Yolov5s/ai_training/mmdetection/configs/yolox/README_kneron.md

YOLOX

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.

Train

python tools/train.py \
    configs/yolox/yolox_s_8x8_300e_coco_img_norm.py

Test

python tools/test.py \
    configs/yolox/yolox_s_8x8_300e_coco_img_norm.py

Test_kneron

python tools/test_kneron.py \
    configs/yolox/yolox_s_8x8_300e_coco_img_norm.py \
    work_dirs/yolox_s_8x8_300e_coco_img_norm/best_bbox_mAP_epoch_298.pth \
    --eval bbox \
    --out-kneron output.json

Export onnx

python tools/deployment/pytorch2onnx.py \
    configs/yolox/yolox_s_8x8_300e_coco_img_norm.py \
    work_dirs/yolox_s_8x8_300e_coco_img_norm/latest.pth \
    --skip-postprocess \
    --shape 640 640

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

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}
}