​​YOLOX: Exceeding YOLO Series in 2021 This paper presents | Data Science by ODS.ai 🦜

​​YOLOX: Exceeding YOLO Series in 2021


This paper presents a new high-performance variation of YOLO - YOLOX. Now it has an anchor-free detector, a decoupled head, and uses the leading label assignment strategy SimOTA.

Thanks to these changes, it reaches state-of-the-art results across a large scale range of models. For example, YOLOX-Nano gets 25.3% AP on COCO (+1.8% to NanoDet), YOLOX-L achieves 50.0% AP on COCO (+1.8 to YOLOv5-L).

For YOLOv3, one of the most widely used detectors in industry, they boost it to 47.3% AP on COCO, outperforming the current best practice by 3.0% AP.

The authors won the 1st Place on Streaming Perception Challenge (Workshop on Autonomous Driving at CVPR 2021) using a single YOLOX-L model.

They also provide deploy versions with ONNX, TensorRT, NCNN, and Openvino supported.

Paper: https://arxiv.org/abs/2107.08430

Code: https://github.com/Megvii-BaseDetection/YOLOX

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-yolox

#deeplearning #cv #objectdetection #endtoend #anchorfree