PyTorch_YOLOv4

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PyTorch implementation of YOLOv4

YOLOv4

This is PyTorch implementation of YOLOv4 which is based on ultralytics/yolov3.

development log

Expand

* `2020-07-23` - support CUDA accelerated Mish activation function. * `2020-07-19` - support and training tiny YOLOv4. [`yolov4-tiny`]() * `2020-07-15` - design and training conditional YOLOv4. [`yolov4-pacsp-conditional`]() * `2020-07-13` - support MixUp data augmentation. * `2020-07-03` - design new stem layers. * `2020-06-16` - support floating16 of GPU inference. * `2020-06-14` - convert .pt to .weights for darknet fine-tuning. * `2020-06-13` - update multi-scale training strategy. * `2020-06-12` - design scaled YOLOv4 follow [ultralytics](https://github.com/ultralytics/yolov5). [`yolov4-pacsp-s`]() [`yolov4-pacsp-m`]() [`yolov4-pacsp-l`]() [`yolov4-pacsp-x`]() * `2020-06-07` - design [scaling methods](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/images/scalingCSP.png) for CSP-based models. [`yolov4-pacsp-25`]() [`yolov4-pacsp-75`]() * `2020-06-03` - update COCO2014 to COCO2017. * `2020-05-30` - update FPN neck to CSPFPN. [`yolov4-yocsp`]() [`yolov4-yocsp-mish`]() * `2020-05-24` - update neck of YOLOv4 to CSPPAN. [`yolov4-pacsp`]() [`yolov4-pacsp-mish`]() * `2020-05-15` - training YOLOv4 with Mish activation function. [`yolov4-yospp-mish`]() [`yolov4-paspp-mish`]() * `2020-05-08` - design and training YOLOv4 with FPN neck. [`yolov4-yospp`]() * `2020-05-01` - training YOLOv4 with Leaky activation function using PyTorch. [`yolov4-paspp`]()

Pretrained Models & Comparison

Model	Test Size	AP^val	AP₅₀^val	AP₇₅^val	AP_S^val	AP_M^val	AP_L^val	cfg	weights
YOLOv4_paspp	736	45.7%	64.2%	50.3%	27.4%	51.3%	58.6%	cfg	weights
YOLOv4_pacsp-s	736	36.0%	54.2%	39.4%	18.7%	41.2%	48.0%	cfg	weights
YOLOv4_pacsp	736	46.4%	64.8%	51.0%	28.5%	51.9%	59.5%	cfg	weights
YOLOv4_pacsp-x	736	47.6%	66.1%	52.2%	29.9%	53.3%	61.5%	cfg	weights

YOLOv4_pacsp-s-mish	736	37.4%	56.3%	40.0%	20.9%	43.0%	49.3%	cfg	weights
YOLOv4_pacsp-mish	736	46.5%	65.7%	50.2%	30.0%	52.0%	59.4%	cfg	weights
YOLOv4_pacsp-x-mish	736	48.5%	67.4%	52.7%	30.9%	54.0%	62.0%	cfg	weights

YOLOv4_tiny	416	22.5%	39.3%	22.5%	7.4%	26.3%	34.8%	cfg	weights

Requirements

pip install -r requirements.txt

※ For running Mish models, please install https://github.com/thomasbrandon/mish-cuda

Training

python train.py --data coco2017.data --cfg yolov4-pacsp.cfg --weights '' --name yolov4-pacsp --img 640 640 640

Testing

python test_half.py --data coco2017.data --cfg yolov4-pacsp.cfg --weights yolov4-pacsp.pt --img 736 --iou-thr 0.7 --batch-size 8

Citation

@article{bochkovskiy2020yolov4,
  title={{YOLOv4}: Optimal Speed and Accuracy of Object Detection},
  author={Bochkovskiy, Alexey and Wang, Chien-Yao and Liao, Hong-Yuan Mark},
  journal={arXiv preprint arXiv:2004.10934},
  year={2020}
}

@inproceedings{wang2020cspnet,
  title={{CSPNet}: A New Backbone That Can Enhance Learning Capability of {CNN}},
  author={Wang, Chien-Yao and Mark Liao, Hong-Yuan and Wu, Yueh-Hua and Chen, Ping-Yang and Hsieh, Jun-Wei and Yeh, I-Hau},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  pages={390--391},
  year={2020}
}

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