Project Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Table of Contents**\n",
    "---\n",
    "* [Introduction](#Introduction)\n",
    "* [Line Coordinates](#Line-Coordinates)\n",
    "* [Install YOLOv8 and ByteTrack](#Install-YOLOv8-ByteTrack)\n",
    "* [Video Information Extraction](#Video-Information-Extraction)\n",
    "* [Vehicle Count](#Vehicle-Count)\n",
    "* [Usage](#Usage)\n",
    "* [Detection on Single frame]()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Introduction**\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the exciting field of object tracking, we embark on a project to deploy the advanced deep learning models, YOLOv8 and ByteTrack, to track the number of vehicles entering and leaving a bustling highway. Our aim is to craft an efficient and reliable system that can perform real-time detection and tracking of vehicles, while simultaneously keeping count of their movements. These models have shown remarkable performance in object detection and tracking tasks, and we will leverage their strengths to develop a custom pipeline that ensures highly accurate and efficient vehicle tracking."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Line Coordinates**\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to draw a line in a desired location in the video, you'll need to obtain the coordinates for that line. One easy way to do this is by extracting a frame from the video and then using a website to get the coordinates.\n",
    "\n",
    "1. **Extract a frame from the video.** You can use a video editor or a Python library like OpenCV or Supervision to extract a frame from the video.\n",
    "\n",
    "2. **Go to the website [PolygoneZone](https://roboflow.github.io/polygonzone/](https://roboflow.github.io/polygonzone/).** This website allows you to easily draw a polygon on the image and get the coordinates for that polygon.\n",
    "\n",
    "3. **Upload the extracted frame to the website.** Click on the \"Choose File\" button and select the extracted frame from your computer.\n",
    "\n",
    "4. **Draw a line on the image.** Use the polygon drawing tool to draw a line in the desired location on the image.\n",
    "\n",
    "5. **Get the line coordinates.** Once you have drawn the line, the website will display the coordinates for the line. These coordinates can be copied and used in your Python code to draw the line in the desired location in the video.\n",
    "\n",
    "By following above given simple steps, you can easily obtain the coordinates for drawing a line in the desired location in the video using a website."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install YOLOv8 and ByteTrack\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Ultralytics YOLOv8.0.81 🚀 Python-3.9.12 torch-1.12.1+cu102 CPU\n",
      "Setup complete ✅ (8 CPUs, 7.7 GB RAM, 309.6/376.9 GB disk)\n"
     ]
    }
   ],
   "source": [
    "# Pip install method (recommended)\n",
    "!pip install ultralytics\n",
    "\n",
    "from IPython import display\n",
    "display.clear_output()\n",
    "\n",
    "import ultralytics\n",
    "ultralytics.checks()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "supervision.__version__: 0.1.0\n",
      "yolox.__version__: 0.1.0\n"
     ]
    }
   ],
   "source": [
    "#!git clone https://github.com/ifzhang/ByteTrack.git\n",
    "%cd ByteTrack\n",
    "\n",
    "# workaround related to https://github.com/roboflow/notebooks/issues/80\n",
    "!sed -i 's/onnx==1.8.1/onnx==1.9.0/g' requirements.txt\n",
    "\n",
    "!pip3 install -q -r requirements.txt\n",
    "!python3 setup.py -q develop\n",
    "!pip install -q cython_bbox\n",
    "!pip install -q onemetric\n",
    "!pip install supervision==0.1.0\n",
    "\n",
    "from IPython import display\n",
    "display.clear_output()\n",
    "\n",
    "import sys\n",
    "sys.path.append(f\"/ByteTrack\")\n",
    "\n",
    "import supervision\n",
    "import yolox\n",
    "from ultralytics import YOLO\n",
    "from IPython import display\n",
    "display.clear_output()\n",
    "from supervision.draw.color import ColorPalette\n",
    "from supervision.geometry.dataclasses import Point\n",
    "from supervision.video.source import get_video_frames_generator\n",
    "from supervision.video.sink import VideoSink\n",
    "from supervision.notebook.utils import show_frame_in_notebook\n",
    "from supervision.tools.detections import Detections, BoxAnnotator\n",
    "from supervision.tools.line_counter import LineCounter, LineCounterAnnotator\n",
    "from supervision.video.dataclasses import VideoInfo\n",
    "from tqdm.notebook import tqdm\n",
    "from typing import List\n",
    "import numpy as np\n",
    "from yolox.tracker.byte_tracker import BYTETracker, STrack\n",
    "from onemetric.cv.utils.iou import box_iou_batch\n",
    "from dataclasses import dataclass\n",
    "\n",
    "@dataclass(frozen=True)\n",
    "class BYTETrackerArgs:\n",
    "    track_thresh: float = 0.25\n",
    "    track_buffer: int = 30\n",
    "    match_thresh: float = 0.8\n",
    "    aspect_ratio_thresh: float = 3.0\n",
    "    min_box_area: float = 1.0\n",
    "    mot20: bool = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load pre-trained YOLOv8 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# settings\n",
    "MODEL = \"yolov8x.pt\"\n",
    "model = YOLO(MODEL)\n",
    "model.fuse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converts Detections into format that can be consumed by match_detections_with_tracks function\n",
    "def detections2boxes(detections: Detections) -> np.ndarray:\n",
    "    return np.hstack((\n",
    "        detections.xyxy,\n",
    "        detections.confidence[:, np.newaxis]\n",
    "    ))\n",
    "\n",
    "\n",
    "# converts List[STrack] into format that can be consumed by match_detections_with_tracks function\n",
    "def tracks2boxes(tracks: List[STrack]) -> np.ndarray:\n",
    "    return np.array([\n",
    "        track.tlbr\n",
    "        for track\n",
    "        in tracks\n",
    "    ], dtype=float)\n",
    "\n",
    "\n",
    "# matches our bounding boxes with predictions\n",
    "def match_detections_with_tracks(\n",
    "    detections: Detections, \n",
    "    tracks: List[STrack]\n",
    ") -> Detections:\n",
    "    if not np.any(detections.xyxy) or len(tracks) == 0:\n",
    "        return np.empty((0,))\n",
    "\n",
    "    tracks_boxes = tracks2boxes(tracks=tracks)\n",
    "    iou = box_iou_batch(tracks_boxes, detections.xyxy)\n",
    "    track2detection = np.argmax(iou, axis=1)\n",
    "    \n",
    "    tracker_ids = [None] * len(detections)\n",
    "    \n",
    "    for tracker_index, detection_index in enumerate(track2detection):\n",
    "        if iou[tracker_index, detection_index] != 0:\n",
    "            tracker_ids[detection_index] = tracks[tracker_index].track_id\n",
    "\n",
    "    return tracker_ids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Video Information Extraction**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VideoInfo(width=1280, height=720, fps=30, total_frames=942)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Select Paths of input video and output video\n",
    "\n",
    "SOURCE_VIDEO_PATH=SOURCE_VIDEO_PATH=\"/home/niraj/projects/vehicle count/input/test.mp4\"\n",
    "TARGET_VIDEO_PATH = f\"/home/niraj/projects/vehicle count/Results/output.mp4\"\n",
    "VideoInfo.from_video_path(SOURCE_VIDEO_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detection on Single frame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0: 384x640 11 cars, 1 bus, 2 trucks, 872.3ms\n",
      "Speed: 0.7ms preprocess, 872.3ms inference, 63.9ms postprocess per image at shape (1, 3, 640, 640)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# dict maping class_id to class_name\n",
    "CLASS_NAMES_DICT = model.model.names\n",
    "# class_ids of interest - car, motorcycle, bus and truck\n",
    "CLASS_ID = [2, 3, 5, 7]\n",
    "\n",
    "# create frame generator\n",
    "generator = get_video_frames_generator(SOURCE_VIDEO_PATH)\n",
    "# create instance of BoxAnnotator\n",
    "\n",
    "box_annotator = BoxAnnotator(color=ColorPalette(), thickness=1, text_thickness=1, text_scale=0.5)\n",
    "# acquire first video frame\n",
    "iterator = iter(generator)\n",
    "frame = next(iterator)\n",
    "# model prediction on single frame and conversion to supervision Detections\n",
    "results = model(frame)\n",
    "detections = Detections(\n",
    "    xyxy=results[0].boxes.xyxy.cpu().numpy(),\n",
    "    confidence=results[0].boxes.conf.cpu().numpy(),\n",
    "    class_id=results[0].boxes.cls.cpu().numpy().astype(int)\n",
    ")\n",
    "# format custom labels\n",
    "labels = [\n",
    "    f\"{CLASS_NAMES_DICT[class_id]} {confidence:0.2f}\"\n",
    "    for _, confidence, class_id, tracker_id\n",
    "    in detections\n",
    "]\n",
    "# annotate and display frame\n",
    "frame = box_annotator.annotate(frame=frame, detections=detections, labels=labels)\n",
    "\n",
    "%matplotlib inline\n",
    "show_frame_in_notebook(frame, (16, 16))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# settings\n",
    "LINE_START = Point(330, 390)\n",
    "LINE_END = Point(920, 386)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9172937da9504862b14e57238d512dc8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/942 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0: 384x640 11 cars, 1 bus, 2 trucks, 933.6ms\n",
      "Speed: 0.9ms preprocess, 933.6ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 bus, 2 trucks, 961.9ms\n",
      "Speed: 1.9ms preprocess, 961.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 bus, 2 trucks, 1033.7ms\n",
      "Speed: 0.8ms preprocess, 1033.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 1 bus, 2 trucks, 967.5ms\n",
      "Speed: 0.8ms preprocess, 967.5ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 bus, 2 trucks, 998.4ms\n",
      "Speed: 2.2ms preprocess, 998.4ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 bus, 3 trucks, 996.3ms\n",
      "Speed: 1.0ms preprocess, 996.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 2 trucks, 1080.3ms\n",
      "Speed: 0.9ms preprocess, 1080.3ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 1 truck, 1293.7ms\n",
      "Speed: 0.8ms preprocess, 1293.7ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 1 truck, 869.7ms\n",
      "Speed: 0.7ms preprocess, 869.7ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 1 truck, 846.6ms\n",
      "Speed: 0.9ms preprocess, 846.6ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 1 truck, 980.3ms\n",
      "Speed: 0.8ms preprocess, 980.3ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 1 truck, 916.8ms\n",
      "Speed: 1.7ms preprocess, 916.8ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 1 truck, 1 traffic light, 982.9ms\n",
      "Speed: 0.9ms preprocess, 982.9ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 1 truck, 1 traffic light, 984.6ms\n",
      "Speed: 1.2ms preprocess, 984.6ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 2 trucks, 1 traffic light, 950.4ms\n",
      "Speed: 1.1ms preprocess, 950.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 bus, 2 trucks, 1 traffic light, 1022.5ms\n",
      "Speed: 0.9ms preprocess, 1022.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 bus, 2 trucks, 1 traffic light, 900.8ms\n",
      "Speed: 0.8ms preprocess, 900.8ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 3 trucks, 874.7ms\n",
      "Speed: 1.0ms preprocess, 874.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 3 trucks, 897.1ms\n",
      "Speed: 0.8ms preprocess, 897.1ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 906.6ms\n",
      "Speed: 0.9ms preprocess, 906.6ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 891.3ms\n",
      "Speed: 0.9ms preprocess, 891.3ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 1 bus, 2 trucks, 874.4ms\n",
      "Speed: 0.9ms preprocess, 874.4ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 3 trucks, 925.9ms\n",
      "Speed: 0.9ms preprocess, 925.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 1 bus, 2 trucks, 1388.2ms\n",
      "Speed: 0.9ms preprocess, 1388.2ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 2 trucks, 978.3ms\n",
      "Speed: 1.0ms preprocess, 978.3ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 1 bus, 1 truck, 1042.4ms\n",
      "Speed: 1.2ms preprocess, 1042.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 1 bus, 1 truck, 1337.3ms\n",
      "Speed: 2.2ms preprocess, 1337.3ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 bus, 1 truck, 1137.6ms\n",
      "Speed: 0.9ms preprocess, 1137.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 1 bus, 2 trucks, 883.6ms\n",
      "Speed: 1.0ms preprocess, 883.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 bus, 2 trucks, 864.0ms\n",
      "Speed: 0.7ms preprocess, 864.0ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 bus, 2 trucks, 899.7ms\n",
      "Speed: 1.2ms preprocess, 899.7ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 bus, 2 trucks, 885.2ms\n",
      "Speed: 0.9ms preprocess, 885.2ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 bus, 2 trucks, 893.7ms\n",
      "Speed: 1.1ms preprocess, 893.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 bus, 2 trucks, 871.2ms\n",
      "Speed: 1.0ms preprocess, 871.2ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 bus, 3 trucks, 908.5ms\n",
      "Speed: 0.8ms preprocess, 908.5ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 1 bus, 2 trucks, 1101.9ms\n",
      "Speed: 0.9ms preprocess, 1101.9ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 bus, 2 trucks, 885.5ms\n",
      "Speed: 1.2ms preprocess, 885.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 1 bus, 3 trucks, 1090.2ms\n",
      "Speed: 0.9ms preprocess, 1090.2ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 1 bus, 3 trucks, 1274.9ms\n",
      "Speed: 0.8ms preprocess, 1274.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 1 bus, 2 trucks, 1126.0ms\n",
      "Speed: 1.0ms preprocess, 1126.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 1 bus, 2 trucks, 849.2ms\n",
      "Speed: 0.7ms preprocess, 849.2ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 1 bus, 2 trucks, 862.2ms\n",
      "Speed: 1.0ms preprocess, 862.2ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 10 cars, 1 bus, 2 trucks, 980.0ms\n",
      "Speed: 1.1ms preprocess, 980.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 995.8ms\n",
      "Speed: 0.9ms preprocess, 995.8ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 979.9ms\n",
      "Speed: 2.3ms preprocess, 979.9ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 bus, 3 trucks, 915.3ms\n",
      "Speed: 0.8ms preprocess, 915.3ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 1 truck, 1050.1ms\n",
      "Speed: 0.9ms preprocess, 1050.1ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 1 truck, 910.0ms\n",
      "Speed: 1.1ms preprocess, 910.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 bus, 1 truck, 905.5ms\n",
      "Speed: 0.7ms preprocess, 905.5ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 3 trucks, 891.6ms\n",
      "Speed: 1.1ms preprocess, 891.6ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 3 trucks, 905.9ms\n",
      "Speed: 0.8ms preprocess, 905.9ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 3 trucks, 857.4ms\n",
      "Speed: 0.8ms preprocess, 857.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 bus, 3 trucks, 877.7ms\n",
      "Speed: 2.1ms preprocess, 877.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 913.3ms\n",
      "Speed: 0.9ms preprocess, 913.3ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 1168.0ms\n",
      "Speed: 0.9ms preprocess, 1168.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 1 bus, 2 trucks, 1154.1ms\n",
      "Speed: 0.8ms preprocess, 1154.1ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 1 bus, 2 trucks, 890.3ms\n",
      "Speed: 0.8ms preprocess, 890.3ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0: 384x640 15 cars, 1 bus, 2 trucks, 923.3ms\n",
      "Speed: 1.1ms preprocess, 923.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 1 truck, 915.6ms\n",
      "Speed: 0.8ms preprocess, 915.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 2 trucks, 899.5ms\n",
      "Speed: 1.2ms preprocess, 899.5ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 1 truck, 991.5ms\n",
      "Speed: 2.1ms preprocess, 991.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 2 trucks, 933.7ms\n",
      "Speed: 0.8ms preprocess, 933.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 2 trucks, 980.7ms\n",
      "Speed: 0.8ms preprocess, 980.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 2 trucks, 930.8ms\n",
      "Speed: 0.9ms preprocess, 930.8ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 2 trucks, 1015.4ms\n",
      "Speed: 1.1ms preprocess, 1015.4ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 3 trucks, 911.0ms\n",
      "Speed: 0.9ms preprocess, 911.0ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 3 trucks, 914.5ms\n",
      "Speed: 1.0ms preprocess, 914.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 966.7ms\n",
      "Speed: 0.8ms preprocess, 966.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 936.7ms\n",
      "Speed: 0.8ms preprocess, 936.7ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 2 trucks, 967.5ms\n",
      "Speed: 0.9ms preprocess, 967.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 2 trucks, 1208.5ms\n",
      "Speed: 1.1ms preprocess, 1208.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 3 trucks, 1238.6ms\n",
      "Speed: 1.1ms preprocess, 1238.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 2 trucks, 928.1ms\n",
      "Speed: 2.0ms preprocess, 928.1ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 2 trucks, 982.4ms\n",
      "Speed: 0.8ms preprocess, 982.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 2 trucks, 929.8ms\n",
      "Speed: 0.9ms preprocess, 929.8ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 2 trucks, 928.3ms\n",
      "Speed: 0.8ms preprocess, 928.3ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 1 truck, 984.1ms\n",
      "Speed: 1.0ms preprocess, 984.1ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 1 truck, 947.7ms\n",
      "Speed: 0.9ms preprocess, 947.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 2 trucks, 940.9ms\n",
      "Speed: 1.1ms preprocess, 940.9ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 2 trucks, 898.8ms\n",
      "Speed: 1.0ms preprocess, 898.8ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 1 truck, 900.6ms\n",
      "Speed: 1.0ms preprocess, 900.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 3 trucks, 908.4ms\n",
      "Speed: 1.1ms preprocess, 908.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 3 trucks, 922.9ms\n",
      "Speed: 0.9ms preprocess, 922.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 3 trucks, 886.4ms\n",
      "Speed: 0.9ms preprocess, 886.4ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 3 trucks, 893.6ms\n",
      "Speed: 0.8ms preprocess, 893.6ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 3 trucks, 957.2ms\n",
      "Speed: 0.8ms preprocess, 957.2ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 3 trucks, 1268.1ms\n",
      "Speed: 0.9ms preprocess, 1268.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 2 trucks, 1213.0ms\n",
      "Speed: 0.9ms preprocess, 1213.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 1 truck, 1011.3ms\n",
      "Speed: 0.8ms preprocess, 1011.3ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 1 truck, 988.7ms\n",
      "Speed: 1.0ms preprocess, 988.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 2 trucks, 1047.2ms\n",
      "Speed: 1.1ms preprocess, 1047.2ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 916.2ms\n",
      "Speed: 0.7ms preprocess, 916.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 914.0ms\n",
      "Speed: 0.8ms preprocess, 914.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 902.9ms\n",
      "Speed: 0.8ms preprocess, 902.9ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 941.3ms\n",
      "Speed: 1.1ms preprocess, 941.3ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 1067.7ms\n",
      "Speed: 0.8ms preprocess, 1067.7ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 983.8ms\n",
      "Speed: 0.8ms preprocess, 983.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 976.0ms\n",
      "Speed: 0.8ms preprocess, 976.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 954.7ms\n",
      "Speed: 1.0ms preprocess, 954.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 963.9ms\n",
      "Speed: 0.8ms preprocess, 963.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 997.6ms\n",
      "Speed: 2.0ms preprocess, 997.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 1100.1ms\n",
      "Speed: 1.1ms preprocess, 1100.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 1388.0ms\n",
      "Speed: 0.8ms preprocess, 1388.0ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 985.0ms\n",
      "Speed: 1.0ms preprocess, 985.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 1034.3ms\n",
      "Speed: 1.0ms preprocess, 1034.3ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 986.3ms\n",
      "Speed: 0.8ms preprocess, 986.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 949.7ms\n",
      "Speed: 0.8ms preprocess, 949.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 1011.6ms\n",
      "Speed: 0.8ms preprocess, 1011.6ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 957.7ms\n",
      "Speed: 0.9ms preprocess, 957.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 1021.9ms\n",
      "Speed: 1.0ms preprocess, 1021.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 951.9ms\n",
      "Speed: 1.4ms preprocess, 951.9ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 992.0ms\n",
      "Speed: 0.8ms preprocess, 992.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 942.7ms\n",
      "Speed: 1.1ms preprocess, 942.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 926.1ms\n",
      "Speed: 1.1ms preprocess, 926.1ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 920.9ms\n",
      "Speed: 1.0ms preprocess, 920.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 903.5ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Speed: 0.8ms preprocess, 903.5ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 912.3ms\n",
      "Speed: 1.1ms preprocess, 912.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 1388.0ms\n",
      "Speed: 1.0ms preprocess, 1388.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 1249.3ms\n",
      "Speed: 1.0ms preprocess, 1249.3ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 999.8ms\n",
      "Speed: 0.7ms preprocess, 999.8ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 915.4ms\n",
      "Speed: 0.9ms preprocess, 915.4ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 977.7ms\n",
      "Speed: 1.4ms preprocess, 977.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 964.0ms\n",
      "Speed: 1.0ms preprocess, 964.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1114.0ms\n",
      "Speed: 0.8ms preprocess, 1114.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 987.7ms\n",
      "Speed: 0.8ms preprocess, 987.7ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 969.3ms\n",
      "Speed: 0.9ms preprocess, 969.3ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 938.0ms\n",
      "Speed: 1.0ms preprocess, 938.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 890.7ms\n",
      "Speed: 1.1ms preprocess, 890.7ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 912.1ms\n",
      "Speed: 0.9ms preprocess, 912.1ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 900.0ms\n",
      "Speed: 1.0ms preprocess, 900.0ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 891.6ms\n",
      "Speed: 1.2ms preprocess, 891.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 891.0ms\n",
      "Speed: 0.8ms preprocess, 891.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 937.5ms\n",
      "Speed: 1.0ms preprocess, 937.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 1326.1ms\n",
      "Speed: 1.0ms preprocess, 1326.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 922.0ms\n",
      "Speed: 2.0ms preprocess, 922.0ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 899.2ms\n",
      "Speed: 0.9ms preprocess, 899.2ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 920.2ms\n",
      "Speed: 0.9ms preprocess, 920.2ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 4 trucks, 902.1ms\n",
      "Speed: 1.5ms preprocess, 902.1ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 915.2ms\n",
      "Speed: 1.0ms preprocess, 915.2ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 896.4ms\n",
      "Speed: 1.5ms preprocess, 896.4ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 905.4ms\n",
      "Speed: 0.9ms preprocess, 905.4ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 894.1ms\n",
      "Speed: 0.8ms preprocess, 894.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 932.0ms\n",
      "Speed: 2.7ms preprocess, 932.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 1046.0ms\n",
      "Speed: 0.8ms preprocess, 1046.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 980.7ms\n",
      "Speed: 0.9ms preprocess, 980.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 902.4ms\n",
      "Speed: 0.8ms preprocess, 902.4ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 892.3ms\n",
      "Speed: 0.8ms preprocess, 892.3ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 913.4ms\n",
      "Speed: 2.2ms preprocess, 913.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 913.0ms\n",
      "Speed: 0.8ms preprocess, 913.0ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 1448.2ms\n",
      "Speed: 1.0ms preprocess, 1448.2ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1156.7ms\n",
      "Speed: 0.8ms preprocess, 1156.7ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 952.8ms\n",
      "Speed: 2.0ms preprocess, 952.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 1020.5ms\n",
      "Speed: 0.8ms preprocess, 1020.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 973.5ms\n",
      "Speed: 0.9ms preprocess, 973.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 981.5ms\n",
      "Speed: 0.8ms preprocess, 981.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 972.3ms\n",
      "Speed: 1.3ms preprocess, 972.3ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 1017.2ms\n",
      "Speed: 0.9ms preprocess, 1017.2ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 984.0ms\n",
      "Speed: 1.1ms preprocess, 984.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 936.6ms\n",
      "Speed: 1.1ms preprocess, 936.6ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 918.5ms\n",
      "Speed: 0.8ms preprocess, 918.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 1 traffic light, 923.3ms\n",
      "Speed: 2.3ms preprocess, 923.3ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 3 trucks, 1 traffic light, 929.3ms\n",
      "Speed: 1.0ms preprocess, 929.3ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 1 traffic light, 940.7ms\n",
      "Speed: 0.8ms preprocess, 940.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 4 trucks, 1 traffic light, 934.0ms\n",
      "Speed: 0.8ms preprocess, 934.0ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 4 trucks, 1 traffic light, 1182.3ms\n",
      "Speed: 0.8ms preprocess, 1182.3ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1313.7ms\n",
      "Speed: 0.9ms preprocess, 1313.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 943.9ms\n",
      "Speed: 1.0ms preprocess, 943.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 930.8ms\n",
      "Speed: 1.0ms preprocess, 930.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 966.1ms\n",
      "Speed: 1.1ms preprocess, 966.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 987.1ms\n",
      "Speed: 1.1ms preprocess, 987.1ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 936.1ms\n",
      "Speed: 0.8ms preprocess, 936.1ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 1025.6ms\n",
      "Speed: 1.0ms preprocess, 1025.6ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 960.8ms\n",
      "Speed: 0.9ms preprocess, 960.8ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 956.5ms\n",
      "Speed: 0.8ms preprocess, 956.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 939.3ms\n",
      "Speed: 0.8ms preprocess, 939.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0: 384x640 17 cars, 2 trucks, 894.9ms\n",
      "Speed: 0.9ms preprocess, 894.9ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 1030.6ms\n",
      "Speed: 2.6ms preprocess, 1030.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 942.9ms\n",
      "Speed: 1.0ms preprocess, 942.9ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 943.5ms\n",
      "Speed: 0.8ms preprocess, 943.5ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 957.5ms\n",
      "Speed: 0.9ms preprocess, 957.5ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 1384.1ms\n",
      "Speed: 0.8ms preprocess, 1384.1ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 1001.7ms\n",
      "Speed: 0.9ms preprocess, 1001.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 948.9ms\n",
      "Speed: 0.9ms preprocess, 948.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 1029.8ms\n",
      "Speed: 1.2ms preprocess, 1029.8ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 965.6ms\n",
      "Speed: 1.2ms preprocess, 965.6ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 957.3ms\n",
      "Speed: 0.9ms preprocess, 957.3ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 1048.2ms\n",
      "Speed: 0.8ms preprocess, 1048.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 3 trucks, 1014.9ms\n",
      "Speed: 0.9ms preprocess, 1014.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 3 trucks, 1043.7ms\n",
      "Speed: 0.8ms preprocess, 1043.7ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 916.5ms\n",
      "Speed: 0.8ms preprocess, 916.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 990.0ms\n",
      "Speed: 0.8ms preprocess, 990.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 1227.1ms\n",
      "Speed: 0.8ms preprocess, 1227.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 1136.8ms\n",
      "Speed: 1.0ms preprocess, 1136.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 1052.0ms\n",
      "Speed: 0.8ms preprocess, 1052.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 902.0ms\n",
      "Speed: 0.8ms preprocess, 902.0ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 1461.6ms\n",
      "Speed: 0.9ms preprocess, 1461.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 1041.7ms\n",
      "Speed: 0.9ms preprocess, 1041.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 1 truck, 1026.8ms\n",
      "Speed: 1.5ms preprocess, 1026.8ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 961.7ms\n",
      "Speed: 1.1ms preprocess, 961.7ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 3 trucks, 937.1ms\n",
      "Speed: 0.9ms preprocess, 937.1ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 3 trucks, 948.5ms\n",
      "Speed: 1.4ms preprocess, 948.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 3 trucks, 960.2ms\n",
      "Speed: 0.9ms preprocess, 960.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 959.2ms\n",
      "Speed: 0.8ms preprocess, 959.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 3 trucks, 906.8ms\n",
      "Speed: 0.9ms preprocess, 906.8ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 1 truck, 889.8ms\n",
      "Speed: 0.8ms preprocess, 889.8ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 890.6ms\n",
      "Speed: 0.8ms preprocess, 890.6ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 928.7ms\n",
      "Speed: 0.8ms preprocess, 928.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 1 truck, 919.2ms\n",
      "Speed: 1.0ms preprocess, 919.2ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 1 truck, 896.6ms\n",
      "Speed: 0.8ms preprocess, 896.6ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 905.4ms\n",
      "Speed: 0.9ms preprocess, 905.4ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 939.5ms\n",
      "Speed: 1.2ms preprocess, 939.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 1435.9ms\n",
      "Speed: 0.9ms preprocess, 1435.9ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 962.6ms\n",
      "Speed: 1.0ms preprocess, 962.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 1 truck, 912.3ms\n",
      "Speed: 0.9ms preprocess, 912.3ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 1 truck, 902.2ms\n",
      "Speed: 0.9ms preprocess, 902.2ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 1 truck, 928.7ms\n",
      "Speed: 1.5ms preprocess, 928.7ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 1 truck, 956.4ms\n",
      "Speed: 0.8ms preprocess, 956.4ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 917.6ms\n",
      "Speed: 2.0ms preprocess, 917.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 904.2ms\n",
      "Speed: 2.2ms preprocess, 904.2ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 889.4ms\n",
      "Speed: 0.7ms preprocess, 889.4ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 1 truck, 961.5ms\n",
      "Speed: 0.9ms preprocess, 961.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 1175.8ms\n",
      "Speed: 0.9ms preprocess, 1175.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 1008.2ms\n",
      "Speed: 0.9ms preprocess, 1008.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 1 truck, 977.1ms\n",
      "Speed: 0.8ms preprocess, 977.1ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 1 truck, 1007.0ms\n",
      "Speed: 2.0ms preprocess, 1007.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 1 truck, 1006.6ms\n",
      "Speed: 0.9ms preprocess, 1006.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 1 truck, 1209.8ms\n",
      "Speed: 1.6ms preprocess, 1209.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 1 truck, 1356.6ms\n",
      "Speed: 0.9ms preprocess, 1356.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 910.1ms\n",
      "Speed: 2.1ms preprocess, 910.1ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 968.1ms\n",
      "Speed: 0.9ms preprocess, 968.1ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 949.7ms\n",
      "Speed: 0.9ms preprocess, 949.7ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 1018.2ms\n",
      "Speed: 0.9ms preprocess, 1018.2ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 1 truck, 901.9ms\n",
      "Speed: 0.8ms preprocess, 901.9ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 1 truck, 1075.2ms\n",
      "Speed: 2.1ms preprocess, 1075.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 1 truck, 1026.3ms\n",
      "Speed: 0.9ms preprocess, 1026.3ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 640)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0: 384x640 18 cars, 1 truck, 1046.3ms\n",
      "Speed: 0.8ms preprocess, 1046.3ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 1 truck, 996.4ms\n",
      "Speed: 2.3ms preprocess, 996.4ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 1 truck, 964.9ms\n",
      "Speed: 0.9ms preprocess, 964.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 978.4ms\n",
      "Speed: 0.9ms preprocess, 978.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 915.5ms\n",
      "Speed: 1.8ms preprocess, 915.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 947.0ms\n",
      "Speed: 0.9ms preprocess, 947.0ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 1173.9ms\n",
      "Speed: 1.0ms preprocess, 1173.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 1390.9ms\n",
      "Speed: 1.2ms preprocess, 1390.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 911.4ms\n",
      "Speed: 0.8ms preprocess, 911.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 1074.0ms\n",
      "Speed: 1.1ms preprocess, 1074.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 1 truck, 966.5ms\n",
      "Speed: 0.8ms preprocess, 966.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 964.3ms\n",
      "Speed: 0.7ms preprocess, 964.3ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 900.5ms\n",
      "Speed: 1.5ms preprocess, 900.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 891.9ms\n",
      "Speed: 0.8ms preprocess, 891.9ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 1034.4ms\n",
      "Speed: 0.8ms preprocess, 1034.4ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 2 trucks, 1072.0ms\n",
      "Speed: 1.9ms preprocess, 1072.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 967.6ms\n",
      "Speed: 0.8ms preprocess, 967.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 979.8ms\n",
      "Speed: 1.2ms preprocess, 979.8ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 979.0ms\n",
      "Speed: 0.8ms preprocess, 979.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 896.4ms\n",
      "Speed: 2.3ms preprocess, 896.4ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 896.4ms\n",
      "Speed: 1.0ms preprocess, 896.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 900.2ms\n",
      "Speed: 0.8ms preprocess, 900.2ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1465.5ms\n",
      "Speed: 1.0ms preprocess, 1465.5ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 978.9ms\n",
      "Speed: 1.2ms preprocess, 978.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 923.8ms\n",
      "Speed: 1.0ms preprocess, 923.8ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 895.3ms\n",
      "Speed: 1.1ms preprocess, 895.3ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 925.2ms\n",
      "Speed: 1.9ms preprocess, 925.2ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 910.7ms\n",
      "Speed: 1.1ms preprocess, 910.7ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 898.7ms\n",
      "Speed: 1.0ms preprocess, 898.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 891.7ms\n",
      "Speed: 0.7ms preprocess, 891.7ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 906.1ms\n",
      "Speed: 1.1ms preprocess, 906.1ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 902.0ms\n",
      "Speed: 0.9ms preprocess, 902.0ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 894.0ms\n",
      "Speed: 0.9ms preprocess, 894.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 908.2ms\n",
      "Speed: 1.1ms preprocess, 908.2ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 910.2ms\n",
      "Speed: 1.0ms preprocess, 910.2ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 907.1ms\n",
      "Speed: 0.8ms preprocess, 907.1ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 899.9ms\n",
      "Speed: 0.9ms preprocess, 899.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 892.8ms\n",
      "Speed: 0.9ms preprocess, 892.8ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 1148.5ms\n",
      "Speed: 0.8ms preprocess, 1148.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 2 trucks, 1243.6ms\n",
      "Speed: 1.8ms preprocess, 1243.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 902.4ms\n",
      "Speed: 0.9ms preprocess, 902.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 893.5ms\n",
      "Speed: 0.8ms preprocess, 893.5ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 908.7ms\n",
      "Speed: 1.0ms preprocess, 908.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 892.6ms\n",
      "Speed: 0.7ms preprocess, 892.6ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 902.5ms\n",
      "Speed: 0.8ms preprocess, 902.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 894.7ms\n",
      "Speed: 0.9ms preprocess, 894.7ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 896.5ms\n",
      "Speed: 0.8ms preprocess, 896.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 928.5ms\n",
      "Speed: 1.2ms preprocess, 928.5ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 911.1ms\n",
      "Speed: 0.8ms preprocess, 911.1ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 908.6ms\n",
      "Speed: 1.4ms preprocess, 908.6ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 903.6ms\n",
      "Speed: 1.1ms preprocess, 903.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 905.8ms\n",
      "Speed: 0.9ms preprocess, 905.8ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 889.8ms\n",
      "Speed: 0.9ms preprocess, 889.8ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 902.3ms\n",
      "Speed: 0.9ms preprocess, 902.3ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 895.2ms\n",
      "Speed: 0.9ms preprocess, 895.2ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 1440.3ms\n",
      "Speed: 1.0ms preprocess, 1440.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 936.0ms\n",
      "Speed: 1.0ms preprocess, 936.0ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 892.0ms\n",
      "Speed: 0.8ms preprocess, 892.0ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 913.7ms\n",
      "Speed: 1.3ms preprocess, 913.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 899.9ms\n",
      "Speed: 0.9ms preprocess, 899.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0: 384x640 13 cars, 2 trucks, 1083.5ms\n",
      "Speed: 1.1ms preprocess, 1083.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 1065.9ms\n",
      "Speed: 1.1ms preprocess, 1065.9ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 1005.1ms\n",
      "Speed: 1.0ms preprocess, 1005.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 985.4ms\n",
      "Speed: 0.9ms preprocess, 985.4ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 1145.4ms\n",
      "Speed: 0.9ms preprocess, 1145.4ms inference, 2.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 1019.5ms\n",
      "Speed: 0.8ms preprocess, 1019.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 1037.6ms\n",
      "Speed: 0.9ms preprocess, 1037.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 1015.5ms\n",
      "Speed: 0.8ms preprocess, 1015.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 truck, 1095.4ms\n",
      "Speed: 0.9ms preprocess, 1095.4ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 truck, 1011.7ms\n",
      "Speed: 2.1ms preprocess, 1011.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 1 truck, 1508.0ms\n",
      "Speed: 0.9ms preprocess, 1508.0ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1027.6ms\n",
      "Speed: 1.5ms preprocess, 1027.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 truck, 920.4ms\n",
      "Speed: 0.9ms preprocess, 920.4ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 975.8ms\n",
      "Speed: 1.1ms preprocess, 975.8ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 1001.4ms\n",
      "Speed: 0.9ms preprocess, 1001.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 2 trucks, 924.9ms\n",
      "Speed: 0.8ms preprocess, 924.9ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 1005.6ms\n",
      "Speed: 1.0ms preprocess, 1005.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 1034.2ms\n",
      "Speed: 1.1ms preprocess, 1034.2ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 981.9ms\n",
      "Speed: 0.8ms preprocess, 981.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 927.6ms\n",
      "Speed: 0.7ms preprocess, 927.6ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 957.7ms\n",
      "Speed: 0.9ms preprocess, 957.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 892.0ms\n",
      "Speed: 0.8ms preprocess, 892.0ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 993.9ms\n",
      "Speed: 0.8ms preprocess, 993.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 980.5ms\n",
      "Speed: 0.8ms preprocess, 980.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 926.6ms\n",
      "Speed: 0.9ms preprocess, 926.6ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1257.7ms\n",
      "Speed: 0.8ms preprocess, 1257.7ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1226.6ms\n",
      "Speed: 1.0ms preprocess, 1226.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1002.4ms\n",
      "Speed: 0.7ms preprocess, 1002.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 946.8ms\n",
      "Speed: 0.9ms preprocess, 946.8ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 2 trucks, 959.7ms\n",
      "Speed: 1.1ms preprocess, 959.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 928.1ms\n",
      "Speed: 2.1ms preprocess, 928.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 977.8ms\n",
      "Speed: 0.8ms preprocess, 977.8ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 919.1ms\n",
      "Speed: 2.1ms preprocess, 919.1ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 913.3ms\n",
      "Speed: 1.1ms preprocess, 913.3ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 926.6ms\n",
      "Speed: 1.0ms preprocess, 926.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 1054.2ms\n",
      "Speed: 0.8ms preprocess, 1054.2ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 1109.0ms\n",
      "Speed: 2.2ms preprocess, 1109.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 1021.2ms\n",
      "Speed: 0.9ms preprocess, 1021.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 1010.9ms\n",
      "Speed: 0.9ms preprocess, 1010.9ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 964.4ms\n",
      "Speed: 2.2ms preprocess, 964.4ms inference, 3.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 1175.5ms\n",
      "Speed: 1.2ms preprocess, 1175.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 2 trucks, 1385.4ms\n",
      "Speed: 0.8ms preprocess, 1385.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 1035.9ms\n",
      "Speed: 0.8ms preprocess, 1035.9ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 4 trucks, 1028.9ms\n",
      "Speed: 1.5ms preprocess, 1028.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 4 trucks, 1048.5ms\n",
      "Speed: 0.8ms preprocess, 1048.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 4 trucks, 1090.0ms\n",
      "Speed: 0.8ms preprocess, 1090.0ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 956.0ms\n",
      "Speed: 0.7ms preprocess, 956.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 3 trucks, 977.2ms\n",
      "Speed: 0.8ms preprocess, 977.2ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 3 trucks, 1002.0ms\n",
      "Speed: 1.6ms preprocess, 1002.0ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 3 trucks, 905.8ms\n",
      "Speed: 1.0ms preprocess, 905.8ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 10 cars, 2 trucks, 907.1ms\n",
      "Speed: 1.0ms preprocess, 907.1ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 9 cars, 2 trucks, 946.7ms\n",
      "Speed: 0.9ms preprocess, 946.7ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 9 cars, 2 trucks, 937.2ms\n",
      "Speed: 1.1ms preprocess, 937.2ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 9 cars, 2 trucks, 962.3ms\n",
      "Speed: 0.9ms preprocess, 962.3ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 9 cars, 2 trucks, 981.5ms\n",
      "Speed: 0.9ms preprocess, 981.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 9 cars, 2 trucks, 1022.0ms\n",
      "Speed: 0.8ms preprocess, 1022.0ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 10 cars, 2 trucks, 1366.8ms\n",
      "Speed: 1.0ms preprocess, 1366.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 10 cars, 2 trucks, 1 traffic light, 1073.9ms\n",
      "Speed: 1.0ms preprocess, 1073.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 2 trucks, 1 traffic light, 921.2ms\n",
      "Speed: 0.9ms preprocess, 921.2ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 2 trucks, 949.2ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Speed: 2.2ms preprocess, 949.2ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 2 trucks, 992.3ms\n",
      "Speed: 0.9ms preprocess, 992.3ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 2 trucks, 976.1ms\n",
      "Speed: 2.4ms preprocess, 976.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 2 trucks, 976.0ms\n",
      "Speed: 1.1ms preprocess, 976.0ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 2 trucks, 965.4ms\n",
      "Speed: 1.0ms preprocess, 965.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 2 trucks, 1018.7ms\n",
      "Speed: 0.8ms preprocess, 1018.7ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 2 trucks, 1 traffic light, 979.2ms\n",
      "Speed: 0.8ms preprocess, 979.2ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 2 trucks, 1 traffic light, 1023.7ms\n",
      "Speed: 0.8ms preprocess, 1023.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 2 trucks, 1 traffic light, 960.3ms\n",
      "Speed: 1.1ms preprocess, 960.3ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 2 trucks, 1 traffic light, 907.1ms\n",
      "Speed: 0.9ms preprocess, 907.1ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 2 trucks, 1 traffic light, 966.4ms\n",
      "Speed: 0.8ms preprocess, 966.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 1 traffic light, 901.1ms\n",
      "Speed: 0.9ms preprocess, 901.1ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 2 trucks, 1 traffic light, 1187.4ms\n",
      "Speed: 0.8ms preprocess, 1187.4ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 2 trucks, 1124.6ms\n",
      "Speed: 1.0ms preprocess, 1124.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 2 trucks, 904.1ms\n",
      "Speed: 0.8ms preprocess, 904.1ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 9 cars, 2 trucks, 1035.4ms\n",
      "Speed: 0.7ms preprocess, 1035.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 2 trucks, 1275.1ms\n",
      "Speed: 0.9ms preprocess, 1275.1ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 2 trucks, 1270.9ms\n",
      "Speed: 0.8ms preprocess, 1270.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 9 cars, 3 trucks, 1064.1ms\n",
      "Speed: 0.9ms preprocess, 1064.1ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 2 trucks, 1114.7ms\n",
      "Speed: 0.8ms preprocess, 1114.7ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 2 trucks, 942.4ms\n",
      "Speed: 0.8ms preprocess, 942.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 10 cars, 2 trucks, 916.0ms\n",
      "Speed: 0.8ms preprocess, 916.0ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 truck, 891.2ms\n",
      "Speed: 0.8ms preprocess, 891.2ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 897.3ms\n",
      "Speed: 0.8ms preprocess, 897.3ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 883.8ms\n",
      "Speed: 0.8ms preprocess, 883.8ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 881.6ms\n",
      "Speed: 0.8ms preprocess, 881.6ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 889.3ms\n",
      "Speed: 0.8ms preprocess, 889.3ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 1021.5ms\n",
      "Speed: 0.7ms preprocess, 1021.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 1445.1ms\n",
      "Speed: 1.0ms preprocess, 1445.1ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1052.9ms\n",
      "Speed: 0.7ms preprocess, 1052.9ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1036.8ms\n",
      "Speed: 0.8ms preprocess, 1036.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 988.4ms\n",
      "Speed: 0.7ms preprocess, 988.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 966.0ms\n",
      "Speed: 1.0ms preprocess, 966.0ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 883.8ms\n",
      "Speed: 0.8ms preprocess, 883.8ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 882.1ms\n",
      "Speed: 0.7ms preprocess, 882.1ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 1136.4ms\n",
      "Speed: 0.7ms preprocess, 1136.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 1097.9ms\n",
      "Speed: 0.8ms preprocess, 1097.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 973.6ms\n",
      "Speed: 0.8ms preprocess, 973.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 1025.5ms\n",
      "Speed: 0.8ms preprocess, 1025.5ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 truck, 1032.2ms\n",
      "Speed: 0.8ms preprocess, 1032.2ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 truck, 1108.8ms\n",
      "Speed: 0.8ms preprocess, 1108.8ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 999.3ms\n",
      "Speed: 0.9ms preprocess, 999.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 2 trucks, 1196.5ms\n",
      "Speed: 0.8ms preprocess, 1196.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1320.3ms\n",
      "Speed: 2.0ms preprocess, 1320.3ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1058.5ms\n",
      "Speed: 0.7ms preprocess, 1058.5ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1007.1ms\n",
      "Speed: 1.0ms preprocess, 1007.1ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 1122.8ms\n",
      "Speed: 0.9ms preprocess, 1122.8ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1114.8ms\n",
      "Speed: 2.1ms preprocess, 1114.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1113.2ms\n",
      "Speed: 0.8ms preprocess, 1113.2ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1011.4ms\n",
      "Speed: 0.9ms preprocess, 1011.4ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1100.3ms\n",
      "Speed: 1.5ms preprocess, 1100.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1071.3ms\n",
      "Speed: 0.8ms preprocess, 1071.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 truck, 1024.4ms\n",
      "Speed: 0.8ms preprocess, 1024.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1022.3ms\n",
      "Speed: 2.2ms preprocess, 1022.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 truck, 1167.1ms\n",
      "Speed: 0.8ms preprocess, 1167.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1036.8ms\n",
      "Speed: 0.9ms preprocess, 1036.8ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1313.9ms\n",
      "Speed: 1.0ms preprocess, 1313.9ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1462.8ms\n",
      "Speed: 1.0ms preprocess, 1462.8ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 truck, 1059.5ms\n",
      "Speed: 1.9ms preprocess, 1059.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0: 384x640 11 cars, 1 truck, 964.2ms\n",
      "Speed: 0.8ms preprocess, 964.2ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 1 truck, 1070.5ms\n",
      "Speed: 0.8ms preprocess, 1070.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 truck, 1100.8ms\n",
      "Speed: 1.0ms preprocess, 1100.8ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 truck, 1143.3ms\n",
      "Speed: 0.7ms preprocess, 1143.3ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 1 truck, 1019.7ms\n",
      "Speed: 1.0ms preprocess, 1019.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 1 truck, 1019.7ms\n",
      "Speed: 1.0ms preprocess, 1019.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 1 truck, 961.6ms\n",
      "Speed: 0.8ms preprocess, 961.6ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 2 trucks, 1020.6ms\n",
      "Speed: 1.0ms preprocess, 1020.6ms inference, 2.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 1 truck, 1044.8ms\n",
      "Speed: 0.7ms preprocess, 1044.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 1 truck, 1003.7ms\n",
      "Speed: 0.7ms preprocess, 1003.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 1 truck, 1017.6ms\n",
      "Speed: 0.7ms preprocess, 1017.6ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 1 truck, 942.7ms\n",
      "Speed: 0.9ms preprocess, 942.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 truck, 1465.2ms\n",
      "Speed: 0.9ms preprocess, 1465.2ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 truck, 1220.1ms\n",
      "Speed: 2.1ms preprocess, 1220.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 truck, 1047.7ms\n",
      "Speed: 0.7ms preprocess, 1047.7ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 truck, 961.7ms\n",
      "Speed: 0.7ms preprocess, 961.7ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 993.5ms\n",
      "Speed: 1.5ms preprocess, 993.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 1034.1ms\n",
      "Speed: 1.0ms preprocess, 1034.1ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1028.8ms\n",
      "Speed: 1.0ms preprocess, 1028.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 1 traffic light, 958.3ms\n",
      "Speed: 1.0ms preprocess, 958.3ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1 traffic light, 1059.9ms\n",
      "Speed: 0.9ms preprocess, 1059.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 1 traffic light, 983.7ms\n",
      "Speed: 0.7ms preprocess, 983.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 2 trucks, 1 traffic light, 1006.0ms\n",
      "Speed: 0.8ms preprocess, 1006.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1 traffic light, 971.0ms\n",
      "Speed: 0.8ms preprocess, 971.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 1 traffic light, 990.6ms\n",
      "Speed: 0.8ms preprocess, 990.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 truck, 960.4ms\n",
      "Speed: 1.0ms preprocess, 960.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1 traffic light, 945.1ms\n",
      "Speed: 1.2ms preprocess, 945.1ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 truck, 1 traffic light, 1504.7ms\n",
      "Speed: 1.0ms preprocess, 1504.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 1145.4ms\n",
      "Speed: 0.9ms preprocess, 1145.4ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 1 traffic light, 979.6ms\n",
      "Speed: 1.1ms preprocess, 979.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 1 traffic light, 1022.4ms\n",
      "Speed: 0.8ms preprocess, 1022.4ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 967.3ms\n",
      "Speed: 0.7ms preprocess, 967.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 1 traffic light, 1084.4ms\n",
      "Speed: 0.9ms preprocess, 1084.4ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 1 traffic light, 1083.0ms\n",
      "Speed: 1.1ms preprocess, 1083.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 1 traffic light, 1039.6ms\n",
      "Speed: 1.4ms preprocess, 1039.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 1 traffic light, 1176.7ms\n",
      "Speed: 0.9ms preprocess, 1176.7ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 1 traffic light, 1016.3ms\n",
      "Speed: 0.8ms preprocess, 1016.3ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1 traffic light, 1004.4ms\n",
      "Speed: 1.0ms preprocess, 1004.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1 traffic light, 981.7ms\n",
      "Speed: 1.1ms preprocess, 981.7ms inference, 2.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1 traffic light, 1021.8ms\n",
      "Speed: 0.8ms preprocess, 1021.8ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1097.5ms\n",
      "Speed: 0.8ms preprocess, 1097.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1321.0ms\n",
      "Speed: 0.7ms preprocess, 1321.0ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1219.7ms\n",
      "Speed: 1.0ms preprocess, 1219.7ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 1001.9ms\n",
      "Speed: 0.8ms preprocess, 1001.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 1092.2ms\n",
      "Speed: 2.1ms preprocess, 1092.2ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 1077.7ms\n",
      "Speed: 0.8ms preprocess, 1077.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 986.4ms\n",
      "Speed: 2.1ms preprocess, 986.4ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 954.5ms\n",
      "Speed: 0.8ms preprocess, 954.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 1128.0ms\n",
      "Speed: 0.9ms preprocess, 1128.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 936.8ms\n",
      "Speed: 0.7ms preprocess, 936.8ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 1023.9ms\n",
      "Speed: 0.9ms preprocess, 1023.9ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 1092.1ms\n",
      "Speed: 1.3ms preprocess, 1092.1ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 2 trucks, 1075.6ms\n",
      "Speed: 0.9ms preprocess, 1075.6ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 1048.1ms\n",
      "Speed: 0.8ms preprocess, 1048.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 1005.4ms\n",
      "Speed: 0.9ms preprocess, 1005.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 1127.8ms\n",
      "Speed: 0.8ms preprocess, 1127.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 1516.9ms\n",
      "Speed: 0.8ms preprocess, 1516.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 1113.8ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Speed: 1.0ms preprocess, 1113.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 1086.9ms\n",
      "Speed: 1.0ms preprocess, 1086.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 966.4ms\n",
      "Speed: 1.1ms preprocess, 966.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 2 trucks, 1088.8ms\n",
      "Speed: 1.0ms preprocess, 1088.8ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 2 trucks, 980.3ms\n",
      "Speed: 0.7ms preprocess, 980.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 931.8ms\n",
      "Speed: 0.7ms preprocess, 931.8ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1013.1ms\n",
      "Speed: 1.2ms preprocess, 1013.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1025.3ms\n",
      "Speed: 3.7ms preprocess, 1025.3ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 4 trucks, 1039.1ms\n",
      "Speed: 0.8ms preprocess, 1039.1ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 1120.7ms\n",
      "Speed: 0.9ms preprocess, 1120.7ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 1151.4ms\n",
      "Speed: 1.2ms preprocess, 1151.4ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 979.1ms\n",
      "Speed: 0.7ms preprocess, 979.1ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 4 trucks, 975.5ms\n",
      "Speed: 1.1ms preprocess, 975.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 1318.6ms\n",
      "Speed: 1.1ms preprocess, 1318.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 4 trucks, 1284.4ms\n",
      "Speed: 0.9ms preprocess, 1284.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 987.7ms\n",
      "Speed: 1.0ms preprocess, 987.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 4 trucks, 957.0ms\n",
      "Speed: 0.7ms preprocess, 957.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 4 trucks, 1064.6ms\n",
      "Speed: 1.1ms preprocess, 1064.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 4 trucks, 1160.4ms\n",
      "Speed: 0.8ms preprocess, 1160.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 4 trucks, 1138.5ms\n",
      "Speed: 0.7ms preprocess, 1138.5ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 4 trucks, 1081.6ms\n",
      "Speed: 0.8ms preprocess, 1081.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 4 trucks, 1040.7ms\n",
      "Speed: 1.9ms preprocess, 1040.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 4 trucks, 1049.9ms\n",
      "Speed: 0.8ms preprocess, 1049.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 3 trucks, 1048.2ms\n",
      "Speed: 0.7ms preprocess, 1048.2ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 3 trucks, 1045.6ms\n",
      "Speed: 0.7ms preprocess, 1045.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 1036.3ms\n",
      "Speed: 0.8ms preprocess, 1036.3ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 992.2ms\n",
      "Speed: 0.8ms preprocess, 992.2ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1143.4ms\n",
      "Speed: 1.0ms preprocess, 1143.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1443.8ms\n",
      "Speed: 0.9ms preprocess, 1443.8ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 train, 3 trucks, 1049.8ms\n",
      "Speed: 0.8ms preprocess, 1049.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 train, 3 trucks, 1166.4ms\n",
      "Speed: 0.8ms preprocess, 1166.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1032.5ms\n",
      "Speed: 0.9ms preprocess, 1032.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1050.0ms\n",
      "Speed: 0.8ms preprocess, 1050.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1082.3ms\n",
      "Speed: 0.8ms preprocess, 1082.3ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1050.8ms\n",
      "Speed: 0.9ms preprocess, 1050.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1046.5ms\n",
      "Speed: 0.8ms preprocess, 1046.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1187.3ms\n",
      "Speed: 1.1ms preprocess, 1187.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1313.8ms\n",
      "Speed: 1.0ms preprocess, 1313.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1138.0ms\n",
      "Speed: 0.8ms preprocess, 1138.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1001.2ms\n",
      "Speed: 0.9ms preprocess, 1001.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1067.9ms\n",
      "Speed: 1.0ms preprocess, 1067.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1488.4ms\n",
      "Speed: 0.8ms preprocess, 1488.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1321.4ms\n",
      "Speed: 2.6ms preprocess, 1321.4ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1069.2ms\n",
      "Speed: 0.9ms preprocess, 1069.2ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 1 person, 13 cars, 3 trucks, 1027.7ms\n",
      "Speed: 0.9ms preprocess, 1027.7ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 1 person, 12 cars, 3 trucks, 1157.9ms\n",
      "Speed: 1.1ms preprocess, 1157.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1068.4ms\n",
      "Speed: 0.9ms preprocess, 1068.4ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 1171.6ms\n",
      "Speed: 0.9ms preprocess, 1171.6ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 3 trucks, 1121.2ms\n",
      "Speed: 0.9ms preprocess, 1121.2ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 1002.1ms\n",
      "Speed: 1.4ms preprocess, 1002.1ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 3 trucks, 1162.9ms\n",
      "Speed: 0.7ms preprocess, 1162.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1037.8ms\n",
      "Speed: 0.7ms preprocess, 1037.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1027.2ms\n",
      "Speed: 1.5ms preprocess, 1027.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 1059.4ms\n",
      "Speed: 1.0ms preprocess, 1059.4ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 978.0ms\n",
      "Speed: 0.8ms preprocess, 978.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 1447.7ms\n",
      "Speed: 0.8ms preprocess, 1447.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 1142.7ms\n",
      "Speed: 0.8ms preprocess, 1142.7ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 1155.9ms\n",
      "Speed: 1.4ms preprocess, 1155.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 1143.0ms\n",
      "Speed: 1.2ms preprocess, 1143.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 1142.1ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Speed: 0.8ms preprocess, 1142.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 1078.7ms\n",
      "Speed: 0.8ms preprocess, 1078.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 1020.2ms\n",
      "Speed: 1.1ms preprocess, 1020.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 1102.2ms\n",
      "Speed: 1.0ms preprocess, 1102.2ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 1074.1ms\n",
      "Speed: 1.1ms preprocess, 1074.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 1120.1ms\n",
      "Speed: 0.8ms preprocess, 1120.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 1080.4ms\n",
      "Speed: 1.0ms preprocess, 1080.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 1138.4ms\n",
      "Speed: 1.1ms preprocess, 1138.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 1013.8ms\n",
      "Speed: 0.9ms preprocess, 1013.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 978.9ms\n",
      "Speed: 1.1ms preprocess, 978.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 4 trucks, 1454.7ms\n",
      "Speed: 0.7ms preprocess, 1454.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 3 trucks, 1069.5ms\n",
      "Speed: 1.1ms preprocess, 1069.5ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 961.0ms\n",
      "Speed: 0.7ms preprocess, 961.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 968.7ms\n",
      "Speed: 0.8ms preprocess, 968.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 4 trucks, 970.7ms\n",
      "Speed: 0.8ms preprocess, 970.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 3 trucks, 970.2ms\n",
      "Speed: 0.7ms preprocess, 970.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 4 trucks, 973.7ms\n",
      "Speed: 1.0ms preprocess, 973.7ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 4 trucks, 1042.1ms\n",
      "Speed: 1.1ms preprocess, 1042.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 4 trucks, 1086.5ms\n",
      "Speed: 1.8ms preprocess, 1086.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 4 trucks, 969.3ms\n",
      "Speed: 0.8ms preprocess, 969.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 4 trucks, 1013.6ms\n",
      "Speed: 1.2ms preprocess, 1013.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1000.9ms\n",
      "Speed: 1.1ms preprocess, 1000.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 4 trucks, 1059.6ms\n",
      "Speed: 1.1ms preprocess, 1059.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 4 trucks, 987.8ms\n",
      "Speed: 0.9ms preprocess, 987.8ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 981.0ms\n",
      "Speed: 0.9ms preprocess, 981.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1494.8ms\n",
      "Speed: 0.7ms preprocess, 1494.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 4 trucks, 1100.6ms\n",
      "Speed: 0.9ms preprocess, 1100.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 4 trucks, 963.4ms\n",
      "Speed: 1.0ms preprocess, 963.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 4 trucks, 1 traffic light, 975.0ms\n",
      "Speed: 1.2ms preprocess, 975.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 4 trucks, 1 traffic light, 972.7ms\n",
      "Speed: 1.2ms preprocess, 972.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1 traffic light, 1001.0ms\n",
      "Speed: 0.8ms preprocess, 1001.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1 traffic light, 987.5ms\n",
      "Speed: 1.0ms preprocess, 987.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 4 trucks, 1042.2ms\n",
      "Speed: 0.8ms preprocess, 1042.2ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 4 trucks, 985.9ms\n",
      "Speed: 1.1ms preprocess, 985.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 4 trucks, 990.6ms\n",
      "Speed: 1.1ms preprocess, 990.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 5 trucks, 972.4ms\n",
      "Speed: 0.8ms preprocess, 972.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 4 trucks, 972.6ms\n",
      "Speed: 0.9ms preprocess, 972.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 4 trucks, 973.4ms\n",
      "Speed: 1.0ms preprocess, 973.4ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 5 trucks, 973.8ms\n",
      "Speed: 0.9ms preprocess, 973.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 5 trucks, 982.0ms\n",
      "Speed: 1.2ms preprocess, 982.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 5 trucks, 1527.3ms\n",
      "Speed: 1.2ms preprocess, 1527.3ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 5 trucks, 1123.7ms\n",
      "Speed: 1.3ms preprocess, 1123.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 960.0ms\n",
      "Speed: 0.8ms preprocess, 960.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 981.9ms\n",
      "Speed: 0.9ms preprocess, 981.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1 traffic light, 981.7ms\n",
      "Speed: 0.9ms preprocess, 981.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 4 trucks, 969.1ms\n",
      "Speed: 1.1ms preprocess, 969.1ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 4 trucks, 1 traffic light, 1039.1ms\n",
      "Speed: 0.8ms preprocess, 1039.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 4 trucks, 1063.2ms\n",
      "Speed: 1.1ms preprocess, 1063.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 4 trucks, 1120.2ms\n",
      "Speed: 1.0ms preprocess, 1120.2ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 4 trucks, 1044.0ms\n",
      "Speed: 0.8ms preprocess, 1044.0ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 1085.1ms\n",
      "Speed: 1.1ms preprocess, 1085.1ms inference, 2.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1041.5ms\n",
      "Speed: 0.9ms preprocess, 1041.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1057.8ms\n",
      "Speed: 1.4ms preprocess, 1057.8ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 1040.2ms\n",
      "Speed: 0.8ms preprocess, 1040.2ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 1224.8ms\n",
      "Speed: 1.2ms preprocess, 1224.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 1314.8ms\n",
      "Speed: 0.8ms preprocess, 1314.8ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 3 trucks, 962.9ms\n",
      "Speed: 0.7ms preprocess, 962.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 5 trucks, 1138.6ms\n",
      "Speed: 0.8ms preprocess, 1138.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1083.8ms\n",
      "Speed: 1.2ms preprocess, 1083.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0: 384x640 15 cars, 5 trucks, 1068.1ms\n",
      "Speed: 0.8ms preprocess, 1068.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 5 trucks, 1005.1ms\n",
      "Speed: 0.8ms preprocess, 1005.1ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 5 trucks, 1123.9ms\n",
      "Speed: 1.1ms preprocess, 1123.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 5 trucks, 1130.7ms\n",
      "Speed: 1.0ms preprocess, 1130.7ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 5 trucks, 1149.8ms\n",
      "Speed: 2.7ms preprocess, 1149.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1060.6ms\n",
      "Speed: 0.8ms preprocess, 1060.6ms inference, 3.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1062.5ms\n",
      "Speed: 0.8ms preprocess, 1062.5ms inference, 3.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1017.0ms\n",
      "Speed: 0.8ms preprocess, 1017.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1052.9ms\n",
      "Speed: 2.0ms preprocess, 1052.9ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1219.0ms\n",
      "Speed: 0.8ms preprocess, 1219.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1569.3ms\n",
      "Speed: 0.9ms preprocess, 1569.3ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 4 trucks, 1115.5ms\n",
      "Speed: 0.8ms preprocess, 1115.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 5 trucks, 1060.1ms\n",
      "Speed: 0.9ms preprocess, 1060.1ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 4 trucks, 989.6ms\n",
      "Speed: 0.9ms preprocess, 989.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 4 trucks, 1034.5ms\n",
      "Speed: 0.8ms preprocess, 1034.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 4 trucks, 1264.8ms\n",
      "Speed: 0.7ms preprocess, 1264.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 4 trucks, 1154.6ms\n",
      "Speed: 0.9ms preprocess, 1154.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1220.1ms\n",
      "Speed: 0.8ms preprocess, 1220.1ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 4 trucks, 1013.1ms\n",
      "Speed: 1.9ms preprocess, 1013.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 4 trucks, 982.4ms\n",
      "Speed: 1.5ms preprocess, 982.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 4 trucks, 1074.3ms\n",
      "Speed: 1.0ms preprocess, 1074.3ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 4 trucks, 1010.4ms\n",
      "Speed: 0.8ms preprocess, 1010.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 4 trucks, 1122.9ms\n",
      "Speed: 0.7ms preprocess, 1122.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 4 trucks, 1492.0ms\n",
      "Speed: 1.4ms preprocess, 1492.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 4 trucks, 1197.9ms\n",
      "Speed: 1.0ms preprocess, 1197.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 4 trucks, 1106.9ms\n",
      "Speed: 0.7ms preprocess, 1106.9ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 4 trucks, 995.2ms\n",
      "Speed: 0.9ms preprocess, 995.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 4 trucks, 1172.8ms\n",
      "Speed: 0.9ms preprocess, 1172.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 4 trucks, 1173.9ms\n",
      "Speed: 0.8ms preprocess, 1173.9ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 4 trucks, 1181.5ms\n",
      "Speed: 0.9ms preprocess, 1181.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 4 trucks, 1200.6ms\n",
      "Speed: 0.9ms preprocess, 1200.6ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 4 trucks, 1176.9ms\n",
      "Speed: 0.8ms preprocess, 1176.9ms inference, 3.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 4 trucks, 1125.4ms\n",
      "Speed: 1.1ms preprocess, 1125.4ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 5 trucks, 1059.3ms\n",
      "Speed: 0.9ms preprocess, 1059.3ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 4 trucks, 1305.2ms\n",
      "Speed: 2.2ms preprocess, 1305.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 4 trucks, 1166.8ms\n",
      "Speed: 0.8ms preprocess, 1166.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 4 trucks, 1552.6ms\n",
      "Speed: 1.3ms preprocess, 1552.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 4 trucks, 1456.9ms\n",
      "Speed: 0.9ms preprocess, 1456.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 4 trucks, 1104.2ms\n",
      "Speed: 2.0ms preprocess, 1104.2ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 4 trucks, 1220.0ms\n",
      "Speed: 1.0ms preprocess, 1220.0ms inference, 3.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 4 trucks, 1100.5ms\n",
      "Speed: 0.9ms preprocess, 1100.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 4 trucks, 1156.9ms\n",
      "Speed: 1.1ms preprocess, 1156.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 5 trucks, 1138.7ms\n",
      "Speed: 0.9ms preprocess, 1138.7ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 5 trucks, 1175.7ms\n",
      "Speed: 0.8ms preprocess, 1175.7ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 5 trucks, 1253.1ms\n",
      "Speed: 0.8ms preprocess, 1253.1ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 5 trucks, 1103.1ms\n",
      "Speed: 0.9ms preprocess, 1103.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 10 cars, 1 bus, 5 trucks, 1100.3ms\n",
      "Speed: 0.8ms preprocess, 1100.3ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 10 cars, 5 trucks, 1052.5ms\n",
      "Speed: 0.9ms preprocess, 1052.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 10 cars, 5 trucks, 1148.7ms\n",
      "Speed: 2.4ms preprocess, 1148.7ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 4 trucks, 1507.2ms\n",
      "Speed: 0.9ms preprocess, 1507.2ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 4 trucks, 1424.3ms\n",
      "Speed: 1.5ms preprocess, 1424.3ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 4 trucks, 1169.4ms\n",
      "Speed: 1.9ms preprocess, 1169.4ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 5 trucks, 1067.9ms\n",
      "Speed: 0.8ms preprocess, 1067.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 1 bus, 5 trucks, 1206.6ms\n",
      "Speed: 2.5ms preprocess, 1206.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 1 bus, 4 trucks, 1212.4ms\n",
      "Speed: 1.0ms preprocess, 1212.4ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 10 cars, 1 bus, 5 trucks, 1228.6ms\n",
      "Speed: 1.0ms preprocess, 1228.6ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 1 bus, 5 trucks, 1199.0ms\n",
      "Speed: 0.9ms preprocess, 1199.0ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 5 trucks, 1 traffic light, 1216.5ms\n",
      "Speed: 0.9ms preprocess, 1216.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 5 trucks, 1304.1ms\n",
      "Speed: 0.8ms preprocess, 1304.1ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 640)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0: 384x640 11 cars, 6 trucks, 1123.2ms\n",
      "Speed: 0.8ms preprocess, 1123.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 6 trucks, 1159.5ms\n",
      "Speed: 1.3ms preprocess, 1159.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 5 trucks, 1189.3ms\n",
      "Speed: 1.0ms preprocess, 1189.3ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 5 trucks, 1694.1ms\n",
      "Speed: 0.9ms preprocess, 1694.1ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 5 trucks, 1412.7ms\n",
      "Speed: 1.2ms preprocess, 1412.7ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 5 trucks, 1107.8ms\n",
      "Speed: 1.3ms preprocess, 1107.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 6 trucks, 1239.3ms\n",
      "Speed: 1.2ms preprocess, 1239.3ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 6 trucks, 1181.7ms\n",
      "Speed: 0.9ms preprocess, 1181.7ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 5 trucks, 1141.7ms\n",
      "Speed: 0.9ms preprocess, 1141.7ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 5 trucks, 1331.0ms\n",
      "Speed: 1.6ms preprocess, 1331.0ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 5 trucks, 1142.1ms\n",
      "Speed: 1.8ms preprocess, 1142.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 6 trucks, 1 traffic light, 1083.5ms\n",
      "Speed: 2.2ms preprocess, 1083.5ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 6 trucks, 1200.7ms\n",
      "Speed: 0.8ms preprocess, 1200.7ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 10 cars, 5 trucks, 1172.7ms\n",
      "Speed: 1.1ms preprocess, 1172.7ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 5 trucks, 1012.3ms\n",
      "Speed: 0.8ms preprocess, 1012.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 5 trucks, 1205.6ms\n",
      "Speed: 0.7ms preprocess, 1205.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 10 cars, 6 trucks, 1615.5ms\n",
      "Speed: 2.0ms preprocess, 1615.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 10 cars, 6 trucks, 1029.5ms\n",
      "Speed: 0.8ms preprocess, 1029.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 6 trucks, 1005.8ms\n",
      "Speed: 0.9ms preprocess, 1005.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 6 trucks, 1063.1ms\n",
      "Speed: 1.0ms preprocess, 1063.1ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 6 trucks, 1208.2ms\n",
      "Speed: 1.1ms preprocess, 1208.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 6 trucks, 1122.8ms\n",
      "Speed: 0.9ms preprocess, 1122.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 6 trucks, 1129.8ms\n",
      "Speed: 1.1ms preprocess, 1129.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 5 trucks, 1028.0ms\n",
      "Speed: 1.0ms preprocess, 1028.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 11 cars, 6 trucks, 1003.9ms\n",
      "Speed: 1.8ms preprocess, 1003.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 6 trucks, 1038.6ms\n",
      "Speed: 0.8ms preprocess, 1038.6ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 6 trucks, 1099.7ms\n",
      "Speed: 0.8ms preprocess, 1099.7ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 6 trucks, 1102.5ms\n",
      "Speed: 0.8ms preprocess, 1102.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 6 trucks, 1164.5ms\n",
      "Speed: 0.9ms preprocess, 1164.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 6 trucks, 1496.5ms\n",
      "Speed: 0.8ms preprocess, 1496.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 6 trucks, 1294.7ms\n",
      "Speed: 0.9ms preprocess, 1294.7ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 6 trucks, 1005.1ms\n",
      "Speed: 0.9ms preprocess, 1005.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 6 trucks, 1037.1ms\n",
      "Speed: 1.0ms preprocess, 1037.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 6 trucks, 1008.2ms\n",
      "Speed: 1.5ms preprocess, 1008.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 6 trucks, 1018.1ms\n",
      "Speed: 1.0ms preprocess, 1018.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 5 trucks, 1035.5ms\n",
      "Speed: 1.1ms preprocess, 1035.5ms inference, 2.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 6 trucks, 1030.5ms\n",
      "Speed: 1.1ms preprocess, 1030.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 6 trucks, 1014.0ms\n",
      "Speed: 0.8ms preprocess, 1014.0ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 12 cars, 1 bus, 6 trucks, 1004.5ms\n",
      "Speed: 1.8ms preprocess, 1004.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 6 trucks, 1020.0ms\n",
      "Speed: 0.9ms preprocess, 1020.0ms inference, 2.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 5 trucks, 1011.9ms\n",
      "Speed: 0.8ms preprocess, 1011.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 5 trucks, 1004.2ms\n",
      "Speed: 0.8ms preprocess, 1004.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 6 trucks, 1006.2ms\n",
      "Speed: 1.0ms preprocess, 1006.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 13 cars, 1 bus, 5 trucks, 1130.8ms\n",
      "Speed: 0.9ms preprocess, 1130.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 6 trucks, 1536.9ms\n",
      "Speed: 1.1ms preprocess, 1536.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 1 bus, 5 trucks, 1061.6ms\n",
      "Speed: 0.9ms preprocess, 1061.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 7 trucks, 1005.1ms\n",
      "Speed: 0.8ms preprocess, 1005.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 6 trucks, 1000.0ms\n",
      "Speed: 0.9ms preprocess, 1000.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 6 trucks, 1013.1ms\n",
      "Speed: 0.8ms preprocess, 1013.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 5 trucks, 1003.6ms\n",
      "Speed: 1.0ms preprocess, 1003.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 4 trucks, 999.8ms\n",
      "Speed: 0.9ms preprocess, 999.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 4 trucks, 1006.4ms\n",
      "Speed: 0.8ms preprocess, 1006.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 1 bus, 4 trucks, 1000.1ms\n",
      "Speed: 0.8ms preprocess, 1000.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 5 trucks, 1007.1ms\n",
      "Speed: 0.9ms preprocess, 1007.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 1 bus, 5 trucks, 999.4ms\n",
      "Speed: 1.0ms preprocess, 999.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 5 trucks, 999.1ms\n",
      "Speed: 0.9ms preprocess, 999.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 5 trucks, 1000.0ms\n",
      "Speed: 0.8ms preprocess, 1000.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 5 trucks, 999.4ms\n",
      "Speed: 0.8ms preprocess, 999.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 5 trucks, 1336.4ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Speed: 2.0ms preprocess, 1336.4ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 14 cars, 5 trucks, 1385.6ms\n",
      "Speed: 0.9ms preprocess, 1385.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 5 trucks, 997.2ms\n",
      "Speed: 1.5ms preprocess, 997.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 5 trucks, 993.2ms\n",
      "Speed: 0.8ms preprocess, 993.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 5 trucks, 1098.3ms\n",
      "Speed: 1.0ms preprocess, 1098.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 5 trucks, 1005.4ms\n",
      "Speed: 0.8ms preprocess, 1005.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 5 trucks, 1024.7ms\n",
      "Speed: 1.1ms preprocess, 1024.7ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 5 trucks, 1054.2ms\n",
      "Speed: 0.9ms preprocess, 1054.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 5 trucks, 996.2ms\n",
      "Speed: 0.8ms preprocess, 996.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 4 trucks, 1001.6ms\n",
      "Speed: 1.5ms preprocess, 1001.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 4 trucks, 1003.4ms\n",
      "Speed: 0.9ms preprocess, 1003.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 4 trucks, 1026.5ms\n",
      "Speed: 0.9ms preprocess, 1026.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 4 trucks, 1003.7ms\n",
      "Speed: 0.8ms preprocess, 1003.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 4 trucks, 1022.0ms\n",
      "Speed: 1.0ms preprocess, 1022.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1017.4ms\n",
      "Speed: 0.9ms preprocess, 1017.4ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1595.9ms\n",
      "Speed: 0.9ms preprocess, 1595.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 4 trucks, 1135.3ms\n",
      "Speed: 1.1ms preprocess, 1135.3ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 4 trucks, 1002.1ms\n",
      "Speed: 2.1ms preprocess, 1002.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 4 trucks, 1013.9ms\n",
      "Speed: 0.8ms preprocess, 1013.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1009.8ms\n",
      "Speed: 0.8ms preprocess, 1009.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1028.6ms\n",
      "Speed: 2.2ms preprocess, 1028.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 15 cars, 4 trucks, 1008.9ms\n",
      "Speed: 1.7ms preprocess, 1008.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 4 trucks, 1003.7ms\n",
      "Speed: 1.0ms preprocess, 1003.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 4 trucks, 1009.4ms\n",
      "Speed: 1.5ms preprocess, 1009.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 4 trucks, 1012.0ms\n",
      "Speed: 2.3ms preprocess, 1012.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 3 trucks, 1010.5ms\n",
      "Speed: 2.2ms preprocess, 1010.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 3 trucks, 1006.4ms\n",
      "Speed: 1.0ms preprocess, 1006.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 3 trucks, 1000.9ms\n",
      "Speed: 0.9ms preprocess, 1000.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 3 trucks, 1009.6ms\n",
      "Speed: 0.9ms preprocess, 1009.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 3 trucks, 1299.4ms\n",
      "Speed: 0.9ms preprocess, 1299.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 1446.2ms\n",
      "Speed: 1.2ms preprocess, 1446.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 998.2ms\n",
      "Speed: 2.0ms preprocess, 998.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 991.7ms\n",
      "Speed: 0.8ms preprocess, 991.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1006.4ms\n",
      "Speed: 1.0ms preprocess, 1006.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 1003.6ms\n",
      "Speed: 1.2ms preprocess, 1003.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 1011.0ms\n",
      "Speed: 1.0ms preprocess, 1011.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 997.6ms\n",
      "Speed: 0.9ms preprocess, 997.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 4 trucks, 1014.5ms\n",
      "Speed: 0.9ms preprocess, 1014.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 5 trucks, 1008.7ms\n",
      "Speed: 1.0ms preprocess, 1008.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 5 trucks, 1001.1ms\n",
      "Speed: 0.9ms preprocess, 1001.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 4 trucks, 1013.6ms\n",
      "Speed: 0.9ms preprocess, 1013.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 4 trucks, 1013.6ms\n",
      "Speed: 1.6ms preprocess, 1013.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 4 trucks, 1009.6ms\n",
      "Speed: 0.9ms preprocess, 1009.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 4 trucks, 1021.8ms\n",
      "Speed: 1.8ms preprocess, 1021.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 5 trucks, 1531.9ms\n",
      "Speed: 1.2ms preprocess, 1531.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 5 trucks, 1123.8ms\n",
      "Speed: 1.0ms preprocess, 1123.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 4 trucks, 996.5ms\n",
      "Speed: 0.8ms preprocess, 996.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 4 trucks, 1002.3ms\n",
      "Speed: 0.8ms preprocess, 1002.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 4 trucks, 1002.8ms\n",
      "Speed: 0.9ms preprocess, 1002.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 5 trucks, 1016.3ms\n",
      "Speed: 1.0ms preprocess, 1016.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 5 trucks, 1003.5ms\n",
      "Speed: 0.8ms preprocess, 1003.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 1009.3ms\n",
      "Speed: 1.1ms preprocess, 1009.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 5 trucks, 1021.8ms\n",
      "Speed: 1.1ms preprocess, 1021.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 5 trucks, 1009.3ms\n",
      "Speed: 0.8ms preprocess, 1009.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 4 trucks, 1002.1ms\n",
      "Speed: 0.9ms preprocess, 1002.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 1016.0ms\n",
      "Speed: 1.0ms preprocess, 1016.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1004.3ms\n",
      "Speed: 0.9ms preprocess, 1004.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 4 trucks, 1017.9ms\n",
      "Speed: 0.9ms preprocess, 1017.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 4 trucks, 1116.6ms\n",
      "Speed: 0.9ms preprocess, 1116.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 4 trucks, 1579.2ms\n",
      "Speed: 1.1ms preprocess, 1579.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1013.8ms\n",
      "Speed: 2.2ms preprocess, 1013.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 3 trucks, 1109.2ms\n",
      "Speed: 0.8ms preprocess, 1109.2ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1001.1ms\n",
      "Speed: 0.8ms preprocess, 1001.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1013.8ms\n",
      "Speed: 1.1ms preprocess, 1013.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1001.6ms\n",
      "Speed: 0.8ms preprocess, 1001.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 3 trucks, 1000.5ms\n",
      "Speed: 1.2ms preprocess, 1000.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1055.3ms\n",
      "Speed: 0.9ms preprocess, 1055.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 3 trucks, 1013.6ms\n",
      "Speed: 1.5ms preprocess, 1013.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 3 trucks, 991.1ms\n",
      "Speed: 0.8ms preprocess, 991.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 3 trucks, 1001.2ms\n",
      "Speed: 0.9ms preprocess, 1001.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1007.2ms\n",
      "Speed: 1.2ms preprocess, 1007.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1014.3ms\n",
      "Speed: 0.9ms preprocess, 1014.3ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 1007.6ms\n",
      "Speed: 1.0ms preprocess, 1007.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 1462.5ms\n",
      "Speed: 1.0ms preprocess, 1462.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1198.0ms\n",
      "Speed: 1.0ms preprocess, 1198.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1000.7ms\n",
      "Speed: 0.9ms preprocess, 1000.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1006.4ms\n",
      "Speed: 0.9ms preprocess, 1006.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1006.1ms\n",
      "Speed: 1.1ms preprocess, 1006.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1081.5ms\n",
      "Speed: 1.0ms preprocess, 1081.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1224.8ms\n",
      "Speed: 1.0ms preprocess, 1224.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1202.5ms\n",
      "Speed: 2.3ms preprocess, 1202.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 3 trucks, 1159.4ms\n",
      "Speed: 2.1ms preprocess, 1159.4ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 1174.3ms\n",
      "Speed: 1.1ms preprocess, 1174.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 1282.5ms\n",
      "Speed: 0.9ms preprocess, 1282.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 1081.1ms\n",
      "Speed: 0.9ms preprocess, 1081.1ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 992.5ms\n",
      "Speed: 0.9ms preprocess, 992.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1157.8ms\n",
      "Speed: 1.0ms preprocess, 1157.8ms inference, 2.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 4 trucks, 1535.7ms\n",
      "Speed: 1.0ms preprocess, 1535.7ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 4 trucks, 1150.3ms\n",
      "Speed: 1.1ms preprocess, 1150.3ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 4 trucks, 1022.8ms\n",
      "Speed: 1.0ms preprocess, 1022.8ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 1073.2ms\n",
      "Speed: 0.9ms preprocess, 1073.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 5 trucks, 1007.8ms\n",
      "Speed: 0.9ms preprocess, 1007.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1020.0ms\n",
      "Speed: 0.9ms preprocess, 1020.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 2 trucks, 1166.5ms\n",
      "Speed: 0.8ms preprocess, 1166.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 2 trucks, 1056.2ms\n",
      "Speed: 0.9ms preprocess, 1056.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 2 trucks, 1140.7ms\n",
      "Speed: 1.1ms preprocess, 1140.7ms inference, 2.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 2 trucks, 1032.2ms\n",
      "Speed: 1.0ms preprocess, 1032.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 1181.4ms\n",
      "Speed: 1.1ms preprocess, 1181.4ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 2 trucks, 1154.9ms\n",
      "Speed: 0.8ms preprocess, 1154.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 1153.8ms\n",
      "Speed: 1.1ms preprocess, 1153.8ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 1470.1ms\n",
      "Speed: 0.9ms preprocess, 1470.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 1349.3ms\n",
      "Speed: 1.0ms preprocess, 1349.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 2 trucks, 995.9ms\n",
      "Speed: 1.0ms preprocess, 995.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 2 trucks, 1002.6ms\n",
      "Speed: 1.1ms preprocess, 1002.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 2 trucks, 1053.6ms\n",
      "Speed: 0.9ms preprocess, 1053.6ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 2 trucks, 1100.6ms\n",
      "Speed: 0.8ms preprocess, 1100.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 2 trucks, 1068.8ms\n",
      "Speed: 1.2ms preprocess, 1068.8ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 2 trucks, 1049.6ms\n",
      "Speed: 0.8ms preprocess, 1049.6ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 2 trucks, 997.1ms\n",
      "Speed: 2.4ms preprocess, 997.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 2 trucks, 1084.1ms\n",
      "Speed: 0.9ms preprocess, 1084.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 2 trucks, 995.4ms\n",
      "Speed: 1.0ms preprocess, 995.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 2 trucks, 991.0ms\n",
      "Speed: 0.9ms preprocess, 991.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 2 trucks, 1002.4ms\n",
      "Speed: 0.8ms preprocess, 1002.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 2 trucks, 1106.4ms\n",
      "Speed: 0.9ms preprocess, 1106.4ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 1232.6ms\n",
      "Speed: 0.8ms preprocess, 1232.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1456.6ms\n",
      "Speed: 0.9ms preprocess, 1456.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 4 trucks, 1024.4ms\n",
      "Speed: 1.0ms preprocess, 1024.4ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 991.9ms\n",
      "Speed: 0.9ms preprocess, 991.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 998.5ms\n",
      "Speed: 1.2ms preprocess, 998.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 986.7ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Speed: 0.8ms preprocess, 986.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 987.1ms\n",
      "Speed: 1.7ms preprocess, 987.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 992.0ms\n",
      "Speed: 0.9ms preprocess, 992.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 995.4ms\n",
      "Speed: 0.8ms preprocess, 995.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 1016.5ms\n",
      "Speed: 0.9ms preprocess, 1016.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 1046.8ms\n",
      "Speed: 0.8ms preprocess, 1046.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 1071.6ms\n",
      "Speed: 1.0ms preprocess, 1071.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 1237.7ms\n",
      "Speed: 0.8ms preprocess, 1237.7ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 2 trucks, 1118.5ms\n",
      "Speed: 1.1ms preprocess, 1118.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 2 trucks, 1121.2ms\n",
      "Speed: 0.9ms preprocess, 1121.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 1538.1ms\n",
      "Speed: 1.1ms preprocess, 1538.1ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 1205.9ms\n",
      "Speed: 1.1ms preprocess, 1205.9ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 1010.0ms\n",
      "Speed: 0.8ms preprocess, 1010.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 1205.1ms\n",
      "Speed: 2.0ms preprocess, 1205.1ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 1089.6ms\n",
      "Speed: 0.9ms preprocess, 1089.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 2 trucks, 1249.8ms\n",
      "Speed: 0.8ms preprocess, 1249.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 1016.2ms\n",
      "Speed: 0.9ms preprocess, 1016.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 1005.2ms\n",
      "Speed: 0.9ms preprocess, 1005.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 998.9ms\n",
      "Speed: 0.9ms preprocess, 998.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 3 trucks, 994.8ms\n",
      "Speed: 0.8ms preprocess, 994.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 4 trucks, 1009.8ms\n",
      "Speed: 1.1ms preprocess, 1009.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 4 trucks, 1011.5ms\n",
      "Speed: 1.1ms preprocess, 1011.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 1014.4ms\n",
      "Speed: 0.8ms preprocess, 1014.4ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 2 trucks, 1081.0ms\n",
      "Speed: 0.9ms preprocess, 1081.0ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 2 trucks, 1581.6ms\n",
      "Speed: 1.0ms preprocess, 1581.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 1052.3ms\n",
      "Speed: 1.0ms preprocess, 1052.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 2 trucks, 1044.8ms\n",
      "Speed: 0.8ms preprocess, 1044.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 1 truck, 997.0ms\n",
      "Speed: 0.8ms preprocess, 997.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 2 trucks, 994.5ms\n",
      "Speed: 1.0ms preprocess, 994.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 990.2ms\n",
      "Speed: 1.0ms preprocess, 990.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 993.4ms\n",
      "Speed: 0.8ms preprocess, 993.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 1013.2ms\n",
      "Speed: 1.1ms preprocess, 1013.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 3 trucks, 1013.5ms\n",
      "Speed: 0.9ms preprocess, 1013.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 2 trucks, 995.5ms\n",
      "Speed: 1.0ms preprocess, 995.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 2 trucks, 990.8ms\n",
      "Speed: 0.9ms preprocess, 990.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 984.8ms\n",
      "Speed: 0.8ms preprocess, 984.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 988.3ms\n",
      "Speed: 0.7ms preprocess, 988.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 1002.5ms\n",
      "Speed: 0.9ms preprocess, 1002.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1273.3ms\n",
      "Speed: 1.0ms preprocess, 1273.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 1391.6ms\n",
      "Speed: 1.1ms preprocess, 1391.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 992.0ms\n",
      "Speed: 0.9ms preprocess, 992.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 990.5ms\n",
      "Speed: 0.8ms preprocess, 990.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 994.7ms\n",
      "Speed: 0.9ms preprocess, 994.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 991.3ms\n",
      "Speed: 0.8ms preprocess, 991.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 2 trucks, 1001.2ms\n",
      "Speed: 0.9ms preprocess, 1001.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 2 trucks, 997.3ms\n",
      "Speed: 1.3ms preprocess, 997.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 1 truck, 992.3ms\n",
      "Speed: 1.1ms preprocess, 992.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 995.0ms\n",
      "Speed: 1.0ms preprocess, 995.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 991.8ms\n",
      "Speed: 1.0ms preprocess, 991.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 985.0ms\n",
      "Speed: 0.8ms preprocess, 985.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 2 trucks, 991.3ms\n",
      "Speed: 0.9ms preprocess, 991.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 1 truck, 991.2ms\n",
      "Speed: 0.8ms preprocess, 991.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 1 truck, 1031.0ms\n",
      "Speed: 0.8ms preprocess, 1031.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 1 truck, 1396.6ms\n",
      "Speed: 1.0ms preprocess, 1396.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 1 truck, 1305.3ms\n",
      "Speed: 2.4ms preprocess, 1305.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 1 truck, 997.5ms\n",
      "Speed: 0.9ms preprocess, 997.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 2 trucks, 996.2ms\n",
      "Speed: 1.1ms preprocess, 996.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 1006.4ms\n",
      "Speed: 0.9ms preprocess, 1006.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 22 cars, 1 truck, 1002.3ms\n",
      "Speed: 0.9ms preprocess, 1002.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 2 trucks, 997.8ms\n",
      "Speed: 0.9ms preprocess, 997.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0: 384x640 21 cars, 2 trucks, 987.8ms\n",
      "Speed: 0.8ms preprocess, 987.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 2 trucks, 1007.2ms\n",
      "Speed: 2.1ms preprocess, 1007.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 2 trucks, 991.4ms\n",
      "Speed: 2.0ms preprocess, 991.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 2 trucks, 1007.7ms\n",
      "Speed: 0.9ms preprocess, 1007.7ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 2 trucks, 998.0ms\n",
      "Speed: 0.9ms preprocess, 998.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 2 trucks, 992.9ms\n",
      "Speed: 0.9ms preprocess, 992.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 2 trucks, 982.4ms\n",
      "Speed: 0.8ms preprocess, 982.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 2 trucks, 987.9ms\n",
      "Speed: 1.0ms preprocess, 987.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 2 trucks, 1513.3ms\n",
      "Speed: 0.9ms preprocess, 1513.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 2 trucks, 1241.9ms\n",
      "Speed: 1.8ms preprocess, 1241.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 2 trucks, 1003.0ms\n",
      "Speed: 1.1ms preprocess, 1003.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 2 trucks, 1011.3ms\n",
      "Speed: 0.8ms preprocess, 1011.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 2 trucks, 1 traffic light, 990.2ms\n",
      "Speed: 0.8ms preprocess, 990.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 985.7ms\n",
      "Speed: 0.8ms preprocess, 985.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 997.5ms\n",
      "Speed: 1.0ms preprocess, 997.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 988.6ms\n",
      "Speed: 0.8ms preprocess, 988.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 1 truck, 994.9ms\n",
      "Speed: 0.8ms preprocess, 994.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 1 truck, 1 traffic light, 997.1ms\n",
      "Speed: 0.8ms preprocess, 997.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 1 truck, 1 traffic light, 999.5ms\n",
      "Speed: 1.0ms preprocess, 999.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 1 truck, 1 traffic light, 998.5ms\n",
      "Speed: 1.4ms preprocess, 998.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 1 truck, 986.9ms\n",
      "Speed: 0.9ms preprocess, 986.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 1 truck, 1016.4ms\n",
      "Speed: 0.9ms preprocess, 1016.4ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 1 truck, 1 traffic light, 1118.9ms\n",
      "Speed: 0.9ms preprocess, 1118.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 1 truck, 1495.1ms\n",
      "Speed: 1.6ms preprocess, 1495.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 1 truck, 990.6ms\n",
      "Speed: 0.9ms preprocess, 990.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 1 truck, 1 traffic light, 1004.6ms\n",
      "Speed: 0.8ms preprocess, 1004.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 1 truck, 995.0ms\n",
      "Speed: 1.0ms preprocess, 995.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 1 truck, 992.6ms\n",
      "Speed: 0.9ms preprocess, 992.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 2 trucks, 1003.9ms\n",
      "Speed: 0.9ms preprocess, 1003.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 1 truck, 980.9ms\n",
      "Speed: 0.8ms preprocess, 980.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 1 truck, 1003.5ms\n",
      "Speed: 1.1ms preprocess, 1003.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 2 trucks, 1016.5ms\n",
      "Speed: 1.0ms preprocess, 1016.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 995.1ms\n",
      "Speed: 1.0ms preprocess, 995.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 983.1ms\n",
      "Speed: 0.8ms preprocess, 983.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 998.5ms\n",
      "Speed: 1.0ms preprocess, 998.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 1002.2ms\n",
      "Speed: 0.9ms preprocess, 1002.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 989.5ms\n",
      "Speed: 0.8ms preprocess, 989.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 17 cars, 2 trucks, 1201.7ms\n",
      "Speed: 1.1ms preprocess, 1201.7ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 2 trucks, 1561.9ms\n",
      "Speed: 1.0ms preprocess, 1561.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 2 trucks, 978.6ms\n",
      "Speed: 0.9ms preprocess, 978.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 2 trucks, 989.5ms\n",
      "Speed: 0.9ms preprocess, 989.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 2 trucks, 1009.7ms\n",
      "Speed: 0.9ms preprocess, 1009.7ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 2 trucks, 984.6ms\n",
      "Speed: 1.9ms preprocess, 984.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 2 trucks, 992.3ms\n",
      "Speed: 1.1ms preprocess, 992.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 2 trucks, 997.2ms\n",
      "Speed: 1.0ms preprocess, 997.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 21 cars, 3 trucks, 1 traffic light, 991.5ms\n",
      "Speed: 0.9ms preprocess, 991.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1 traffic light, 996.3ms\n",
      "Speed: 1.1ms preprocess, 996.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 1 traffic light, 1016.6ms\n",
      "Speed: 1.0ms preprocess, 1016.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 1 traffic light, 991.4ms\n",
      "Speed: 1.2ms preprocess, 991.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 1 traffic light, 993.3ms\n",
      "Speed: 1.1ms preprocess, 993.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 20 cars, 3 trucks, 1 traffic light, 986.3ms\n",
      "Speed: 1.5ms preprocess, 986.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 19 cars, 3 trucks, 1 traffic light, 991.9ms\n",
      "Speed: 0.9ms preprocess, 991.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 3 trucks, 1 traffic light, 1370.1ms\n",
      "Speed: 1.0ms preprocess, 1370.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 1 bus, 4 trucks, 1 traffic light, 1254.3ms\n",
      "Speed: 0.9ms preprocess, 1254.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 1 bus, 4 trucks, 1 traffic light, 984.5ms\n",
      "Speed: 0.8ms preprocess, 984.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 4 trucks, 1 traffic light, 995.7ms\n",
      "Speed: 0.8ms preprocess, 995.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 16 cars, 4 trucks, 1 traffic light, 992.8ms\n",
      "Speed: 1.0ms preprocess, 992.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "\n",
      "0: 384x640 18 cars, 4 trucks, 1 traffic light, 998.3ms\n",
      "Speed: 2.0ms preprocess, 998.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n"
     ]
    }
   ],
   "source": [
    "# create BYTETracker instance\n",
    "byte_tracker = BYTETracker(BYTETrackerArgs())\n",
    "# create VideoInfo instance\n",
    "video_info = VideoInfo.from_video_path(SOURCE_VIDEO_PATH)\n",
    "# create frame generator\n",
    "generator = get_video_frames_generator(SOURCE_VIDEO_PATH)\n",
    "# create LineCounter instance\n",
    "line_counter = LineCounter(start=LINE_START, end=LINE_END)\n",
    "# create instance of BoxAnnotator and LineCounterAnnotator\n",
    "box_annotator = BoxAnnotator(color=ColorPalette(), thickness=1, text_thickness=1, text_scale=0.5)\n",
    "line_annotator = LineCounterAnnotator(thickness=2, text_thickness=1, text_scale=0.5)\n",
    "\n",
    "# open target video file\n",
    "with VideoSink(TARGET_VIDEO_PATH, video_info) as sink:\n",
    "    # loop over video frames\n",
    "    for frame in tqdm(generator, total=video_info.total_frames):\n",
    "        # model prediction on single frame and conversion to supervision Detections\n",
    "        results = model(frame)\n",
    "        detections = Detections(\n",
    "            xyxy=results[0].boxes.xyxy.cpu().numpy(),\n",
    "            confidence=results[0].boxes.conf.cpu().numpy(),\n",
    "            class_id=results[0].boxes.cls.cpu().numpy().astype(int)\n",
    "        )\n",
    "        # filtering out detections with unwanted classes\n",
    "        mask = np.array([class_id in CLASS_ID for class_id in detections.class_id], dtype=bool)\n",
    "        detections.filter(mask=mask, inplace=True)\n",
    "        # tracking detections\n",
    "        tracks = byte_tracker.update(\n",
    "            output_results=detections2boxes(detections=detections),\n",
    "            img_info=frame.shape,\n",
    "            img_size=frame.shape\n",
    "        )\n",
    "        tracker_id = match_detections_with_tracks(detections=detections, tracks=tracks)\n",
    "        detections.tracker_id = np.array(tracker_id)\n",
    "        # filtering out detections without trackers\n",
    "        mask = np.array([tracker_id is not None for tracker_id in detections.tracker_id], dtype=bool)\n",
    "        detections.filter(mask=mask, inplace=True)\n",
    "        # format custom labels\n",
    "        labels = [\n",
    "            f\"#{tracker_id} {CLASS_NAMES_DICT[class_id]} {confidence:0.2f}\"\n",
    "            for _, confidence, class_id, tracker_id\n",
    "            in detections\n",
    "        ]\n",
    "        # updating line counter\n",
    "        line_counter.update(detections=detections)\n",
    "        # annotate and display frame\n",
    "        frame = box_annotator.annotate(frame=frame, detections=detections, labels=labels)\n",
    "        line_annotator.annotate(frame=frame, line_counter=line_counter)\n",
    "        sink.write_frame(frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting moviepy\n",
      "\u001b[33m  WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError(\"HTTPSConnectionPool(host='files.pythonhosted.org', port=443): Read timed out. (read timeout=15)\")': /packages/18/54/01a8c4e35c75ca9724d19a7e4de9dc23f0ceb8769102c7de056113af61c3/moviepy-1.0.3.tar.gz\u001b[0m\n",
      "  Downloading moviepy-1.0.3.tar.gz (388 kB)\n",
      "\u001b[K     |████████████████████████████████| 388 kB 394 kB/s eta 0:00:01\n",
      "\u001b[?25hCollecting decorator<5.0,>=4.0.2\n",
      "  Downloading decorator-4.4.2-py2.py3-none-any.whl (9.2 kB)\n",
      "Requirement already satisfied: tqdm<5.0,>=4.11.2 in /home/niraj/anaconda3/lib/python3.9/site-packages (from moviepy) (4.64.0)\n",
      "Requirement already satisfied: requests<3.0,>=2.8.1 in /home/niraj/anaconda3/lib/python3.9/site-packages (from moviepy) (2.27.1)\n",
      "Collecting proglog<=1.0.0\n",
      "  Downloading proglog-0.1.10-py3-none-any.whl (6.1 kB)\n",
      "Requirement already satisfied: numpy>=1.17.3 in /home/niraj/anaconda3/lib/python3.9/site-packages (from moviepy) (1.22.4)\n",
      "Requirement already satisfied: imageio<3.0,>=2.5 in /home/niraj/anaconda3/lib/python3.9/site-packages (from moviepy) (2.9.0)\n",
      "Collecting imageio_ffmpeg>=0.2.0\n",
      "  Downloading imageio_ffmpeg-0.4.8-py3-none-manylinux2010_x86_64.whl (26.9 MB)\n",
      "\u001b[K     |████████████████████████████████| 26.9 MB 111 kB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: pillow in /home/niraj/anaconda3/lib/python3.9/site-packages (from imageio<3.0,>=2.5->moviepy) (9.0.1)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/niraj/anaconda3/lib/python3.9/site-packages (from requests<3.0,>=2.8.1->moviepy) (3.3)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in /home/niraj/anaconda3/lib/python3.9/site-packages (from requests<3.0,>=2.8.1->moviepy) (2.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/niraj/anaconda3/lib/python3.9/site-packages (from requests<3.0,>=2.8.1->moviepy) (2021.10.8)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/niraj/anaconda3/lib/python3.9/site-packages (from requests<3.0,>=2.8.1->moviepy) (1.26.15)\n",
      "Building wheels for collected packages: moviepy\n",
      "  Building wheel for moviepy (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for moviepy: filename=moviepy-1.0.3-py3-none-any.whl size=110743 sha256=27329b47c1e35a68773879831fd333fb4b9df2f0ba486b8eab6235c85b63a046\n",
      "  Stored in directory: /home/niraj/.cache/pip/wheels/29/15/e4/4f790bec6acd51a00b67e8ee1394f0bc6e0135c315f8ff399a\n",
      "Successfully built moviepy\n",
      "Installing collected packages: proglog, imageio-ffmpeg, decorator, moviepy\n",
      "  Attempting uninstall: decorator\n",
      "    Found existing installation: decorator 5.1.1\n",
      "    Uninstalling decorator-5.1.1:\n",
      "      Successfully uninstalled decorator-5.1.1\n",
      "Successfully installed decorator-4.4.2 imageio-ffmpeg-0.4.8 moviepy-1.0.3 proglog-0.1.10\n",
      "MoviePy - Building file output.gif with imageio.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                              \r"
     ]
    }
   ],
   "source": [
    "#!pip install moviepy\n",
    "from moviepy.editor import VideoFileClip\n",
    "\n",
    "videoClip = VideoFileClip(\"/home/niraj/projects/vehicle count/Results/output.mp4\")\n",
    "\n",
    "videoClip.write_gif(\"output.gif\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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