WongKinYiu / PyTorch_YOLOv4
- пятница, 31 июля 2020 г. в 00:23:04
Python
PyTorch implementation of YOLOv4
This is PyTorch implementation of YOLOv4 which is based on ultralytics/yolov3.
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-s yolov4-pacsp-m yolov4-pacsp-l yolov4-pacsp-x2020-06-07 - design scaling methods for CSP-based models. 
yolov4-pacsp-25 yolov4-pacsp-752020-06-03 - update COCO2014 to COCO2017.2020-05-30 - update FPN neck to CSPFPN. yolov4-yocsp yolov4-yocsp-mish2020-05-24 - update neck of YOLOv4 to CSPPAN. yolov4-pacsp yolov4-pacsp-mish2020-05-15 - training YOLOv4 with Mish activation function. yolov4-yospp-mish yolov4-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 |
|---|---|---|---|---|---|---|---|---|---|
| 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 |
pip install -r requirements.txt
※ For running Mish models, please install https://github.com/thomasbrandon/mish-cuda
python train.py --data coco2017.data --cfg yolov4-pacsp.cfg --weights '' --name yolov4-pacsp --img 640 640 640
python test_half.py --data coco2017.data --cfg yolov4-pacsp.cfg --weights yolov4-pacsp.pt --img 736 --iou-thr 0.7 --batch-size 8
@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}
}