YOLOX is a new high-performance detector with some experienced improvements to YOLO series. We switch the YOLO detector to an anchor-free manner and conduct other advanced detection techniques, i.e., a decoupled head and the leading label assignment strategy SimOTA to achieve state-of-the-art results across a large scale range of models: For YOLO-Nano with only 0.91M parameters and 1.08G FLOPs, we get 25.3% AP on COCO, surpassing NanoDet by 1.8% AP; for YOLOv3, one of the most widely used detectors in industry, we boost it to 47.3% AP on COCO, outperforming the current best practice by 3.0% AP; for YOLOX-L with roughly the same amount of parameters as YOLOv4-CSP, YOLOv5-L, we achieve 50.0% AP on COCO at a speed of 68.9 FPS on Tesla V100, exceeding YOLOv5-L by 1.8% AP. Further, we won the 1st Place on Streaming Perception Challenge (Workshop on Autonomous Driving at CVPR 2021) using a single YOLOX-L model.
| mindspore | ascend driver | firmware | cann toolkit/kernel |
|---|---|---|---|
| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1 |
Please refer to the GETTING_STARTED in MindYOLO for details.
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It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run
# distributed training on multiple GPU/Ascend devices
msrun --worker_num=8 --local_worker_num=8 --bind_core=True --log_dir=./yolox_log python train.py --config ./configs/yolox/yolox-s.yaml --device_target Ascend --is_parallel TrueSimilarly, you can train the model on multiple GPU devices with the above msrun command. Note: For more information about msrun configuration, please refer to here.
For detailed illustration of all hyper-parameters, please refer to config.py.
Note: As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction.
If you want to train or finetune the model on a smaller dataset without distributed training, please firstly run:
# standalone 1st stage training on a CPU/GPU/Ascend device
python train.py --config ./configs/yolox/yolox-s.yaml --device_target AscendTo validate the accuracy of the trained model, you can use test.py and parse the checkpoint path with --weight.
python test.py --config ./configs/yolox/yolox-s.yaml --device_target Ascend --weight /PATH/TO/WEIGHT.ckpt
Experiments are tested on Ascend 910* with mindspore 2.3.1 graph mode.
| model name | scale | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | map | recipe | weight |
|---|---|---|---|---|---|---|---|---|---|---|---|
| YOLOX | S | 8 | 8 | 640x640 | O2 | 299.01s | 177.65 | 360.26 | 41.0% | yaml | weights |
Experiments are tested on Ascend 910 with mindspore 2.3.1 graph mode.
| model name | scale | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | map | recipe | weight |
|---|---|---|---|---|---|---|---|---|---|---|---|
| YOLOX | N | 8 | 8 | 416x416 | O2 | 202.49s | 138.84 | 460.96 | 24.1% | yaml | weights |
| YOLOX | Tiny | 8 | 8 | 416x416 | O2 | 169.71s | 126.85 | 504.53 | 33.3% | yaml | weights |
| YOLOX | S | 8 | 8 | 640x640 | O2 | 202.46s | 243.99 | 262.31 | 40.7% | yaml | weights |
| YOLOX | M | 8 | 8 | 640x640 | O2 | 212.78s | 267.68 | 239.09 | 46.7% | yaml | weights |
| YOLOX | L | 8 | 8 | 640x640 | O2 | 262.52s | 316.78 | 202.03 | 49.2% | yaml | weights |
| YOLOX | X | 8 | 8 | 640x640 | O2 | 341.33s | 415.67 | 153.97 | 51.6% | yaml | weights |
| YOLOX | Darknet53 | 8 | 8 | 640x640 | O2 | 198.15s | 407.53 | 157.04 | 47.7% | yaml | weights |
- map: Accuracy reported on the validation set.
- We refer to the official YOLOX to reproduce the results.
[1] Zheng Ge. YOLOX: Exceeding YOLO Series in 2021. https://arxiv.org/abs/2107.08430, 2021.