3, 2]              \n",
            " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 18                  -1  2   1846272  ultralytics.nn.modules.block.C2f             [576, 384, 2]                 \n",
            " 19                  -1  1   1327872  ultralytics.nn.modules.conv.Conv             [384, 384, 3, 2]              \n",
            " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 21                  -1  2   4207104  ultralytics.nn.modules.block.C2f             [960, 576, 2]                 \n",
            " 22        [15, 18, 21]  1   3776275  ultralytics.nn.modules.head.Detect           [1, [192, 384, 576]]          \n",
            "Model summary: 295 layers, 25856899 parameters, 25856883 gradients\n",
            "\n",
            "Transferred 469/475 items from pretrained weights\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m To get all data logged automatically, import comet_ml before the following modules: torch.\n",
            "\u001b[1;38;5;196mCOMET ERROR:\u001b[0m Failed to set run cmd args\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in '/content' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/tsuize/yolov8-on-1080/cc760b53ca8a4b14bd77cc81753a7bc8\u001b[0m\n",
            "\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train6', view at http://localhost:6006/\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/vesselDetection_LaJolla-4/train/labels.cache... 111 images, 22 backgrounds, 0 corrupt: 100%|██████████| 111/111 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/vesselDetection_LaJolla-4/valid/labels.cache... 38 images, 11 backgrounds, 0 corrupt: 100%|██████████| 38/38 [00:00<?, ?it/s]\n",
            "Plotting labels to runs/detect/train6/labels.jpg... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.002, momentum=0.9) with parameter groups 77 weight(decay=0.0), 84 weight(decay=0.0005), 83 bias(decay=0.0)\n",
            "Image sizes 640 train, 640 val\n",
            "Using 8 dataloader workers\n",
            "Logging results to \u001b[1mruns/detect/train6\u001b[0m\n",
            "Starting training for 100 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      1/100      6.51G      1.569      4.277     0.9672         42        640: 100%|██████████| 7/7 [00:04<00:00,  1.63it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  2.51it/s]\n",
            "                   all         38         30      0.707      0.767      0.819      0.412\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      2/100      6.98G      1.403      2.287     0.9437         46        640: 100%|██████████| 7/7 [00:01<00:00,  6.83it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  7.65it/s]\n",
            "                   all         38         30     0.0211      0.733     0.0202    0.00928\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      3/100      6.99G      1.429      1.143     0.9455         26        640: 100%|██████████| 7/7 [00:00<00:00,  7.81it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  8.03it/s]\n",
            "                   all         38         30      0.586      0.709      0.662      0.369\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      4/100         7G      1.505      1.029      1.011         58        640: 100%|██████████| 7/7 [00:00<00:00,  7.64it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  8.52it/s]\n",
            "                   all         38         30      0.914      0.867      0.875      0.491\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      5/100      7.01G       1.38      1.003     0.9683         24        640: 100%|██████████| 7/7 [00:00<00:00,  8.04it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  9.79it/s]\n",
            "                   all         38         30      0.891        0.9      0.909      0.512\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      6/100      6.99G      1.358      1.049     0.9829         27        640: 100%|██████████| 7/7 [00:00<00:00,  8.30it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  9.07it/s]\n",
            "                   all         38         30      0.811       0.86      0.773      0.373\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      7/100      7.01G      1.398     0.8694     0.9808         30        640: 100%|██████████| 7/7 [00:00<00:00,  7.62it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 11.20it/s]\n",
            "                   all         38         30     0.0106      0.133    0.00395    0.00165\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      8/100      6.99G       1.52     0.8608      1.004         29        640: 100%|██████████| 7/7 [00:00<00:00,  8.54it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  8.49it/s]\n",
            "                   all         38         30    0.00812        0.2    0.00339    0.00159\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      9/100         7G      1.492     0.8699     0.9911         39        640: 100%|██████████| 7/7 [00:00<00:00,  8.07it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 10.33it/s]\n",
            "                   all         38         30    0.00158      0.333    0.00125   0.000473\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     10/100      6.98G      1.491     0.9065       1.02         39        640: 100%|██████████| 7/7 [00:00<00:00,  8.58it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  9.53it/s]\n",
            "                   all         38         30   0.000152     0.0333   4.82e-05    2.4e-05\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     11/100      7.01G      1.434      0.868     0.9626         28        640: 100%|██████████| 7/7 [00:00<00:00,  8.67it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  9.51it/s]\n",
            "                   all         38         30    0.00445      0.133    0.00146   0.000618\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     12/100      7.01G      1.549      1.076      1.077         43        640: 100%|██████████| 7/7 [00:00<00:00,  8.03it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  9.66it/s]\n",
            "                   all         38         30   0.000459      0.167    0.00028   0.000113\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     13/100      6.98G      1.489      1.573      1.009         30        640: 100%|██████████| 7/7 [00:00<00:00,  8.72it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 10.38it/s]\n",
            "                   all         38         30   0.000657        0.2   0.000397   0.000199\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     14/100      7.01G       1.67      1.102      1.074         47        640: 100%|██████████| 7/7 [00:00<00:00,  8.36it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  9.52it/s]\n",
            "                   all         38         30   0.000326        0.1    0.00016   7.18e-05\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     15/100         7G      1.693     0.9411      1.076         49        640: 100%|██████████| 7/7 [00:00<00:00,  8.46it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 10.66it/s]\n",
            "                   all         38         30     0.0391        0.2     0.0122    0.00659\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     16/100      7.01G      1.445     0.9204      1.015         41        640: 100%|██████████| 7/7 [00:00<00:00,  8.30it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 11.38it/s]\n",
            "                   all         38         30      0.908      0.233      0.247      0.102\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     17/100         7G      1.406     0.7864     0.9939         53        640: 100%|██████████| 7/7 [00:00<00:00,  8.72it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 11.72it/s]\n",
            "                   all         38         30      0.947     0.0333     0.0355     0.0248\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     18/100      6.99G      1.593     0.8324      1.004         29        640: 100%|██████████| 7/7 [00:00<00:00,  8.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 11.91it/s]\n",
            "                   all         38         30      0.947     0.0333     0.0355     0.0248\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     19/100      7.02G      1.642     0.9157      1.031         51        640: 100%|██████████| 7/7 [00:00<00:00,  8.41it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 11.30it/s]\n",
            "                   all         38         30    0.00148     0.0333   0.000402   0.000214\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     20/100      6.99G      1.591     0.8276       1.03         46        640: 100%|██████████| 7/7 [00:00<00:00,  8.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 10.40it/s]\n",
            "                   all         38         30   0.000386        0.1   0.000161   3.52e-05\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     21/100         7G      1.569     0.8594      1.001         32        640: 100%|██████████| 7/7 [00:00<00:00,  8.72it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 10.91it/s]\n",
            "                   all         38         30      0.561        0.3      0.253     0.0673\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     22/100      7.01G      1.505     0.8808      1.019         29        640: 100%|██████████| 7/7 [00:00<00:00,  8.63it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 11.56it/s]\n",
            "                   all         38         30      0.941        0.1     0.0978     0.0559\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     23/100      7.03G      1.517      0.821      1.031         55        640: 100%|██████████| 7/7 [00:00<00:00,  8.28it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 11.75it/s]\n",
            "                   all         38         30       0.92        0.4      0.482      0.269\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     24/100      7.02G      1.494      0.793      1.009         23        640: 100%|██████████| 7/7 [00:00<00:00,  8.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00, 10.28it/s]\n",
            "                   all         38         30      0.798      0.567      0.559      0.303\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     25/100      6.99G      1.537     0.8182      1.011         25        640: 100%|██████████| 7/7 [00:00<00:00,  8.75it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  7.16it/s]\n",
            "                   all         38         30      0.897      0.467       0.52      0.233\n",
            "Stopping training early as no improvement observed in last 20 epochs. Best results observed at epoch 5, best model saved as best.pt.\n",
            "To update EarlyStopping(patience=20) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.\n",
            "\n",
            "25 epochs completed in 0.019 hours.\n",
            "Optimizer stripped from runs/detect/train6/weights/last.pt, 52.0MB\n",
            "Optimizer stripped from runs/detect/train6/weights/best.pt, 52.0MB\n",
            "\n",
            "Validating runs/detect/train6/weights/best.pt...\n",
            "Ultralytics YOLOv8.0.134 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (NVIDIA A100-SXM4-40GB, 40514MiB)\n",
            "Model summary (fused): 218 layers, 25840339 parameters, 0 gradients\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  7.42it/s]\n",
            "                   all         38         30      0.891        0.9      0.911      0.515\n",
            "Speed: 1.0ms preprocess, 1.0ms inference, 0.0ms loss, 0.6ms postprocess per image\n",
            "Results saved to \u001b[1mruns/detect/train6\u001b[0m\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/tsuize/yolov8-on-1080/cc760b53ca8a4b14bd77cc81753a7bc8\u001b[0m\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr/pg0 [26]               : (0.00012000000000000002, 0.0017228)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr/pg1 [26]               : (0.00012000000000000002, 0.0017228)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr/pg2 [26]               : (0.00012000000000000002, 0.0017228)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP50(B) [52]     : (5e-05, 0.9110418260353794)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP50-95(B) [52]  : (2e-05, 0.515190810019128)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision(B) [52] : (0.00015, 0.94694)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall(B) [52]    : (0.03333, 0.9)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model/GFLOPs              : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model/parameters          : 25856899\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model/speed_PyTorch(ms)   : 15.085\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [200]      : (1.16212, 1.97381)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [200]      : (0.71566, 4.37943)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/dfl_loss [200]      : (0.88295, 1.2039)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [50]         : (0.52935, 5.12136)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [50]         : (0.80605, inf)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/dfl_loss [50]         : (0.59167, 15.18568)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_batch_logging_interval  : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     log_confusion_matrix_on_eval : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     log_image_predictions        : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     max_image_predictions        : 100\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     agnostic_nms    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     amp             : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     augment         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch           : 16\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box             : 7.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boxes           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cfg             : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     classes         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     close_mosaic    : 10\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls             : 0.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conf            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste      : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     data            : /content/vesselDetection_LaJolla-4/data.yaml\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees         : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     deterministic   : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device          : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dfl             : 1.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dnn             : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dropout         : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dynamic         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epochs          : 100\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr          : 0.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud          : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     format          : torchscript\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fraction        : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     half            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h           : 0.015\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s           : 0.7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v           : 0.4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz           : 640\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     int8            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou             : 0.7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     keras           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     kobj            : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     line_width      : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0             : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf             : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mask_ratio      : 4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     max_det         : 300\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup           : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mode            : train\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model           : yolov8m.pt\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum        : 0.937\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic          : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nbs             : 64\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nms             : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     opset           : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimize        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer       : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     overlap_mask    : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience        : 20\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective     : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     plots           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pose            : 12.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pretrained      : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     profile         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     retina_masks    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save            : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_conf       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_crop       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir        : runs/detect/train6\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_hybrid     : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_json       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period     : -1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_txt        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale           : 0.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed            : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear           : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     show            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     show_conf       : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     show_labels     : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     simplify        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     source          : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     split           : val\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     task            : detect\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tracker         : botsort.yaml\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate       : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     v5loader        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val             : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     verbose         : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     vid_stride      : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     visualize       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr  : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs   : 3.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum : 0.8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay    : 0.0005\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers         : 8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workspace       : 4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix    : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     filename            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images              : 10\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages  : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model-element       : 1 (49.59 MB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     source_code         : 2 (12.96 KB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
            "\u001b[1;38;5;196mCOMET ERROR:\u001b[0m Error sending a notification, make sure you have opted-in for notifications\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m To get all data logged automatically, import comet_ml before the following modules: torch.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for metadata to finish uploading (timeout is 3600 seconds)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Uploading 1 metrics, params and output messages\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still uploading 1 file(s), remaining 34.09 MB/51.54 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install roboflow\n",
        "\n",
        "from roboflow import Roboflow\n",
        "rf = Roboflow(api_key=\"9FKfFVyybJD9PRo1D1DI\")\n",
        "project = rf.workspace(\"cuize\").project(\"vesseldetection-dhmgv\")\n",
        "dataset = project.version(15).download(\"yolov8\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WLTztGK2AI3Z",
        "outputId": "e9957611-e967-4f09-de35-917c7f21d8b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: roboflow in /usr/local/lib/python3.10/dist-packages (1.1.7)\n",
            "Requirement already satisfied: certifi==2022.12.7 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2022.12.7)\n",
            "Requirement already satisfied: chardet==4.0.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.0.0)\n",
            "Requirement already satisfied: cycler==0.10.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (0.10.0)\n",
            "Requirement already satisfied: idna==2.10 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.10)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.4.5)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from roboflow) (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.23.5)\n",
            "Requirement already satisfied: opencv-python-headless==4.8.0.74 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.8.0.74)\n",
            "Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from roboflow) (9.4.0)\n",
            "Requirement already satisfied: pyparsing==2.4.7 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.4.7)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.8.2)\n",
            "Requirement already satisfied: python-dotenv in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.0.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.31.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.16.0)\n",
            "Requirement already satisfied: supervision in /usr/local/lib/python3.10/dist-packages (from roboflow) (0.14.0)\n",
            "Requirement already satisfied: urllib3>=1.26.6 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.0.4)\n",
            "Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.66.1)\n",
            "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (6.0.1)\n",
            "Requirement already satisfied: requests-toolbelt in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.0.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (1.1.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (4.42.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (23.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->roboflow) (3.2.0)\n",
            "Requirement already satisfied: scipy<2.0.0,>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from supervision->roboflow) (1.11.2)\n",
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n",
            "Exporting format yolov8 in progress : 85.0%\n",
            "Version export complete for yolov8 format\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading Dataset Version Zip in vesselDetection-15 to yolov8:: 100%|██████████| 105812/105812 [00:05<00:00, 18973.79it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "Extracting Dataset Version Zip to vesselDetection-15 in yolov8:: 100%|██████████| 5294/5294 [00:00<00:00, 8048.07it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!yolo task=detect \\\n",
        "mode=train \\\n",
        "model=yolov8m.pt \\\n",
        "data= /content/vesselDetection-17/data.yaml \\\n",
        "patience =20 \\\n",
        "imgsz=640"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OQu7_3N3MB59",
        "outputId": "1cf6aad3-e76b-4972-f691-de0b3398f773"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "New https://pypi.org/project/ultralytics/8.0.190 available 😃 Update with 'pip install -U ultralytics'\n",
            "Ultralytics YOLOv8.0.134 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (NVIDIA A100-SXM4-40GB, 40514MiB)\n",
            "\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8m.pt, data=/content/vesselDetection-15/data.yaml, epochs=100, patience=20, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/train7\n",
            "Overriding model.yaml nc=80 with nc=1\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1      1392  ultralytics.nn.modules.conv.Conv             [3, 48, 3, 2]                 \n",
            "  1                  -1  1     41664  ultralytics.nn.modules.conv.Conv             [48, 96, 3, 2]                \n",
            "  2                  -1  2    111360  ultralytics.nn.modules.block.C2f             [96, 96, 2, True]             \n",
            "  3                  -1  1    166272  ultralytics.nn.modules.conv.Conv             [96, 192, 3, 2]               \n",
            "  4                  -1  4    813312  ultralytics.nn.modules.block.C2f             [192, 192, 4, True]           \n",
            "  5                  -1  1    664320  ultralytics.nn.modules.conv.Conv             [192, 384, 3, 2]              \n",
            "  6                  -1  4   3248640  ultralytics.nn.modules.block.C2f             [384, 384, 4, True]           \n",
            "  7                  -1  1   1991808  ultralytics.nn.modules.conv.Conv             [384, 576, 3, 2]              \n",
            "  8                  -1  2   3985920  ultralytics.nn.modules.block.C2f             [576, 576, 2, True]           \n",
            "  9                  -1  1    831168  ultralytics.nn.modules.block.SPPF            [576, 576, 5]                 \n",
            " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 12                  -1  2   1993728  ultralytics.nn.modules.block.C2f             [960, 384, 2]                 \n",
            " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 15                  -1  2    517632  ultralytics.nn.modules.block.C2f             [576, 192, 2]                 \n",
            " 16                  -1  1    332160  ultralytics.nn.modules.conv.Conv             [192, 192, 3, 2]              \n",
            " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 18                  -1  2   1846272  ultralytics.nn.modules.block.C2f             [576, 384, 2]                 \n",
            " 19                  -1  1   1327872  ultralytics.nn.modules.conv.Conv             [384, 384, 3, 2]              \n",
            " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 21                  -1  2   4207104  ultralytics.nn.modules.block.C2f             [960, 576, 2]                 \n",
            " 22        [15, 18, 21]  1   3776275  ultralytics.nn.modules.head.Detect           [1, [192, 384, 576]]          \n",
            "Model summary: 295 layers, 25856899 parameters, 25856883 gradients\n",
            "\n",
            "Transferred 469/475 items from pretrained weights\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m To get all data logged automatically, import comet_ml before the following modules: torch.\n",
            "\u001b[1;38;5;196mCOMET ERROR:\u001b[0m Failed to set run cmd args\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in '/content' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/tsuize/yolov8-on-1080/692049b449644a81b82641e4526dd6b9\u001b[0m\n",
            "\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train7', view at http://localhost:6006/\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/vesselDetection-15/train/labels... 1580 images, 339 backgrounds, 0 corrupt: 100%|██████████| 1580/1580 [00:01<00:00, 1559.82it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/vesselDetection-15/train/labels.cache\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/vesselDetection-15/valid/labels... 530 images, 192 backgrounds, 0 corrupt: 100%|██████████| 530/530 [00:00<00:00, 1535.21it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/vesselDetection-15/valid/labels.cache\n",
            "Plotting labels to runs/detect/train7/labels.jpg... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.002, momentum=0.9) with parameter groups 77 weight(decay=0.0), 84 weight(decay=0.0005), 83 bias(decay=0.0)\n",
            "Image sizes 640 train, 640 val\n",
            "Using 8 dataloader workers\n",
            "Logging results to \u001b[1mruns/detect/train7\u001b[0m\n",
            "Starting training for 100 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      1/100      6.52G      1.184      1.372      1.142         25        640: 100%|██████████| 99/99 [00:14<00:00,  6.95it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.27it/s]\n",
            "                   all        530        387      0.018      0.326     0.0102    0.00579\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      2/100      7.02G       1.36       1.17      1.263         27        640: 100%|██████████| 99/99 [00:11<00:00,  8.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.60it/s]\n",
            "                   all        530        387      0.016      0.729     0.0143    0.00808\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      3/100      7.02G      1.367       1.14      1.255         24        640: 100%|██████████| 99/99 [00:11<00:00,  8.67it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.55it/s]\n",
            "                   all        530        387      0.366      0.408      0.339      0.208\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      4/100      7.01G      1.333      1.084      1.227         22        640: 100%|██████████| 99/99 [00:11<00:00,  8.77it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.90it/s]\n",
            "                   all        530        387      0.508      0.318      0.295      0.165\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      5/100      7.03G       1.31      1.033      1.217         29        640: 100%|██████████| 99/99 [00:11<00:00,  8.77it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.31it/s]\n",
            "                   all        530        387      0.147      0.204      0.093     0.0422\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      6/100         7G      1.243     0.9223      1.183         24        640: 100%|██████████| 99/99 [00:11<00:00,  8.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.58it/s]\n",
            "                   all        530        387      0.846      0.697      0.802      0.555\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      7/100      7.01G      1.212     0.9072      1.171         22        640: 100%|██████████| 99/99 [00:11<00:00,  8.75it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.80it/s]\n",
            "                   all        530        387      0.853       0.57      0.703      0.438\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      8/100         7G       1.17     0.8317      1.147         13        640: 100%|██████████| 99/99 [00:11<00:00,  8.81it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.52it/s]\n",
            "                   all        530        387      0.887      0.809      0.869      0.616\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      9/100      7.03G      1.144     0.7779      1.126         17        640: 100%|██████████| 99/99 [00:11<00:00,  8.87it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.68it/s]\n",
            "                   all        530        387      0.903      0.794      0.852      0.606\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     10/100         7G      1.106     0.7514      1.095         14        640: 100%|██████████| 99/99 [00:11<00:00,  8.81it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.51it/s]\n",
            "                   all        530        387      0.927      0.852      0.905      0.665\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     11/100      7.01G      1.122     0.7793      1.112         16        640: 100%|██████████| 99/99 [00:11<00:00,  8.81it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.59it/s]\n",
            "                   all        530        387      0.954       0.84      0.923      0.658\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     12/100      7.01G      1.079     0.7276      1.088         23        640: 100%|██████████| 99/99 [00:11<00:00,  8.82it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.67it/s]\n",
            "                   all        530        387      0.879      0.801      0.849      0.617\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     13/100      7.03G      1.094     0.7512      1.105         23        640: 100%|██████████| 99/99 [00:11<00:00,  8.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.92it/s]\n",
            "                   all        530        387      0.827      0.715      0.764      0.541\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     14/100         7G      1.013     0.6941      1.066         13        640: 100%|██████████| 99/99 [00:11<00:00,  8.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.67it/s]\n",
            "                   all        530        387      0.936      0.848      0.921      0.677\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     15/100      7.01G      1.046     0.7047      1.084         24        640: 100%|██████████| 99/99 [00:11<00:00,  8.81it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.64it/s]\n",
            "                   all        530        387      0.901      0.863      0.922      0.679\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     16/100      7.01G      1.043     0.6973      1.091         23        640: 100%|██████████| 99/99 [00:11<00:00,  8.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.79it/s]\n",
            "                   all        530        387      0.929      0.778       0.84      0.642\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     17/100      7.01G       1.02     0.6656      1.062         22        640: 100%|██████████| 99/99 [00:11<00:00,  8.77it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.78it/s]\n",
            "                   all        530        387      0.956      0.897      0.926      0.719\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     18/100         7G     0.9906     0.6347      1.063         25        640: 100%|██████████| 99/99 [00:11<00:00,  8.83it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.76it/s]\n",
            "                   all        530        387      0.922      0.819      0.899      0.639\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     19/100         7G      0.984     0.6311       1.05         22        640: 100%|██████████| 99/99 [00:11<00:00,  8.81it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.72it/s]\n",
            "                   all        530        387      0.933      0.837      0.911      0.704\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     20/100      7.01G     0.9561     0.6176      1.043         18        640: 100%|██████████| 99/99 [00:11<00:00,  8.83it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.54it/s]\n",
            "                   all        530        387      0.949      0.884      0.929      0.712\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     21/100      7.02G     0.9736     0.6326      1.043         18        640: 100%|██████████| 99/99 [00:11<00:00,  8.75it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.78it/s]\n",
            "                   all        530        387      0.946      0.819      0.908      0.709\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     22/100         7G     0.9508     0.6166      1.036         29        640: 100%|██████████| 99/99 [00:11<00:00,  8.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.48it/s]\n",
            "                   all        530        387      0.954      0.853      0.915      0.716\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     23/100      7.02G     0.9645      0.615      1.033         20        640: 100%|██████████| 99/99 [00:11<00:00,  8.75it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.88it/s]\n",
            "                   all        530        387      0.944      0.876      0.918      0.698\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     24/100      7.03G     0.9365     0.5921      1.025         20        640: 100%|██████████| 99/99 [00:11<00:00,  8.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.63it/s]\n",
            "                   all        530        387      0.942      0.845      0.911      0.697\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     25/100      7.01G     0.9262     0.6017      1.028         20        640: 100%|██████████| 99/99 [00:11<00:00,  8.76it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.86it/s]\n",
            "                   all        530        387      0.919      0.874      0.915      0.699\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     26/100      6.99G     0.9106      0.583       1.01         16        640: 100%|██████████| 99/99 [00:11<00:00,  8.77it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.71it/s]\n",
            "                   all        530        387      0.961      0.902      0.951       0.74\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     27/100      7.01G     0.9236     0.5861      1.026         28        640: 100%|██████████| 99/99 [00:11<00:00,  8.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.61it/s]\n",
            "                   all        530        387      0.943      0.884      0.934      0.718\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     28/100      7.02G     0.9079     0.5749      1.006         24        640: 100%|██████████| 99/99 [00:11<00:00,  8.85it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.59it/s]\n",
            "                   all        530        387      0.878      0.859      0.902      0.687\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     29/100      7.01G     0.8946     0.5626      1.018         21        640: 100%|██████████| 99/99 [00:11<00:00,  8.83it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.91it/s]\n",
            "                   all        530        387      0.966      0.876      0.933       0.74\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     30/100         7G     0.8731     0.5441      1.002         24        640: 100%|██████████| 99/99 [00:11<00:00,  8.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.67it/s]\n",
            "                   all        530        387      0.967      0.886      0.945      0.736\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     31/100      7.02G     0.8981     0.5475      1.017         24        640: 100%|██████████| 99/99 [00:11<00:00,  8.81it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.62it/s]\n",
            "                   all        530        387      0.923      0.858      0.899      0.726\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     32/100      7.01G     0.8713     0.5344     0.9971         20        640: 100%|██████████| 99/99 [00:11<00:00,  8.84it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.52it/s]\n",
            "                   all        530        387       0.95      0.897      0.937      0.728\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     33/100      7.02G     0.8753     0.5464      1.011         25        640: 100%|██████████| 99/99 [00:11<00:00,  8.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.37it/s]\n",
            "                   all        530        387      0.941      0.871      0.939      0.753\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     34/100      6.99G     0.8844     0.5483      1.004         22        640: 100%|██████████| 99/99 [00:11<00:00,  8.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.83it/s]\n",
            "                   all        530        387      0.952       0.88      0.932       0.74\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     35/100      7.01G      0.879     0.5467      1.011         16        640: 100%|██████████| 99/99 [00:11<00:00,  8.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.90it/s]\n",
            "                   all        530        387      0.954      0.905      0.944      0.746\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     36/100      7.03G     0.8762     0.5362      1.002         17        640: 100%|██████████| 99/99 [00:11<00:00,  8.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.83it/s]\n",
            "                   all        530        387      0.977      0.863      0.948      0.754\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     37/100      7.02G     0.8495     0.5278     0.9981         16        640: 100%|██████████| 99/99 [00:11<00:00,  8.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.97it/s]\n",
            "                   all        530        387      0.969      0.898      0.938      0.735\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     38/100         7G     0.8519     0.5175     0.9963         19        640: 100%|██████████| 99/99 [00:11<00:00,  8.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.93it/s]\n",
            "                   all        530        387      0.932      0.882      0.936      0.727\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     39/100      6.99G     0.8486     0.5202     0.9924         17        640: 100%|██████████| 99/99 [00:11<00:00,  8.77it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.65it/s]\n",
            "                   all        530        387      0.975      0.891      0.935      0.748\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     40/100      7.02G     0.8283     0.5165     0.9847         22        640: 100%|██████████| 99/99 [00:11<00:00,  8.81it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.08it/s]\n",
            "                   all        530        387      0.966      0.892      0.942      0.746\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     41/100      7.04G     0.8153     0.5055     0.9822         22        640: 100%|██████████| 99/99 [00:11<00:00,  8.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.72it/s]\n",
            "                   all        530        387      0.964      0.895      0.953      0.753\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     42/100      6.99G     0.8236     0.5024     0.9833         19        640: 100%|██████████| 99/99 [00:11<00:00,  8.73it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.80it/s]\n",
            "                   all        530        387      0.961      0.889       0.94      0.759\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     43/100      7.01G      0.823     0.5054     0.9824         16        640: 100%|██████████| 99/99 [00:11<00:00,  8.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.96it/s]\n",
            "                   all        530        387      0.977      0.886      0.941      0.745\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     44/100      7.01G     0.8142     0.5086     0.9767         15        640: 100%|██████████| 99/99 [00:11<00:00,  8.76it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.94it/s]\n",
            "                   all        530        387      0.955      0.885      0.951      0.753\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     45/100      7.02G      0.801     0.4865     0.9752         21        640: 100%|██████████| 99/99 [00:11<00:00,  8.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.80it/s]\n",
            "                   all        530        387      0.956      0.892      0.941      0.737\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     46/100      7.01G     0.8112     0.4784     0.9713         28        640: 100%|██████████| 99/99 [00:11<00:00,  8.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.02it/s]\n",
            "                   all        530        387      0.962      0.879      0.937      0.762\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     47/100         7G     0.7906     0.4916     0.9651         15        640: 100%|██████████| 99/99 [00:11<00:00,  8.73it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.87it/s]\n",
            "                   all        530        387      0.959      0.901      0.949      0.759\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     48/100         7G     0.7726     0.4817      0.969         14        640: 100%|██████████| 99/99 [00:11<00:00,  8.77it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.80it/s]\n",
            "                   all        530        387      0.969      0.881       0.94      0.738\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     49/100      7.02G     0.8029     0.4835     0.9733         17        640: 100%|██████████| 99/99 [00:11<00:00,  8.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.82it/s]\n",
            "                   all        530        387      0.939      0.907       0.95      0.755\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     50/100         7G     0.7888     0.4701     0.9633         20        640: 100%|██████████| 99/99 [00:11<00:00,  8.83it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.96it/s]\n",
            "                   all        530        387       0.98      0.891      0.944      0.725\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     51/100      7.01G     0.7602     0.4536     0.9559         23        640: 100%|██████████| 99/99 [00:11<00:00,  8.72it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.99it/s]\n",
            "                   all        530        387      0.975      0.886      0.939      0.731\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     52/100      7.01G     0.7559      0.451     0.9585         15        640: 100%|██████████| 99/99 [00:11<00:00,  8.75it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.69it/s]\n",
            "                   all        530        387      0.959      0.899      0.937      0.751\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     53/100      7.01G     0.7765      0.472     0.9655         24        640: 100%|██████████| 99/99 [00:11<00:00,  8.76it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.88it/s]\n",
            "                   all        530        387      0.953      0.886      0.949      0.748\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     54/100         7G     0.7642     0.4692     0.9609         23        640: 100%|██████████| 99/99 [00:11<00:00,  8.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.60it/s]\n",
            "                   all        530        387      0.961      0.886      0.953      0.777\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     55/100      7.01G     0.7432     0.4502     0.9558         19        640: 100%|██████████| 99/99 [00:11<00:00,  8.82it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.15it/s]\n",
            "                   all        530        387       0.95      0.899      0.945      0.738\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     56/100      7.02G     0.7616     0.4565     0.9635         19        640: 100%|██████████| 99/99 [00:11<00:00,  8.74it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.92it/s]\n",
            "                   all        530        387      0.961      0.888       0.95      0.756\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     57/100      7.02G     0.7482     0.4555     0.9534         27        640: 100%|██████████| 99/99 [00:11<00:00,  8.76it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.82it/s]\n",
            "                   all        530        387      0.978      0.899      0.951      0.773\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     58/100      6.99G      0.749     0.4466     0.9571         21        640: 100%|██████████| 99/99 [00:11<00:00,  8.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.14it/s]\n",
            "                   all        530        387      0.961      0.897      0.944      0.746\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     59/100         7G     0.7325     0.4415     0.9481         21        640: 100%|██████████| 99/99 [00:11<00:00,  8.76it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.78it/s]\n",
            "                   all        530        387      0.916      0.899      0.941      0.741\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     60/100      7.02G     0.7352     0.4464     0.9483         22        640: 100%|██████████| 99/99 [00:11<00:00,  8.64it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.01it/s]\n",
            "                   all        530        387      0.943      0.907      0.943      0.757\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     61/100      7.01G     0.7293     0.4381     0.9526         14        640: 100%|██████████| 99/99 [00:11<00:00,  8.76it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.02it/s]\n",
            "                   all        530        387      0.958      0.881      0.944      0.759\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     62/100      6.99G     0.7224     0.4299     0.9425         21        640: 100%|██████████| 99/99 [00:11<00:00,  8.77it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.06it/s]\n",
            "                   all        530        387      0.956      0.908      0.951       0.78\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     63/100      7.01G      0.739     0.4387     0.9477         15        640: 100%|██████████| 99/99 [00:11<00:00,  8.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.22it/s]\n",
            "                   all        530        387      0.955      0.902      0.957      0.754\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     64/100      7.01G     0.7186     0.4239      0.942         23        640: 100%|██████████| 99/99 [00:11<00:00,  8.81it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.13it/s]\n",
            "                   all        530        387      0.934      0.907      0.948      0.771\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     65/100      7.02G     0.7079     0.4076     0.9378         21        640: 100%|██████████| 99/99 [00:11<00:00,  8.75it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.10it/s]\n",
            "                   all        530        387      0.937      0.922      0.953      0.751\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     66/100      6.99G     0.7165     0.4147     0.9404         19        640: 100%|██████████| 99/99 [00:11<00:00,  8.81it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.03it/s]\n",
            "                   all        530        387      0.963      0.915      0.953      0.771\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     67/100         7G      0.713     0.4159     0.9412         27        640: 100%|██████████| 99/99 [00:11<00:00,  8.81it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.82it/s]\n",
            "                   all        530        387      0.961      0.903      0.948      0.773\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     68/100      7.01G     0.6967     0.4072     0.9371         26        640: 100%|██████████| 99/99 [00:11<00:00,  8.72it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.67it/s]\n",
            "                   all        530        387       0.94      0.904       0.95      0.743\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     69/100         7G     0.7005     0.4073     0.9426         14        640: 100%|██████████| 99/99 [00:11<00:00,  8.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.82it/s]\n",
            "                   all        530        387      0.943      0.906      0.939      0.753\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     70/100      6.99G     0.6794     0.3956     0.9295         32        640: 100%|██████████| 99/99 [00:11<00:00,  8.77it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.96it/s]\n",
            "                   all        530        387      0.966      0.902      0.946      0.768\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     71/100      7.01G     0.6795     0.4092     0.9304         21        640: 100%|██████████| 99/99 [00:11<00:00,  8.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.08it/s]\n",
            "                   all        530        387      0.962      0.902      0.947      0.775\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     72/100      7.01G     0.6702     0.3972       0.92         15        640: 100%|██████████| 99/99 [00:11<00:00,  8.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.00it/s]\n",
            "                   all        530        387      0.954      0.907      0.948      0.759\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     73/100      7.02G     0.6755     0.4037     0.9247         18        640: 100%|██████████| 99/99 [00:11<00:00,  8.86it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.89it/s]\n",
            "                   all        530        387      0.972      0.891      0.946      0.744\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     74/100      6.99G     0.6694     0.3982     0.9299         15        640: 100%|██████████| 99/99 [00:11<00:00,  8.84it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.04it/s]\n",
            "                   all        530        387      0.966      0.904      0.951      0.766\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     75/100      7.01G     0.6853     0.3977     0.9254         29        640: 100%|██████████| 99/99 [00:11<00:00,  8.86it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.08it/s]\n",
            "                   all        530        387       0.95      0.902      0.944      0.752\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     76/100         7G      0.662     0.3885     0.9198         24        640: 100%|██████████| 99/99 [00:11<00:00,  8.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.81it/s]\n",
            "                   all        530        387       0.94      0.895      0.943      0.745\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     77/100      7.02G     0.6744      0.394     0.9225         20        640: 100%|██████████| 99/99 [00:11<00:00,  8.77it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.90it/s]\n",
            "                   all        530        387      0.942      0.889      0.944      0.763\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     78/100         7G     0.6363      0.379     0.9144         22        640: 100%|██████████| 99/99 [00:11<00:00,  8.85it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.86it/s]\n",
            "                   all        530        387       0.97      0.899      0.943      0.762\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     79/100         7G     0.6534     0.3746     0.9117         17        640: 100%|██████████| 99/99 [00:11<00:00,  8.86it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.94it/s]\n",
            "                   all        530        387      0.974      0.886      0.942      0.746\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     80/100         7G     0.6436     0.3792     0.9219         15        640: 100%|██████████| 99/99 [00:11<00:00,  8.85it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.91it/s]\n",
            "                   all        530        387      0.954       0.91      0.952      0.774\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     81/100      7.01G      0.626     0.3744     0.9132         19        640: 100%|██████████| 99/99 [00:11<00:00,  8.84it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.07it/s]\n",
            "                   all        530        387      0.935      0.923      0.954      0.774\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     82/100         7G     0.6348     0.3749       0.92         23        640: 100%|██████████| 99/99 [00:11<00:00,  8.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:04<00:00,  4.09it/s]\n",
            "                   all        530        387      0.967      0.901       0.95      0.783\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     83/100      7.01G     0.6271     0.3677     0.9182         21        640: 100%|██████████| 99/99 [00:11<00:00,  8.82it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.92it/s]\n",
            "                   all        530        387      0.957      0.891      0.952      0.756\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     84/100      7.04G     0.6373     0.3758      0.913         19        640: 100%|██████████| 99/99 [00:11<00:00,  8.74it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.11it/s]\n",
            "                   all        530        387      0.955      0.894      0.942      0.751\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     85/100      7.02G     0.6031     0.3601     0.9012         23        640: 100%|██████████| 99/99 [00:11<00:00,  8.75it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.07it/s]\n",
            "                   all        530        387      0.974      0.894      0.953      0.761\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     86/100         7G     0.6159     0.3611     0.9045         15        640: 100%|██████████| 99/99 [00:11<00:00,  8.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.22it/s]\n",
            "                   all        530        387      0.958      0.899      0.949      0.757\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     87/100         7G     0.6104      0.362     0.9042         18        640: 100%|██████████| 99/99 [00:11<00:00,  8.82it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.12it/s]\n",
            "                   all        530        387      0.927      0.919      0.954      0.774\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     88/100      7.01G     0.6104     0.3605     0.9089         21        640: 100%|██████████| 99/99 [00:11<00:00,  8.76it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.16it/s]\n",
            "                   all        530        387       0.96      0.897      0.947      0.759\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     89/100      7.02G     0.5854     0.3536     0.8985         23        640: 100%|██████████| 99/99 [00:11<00:00,  8.83it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.15it/s]\n",
            "                   all        530        387      0.959      0.891      0.955      0.774\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     90/100      6.99G     0.5956     0.3566     0.8986         15        640: 100%|██████████| 99/99 [00:11<00:00,  8.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.15it/s]\n",
            "                   all        530        387      0.958       0.89      0.955       0.77\n",
            "Closing dataloader mosaic\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     91/100         7G     0.5652     0.3054     0.8559          9        640: 100%|██████████| 99/99 [00:12<00:00,  7.90it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  6.91it/s]\n",
            "                   all        530        387      0.963      0.897      0.955       0.77\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     92/100      7.01G     0.5671        0.3     0.8627          7        640: 100%|██████████| 99/99 [00:11<00:00,  8.73it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.00it/s]\n",
            "                   all        530        387      0.952      0.907      0.956      0.789\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     93/100      7.03G     0.5528     0.2925     0.8584         10        640: 100%|██████████| 99/99 [00:11<00:00,  8.82it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.21it/s]\n",
            "                   all        530        387      0.956      0.892      0.953       0.77\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     94/100         7G     0.5435     0.2874     0.8583         10        640: 100%|██████████| 99/99 [00:11<00:00,  8.86it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.19it/s]\n",
            "                   all        530        387      0.961       0.91      0.956      0.787\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     95/100      7.02G     0.5455     0.2897     0.8586          9        640: 100%|██████████| 99/99 [00:11<00:00,  8.83it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.21it/s]\n",
            "                   all        530        387      0.944      0.909      0.953      0.778\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     96/100      7.02G      0.541     0.2843     0.8527          9        640: 100%|██████████| 99/99 [00:11<00:00,  8.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.19it/s]\n",
            "                   all        530        387      0.953        0.9      0.951      0.779\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     97/100      7.03G      0.526     0.2766     0.8531         11        640: 100%|██████████| 99/99 [00:11<00:00,  8.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.08it/s]\n",
            "                   all        530        387      0.954      0.907      0.952      0.783\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     98/100      6.99G     0.5262     0.2758     0.8556          9        640: 100%|██████████| 99/99 [00:11<00:00,  8.87it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.12it/s]\n",
            "                   all        530        387      0.949      0.907      0.951      0.782\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     99/100      7.02G     0.5156      0.276     0.8443         10        640: 100%|██████████| 99/99 [00:11<00:00,  8.85it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:02<00:00,  7.12it/s]\n",
            "                   all        530        387      0.947       0.91      0.952       0.78\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    100/100      7.02G     0.5119     0.2728     0.8496          9        640: 100%|██████████| 99/99 [00:11<00:00,  8.81it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:04<00:00,  4.18it/s]\n",
            "                   all        530        387      0.944       0.91      0.954      0.784\n",
            "\n",
            "100 epochs completed in 0.413 hours.\n",
            "Optimizer stripped from runs/detect/train7/weights/last.pt, 52.0MB\n",
            "Optimizer stripped from runs/detect/train7/weights/best.pt, 52.0MB\n",
            "\n",
            "Validating runs/detect/train7/weights/best.pt...\n",
            "Ultralytics YOLOv8.0.134 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (NVIDIA A100-SXM4-40GB, 40514MiB)\n",
            "Model summary (fused): 218 layers, 25840339 parameters, 0 gradients\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 17/17 [00:03<00:00,  4.38it/s]\n",
            "                   all        530        387      0.952      0.907      0.956      0.788\n",
            "Speed: 0.3ms preprocess, 0.9ms inference, 0.0ms loss, 0.5ms postprocess per image\n",
            "Results saved to \u001b[1mruns/detect/train7\u001b[0m\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/tsuize/yolov8-on-1080/692049b449644a81b82641e4526dd6b9\u001b[0m\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr/pg0 [101]               : (5.9600000000000046e-05, 0.001953799326599327)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr/pg1 [101]               : (5.9600000000000046e-05, 0.001953799326599327)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr/pg2 [101]               : (5.9600000000000046e-05, 0.001953799326599327)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP50(B) [202]     : (0.01016, 0.95735)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP50-95(B) [202]  : (0.00579, 0.78863)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision(B) [202] : (0.01603, 0.98037)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall(B) [202]    : (0.20413, 0.92285)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model/GFLOPs               : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model/parameters           : 25856899\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model/speed_PyTorch(ms)    : 1.448\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [10000]     : (0.29943, 1.43077)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [10000]     : (0.21165, 4.32532)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/dfl_loss [10000]     : (0.8146, 1.31475)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [200]         : (0.68408, 2.21229)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [200]         : (0.37936, 130.34537)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/dfl_loss [200]         : (0.95558, 2.89014)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_batch_logging_interval  : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     log_confusion_matrix_on_eval : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     log_image_predictions        : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     max_image_predictions        : 100\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     agnostic_nms    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     amp             : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     augment         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch           : 16\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box             : 7.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boxes           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cfg             : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     classes         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     close_mosaic    : 10\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls             : 0.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conf            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste      : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     data            : /content/vesselDetection-15/data.yaml\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees         : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     deterministic   : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device          : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dfl             : 1.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dnn             : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dropout         : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dynamic         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epochs          : 100\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr          : 0.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud          : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     format          : torchscript\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fraction        : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     half            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h           : 0.015\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s           : 0.7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v           : 0.4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz           : 640\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     int8            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou             : 0.7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     keras           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     kobj            : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     line_width      : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0             : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf             : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mask_ratio      : 4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     max_det         : 300\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup           : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mode            : train\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model           : yolov8m.pt\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum        : 0.937\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic          : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nbs             : 64\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nms             : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     opset           : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimize        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer       : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     overlap_mask    : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience        : 20\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective     : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     plots           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pose            : 12.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pretrained      : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     profile         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     retina_masks    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save            : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_conf       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_crop       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir        : runs/detect/train7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_hybrid     : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_json       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period     : -1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_txt        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale           : 0.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed            : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear           : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     show            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     show_conf       : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     show_labels     : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     simplify        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     source          : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     split           : val\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     task            : detect\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tracker         : botsort.yaml\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate       : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     v5loader        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val             : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     verbose         : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     vid_stride      : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     visualize       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr  : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs   : 3.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum : 0.8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay    : 0.0005\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers         : 8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workspace       : 4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix    : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     filename            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images              : 10\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages  : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model-element       : 1 (49.59 MB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     source_code         : 2 (12.96 KB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m To get all data logged automatically, import comet_ml before the following modules: torch.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for metadata to finish uploading (timeout is 3600 seconds)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Uploading 1 metrics, params and output messages\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still uploading 1 file(s), remaining 13.96 MB/51.31 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!yolo task=detect \\\n",
        "mode=train \\\n",
        "model=yolov8m.pt \\\n",
        "data= /content/vesselDetection-17/data.yaml \\\n",
        "epochs = 100 \\\n",
        "patience =20 \\\n",
        "imgsz=640"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ezpjMrSrMmpB",
        "outputId": "ccd0ac2e-9de3-4ecc-afc3-be63ddfe3968"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "New https://pypi.org/project/ultralytics/8.0.192 available 😃 Update with 'pip install -U ultralytics'\n",
            "Ultralytics YOLOv8.0.134 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (NVIDIA A100-SXM4-40GB, 40514MiB)\n",
            "\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8m.pt, data=/content/vesselDetection-17/data.yaml, epochs=100, patience=20, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/train2\n",
            "Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
            "100% 755k/755k [00:00<00:00, 23.7MB/s]\n",
            "Overriding model.yaml nc=80 with nc=1\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1      1392  ultralytics.nn.modules.conv.Conv             [3, 48, 3, 2]                 \n",
            "  1                  -1  1     41664  ultralytics.nn.modules.conv.Conv             [48, 96, 3, 2]                \n",
            "  2                  -1  2    111360  ultralytics.nn.modules.block.C2f             [96, 96, 2, True]             \n",
            "  3                  -1  1    166272  ultralytics.nn.modules.conv.Conv             [96, 192, 3, 2]               \n",
            "  4                  -1  4    813312  ultralytics.nn.modules.block.C2f             [192, 192, 4, True]           \n",
            "  5                  -1  1    664320  ultralytics.nn.modules.conv.Conv             [192, 384, 3, 2]              \n",
            "  6                  -1  4   3248640  ultralytics.nn.modules.block.C2f             [384, 384, 4, True]           \n",
            "  7                  -1  1   1991808  ultralytics.nn.modules.conv.Conv             [384, 576, 3, 2]              \n",
            "  8                  -1  2   3985920  ultralytics.nn.modules.block.C2f             [576, 576, 2, True]           \n",
            "  9                  -1  1    831168  ultralytics.nn.modules.block.SPPF            [576, 576, 5]                 \n",
            " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 12                  -1  2   1993728  ultralytics.nn.modules.block.C2f             [960, 384, 2]                 \n",
            " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 15                  -1  2    517632  ultralytics.nn.modules.block.C2f             [576, 192, 2]                 \n",
            " 16                  -1  1    332160  ultralytics.nn.modules.conv.Conv             [192, 192, 3, 2]              \n",
            " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 18                  -1  2   1846272  ultralytics.nn.modules.block.C2f             [576, 384, 2]                 \n",
            " 19                  -1  1   1327872  ultralytics.nn.modules.conv.Conv             [384, 384, 3, 2]              \n",
            " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 21                  -1  2   4207104  ultralytics.nn.modules.block.C2f             [960, 576, 2]                 \n",
            " 22        [15, 18, 21]  1   3776275  ultralytics.nn.modules.head.Detect           [1, [192, 384, 576]]          \n",
            "Model summary: 295 layers, 25856899 parameters, 25856883 gradients\n",
            "\n",
            "Transferred 469/475 items from pretrained weights\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m To get all data logged automatically, import comet_ml before the following modules: torch.\n",
            "\u001b[1;38;5;196mCOMET ERROR:\u001b[0m Failed to set run cmd args\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in '/content' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/tsuize/yolov8-differentversion/0dcdb57f30f14ade9d1d36363e454c0c\u001b[0m\n",
            "\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train2', view at http://localhost:6006/\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to yolov8n.pt...\n",
            "100%|██████████| 6.23M/6.23M [00:00<00:00, 73.8MB/s]\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/vesselDetection-17/train/labels... 1981 images, 493 backgrounds, 0 corrupt: 100%|██████████| 1981/1981 [00:01<00:00, 1605.73it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/vesselDetection-17/train/labels.cache\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/vesselDetection-17/valid/labels... 660 images, 265 backgrounds, 0 corrupt: 100%|██████████| 660/660 [00:00<00:00, 1625.04it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/vesselDetection-17/valid/labels.cache\n",
            "Plotting labels to runs/detect/train2/labels.jpg... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.002, momentum=0.9) with parameter groups 77 weight(decay=0.0), 84 weight(decay=0.0005), 83 bias(decay=0.0)\n",
            "Image sizes 640 train, 640 val\n",
            "Using 8 dataloader workers\n",
            "Logging results to \u001b[1mruns/detect/train2\u001b[0m\n",
            "Starting training for 100 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      1/100      6.52G      1.217      1.348      1.189         25        640: 100%|██████████| 124/124 [00:18<00:00,  6.62it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:04<00:00,  4.63it/s]\n",
            "                   all        660        453    0.00696      0.486    0.00502    0.00293\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      2/100         7G      1.348      1.114      1.285         26        640: 100%|██████████| 124/124 [00:15<00:00,  8.20it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.69it/s]\n",
            "                   all        660        453     0.0322     0.0419    0.00489    0.00244\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      3/100      7.01G      1.364      1.104      1.284         23        640: 100%|██████████| 124/124 [00:14<00:00,  8.43it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.36it/s]\n",
            "                   all        660        453      0.615      0.556      0.562      0.338\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      4/100      7.01G      1.313      1.039      1.272         29        640: 100%|██████████| 124/124 [00:14<00:00,  8.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.38it/s]\n",
            "                   all        660        453      0.662      0.386       0.43      0.262\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      5/100      7.02G      1.272     0.9495      1.231         25        640: 100%|██████████| 124/124 [00:14<00:00,  8.44it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.93it/s]\n",
            "                   all        660        453      0.777      0.567      0.626      0.417\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      6/100      7.02G      1.252     0.9206      1.221         21        640: 100%|██████████| 124/124 [00:14<00:00,  8.46it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.27it/s]\n",
            "                   all        660        453      0.867      0.761       0.83       0.58\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      7/100      7.03G      1.193     0.8567      1.185         18        640: 100%|██████████| 124/124 [00:14<00:00,  8.46it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.38it/s]\n",
            "                   all        660        453       0.84      0.812      0.846      0.584\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      8/100      7.02G      1.174     0.8204      1.171         21        640: 100%|██████████| 124/124 [00:14<00:00,  8.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.86it/s]\n",
            "                   all        660        453      0.889      0.813      0.896      0.653\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      9/100      7.02G      1.144     0.7785      1.157         13        640: 100%|██████████| 124/124 [00:14<00:00,  8.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.40it/s]\n",
            "                   all        660        453      0.896      0.863      0.901      0.672\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     10/100      7.01G      1.079     0.7475      1.116         23        640: 100%|██████████| 124/124 [00:15<00:00,  8.25it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.15it/s]\n",
            "                   all        660        453      0.886      0.817      0.876      0.654\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     11/100      7.03G      1.058     0.7277      1.108         20        640: 100%|██████████| 124/124 [00:14<00:00,  8.45it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.97it/s]\n",
            "                   all        660        453      0.868      0.728      0.815      0.599\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     12/100      7.04G      1.077     0.7221      1.111         21        640: 100%|██████████| 124/124 [00:14<00:00,  8.46it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.26it/s]\n",
            "                   all        660        453      0.857      0.823      0.869       0.64\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     13/100      7.03G      1.044     0.6809      1.101         21        640: 100%|██████████| 124/124 [00:14<00:00,  8.46it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.51it/s]\n",
            "                   all        660        453      0.921       0.87      0.921      0.666\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     14/100      7.02G      1.028     0.6782        1.1         22        640: 100%|██████████| 124/124 [00:14<00:00,  8.44it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.76it/s]\n",
            "                   all        660        453      0.912      0.819      0.895       0.67\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     15/100      7.03G      1.025     0.6733      1.097         27        640: 100%|██████████| 124/124 [00:14<00:00,  8.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.41it/s]\n",
            "                   all        660        453      0.885      0.817      0.877      0.661\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     16/100      7.04G      1.006     0.6506      1.078         20        640: 100%|██████████| 124/124 [00:14<00:00,  8.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.32it/s]\n",
            "                   all        660        453      0.897      0.809      0.898      0.641\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     17/100      7.04G     0.9949     0.6418      1.082         18        640: 100%|██████████| 124/124 [00:14<00:00,  8.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.96it/s]\n",
            "                   all        660        453      0.937      0.843      0.897      0.683\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     18/100      7.04G     0.9901     0.6328      1.079         15        640: 100%|██████████| 124/124 [00:14<00:00,  8.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.19it/s]\n",
            "                   all        660        453      0.928      0.829      0.896      0.679\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     19/100      7.04G     0.9765     0.6243      1.069         23        640: 100%|██████████| 124/124 [00:14<00:00,  8.45it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.40it/s]\n",
            "                   all        660        453      0.924      0.865      0.914      0.673\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     20/100      7.03G     0.9635     0.6026      1.055         20        640: 100%|██████████| 124/124 [00:14<00:00,  8.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.17it/s]\n",
            "                   all        660        453      0.935      0.879      0.934      0.719\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     21/100      7.04G     0.9557     0.6073      1.067         20        640: 100%|██████████| 124/124 [00:14<00:00,  8.42it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.96it/s]\n",
            "                   all        660        453      0.959      0.857      0.934       0.72\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     22/100      7.02G     0.9372     0.5949      1.058         15        640: 100%|██████████| 124/124 [00:14<00:00,  8.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.32it/s]\n",
            "                   all        660        453      0.912      0.863      0.919       0.72\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     23/100      7.03G     0.9311     0.5998      1.056         23        640: 100%|██████████| 124/124 [00:14<00:00,  8.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.26it/s]\n",
            "                   all        660        453      0.941      0.878      0.938      0.701\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     24/100      7.05G     0.9593     0.5985      1.061         13        640: 100%|██████████| 124/124 [00:14<00:00,  8.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.13it/s]\n",
            "                   all        660        453      0.968      0.872      0.937      0.733\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     25/100      7.03G     0.9408     0.5986      1.054         19        640: 100%|██████████| 124/124 [00:14<00:00,  8.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.25it/s]\n",
            "                   all        660        453       0.91      0.873      0.923      0.697\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     26/100      7.04G     0.9289     0.5877      1.047         25        640: 100%|██████████| 124/124 [00:14<00:00,  8.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.37it/s]\n",
            "                   all        660        453      0.943      0.869      0.928      0.706\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     27/100      7.04G     0.9095     0.5684      1.038         25        640: 100%|██████████| 124/124 [00:14<00:00,  8.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.02it/s]\n",
            "                   all        660        453      0.916      0.892      0.924      0.726\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     28/100      7.03G     0.8719     0.5357      1.022         20        640: 100%|██████████| 124/124 [00:14<00:00,  8.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.14it/s]\n",
            "                   all        660        453      0.954      0.879      0.939      0.733\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     29/100      7.03G     0.8849     0.5572      1.028         26        640: 100%|██████████| 124/124 [00:14<00:00,  8.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.31it/s]\n",
            "                   all        660        453      0.966      0.875      0.941      0.733\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     30/100      7.03G     0.9179     0.5473      1.038         25        640: 100%|██████████| 124/124 [00:14<00:00,  8.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.21it/s]\n",
            "                   all        660        453      0.929      0.833       0.91      0.706\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     31/100      7.03G     0.8737     0.5254      1.023         27        640: 100%|██████████| 124/124 [00:14<00:00,  8.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.33it/s]\n",
            "                   all        660        453       0.95      0.879      0.933       0.73\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     32/100      7.04G     0.8866     0.5577      1.038         15        640: 100%|██████████| 124/124 [00:14<00:00,  8.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.42it/s]\n",
            "                   all        660        453      0.964       0.87      0.937      0.735\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     33/100      7.03G      0.854      0.526      1.013         28        640: 100%|██████████| 124/124 [00:14<00:00,  8.41it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.83it/s]\n",
            "                   all        660        453      0.944      0.896      0.944      0.727\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     34/100      7.02G     0.8678     0.5239      1.024         22        640: 100%|██████████| 124/124 [00:14<00:00,  8.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.46it/s]\n",
            "                   all        660        453      0.928      0.868      0.923      0.737\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     35/100      7.03G     0.8617     0.5323      1.024         24        640: 100%|██████████| 124/124 [00:14<00:00,  8.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.28it/s]\n",
            "                   all        660        453      0.934       0.88      0.931      0.727\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     36/100      7.03G     0.8554     0.5272      1.008         20        640: 100%|██████████| 124/124 [00:14<00:00,  8.46it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.15it/s]\n",
            "                   all        660        453      0.963      0.868      0.951      0.743\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     37/100      7.03G     0.8372     0.5106      1.013         23        640: 100%|██████████| 124/124 [00:14<00:00,  8.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.30it/s]\n",
            "                   all        660        453      0.942      0.892      0.944      0.738\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     38/100      7.03G     0.8388     0.5046      1.005         15        640: 100%|██████████| 124/124 [00:14<00:00,  8.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.31it/s]\n",
            "                   all        660        453       0.97      0.857      0.929      0.716\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     39/100      7.03G     0.8261     0.5011      0.994         18        640: 100%|██████████| 124/124 [00:14<00:00,  8.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.86it/s]\n",
            "                   all        660        453      0.941      0.874      0.949      0.738\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     40/100      7.04G     0.8393     0.5209      1.006         15        640: 100%|██████████| 124/124 [00:14<00:00,  8.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.22it/s]\n",
            "                   all        660        453      0.951      0.872      0.942      0.751\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     41/100      7.03G     0.8615     0.5144     0.9997         21        640: 100%|██████████| 124/124 [00:15<00:00,  8.12it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.46it/s]\n",
            "                   all        660        453      0.913      0.885      0.937      0.751\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     42/100      7.02G     0.8279      0.494     0.9886         13        640: 100%|██████████| 124/124 [00:14<00:00,  8.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.24it/s]\n",
            "                   all        660        453       0.96      0.854      0.937      0.749\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     43/100      7.03G     0.8083     0.4928     0.9933         18        640: 100%|██████████| 124/124 [00:14<00:00,  8.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.42it/s]\n",
            "                   all        660        453      0.939      0.894      0.941      0.767\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     44/100      7.05G     0.7716      0.467     0.9762         12        640: 100%|██████████| 124/124 [00:14<00:00,  8.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.37it/s]\n",
            "                   all        660        453      0.944       0.85      0.934      0.729\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     45/100      7.03G     0.8165      0.486     0.9905         34        640: 100%|██████████| 124/124 [00:14<00:00,  8.42it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.40it/s]\n",
            "                   all        660        453      0.974      0.879      0.953      0.762\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     46/100      7.04G     0.8087     0.4862     0.9837         20        640: 100%|██████████| 124/124 [00:14<00:00,  8.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.54it/s]\n",
            "                   all        660        453      0.927      0.901      0.945      0.757\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     47/100      7.04G      0.794     0.4696     0.9879         16        640: 100%|██████████| 124/124 [00:14<00:00,  8.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.57it/s]\n",
            "                   all        660        453      0.975      0.879      0.941      0.774\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     48/100      7.04G     0.7755     0.4627     0.9758         20        640: 100%|██████████| 124/124 [00:14<00:00,  8.46it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.35it/s]\n",
            "                   all        660        453      0.966      0.879      0.949      0.773\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     49/100      7.04G     0.8001     0.4685     0.9784         19        640: 100%|██████████| 124/124 [00:14<00:00,  8.54it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.20it/s]\n",
            "                   all        660        453      0.936      0.897      0.936      0.729\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     50/100      7.03G     0.7768     0.4616     0.9776         13        640: 100%|██████████| 124/124 [00:14<00:00,  8.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.48it/s]\n",
            "                   all        660        453      0.954      0.876      0.943      0.749\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     51/100      7.03G     0.7801     0.4698     0.9793         27        640: 100%|██████████| 124/124 [00:14<00:00,  8.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.42it/s]\n",
            "                   all        660        453      0.958      0.887      0.952       0.77\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     52/100      7.04G     0.7686     0.4644     0.9689         26        640: 100%|██████████| 124/124 [00:14<00:00,  8.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.18it/s]\n",
            "                   all        660        453      0.961      0.876      0.952      0.765\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     53/100      7.04G     0.7614     0.4657     0.9731         16        640: 100%|██████████| 124/124 [00:14<00:00,  8.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.45it/s]\n",
            "                   all        660        453       0.95      0.857       0.94       0.75\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     54/100      7.03G     0.7622     0.4459     0.9665         23        640: 100%|██████████| 124/124 [00:14<00:00,  8.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.44it/s]\n",
            "                   all        660        453      0.971      0.901      0.955       0.74\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     55/100      7.05G     0.7486     0.4431     0.9632         22        640: 100%|██████████| 124/124 [00:14<00:00,  8.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.43it/s]\n",
            "                   all        660        453      0.967      0.897      0.956      0.777\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     56/100      7.03G     0.7455     0.4564     0.9654         17        640: 100%|██████████| 124/124 [00:14<00:00,  8.45it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.51it/s]\n",
            "                   all        660        453       0.97      0.874      0.955      0.774\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     57/100      7.05G     0.7456     0.4365     0.9618         24        640: 100%|██████████| 124/124 [00:14<00:00,  8.40it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.40it/s]\n",
            "                   all        660        453      0.956      0.907      0.958      0.757\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     58/100      7.04G     0.7374     0.4308     0.9546         29        640: 100%|██████████| 124/124 [00:14<00:00,  8.55it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.22it/s]\n",
            "                   all        660        453      0.977      0.879      0.955      0.751\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     59/100      7.03G     0.7348     0.4307     0.9583         21        640: 100%|██████████| 124/124 [00:14<00:00,  8.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.07it/s]\n",
            "                   all        660        453       0.96      0.896      0.948      0.756\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     60/100      7.03G     0.7308     0.4264     0.9589         23        640: 100%|██████████| 124/124 [00:14<00:00,  8.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.26it/s]\n",
            "                   all        660        453      0.933      0.905      0.944      0.754\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     61/100      7.04G     0.7308     0.4243     0.9468         17        640: 100%|██████████| 124/124 [00:14<00:00,  8.42it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.41it/s]\n",
            "                   all        660        453      0.935      0.855      0.937       0.75\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     62/100      7.03G     0.7196     0.4271     0.9529         18        640: 100%|██████████| 124/124 [00:14<00:00,  8.55it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.17it/s]\n",
            "                   all        660        453      0.916      0.876      0.935      0.745\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     63/100      7.04G     0.7156      0.425      0.951         22        640: 100%|██████████| 124/124 [00:14<00:00,  8.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.44it/s]\n",
            "                   all        660        453      0.962      0.887      0.949      0.758\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     64/100      7.04G     0.7138     0.4145     0.9496         19        640: 100%|██████████| 124/124 [00:14<00:00,  8.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.64it/s]\n",
            "                   all        660        453       0.97      0.887      0.951      0.782\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     65/100      7.04G     0.7128     0.4157     0.9477         20        640: 100%|██████████| 124/124 [00:14<00:00,  8.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.26it/s]\n",
            "                   all        660        453       0.93      0.908      0.957      0.755\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     66/100      7.04G     0.7165     0.4177      0.954         18        640: 100%|██████████| 124/124 [00:14<00:00,  8.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.27it/s]\n",
            "                   all        660        453      0.927      0.896      0.951       0.77\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     67/100      7.03G     0.7107     0.4127     0.9492         19        640: 100%|██████████| 124/124 [00:14<00:00,  8.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.50it/s]\n",
            "                   all        660        453      0.946      0.896      0.958      0.773\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     68/100      7.05G        0.7     0.4125     0.9468         23        640: 100%|██████████| 124/124 [00:14<00:00,  8.45it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.21it/s]\n",
            "                   all        660        453      0.971      0.886      0.961      0.778\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     69/100      7.06G     0.6768     0.3869     0.9302         26        640: 100%|██████████| 124/124 [00:14<00:00,  8.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.02it/s]\n",
            "                   all        660        453      0.953      0.898       0.95      0.766\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     70/100      7.04G     0.6968     0.3922     0.9433         17        640: 100%|██████████| 124/124 [00:14<00:00,  8.46it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.59it/s]\n",
            "                   all        660        453      0.963      0.896      0.955      0.787\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     71/100      7.04G     0.6894      0.396     0.9385         22        640: 100%|██████████| 124/124 [00:14<00:00,  8.45it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.46it/s]\n",
            "                   all        660        453      0.933      0.886      0.941      0.748\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     72/100      7.04G     0.6734     0.3878     0.9262         26        640: 100%|██████████| 124/124 [00:14<00:00,  8.44it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.05it/s]\n",
            "                   all        660        453      0.959       0.85      0.951       0.76\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     73/100      7.04G     0.6761     0.3926     0.9315         19        640: 100%|██████████| 124/124 [00:14<00:00,  8.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.63it/s]\n",
            "                   all        660        453      0.964      0.897      0.954      0.775\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     74/100      7.03G     0.6733     0.3866     0.9348         23        640: 100%|██████████| 124/124 [00:14<00:00,  8.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.37it/s]\n",
            "                   all        660        453      0.943       0.91      0.957      0.779\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     75/100      7.01G     0.6738     0.3925     0.9418         20        640: 100%|██████████| 124/124 [00:14<00:00,  8.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.29it/s]\n",
            "                   all        660        453      0.947      0.903      0.951      0.778\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     76/100      7.03G     0.6716     0.3804     0.9207         21        640: 100%|██████████| 124/124 [00:14<00:00,  8.43it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.18it/s]\n",
            "                   all        660        453      0.948      0.901      0.947      0.777\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     77/100      7.04G     0.6584     0.3801     0.9246         25        640: 100%|██████████| 124/124 [00:14<00:00,  8.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.55it/s]\n",
            "                   all        660        453      0.975      0.876      0.952      0.783\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     78/100      7.04G     0.6693     0.3824     0.9335         25        640: 100%|██████████| 124/124 [00:14<00:00,  8.44it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.49it/s]\n",
            "                   all        660        453      0.968      0.887      0.948      0.767\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     79/100      7.03G     0.6681     0.3762     0.9301         18        640: 100%|██████████| 124/124 [00:14<00:00,  8.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.17it/s]\n",
            "                   all        660        453      0.961      0.896      0.956      0.778\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     80/100      7.04G     0.6535      0.379      0.929         17        640: 100%|██████████| 124/124 [00:14<00:00,  8.42it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.16it/s]\n",
            "                   all        660        453       0.96      0.896      0.957      0.771\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     81/100      7.06G     0.6414     0.3696     0.9273         22        640: 100%|██████████| 124/124 [00:14<00:00,  8.44it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.50it/s]\n",
            "                   all        660        453      0.954      0.912      0.957      0.773\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     82/100      7.05G     0.6369      0.363     0.9213         14        640: 100%|██████████| 124/124 [00:14<00:00,  8.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.34it/s]\n",
            "                   all        660        453      0.953      0.909      0.957       0.78\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     83/100      7.03G     0.6318     0.3557     0.9126         18        640: 100%|██████████| 124/124 [00:14<00:00,  8.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.33it/s]\n",
            "                   all        660        453      0.947      0.916       0.96       0.79\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     84/100      7.03G     0.6428     0.3603     0.9199         23        640: 100%|██████████| 124/124 [00:14<00:00,  8.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.27it/s]\n",
            "                   all        660        453      0.937      0.916      0.949      0.774\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     85/100      7.02G     0.6158     0.3439     0.9111         17        640: 100%|██████████| 124/124 [00:14<00:00,  8.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.46it/s]\n",
            "                   all        660        453      0.949      0.904      0.957      0.776\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     86/100      7.04G     0.6263     0.3526      0.918         16        640: 100%|██████████| 124/124 [00:14<00:00,  8.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.06it/s]\n",
            "                   all        660        453      0.939      0.911      0.955      0.784\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     87/100      7.04G     0.6201     0.3495      0.917         22        640: 100%|██████████| 124/124 [00:14<00:00,  8.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.57it/s]\n",
            "                   all        660        453      0.958       0.89      0.954      0.776\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     88/100      7.04G     0.6195      0.349     0.9144         21        640: 100%|██████████| 124/124 [00:14<00:00,  8.46it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.50it/s]\n",
            "                   all        660        453      0.956      0.894      0.955      0.778\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     89/100      7.05G     0.6047     0.3432     0.9124         20        640: 100%|██████████| 124/124 [00:14<00:00,  8.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.37it/s]\n",
            "                   all        660        453      0.955      0.899      0.955      0.773\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     90/100      7.03G      0.626     0.3515     0.9136         17        640: 100%|██████████| 124/124 [00:14<00:00,  8.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.48it/s]\n",
            "                   all        660        453      0.953      0.899      0.954      0.783\n",
            "Closing dataloader mosaic\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     91/100      7.05G     0.6017     0.3058       0.87         10        640: 100%|██████████| 124/124 [00:16<00:00,  7.69it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.44it/s]\n",
            "                   all        660        453      0.945      0.908      0.958      0.782\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     92/100      7.03G     0.5819     0.2948     0.8663         11        640: 100%|██████████| 124/124 [00:14<00:00,  8.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.09it/s]\n",
            "                   all        660        453      0.946      0.895      0.955      0.781\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     93/100      7.04G     0.5778     0.2931     0.8585         12        640: 100%|██████████| 124/124 [00:14<00:00,  8.34it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.54it/s]\n",
            "                   all        660        453       0.95      0.894      0.954      0.786\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     94/100      7.03G     0.5705      0.289     0.8549         10        640: 100%|██████████| 124/124 [00:14<00:00,  8.55it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.82it/s]\n",
            "                   all        660        453      0.938      0.904      0.954      0.785\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     95/100      7.03G     0.5693      0.286     0.8557         10        640: 100%|██████████| 124/124 [00:14<00:00,  8.57it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.39it/s]\n",
            "                   all        660        453      0.955      0.894      0.955      0.791\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     96/100      7.04G     0.5588     0.2861     0.8528         10        640: 100%|██████████| 124/124 [00:14<00:00,  8.58it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.32it/s]\n",
            "                   all        660        453      0.952      0.907      0.959      0.791\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     97/100      7.03G     0.5505     0.2842     0.8543         10        640: 100%|██████████| 124/124 [00:14<00:00,  8.61it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.64it/s]\n",
            "                   all        660        453      0.949      0.912      0.963      0.798\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     98/100      7.03G     0.5581       0.28     0.8495         10        640: 100%|██████████| 124/124 [00:14<00:00,  8.57it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.31it/s]\n",
            "                   all        660        453      0.949      0.913      0.959        0.8\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     99/100      7.02G     0.5536     0.2787     0.8445         13        640: 100%|██████████| 124/124 [00:14<00:00,  8.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.72it/s]\n",
            "                   all        660        453      0.945      0.909      0.959      0.799\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    100/100      7.04G     0.5471     0.2731     0.8563         13        640: 100%|██████████| 124/124 [00:14<00:00,  8.56it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:04<00:00,  4.60it/s]\n",
            "                   all        660        453      0.949       0.91      0.958      0.795\n",
            "\n",
            "100 epochs completed in 0.534 hours.\n",
            "Optimizer stripped from runs/detect/train2/weights/last.pt, 52.0MB\n",
            "Optimizer stripped from runs/detect/train2/weights/best.pt, 52.0MB\n",
            "\n",
            "Validating runs/detect/train2/weights/best.pt...\n",
            "Ultralytics YOLOv8.0.134 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (NVIDIA A100-SXM4-40GB, 40514MiB)\n",
            "Model summary (fused): 218 layers, 25840339 parameters, 0 gradients\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:04<00:00,  4.72it/s]\n",
            "                   all        660        453      0.949      0.913      0.959      0.799\n",
            "Speed: 0.2ms preprocess, 1.0ms inference, 0.0ms loss, 0.5ms postprocess per image\n",
            "Results saved to \u001b[1mruns/detect/train2\u001b[0m\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/tsuize/yolov8-differentversion/0dcdb57f30f14ade9d1d36363e454c0c\u001b[0m\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr/pg0 [101]               : (5.9600000000000046e-05, 0.0019551301075268817)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr/pg1 [101]               : (5.9600000000000046e-05, 0.0019551301075268817)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr/pg2 [101]               : (5.9600000000000046e-05, 0.0019551301075268817)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP50(B) [202]     : (0.00489, 0.96278)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP50-95(B) [202]  : (0.00244, 0.79952)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision(B) [202] : (0.00696, 0.97658)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall(B) [202]    : (0.04194, 0.91611)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model/GFLOPs               : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model/parameters           : 25856899\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model/speed_PyTorch(ms)    : 1.48\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [12500]     : (0.46999, 1.44744)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [12500]     : (0.22891, 4.07054)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/dfl_loss [12500]     : (0.77358, 1.41801)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [200]         : (0.70798, 2.83334)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [200]         : (0.36836, inf)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/dfl_loss [200]         : (0.95722, 2.74085)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_batch_logging_interval  : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     log_confusion_matrix_on_eval : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     log_image_predictions        : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     max_image_predictions        : 100\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     agnostic_nms    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     amp             : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     augment         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch           : 16\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box             : 7.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boxes           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cfg             : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     classes         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     close_mosaic    : 10\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls             : 0.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conf            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste      : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     data            : /content/vesselDetection-17/data.yaml\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees         : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     deterministic   : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device          : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dfl             : 1.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dnn             : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dropout         : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dynamic         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epochs          : 100\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr          : 0.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud          : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     format          : torchscript\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fraction        : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     half            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h           : 0.015\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s           : 0.7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v           : 0.4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz           : 640\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     int8            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou             : 0.7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     keras           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     kobj            : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     line_width      : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0             : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf             : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mask_ratio      : 4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     max_det         : 300\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup           : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mode            : train\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model           : yolov8m.pt\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum        : 0.937\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic          : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nbs             : 64\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nms             : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     opset           : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimize        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer       : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     overlap_mask    : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience        : 20\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective     : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     plots           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pose            : 12.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pretrained      : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     profile         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     retina_masks    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save            : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_conf       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_crop       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir        : runs/detect/train2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_hybrid     : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_json       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period     : -1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_txt        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale           : 0.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed            : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear           : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     show            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     show_conf       : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     show_labels     : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     simplify        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     source          : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     split           : val\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     task            : detect\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tracker         : botsort.yaml\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate       : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     v5loader        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val             : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     verbose         : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     vid_stride      : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     visualize       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr  : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs   : 3.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum : 0.8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay    : 0.0005\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers         : 8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workspace       : 4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix    : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     filename            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images              : 10\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages  : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model-element       : 1 (49.59 MB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     source_code         : 2 (12.96 KB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
            "\u001b[1;38;5;196mCOMET ERROR:\u001b[0m Error sending a notification, make sure you have opted-in for notifications\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m To get all data logged automatically, import comet_ml before the following modules: torch.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for metadata to finish uploading (timeout is 3600 seconds)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Uploading 1 metrics, params and output messages\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Train yolov8x**"
      ],
      "metadata": {
        "id": "dI82q5EdwpoZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!yolo task=detect \\\n",
        "mode=train \\\n",
        "model=yolov8x.pt \\\n",
        "data= /content/vesselDetection-17/data.yaml \\\n",
        "epochs = 200 \\\n",
        "patience =20 \\\n",
        "imgsz=640"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SZbad_zhwwTf",
        "outputId": "698ad0b1-0e2c-4f8e-a04f-556df0fef9d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt to yolov8x.pt...\n",
            "100% 131M/131M [00:01<00:00, 75.2MB/s]\n",
            "New https://pypi.org/project/ultralytics/8.0.192 available 😃 Update with 'pip install -U ultralytics'\n",
            "Ultralytics YOLOv8.0.134 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (NVIDIA A100-SXM4-40GB, 40514MiB)\n",
            "\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8x.pt, data=/content/vesselDetection-17/data.yaml, epochs=200, patience=20, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/train3\n",
            "Overriding model.yaml nc=80 with nc=1\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1      2320  ultralytics.nn.modules.conv.Conv             [3, 80, 3, 2]                 \n",
            "  1                  -1  1    115520  ultralytics.nn.modules.conv.Conv             [80, 160, 3, 2]               \n",
            "  2                  -1  3    436800  ultralytics.nn.modules.block.C2f             [160, 160, 3, True]           \n",
            "  3                  -1  1    461440  ultralytics.nn.modules.conv.Conv             [160, 320, 3, 2]              \n",
            "  4                  -1  6   3281920  ultralytics.nn.modules.block.C2f             [320, 320, 6, True]           \n",
            "  5                  -1  1   1844480  ultralytics.nn.modules.conv.Conv             [320, 640, 3, 2]              \n",
            "  6                  -1  6  13117440  ultralytics.nn.modules.block.C2f             [640, 640, 6, True]           \n",
            "  7                  -1  1   3687680  ultralytics.nn.modules.conv.Conv             [640, 640, 3, 2]              \n",
            "  8                  -1  3   6969600  ultralytics.nn.modules.block.C2f             [640, 640, 3, True]           \n",
            "  9                  -1  1   1025920  ultralytics.nn.modules.block.SPPF            [640, 640, 5]                 \n",
            " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 12                  -1  3   7379200  ultralytics.nn.modules.block.C2f             [1280, 640, 3]                \n",
            " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 15                  -1  3   1948800  ultralytics.nn.modules.block.C2f             [960, 320, 3]                 \n",
            " 16                  -1  1    922240  ultralytics.nn.modules.conv.Conv             [320, 320, 3, 2]              \n",
            " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 18                  -1  3   7174400  ultralytics.nn.modules.block.C2f             [960, 640, 3]                 \n",
            " 19                  -1  1   3687680  ultralytics.nn.modules.conv.Conv             [640, 640, 3, 2]              \n",
            " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 21                  -1  3   7379200  ultralytics.nn.modules.block.C2f             [1280, 640, 3]                \n",
            " 22        [15, 18, 21]  1   8718931  ultralytics.nn.modules.head.Detect           [1, [320, 640, 640]]          \n",
            "Model summary: 365 layers, 68153571 parameters, 68153555 gradients\n",
            "\n",
            "Transferred 589/595 items from pretrained weights\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m To get all data logged automatically, import comet_ml before the following modules: torch.\n",
            "\u001b[1;38;5;196mCOMET ERROR:\u001b[0m Failed to set run cmd args\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in '/content' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/tsuize/yolov8-differentversion/cb3c722522a64581b1d1cbc8cf2b5d4e\u001b[0m\n",
            "\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train3', view at http://localhost:6006/\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/vesselDetection-17/train/labels.cache... 1981 images, 493 backgrounds, 0 corrupt: 100%|██████████| 1981/1981 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/vesselDetection-17/valid/labels.cache... 660 images, 265 backgrounds, 0 corrupt: 100%|██████████| 660/660 [00:00<?, ?it/s]\n",
            "Plotting labels to runs/detect/train3/labels.jpg... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.002, momentum=0.9) with parameter groups 97 weight(decay=0.0), 104 weight(decay=0.0005), 103 bias(decay=0.0)\n",
            "Image sizes 640 train, 640 val\n",
            "Using 8 dataloader workers\n",
            "Logging results to \u001b[1mruns/detect/train3\u001b[0m\n",
            "Starting training for 200 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      1/200      13.6G      1.296      1.698      1.274         25        640: 100%|██████████| 124/124 [00:29<00:00,  4.22it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.38it/s]\n",
            "                   all        660        453    0.00218      0.161     0.0011   0.000419\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      2/200      14.2G      1.525      1.381      1.432         26        640: 100%|██████████| 124/124 [00:26<00:00,  4.65it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.88it/s]\n",
            "                   all        660        453    0.00152     0.0331   0.000567   0.000175\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      3/200      14.2G      1.472      1.268      1.389         23        640: 100%|██████████| 124/124 [00:26<00:00,  4.69it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:04<00:00,  5.21it/s]\n",
            "                   all        660        453     0.0146      0.629     0.0126    0.00643\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      4/200      14.2G      1.405       1.17      1.355         29        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  6.03it/s]\n",
            "                   all        660        453      0.463     0.0552     0.0469     0.0214\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      5/200      14.2G       1.33      1.036      1.299         25        640: 100%|██████████| 124/124 [00:25<00:00,  4.77it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:04<00:00,  5.24it/s]\n",
            "                   all        660        453      0.784       0.68      0.731      0.454\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      6/200      14.2G      1.307      1.034      1.284         21        640: 100%|██████████| 124/124 [00:26<00:00,  4.73it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.61it/s]\n",
            "                   all        660        453      0.404      0.203      0.204      0.115\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      7/200      14.2G      1.234     0.9413      1.227         18        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:04<00:00,  5.13it/s]\n",
            "                   all        660        453       0.86      0.731      0.822      0.573\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      8/200      14.2G      1.219     0.8978      1.224         21        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:04<00:00,  5.07it/s]\n",
            "                   all        660        453      0.917      0.784      0.869      0.613\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      9/200      14.2G      1.185      0.838       1.21         13        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:04<00:00,  5.15it/s]\n",
            "                   all        660        453      0.887      0.766      0.862      0.607\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     10/200      14.2G      1.136     0.7898      1.174         23        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.47it/s]\n",
            "                   all        660        453      0.645      0.598      0.633      0.388\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     11/200      13.9G      1.117     0.7821      1.166         20        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.38it/s]\n",
            "                   all        660        453       0.93       0.76      0.861       0.62\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     12/200      13.9G      1.116      0.772      1.154         21        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.40it/s]\n",
            "                   all        660        453      0.914      0.798      0.857      0.617\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     13/200      13.9G      1.097     0.7414      1.159         21        640: 100%|██████████| 124/124 [00:25<00:00,  4.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.38it/s]\n",
            "                   all        660        453      0.877       0.77      0.848      0.611\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     14/200      14.2G      1.073     0.7324      1.146         22        640: 100%|██████████| 124/124 [00:25<00:00,  4.77it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:04<00:00,  5.13it/s]\n",
            "                   all        660        453      0.882      0.779      0.848      0.633\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     15/200      14.2G      1.068     0.7113      1.144         27        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.47it/s]\n",
            "                   all        660        453      0.934      0.852      0.912      0.687\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     16/200      14.2G      1.044     0.6854      1.124         20        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:04<00:00,  5.19it/s]\n",
            "                   all        660        453      0.929      0.863      0.917      0.674\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     17/200      14.2G       1.03     0.6649      1.126         18        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.37it/s]\n",
            "                   all        660        453      0.948      0.854      0.917      0.684\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     18/200      14.2G      1.025     0.6669      1.121         15        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.40it/s]\n",
            "                   all        660        453      0.928      0.854      0.903      0.675\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     19/200      14.2G      1.014     0.6534      1.107         23        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.64it/s]\n",
            "                   all        660        453      0.938      0.867      0.923      0.688\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     20/200      14.2G      1.006     0.6555      1.104         20        640: 100%|██████████| 124/124 [00:26<00:00,  4.73it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:04<00:00,  5.17it/s]\n",
            "                   all        660        453      0.936      0.804      0.881      0.683\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     21/200      14.2G      1.004     0.6567      1.112         20        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.35it/s]\n",
            "                   all        660        453      0.939      0.837      0.901      0.673\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     22/200      14.2G     0.9849      0.655      1.101         15        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.41it/s]\n",
            "                   all        660        453      0.916      0.842      0.909      0.669\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     23/200      14.3G     0.9914     0.6268      1.108         23        640: 100%|██████████| 124/124 [00:25<00:00,  4.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:04<00:00,  5.22it/s]\n",
            "                   all        660        453      0.951      0.826      0.908      0.674\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     24/200      14.3G     0.9882     0.6405      1.103         13        640: 100%|██████████| 124/124 [00:25<00:00,  4.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.53it/s]\n",
            "                   all        660        453      0.941      0.846       0.92      0.699\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     25/200      14.3G     0.9686     0.6325      1.091         19        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.40it/s]\n",
            "                   all        660        453      0.931      0.835      0.904      0.678\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     26/200      14.3G     0.9557     0.6031      1.081         25        640: 100%|██████████| 124/124 [00:25<00:00,  4.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:04<00:00,  5.25it/s]\n",
            "                   all        660        453      0.955      0.828        0.9      0.688\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     27/200      14.3G     0.9381     0.6045       1.08         25        640: 100%|██████████| 124/124 [00:26<00:00,  4.75it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.50it/s]\n",
            "                   all        660        453      0.915      0.848      0.913      0.713\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     28/200      14.2G     0.8998     0.5686       1.06         20        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:04<00:00,  5.02it/s]\n",
            "                   all        660        453      0.947       0.85      0.926      0.709\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     29/200      14.3G     0.9261     0.5873      1.075         26        640: 100%|██████████| 124/124 [00:25<00:00,  4.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.67it/s]\n",
            "                   all        660        453       0.86       0.77      0.843      0.579\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     30/200      14.3G     0.9283     0.5733      1.065         25        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.60it/s]\n",
            "                   all        660        453      0.953      0.859      0.933      0.691\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     31/200      14.3G     0.9093     0.5669      1.065         27        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:04<00:00,  5.22it/s]\n",
            "                   all        660        453      0.934      0.861      0.921      0.722\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     32/200      14.3G     0.9252     0.6002      1.081         15        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.49it/s]\n",
            "                   all        660        453      0.948      0.857      0.918      0.672\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     33/200      14.3G     0.9029     0.5714      1.062         28        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.32it/s]\n",
            "                   all        660        453      0.963      0.883      0.943      0.729\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     34/200      14.3G     0.8988     0.5589      1.057         22        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.64it/s]\n",
            "                   all        660        453      0.956      0.859      0.942      0.712\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     35/200      14.3G     0.8985     0.5616      1.066         24        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:04<00:00,  5.19it/s]\n",
            "                   all        660        453      0.959      0.885      0.935      0.706\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     36/200      14.3G     0.9002     0.5706      1.058         20        640: 100%|██████████| 124/124 [00:25<00:00,  4.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.35it/s]\n",
            "                   all        660        453      0.942      0.855      0.921      0.733\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     37/200      14.2G     0.8619     0.5382      1.051         23        640: 100%|██████████| 124/124 [00:26<00:00,  4.76it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.61it/s]\n",
            "                   all        660        453      0.943      0.874      0.939      0.726\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     38/200      14.3G     0.8743     0.5414      1.045         15        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.43it/s]\n",
            "                   all        660        453       0.97       0.86      0.935      0.697\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     39/200      14.3G     0.8824     0.5456      1.052         18        640: 100%|██████████| 124/124 [00:25<00:00,  4.81it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.35it/s]\n",
            "                   all        660        453      0.929      0.861      0.922      0.701\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     40/200      14.3G     0.8894     0.5612       1.06         15        640: 100%|██████████| 124/124 [00:25<00:00,  4.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.51it/s]\n",
            "                   all        660        453      0.961      0.852      0.928      0.722\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     41/200      14.3G     0.8841     0.5431      1.044         21        640: 100%|██████████| 124/124 [00:26<00:00,  4.73it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.53it/s]\n",
            "                   all        660        453      0.947      0.872      0.931      0.718\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     42/200      14.3G     0.8691     0.5211      1.038         13        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.28it/s]\n",
            "                   all        660        453      0.947      0.862      0.928      0.711\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     43/200      14.3G     0.8742     0.5447      1.046         18        640: 100%|██████████| 124/124 [00:25<00:00,  4.77it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.50it/s]\n",
            "                   all        660        453      0.929      0.873      0.933      0.712\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     44/200      14.3G     0.8218     0.5088      1.029         12        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.53it/s]\n",
            "                   all        660        453      0.936       0.84      0.923      0.718\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     45/200      14.2G     0.8657     0.5282       1.04         34        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.48it/s]\n",
            "                   all        660        453      0.926      0.832      0.923      0.711\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     46/200      14.3G     0.8596     0.5286      1.032         20        640: 100%|██████████| 124/124 [00:25<00:00,  4.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.64it/s]\n",
            "                   all        660        453      0.945      0.874      0.945      0.738\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     47/200      14.3G     0.8368     0.5057      1.031         16        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.47it/s]\n",
            "                   all        660        453       0.96      0.823      0.919      0.717\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     48/200      14.3G     0.8241     0.4984      1.021         20        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.57it/s]\n",
            "                   all        660        453      0.956      0.821      0.921      0.728\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     49/200      14.3G     0.8448      0.513      1.027         19        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.46it/s]\n",
            "                   all        660        453      0.956      0.873      0.919      0.708\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     50/200      14.3G     0.8157     0.5004      1.022         13        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.57it/s]\n",
            "                   all        660        453      0.965      0.861      0.935      0.757\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     51/200      14.3G     0.8116     0.4959      1.023         27        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.56it/s]\n",
            "                   all        660        453      0.963      0.872      0.946      0.729\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     52/200      14.3G     0.8275     0.5128      1.026         26        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.39it/s]\n",
            "                   all        660        453      0.939      0.876      0.932      0.729\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     53/200      14.3G     0.8183     0.5073       1.03         16        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.41it/s]\n",
            "                   all        660        453      0.952      0.828      0.917      0.705\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     54/200      14.3G     0.8095     0.4849      1.017         23        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.54it/s]\n",
            "                   all        660        453      0.966      0.859      0.934      0.718\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     55/200      14.3G     0.8133      0.501      1.017         22        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:04<00:00,  5.14it/s]\n",
            "                   all        660        453      0.944      0.845      0.935       0.73\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     56/200      14.3G     0.8052     0.5049      1.017         17        640: 100%|██████████| 124/124 [00:25<00:00,  4.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.36it/s]\n",
            "                   all        660        453      0.966      0.879      0.947       0.74\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     57/200      14.3G     0.8005     0.4845      1.014         24        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.51it/s]\n",
            "                   all        660        453      0.954      0.903      0.951      0.757\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     58/200      14.3G     0.8002     0.4773      1.008         29        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.44it/s]\n",
            "                   all        660        453      0.961      0.881      0.938      0.743\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     59/200      14.3G     0.7962     0.4877      1.013         21        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.42it/s]\n",
            "                   all        660        453       0.96      0.868      0.943      0.733\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     60/200      14.3G     0.7825     0.4796      1.004         23        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.66it/s]\n",
            "                   all        660        453      0.948      0.879      0.943      0.747\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     61/200      14.3G     0.7905     0.4812     0.9971         17        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.69it/s]\n",
            "                   all        660        453      0.953      0.847      0.931      0.735\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     62/200      14.3G     0.7887     0.4737      1.007         18        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.32it/s]\n",
            "                   all        660        453      0.964      0.876      0.941      0.754\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     63/200      14.3G     0.7861     0.4799      1.005         22        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.63it/s]\n",
            "                   all        660        453      0.959      0.875      0.951      0.753\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     64/200      14.3G     0.7673     0.4702     0.9995         19        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.78it/s]\n",
            "                   all        660        453      0.956      0.852      0.933      0.745\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     65/200      14.3G     0.7667     0.4633     0.9954         20        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:04<00:00,  5.20it/s]\n",
            "                   all        660        453      0.964      0.896      0.949      0.747\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     66/200      14.3G     0.7904     0.4705      1.009         18        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.74it/s]\n",
            "                   all        660        453       0.94      0.823       0.91      0.676\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     67/200      14.3G     0.7675     0.4594      1.005         19        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.56it/s]\n",
            "                   all        660        453       0.96      0.872      0.947      0.747\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     68/200      14.3G     0.7574     0.4598     0.9969         23        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.29it/s]\n",
            "                   all        660        453      0.949      0.872      0.936      0.735\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     69/200      14.3G     0.7514      0.452     0.9855         26        640: 100%|██████████| 124/124 [00:25<00:00,  4.77it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.65it/s]\n",
            "                   all        660        453      0.949      0.865       0.93      0.716\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     70/200      14.3G     0.7562     0.4523      0.996         17        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.71it/s]\n",
            "                   all        660        453      0.964      0.879      0.949      0.767\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     71/200      14.3G     0.7699      0.446      1.002         22        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.34it/s]\n",
            "                   all        660        453      0.934       0.89      0.951       0.75\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     72/200      14.3G     0.7434     0.4376     0.9815         26        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.66it/s]\n",
            "                   all        660        453      0.924      0.887      0.942      0.761\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     73/200      14.3G     0.7522     0.4447     0.9923         19        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.74it/s]\n",
            "                   all        660        453      0.939      0.887      0.946      0.759\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     74/200      14.3G     0.7597     0.4446      0.996         23        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.44it/s]\n",
            "                   all        660        453      0.944      0.885       0.94      0.737\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     75/200      14.3G     0.7467     0.4524     0.9977         20        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.61it/s]\n",
            "                   all        660        453      0.959      0.871      0.956      0.756\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     76/200      14.3G     0.7293     0.4285       0.97         21        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.68it/s]\n",
            "                   all        660        453      0.962      0.894      0.957      0.769\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     77/200      14.3G      0.736     0.4346     0.9782         25        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.32it/s]\n",
            "                   all        660        453      0.975      0.874      0.944      0.744\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     78/200      14.3G     0.7408     0.4386     0.9871         25        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.56it/s]\n",
            "                   all        660        453      0.962      0.884      0.939      0.748\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     79/200      14.3G     0.7445     0.4403     0.9854         18        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.53it/s]\n",
            "                   all        660        453      0.954      0.866      0.951      0.753\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     80/200      14.3G     0.7299     0.4328     0.9824         17        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.49it/s]\n",
            "                   all        660        453      0.951      0.901       0.95      0.749\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     81/200      14.3G     0.7156     0.4326     0.9794         22        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.54it/s]\n",
            "                   all        660        453      0.945      0.883      0.949      0.755\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     82/200      14.3G     0.7167     0.4209     0.9739         14        640: 100%|██████████| 124/124 [00:25<00:00,  4.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.65it/s]\n",
            "                   all        660        453      0.947      0.881      0.946      0.753\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     83/200      14.3G     0.7058     0.4194     0.9656         18        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.56it/s]\n",
            "                   all        660        453      0.931      0.879      0.942      0.746\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     84/200      14.3G     0.7131     0.4104     0.9695         23        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.64it/s]\n",
            "                   all        660        453      0.929      0.921      0.958      0.762\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     85/200      14.3G      0.682     0.4046     0.9547         17        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.69it/s]\n",
            "                   all        660        453      0.967      0.885      0.948      0.762\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     86/200      14.3G     0.7135     0.4169     0.9756         16        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:04<00:00,  5.16it/s]\n",
            "                   all        660        453      0.958      0.898       0.95      0.761\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     87/200      14.3G     0.6977     0.4092     0.9672         22        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.69it/s]\n",
            "                   all        660        453      0.947      0.896      0.943      0.749\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     88/200      14.3G     0.6966     0.4026     0.9671         21        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.58it/s]\n",
            "                   all        660        453      0.952      0.894      0.953      0.758\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     89/200      14.3G     0.6894     0.4117     0.9651         20        640: 100%|██████████| 124/124 [00:26<00:00,  4.72it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.45it/s]\n",
            "                   all        660        453      0.959      0.865      0.942      0.753\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     90/200      14.3G     0.7253      0.423     0.9728         17        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.65it/s]\n",
            "                   all        660        453      0.942      0.899       0.96      0.754\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     91/200      14.3G     0.6918     0.4044     0.9626         27        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.67it/s]\n",
            "                   all        660        453      0.962      0.883      0.948      0.757\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     92/200      14.3G     0.6987     0.4197     0.9619         25        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.54it/s]\n",
            "                   all        660        453      0.953      0.903      0.951      0.763\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     93/200      14.3G     0.6836     0.4077     0.9523         22        640: 100%|██████████| 124/124 [00:25<00:00,  4.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.54it/s]\n",
            "                   all        660        453      0.937      0.896      0.949      0.753\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     94/200      14.3G     0.6758     0.3994     0.9608         21        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.85it/s]\n",
            "                   all        660        453      0.986      0.881       0.96      0.789\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     95/200      14.3G     0.6869     0.4022      0.959         23        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.39it/s]\n",
            "                   all        660        453      0.961      0.878      0.948      0.745\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     96/200      14.3G     0.6703     0.3926     0.9595         23        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.66it/s]\n",
            "                   all        660        453      0.954      0.907      0.957      0.771\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     97/200      14.3G      0.679      0.409     0.9562         20        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.62it/s]\n",
            "                   all        660        453      0.953      0.886       0.95       0.76\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     98/200      14.3G     0.6604     0.3968     0.9501         19        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.64it/s]\n",
            "                   all        660        453      0.961      0.874       0.95       0.77\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     99/200      14.3G     0.6782     0.3988     0.9535         22        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.46it/s]\n",
            "                   all        660        453      0.951      0.892      0.941      0.753\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    100/200      14.3G     0.6645     0.3845     0.9476         18        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.72it/s]\n",
            "                   all        660        453      0.958      0.887      0.952      0.771\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    101/200      14.2G     0.6702     0.3971      0.949         16        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.69it/s]\n",
            "                   all        660        453       0.96      0.895      0.956      0.776\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    102/200      14.3G     0.6569     0.3896     0.9571         30        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.35it/s]\n",
            "                   all        660        453      0.953      0.905      0.958      0.767\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    103/200      14.3G     0.6628     0.3933     0.9625         26        640: 100%|██████████| 124/124 [00:27<00:00,  4.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.68it/s]\n",
            "                   all        660        453      0.948      0.903      0.958       0.77\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    104/200      14.3G     0.6601     0.3946     0.9509         26        640: 100%|██████████| 124/124 [00:25<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.52it/s]\n",
            "                   all        660        453      0.966      0.894      0.957      0.766\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    105/200      14.3G     0.6348     0.3718     0.9406         17        640: 100%|██████████| 124/124 [00:25<00:00,  4.77it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.58it/s]\n",
            "                   all        660        453      0.967      0.898      0.962       0.76\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    106/200      14.3G     0.6515     0.3814     0.9471         17        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.60it/s]\n",
            "                   all        660        453       0.96      0.892      0.959      0.772\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    107/200      14.3G     0.6365      0.371     0.9373         19        640: 100%|██████████| 124/124 [00:26<00:00,  4.73it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.71it/s]\n",
            "                   all        660        453      0.968      0.898      0.961      0.766\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    108/200      14.3G     0.6487     0.3815     0.9443         26        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.53it/s]\n",
            "                   all        660        453      0.956       0.89      0.957       0.76\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    109/200      14.3G     0.6519      0.389     0.9424         21        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.49it/s]\n",
            "                   all        660        453      0.942      0.914      0.952      0.752\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    110/200      14.3G     0.6542      0.373     0.9496         25        640: 100%|██████████| 124/124 [00:25<00:00,  4.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.74it/s]\n",
            "                   all        660        453      0.958      0.852       0.94      0.746\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    111/200      14.3G     0.6373     0.3753     0.9424         14        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.38it/s]\n",
            "                   all        660        453      0.973      0.891      0.957      0.763\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    112/200      14.3G     0.6447     0.3822     0.9454         18        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.69it/s]\n",
            "                   all        660        453      0.955      0.886      0.958      0.755\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    113/200      14.2G     0.6504     0.3744     0.9474         20        640: 100%|██████████| 124/124 [00:25<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:03<00:00,  5.67it/s]\n",
            "                   all        660        453      0.956      0.916      0.959      0.772\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    114/200      14.3G      0.614     0.3572     0.9309         21        640: 100%|██████████| 124/124 [00:26<00:00,  4.75it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:05<00:00,  3.91it/s]\n",
            "                   all        660        453      0.949      0.905      0.962      0.772\n",
            "Stopping training early as no improvement observed in last 20 epochs. Best results observed at epoch 94, best model saved as best.pt.\n",
            "To update EarlyStopping(patience=20) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.\n",
            "\n",
            "114 epochs completed in 1.017 hours.\n",
            "Optimizer stripped from runs/detect/train3/weights/last.pt, 136.7MB\n",
            "Optimizer stripped from runs/detect/train3/weights/best.pt, 136.7MB\n",
            "\n",
            "Validating runs/detect/train3/weights/best.pt...\n",
            "Ultralytics YOLOv8.0.134 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (NVIDIA A100-SXM4-40GB, 40514MiB)\n",
            "Model summary (fused): 268 layers, 68124531 parameters, 0 gradients\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 21/21 [00:05<00:00,  4.10it/s]\n",
            "                   all        660        453      0.986      0.881      0.959      0.789\n",
            "Speed: 0.2ms preprocess, 1.9ms inference, 0.0ms loss, 0.5ms postprocess per image\n",
            "Results saved to \u001b[1mruns/detect/train3\u001b[0m\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/tsuize/yolov8-differentversion/cb3c722522a64581b1d1cbc8cf2b5d4e\u001b[0m\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr/pg0 [115]               : (0.0006612903225806452, 0.00197487688172043)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr/pg1 [115]               : (0.0006612903225806452, 0.00197487688172043)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr/pg2 [115]               : (0.0006612903225806452, 0.00197487688172043)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP50(B) [230]     : (0.00057, 0.96162)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP50-95(B) [230]  : (0.00018, 0.78923)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision(B) [230] : (0.00152, 0.9856402382249053)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall(B) [230]    : (0.03311, 0.92052)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model/GFLOPs               : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model/parameters           : 68153571\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model/speed_PyTorch(ms)    : 2.338\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [14250]     : (0.54888, 1.61954)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [14250]     : (0.31149, 4.9882)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/dfl_loss [14250]     : (0.8254, 1.44872)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [228]         : (0.74315, 3.95774)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [228]         : (0.40216, inf)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/dfl_loss [228]         : (1.0238, 8.10995)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_batch_logging_interval  : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     log_confusion_matrix_on_eval : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     log_image_predictions        : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     max_image_predictions        : 100\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     agnostic_nms    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     amp             : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     augment         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch           : 16\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box             : 7.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boxes           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cfg             : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     classes         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     close_mosaic    : 10\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls             : 0.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conf            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste      : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     data            : /content/vesselDetection-17/data.yaml\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees         : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     deterministic   : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device          : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dfl             : 1.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dnn             : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dropout         : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dynamic         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epochs          : 200\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr          : 0.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud          : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     format          : torchscript\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fraction        : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     half            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h           : 0.015\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s           : 0.7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v           : 0.4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz           : 640\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     int8            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou             : 0.7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     keras           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     kobj            : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     line_width      : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0             : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf             : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mask_ratio      : 4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     max_det         : 300\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup           : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mode            : train\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model           : yolov8x.pt\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum        : 0.937\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic          : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nbs             : 64\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nms             : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     opset           : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimize        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer       : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     overlap_mask    : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience        : 20\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective     : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     plots           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pose            : 12.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pretrained      : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     profile         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     retina_masks    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save            : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_conf       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_crop       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir        : runs/detect/train3\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_hybrid     : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_json       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period     : -1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_txt        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale           : 0.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed            : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear           : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     show            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     show_conf       : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     show_labels     : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     simplify        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     source          : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     split           : val\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     task            : detect\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tracker         : botsort.yaml\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate       : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     v5loader        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val             : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     verbose         : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     vid_stride      : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     visualize       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr  : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs   : 3.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum : 0.8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay    : 0.0005\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers         : 8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workspace       : 4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix    : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     filename            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images              : 10\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages  : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model-element       : 1 (130.35 MB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     source_code         : 2 (12.96 KB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
            "\u001b[1;38;5;196mCOMET ERROR:\u001b[0m Error sending a notification, make sure you have opted-in for notifications\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m To get all data logged automatically, import comet_ml before the following modules: torch.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for metadata to finish uploading (timeout is 3600 seconds)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Uploading 1 metrics, params and output messages\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still uploading 1 file(s), remaining 10.18 MB/132.18 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Validate on orcasound data"
      ],
      "metadata": {
        "id": "pjjhPex0BVEt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!yolo task=detect mode=val model=/content/runs/detect/train5/weights/best.pt data=/content/vesselDetection-14/data.yaml"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NH1OMbsdBTdu",
        "outputId": "f0eb7b6b-0c91-4a89-e891-44f8dbb16576"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ultralytics YOLOv8.0.134 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (NVIDIA A100-SXM4-40GB, 40514MiB)\n",
            "Model summary (fused): 218 layers, 25840339 parameters, 0 gradients\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/vesselDetection-14/valid/labels.cache... 2641 images, 758 backgrounds, 0 corrupt: 100% 2641/2641 [00:00<?, ?it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 166/166 [00:15<00:00, 11.04it/s]\n",
            "                   all       2641       2075      0.369      0.224      0.196     0.0932\n",
            "Speed: 0.1ms preprocess, 1.7ms inference, 0.0ms loss, 0.6ms postprocess per image\n",
            "Results saved to \u001b[1mruns/detect/val3\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install roboflow\n",
        "\n",
        "from roboflow import Roboflow\n",
        "rf = Roboflow(api_key=\"9FKfFVyybJD9PRo1D1DI\")\n",
        "project = rf.workspace(\"cuize\").project(\"vesseldetection_lajolla\")\n",
        "dataset = project.version(4).download(\"yolov8\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m3TRiGHBCMKC",
        "outputId": "d269bbce-b571-412b-f216-505f5098584d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: roboflow in /usr/local/lib/python3.10/dist-packages (1.1.7)\n",
            "Requirement already satisfied: certifi==2022.12.7 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2022.12.7)\n",
            "Requirement already satisfied: chardet==4.0.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.0.0)\n",
            "Requirement already satisfied: cycler==0.10.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (0.10.0)\n",
            "Requirement already satisfied: idna==2.10 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.10)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.4.5)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from roboflow) (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.23.5)\n",
            "Requirement already satisfied: opencv-python-headless==4.8.0.74 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.8.0.74)\n",
            "Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from roboflow) (9.4.0)\n",
            "Requirement already satisfied: pyparsing==2.4.7 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.4.7)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.8.2)\n",
            "Requirement already satisfied: python-dotenv in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.0.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.31.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.16.0)\n",
            "Requirement already satisfied: supervision in /usr/local/lib/python3.10/dist-packages (from roboflow) (0.14.0)\n",
            "Requirement already satisfied: urllib3>=1.26.6 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.0.4)\n",
            "Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.66.1)\n",
            "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (6.0.1)\n",
            "Requirement already satisfied: requests-toolbelt in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.0.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (1.1.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (4.42.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (23.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->roboflow) (3.2.0)\n",
            "Requirement already satisfied: scipy<2.0.0,>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from supervision->roboflow) (1.11.2)\n",
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading Dataset Version Zip in vesselDetection_LaJolla-4 to yolov8:: 100%|██████████| 98751/98751 [00:05<00:00, 17387.68it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "Extracting Dataset Version Zip to vesselDetection_LaJolla-4 in yolov8:: 100%|██████████| 307/307 [00:00<00:00, 1412.06it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "U0fXYgkHBauI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!yolo task=detect \\\n",
        "mode=predict \\\n",
        "model= /content/drive/MyDrive/train2/weights/best.pt \\\n",
        "source= /content/2023_02_02T14m11m56_jpg.rf.1a10cf5ea00e78e4bba14e82b53d1d55.jpg\n",
        "save_txt = True\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sAnJpIGltLun",
        "outputId": "8e79ee86-8ac5-440d-a788-84ad2dd1c7aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ultralytics YOLOv8.0.134 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
            "Model summary (fused): 218 layers, 25840339 parameters, 0 gradients\n",
            "\n",
            "image 1/1 /content/2023_02_02T14m11m56_jpg.rf.1a10cf5ea00e78e4bba14e82b53d1d55.jpg: 352x640 2 vessels, 63.1ms\n",
            "Speed: 2.3ms preprocess, 63.1ms inference, 1.8ms postprocess per image at shape (1, 3, 352, 640)\n",
            "Results saved to \u001b[1mruns/detect/predict3\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r predict.zip /content/runs/detect/predict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IUtAQBX_AICU",
        "outputId": "75f19beb-7006-4dda-ac39-80b78fe20724"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: content/runs/detect/predict/ (stored 0%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T11m20m53.jpg (deflated 20%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T12m29m51.jpg (deflated 18%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T09m08m44.jpg (deflated 25%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T13m04m28.jpg (deflated 8%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T12m10m25.jpg (deflated 13%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T12m53m00.jpg (deflated 13%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T10m28m51.jpg (deflated 18%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T09m59m04.jpg (deflated 14%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T09m38m52.jpg (deflated 18%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T16m33m44.jpg (deflated 31%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T10m07m23.jpg (deflated 16%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T10m16m05.jpg (deflated 16%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T10m33m16.jpg (deflated 31%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T13m14m33.jpg (deflated 11%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T06m47m28.jpg (deflated 15%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T15m06m32.jpg (deflated 8%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T09m13m41.jpg (deflated 20%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T08m04m52.jpg (deflated 13%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T14m31m17.jpg (deflated 15%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T09m31m12.jpg (deflated 18%)\n",
            "  adding: content/runs/detect/predict/2023_02_01T12m58m31.jpg (deflated 9%)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "shutil.move('/content/predict.zip','/content/drive/MyDrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "AuyAUDiG9mHx",
        "outputId": "8901c32a-031d-4a9f-d7cc-3bb7ab76359e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "Error",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mError\u001b[0m                                     Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-1c646b6e1acc>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mshutil\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mshutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/predict.zip'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'/content/drive/MyDrive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/lib/python3.10/shutil.py\u001b[0m in \u001b[0;36mmove\u001b[0;34m(src, dst, copy_function)\u001b[0m\n\u001b[1;32m    812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    813\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreal_dst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 814\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Destination path '%s' already exists\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mreal_dst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    815\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    816\u001b[0m         \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal_dst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mError\u001b[0m: Destination path '/content/drive/MyDrive/predict.zip' already exists"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.download(\"/content/predict.zip\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "S1uciwcU4sHg",
        "outputId": "18cfe509-90ba-4bfd-8b29-fde517e5717f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_0eac9a90-9ce5-4b3c-9689-292c8e310f1e\", \"predict.zip\", 490488831)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}