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--- |
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license: mit |
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--- |
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[](https://github.com/AlexeyAB/darknet/actions?query=workflow%3A%22Darknet+Continuous+Integration%22) |
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## Model |
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YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. YOLOv7-E6 object detector (56 FPS V100, 55.9% AP) outperforms both transformer-based detector SWIN-L Cascade-Mask R-CNN (9.2 FPS A100, 53.9% AP) by 509% in speed and 2% in accuracy, and convolutional-based detector ConvNeXt-XL Cascade-Mask R-CNN (8.6 FPS A100, 55.2% AP) by 551% in speed and 0.7% AP in accuracy, as well as YOLOv7 outperforms: YOLOR, YOLOX, Scaled-YOLOv4, YOLOv5, DETR, Deformable DETR, DINO-5scale-R50, ViT-Adapter-B and many other object detectors in speed and accuracy. |
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## How to use: |
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``` |
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# clone the repo |
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git clone https://huggingface.co/hashb/darknet-yolov4-object-detection |
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# open file darknet-yolov4-object-detection.ipynb and run in colab |
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``` |
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## Citation |
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``` |
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@misc{bochkovskiy2020yolov4, |
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title={YOLOv4: Optimal Speed and Accuracy of Object Detection}, |
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author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao}, |
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year={2020}, |
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eprint={2004.10934}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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``` |
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@InProceedings{Wang_2021_CVPR, |
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author = {Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark}, |
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title = {{Scaled-YOLOv4}: Scaling Cross Stage Partial Network}, |
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booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
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month = {June}, |
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year = {2021}, |
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pages = {13029-13038} |
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} |
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``` |
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