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 | APval | AP50val | AP75val | APSval | APMval | APLval | cfg | weights |
|---|---|---|---|---|---|---|---|---|---|
| YOLOv4paspp | 736 | 45.7% | 64.2% | 50.3% | 27.4% | 51.3% | 58.6% | cfg | weights |
| YOLOv4pacsp-s | 736 | 36.0% | 54.2% | 39.4% | 18.7% | 41.2% | 48.0% | cfg | weights |
| YOLOv4pacsp | 736 | 46.4% | 64.8% | 51.0% | 28.5% | 51.9% | 59.5% | cfg | weights |
| YOLOv4pacsp-x | 736 | 47.6% | 66.1% | 52.2% | 29.9% | 53.3% | 61.5% | cfg | weights |
| YOLOv4pacsp-s-mish | 736 | 37.4% | 56.3% | 40.0% | 20.9% | 43.0% | 49.3% | cfg | weights |
| YOLOv4pacsp-mish | 736 | 46.5% | 65.7% | 50.2% | 30.0% | 52.0% | 59.4% | cfg | weights |
| YOLOv4pacsp-x-mish | 736 | 48.5% | 67.4% | 52.7% | 30.9% | 54.0% | 62.0% | cfg | weights |
| YOLOv4tiny | 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}
}