This is PyTorch implementation of YOLOv4 which is based on ultralytics/yolov3.
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2020-08-29- support deformable kernel.2020-08-24- support channel last training/testing.2020-08-16- design CSPPRN.2020-08-15- design deeper model.csp-p6-mish2020-08-11- support HarDNet.hard39-pacsphard68-pacsphard85-pacsp2020-08-10- add DDP training.2020-08-06- support DCN, DCNv2.yolov4-dcn2020-08-01- add pytorch hub.2020-07-31- support ResNet, ResNeXt, CSPResNet, CSPResNeXt.r50-pacspx50-pacspcspr50-pacspcspx50-pacsp2020-07-28- support SAM.yolov4-pacsp-sam2020-07-24- update api.2020-07-23- support CUDA accelerated Mish activation function.2020-07-19- support and training tiny YOLOv4.yolov4-tiny2020-07-15- design and training conditional YOLOv4.yolov4-pacsp-conditional2020-07-13- 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.yolov4-pacsp-syolov4-pacsp-myolov4-pacsp-lyolov4-pacsp-x2020-06-07- design scaling methods for CSP-based models.yolov4-pacsp-25yolov4-pacsp-752020-06-03- update COCO2014 to COCO2017.2020-05-30- update FPN neck to CSPFPN.yolov4-yocspyolov4-yocsp-mish2020-05-24- update neck of YOLOv4 to CSPPAN.yolov4-pacspyolov4-pacsp-mish2020-05-15- training YOLOv4 with Mish activation function.yolov4-yospp-mishyolov4-paspp-mish2020-05-08- design and training YOLOv4 with FPN neck.yolov4-yospp2020-05-01- training YOLOv4 with Leaky activation function using PyTorch.yolov4-paspp
| Model | Test Size | APval | AP50val | AP75val | APSval | APMval | APLval | cfg | weights |
|---|---|---|---|---|---|---|---|---|---|
| YOLOv4 | 672 | 47.7% | 66.7% | 52.1% | 30.5% | 52.6% | 61.4% | cfg | weights |
| YOLOv4pacsp-s | 672 | 36.6% | 55.5% | 39.6% | 21.2% | 41.1% | 47.0% | cfg | weights |
| YOLOv4pacsp | 672 | 47.2% | 66.2% | 51.6% | 30.4% | 52.3% | 60.8% | cfg | weights |
| YOLOv4pacsp-x | 672 | 49.3% | 68.1% | 53.6% | 31.8% | 54.5% | 63.6% | cfg | weights |
| YOLOv4pacsp-s-mish | 672 | 38.6% | 57.7% | 41.8% | 22.3% | 43.5% | 49.3% | cfg | weights |
| YOLOv4pacsp-mish | 672 | 48.1% | 66.9% | 52.3% | 30.8% | 53.4% | 61.7% | cfg | weights |
| YOLOv4pacsp-x-mish | 672 | 50.0% | 68.5% | 54.4% | 32.9% | 54.9% | 64.0% | cfg | weights |
pip install -r requirements.txt
※ For running Mish models, please install https://github.com/thomasbrandon/mish-cuda
python train.py --device 0 --batch-size 16 --img 640 640 --data coco.yaml --cfg cfg/yolov4-pacsp.cfg --weights '' --name yolov4-pacsp
python test.py --img 640 --conf 0.001 --batch 8 --device 0 --data coco.yaml --cfg cfg/yolov4-pacsp.cfg --weights weights/yolov4-pacsp.pt
@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}
}