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127 lines
5.0 KiB
Markdown
127 lines
5.0 KiB
Markdown
# Darknet
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# Darknet #
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Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.
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**Discord** invite link for for communication and questions: https://discord.gg/zSq8rtW
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## YOLOv7:
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* **paper** - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors: https://arxiv.org/abs/2207.02696
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* **source code - Pytorch (use to reproduce results):** https://github.com/WongKinYiu/yolov7
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----
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Official YOLOv7 is more accurate and faster than YOLOv5 by **120%** FPS, than YOLOX by **180%** FPS, than Dual-Swin-T by **1200%** FPS, than ConvNext by **550%** FPS, than SWIN-L by **500%** FPS.
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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, batch=1.
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* YOLOv7-e6 (55.9% AP, 56 FPS V100 b=1) by `+500%` FPS faster than SWIN-L Cascade-Mask R-CNN (53.9% AP, 9.2 FPS A100 b=1)
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* YOLOv7-e6 (55.9% AP, 56 FPS V100 b=1) by `+550%` FPS faster than ConvNeXt-XL C-M-RCNN (55.2% AP, 8.6 FPS A100 b=1)
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* YOLOv7-w6 (54.6% AP, 84 FPS V100 b=1) by `+120%` FPS faster than YOLOv5-X6-r6.1 (55.0% AP, 38 FPS V100 b=1)
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* YOLOv7-w6 (54.6% AP, 84 FPS V100 b=1) by `+1200%` FPS faster than Dual-Swin-T C-M-RCNN (53.6% AP, 6.5 FPS V100 b=1)
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* YOLOv7x (52.9% AP, 114 FPS V100 b=1) by `+150%` FPS faster than PPYOLOE-X (51.9% AP, 45 FPS V100 b=1)
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* YOLOv7 (51.2% AP, 161 FPS V100 b=1) by `+180%` FPS faster than YOLOX-X (51.1% AP, 58 FPS V100 b=1)
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----
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----
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----
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----
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## Scaled-YOLOv4:
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* **paper (CVPR 2021)**: https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html
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* **source code - Pytorch (use to reproduce results):** https://github.com/WongKinYiu/ScaledYOLOv4
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* **source code - Darknet:** https://github.com/AlexeyAB/darknet
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* **Medium:** https://alexeyab84.medium.com/scaled-yolo-v4-is-the-best-neural-network-for-object-detection-on-ms-coco-dataset-39dfa22fa982?source=friends_link&sk=c8553bfed861b1a7932f739d26f487c8
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## YOLOv4:
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* **paper:** https://arxiv.org/abs/2004.10934
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* **source code:** https://github.com/AlexeyAB/darknet
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* **Wiki:** https://github.com/AlexeyAB/darknet/wiki
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* **useful links:** https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7
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For more information see the [Darknet project website](http://pjreddie.com/darknet).
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<details><summary> <b>Expand</b> </summary>
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 https://paperswithcode.com/sota/object-detection-on-coco
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----
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 AP50:95 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2011.08036
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----
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----
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</details>
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----
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## Citation
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```
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@misc{https://doi.org/10.48550/arxiv.2207.02696,
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doi = {10.48550/ARXIV.2207.02696},
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url = {https://arxiv.org/abs/2207.02696},
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author = {Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
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keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
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title = {YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},
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publisher = {arXiv},
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year = {2022},
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copyright = {arXiv.org perpetual, non-exclusive license}
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}
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```
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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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