  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. Source code is at
https://github.com/Megvii-BaseDetection/YOLOX.
