YOLOX: Exceeding YOLO Series in 2021
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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 NanoDet by 1.8 industry, we boost it to 47.3 practice by 3.0 YOLOv4-CSP, YOLOv5-L, we achieve 50.0 Tesla V100, exceeding YOLOv5-L by 1.8 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.
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