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YOLOX: Exceeding YOLO Series in 2021

Abstract

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.

Citation

@article{yolox2021,
  title={{YOLOX}: Exceeding YOLO Series in 2021},
  author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
  journal={arXiv preprint arXiv:2107.08430},
  year={2021}
}

Results and Models

Backbone size Mem (GB) box AP Config Download
YOLOX-tiny 416 3.5 32.0 config model | log
YOLOX-s 640 7.6 40.5 config model | log
YOLOX-l 640 19.9 49.4 config model | log
YOLOX-x 640 28.1 50.9 config model | log

Note:

  1. The test score threshold is 0.001, and the box AP indicates the best AP.
  2. Due to the need for pre-training weights, we cannot reproduce the performance of the yolox-nano model. Please refer to Megvii-BaseDetection/YOLOX#674 for more information.
  3. We also trained the model by the official release of YOLOX based on Megvii-BaseDetection/YOLOX#735 with commit ID 38c633. 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.