mludolph/yolor

implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)

YOLOR

implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks

To reproduce the results in the paper, please use this branch.

Model	Test Size	APtest	AP50test	AP75test	APStest	APMtest	APLtest	batch1 throughput
YOLOR-P6	1280	52.6%	70.6%	57.6%	34.7%	56.6%	64.2%	49 fps
YOLOR-W6	1280	54.1%	72.0%	59.2%	36.3%	57.9%	66.1%	47 fps
YOLOR-E6	1280	54.8%	72.7%	60.0%	36.9%	58.7%	66.9%	37 fps
YOLOR-D6	1280	55.4%	73.3%	60.6%	38.0%	59.2%	67.1%	30 fps
YOLOv4-P5	896	51.8%	70.3%	56.6%	33.4%	55.7%	63.4%	41 fps
YOLOv4-P6	1280	54.5%	72.6%	59.8%	36.6%	58.2%	65.5%	30 fps
YOLOv4-P7	1536	55.5%	73.4%	60.8%	38.4%	59.4%	67.7%	16 fps

To reproduce the inference speed, please see darknet.

Model	Test Size	APval	AP50val	AP75val	APSval	APMval	APLval	batch1 throughput
YOLOv4-CSP	640	49.1%	67.7%	53.8%	32.1%	54.4%	63.2%	76 fps
YOLOR-CSP	640	49.2%	67.6%	53.7%	32.9%	54.4%	63.0%	weights
YOLOR-CSP*	640	50.0%	68.7%	54.3%	34.2%	55.1%	64.3%	weights
YOLOv4-CSP-X	640	50.9%	69.3%	55.4%	35.3%	55.8%	64.8%	53 fps
YOLOR-CSP-X	640	51.1%	69.6%	55.7%	35.7%	56.0%	65.2%	weights
YOLOR-CSP-X*	640	51.5%	69.9%	56.1%	35.8%	56.8%	66.1%	weights

Convert to ONNX

yolor_csp_x*

  python convert_to_onnx.py --weights yolor_csp_x_star.pt --cfg cfg/yolor_csp_x.cfg --output yolo_csp_x_star.onnx

  python object_detector_onnx.py

You will get the results:

Convert to TensorRT

  /usr/src/tensorrt/bin/trtexec --onnx=yolor_csp_x_star.onnx \
                                --saveEngine=yolor_csp_x_star-fp16.trt \
                                --explicitBatch \
                                --minShapes=input:1x3x416x416 \
                                --optShapes=input:1x3x896x896 \
                                --maxShapes=input:1x3x896x896 \
                                --verbose \
                                --fp16 \
                                --device=0

  python object_detector_trt.py

Note that yolor_p6 have 4 detect layer, change maximum boxes in exec_backends/trt_loader.py

Citation

@article{wang2021you,
  title={You Only Learn One Representation: Unified Network for Multiple Tasks},
  author={Wang, Chien-Yao and Yeh, I-Hau and Liao, Hong-Yuan Mark},
  journal={arXiv preprint arXiv:2105.04206},
  year={2021}
}

Acknowledgements

Expand

• https://github.com/AlexeyAB/darknet
• https://github.com/WongKinYiu/PyTorch_YOLOv4
• https://github.com/WongKinYiu/ScaledYOLOv4
• https://github.com/ultralytics/yolov3
• https://github.com/ultralytics/yolov5