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40 lines
4.5 KiB
Markdown
40 lines
4.5 KiB
Markdown
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# YOLOX
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> [YOLOX: Exceeding YOLO Series in 2021](https://arxiv.org/abs/2107.08430)
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<!-- [ALGORITHM] -->
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## Abstract
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In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX. 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. We hope this report can provide useful experience for developers and researchers in practical scenes, and we also provide deploy versions with ONNX, TensorRT, NCNN, and Openvino supported.
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<div align=center>
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<img src="https://user-images.githubusercontent.com/40661020/144001736-9fb303dd-eac7-46b0-ad45-214cfa51e928.png"/>
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</div>
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## Results and Models
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| Backbone | size | Mem (GB) | box AP | Config | Download |
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| :--------: | :--: | :------: | :----: | :---------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| YOLOX-tiny | 416 | 3.5 | 32.0 | [config](https://github.com/open-mmlab/mmyolo/tree/master/configs/yolox/yolox_tiny_8xb8-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0.0.1/yolox/yolox_tiny_8xb8-300e_coco) \| [log](https://download.openmmlab.com/mmyolo/v0.0.1/yolox/yolox_tiny_8xb8-300e_coco) |
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| YOLOX-s | 640 | 7.6 | 40.5 | [config](https://github.com/open-mmlab/mmyolo/tree/master/configs/yolox/yolox_s_8xb8-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0.0.1/yolox/yolox_s_8xb8-300e_coco) \| [log](https://download.openmmlab.com/mmyolo/v0.0.1/yolox/yolox_s_8xb8-300e_coco) |
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| YOLOX-m | 640 | 7.6 | 40.5 | [config](https://github.com/open-mmlab/mmyolo/tree/master/configs/yolox/yolox_m_8xb8-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0.0.1/yolox/yolox_m_8xb8-300e_coco) \| [log](https://download.openmmlab.com/mmyolo/v0.0.1/yolox/yolox_m_8xb8-300e_coco) |
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| YOLOX-l | 640 | 19.9 | 49.4 | [config](https://github.com/open-mmlab/mmyolo/tree/master/configs/yolox/yolox_l_8xb8-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0.0.1/yolox/yolox_l_8xb8-300e_coco) \| [log](https://download.openmmlab.com/mmyolo/v0.0.1/yolox/yolox_l_8xb8-300e_coco) |
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**Note**:
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1. The test score threshold is 0.001, and the box AP indicates the best AP.
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2. Due to the need for pre-training weights, we cannot reproduce the performance of the `yolox-nano` model. Please refer to https://github.com/Megvii-BaseDetection/YOLOX/issues/674 for more information.
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3. We also trained the model by the official release of YOLOX based on [Megvii-BaseDetection/YOLOX#735](https://github.com/Megvii-BaseDetection/YOLOX/issues/735) with commit ID [38c633](https://github.com/Megvii-BaseDetection/YOLOX/tree/38c633bf176462ee42b110c70e4ffe17b5753208). We found that the best AP of `YOLOX-tiny`, `YOLOX-s`, `YOLOX-l`, and `YOLOX-x` is 31.8, 40.3, 49.2, and 50.9, respectively. The performance is consistent with that of our re-implementation (see Table above) but still has a gap (0.3~0.8 AP) in comparison with the reported performance in their [README](https://github.com/Megvii-BaseDetection/YOLOX/blob/38c633bf176462ee42b110c70e4ffe17b5753208/README.md#benchmark).
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## Citation
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```latex
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@article{yolox2021,
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title={{YOLOX}: Exceeding YOLO Series in 2021},
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author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
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journal={arXiv preprint arXiv:2107.08430},
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year={2021}
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}
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```
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