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@ -2,7 +2,9 @@
## (neural networks for object detection) ## (neural networks for object detection)
Paper Yolo v4: https://arxiv.org/abs/2004.10934 Paper YOLO v4: https://arxiv.org/abs/2004.10934
Paper Scaled YOLO v4: https://arxiv.org/abs/2011.08036
More details: [medium link](https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7) More details: [medium link](https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7)
@ -54,6 +56,10 @@ About Darknet framework: http://pjreddie.com/darknet/
![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png)
![scaled_yolov4](https://user-images.githubusercontent.com/4096485/101356322-f1f5a180-38a8-11eb-9907-4fe4f188d887.png) AP50:95 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2011.08036
----
![modern_gpus](https://user-images.githubusercontent.com/4096485/82835867-f1c62380-9ecd-11ea-9134-1598ed2abc4b.png) AP50:95 / AP50 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2004.10934 ![modern_gpus](https://user-images.githubusercontent.com/4096485/82835867-f1c62380-9ecd-11ea-9134-1598ed2abc4b.png) AP50:95 / AP50 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2004.10934
@ -81,7 +87,8 @@ tkDNN-TensorRT accelerates YOLOv4 **~2x** times for batch=1 and **3x-4x** times
#### Youtube video of results #### Youtube video of results
[![Yolo v4](http://img.youtube.com/vi/1_SiUOYUoOI/0.jpg)](https://youtu.be/1_SiUOYUoOI "Yolo v4") | [![Yolo v4](https://user-images.githubusercontent.com/4096485/101360000-1a33cf00-38ae-11eb-9e5e-b29c5fb0afbe.png)](https://youtu.be/1_SiUOYUoOI "Yolo v4") | [![Scaled Yolo v4](https://user-images.githubusercontent.com/4096485/101359389-43a02b00-38ad-11eb-866c-f813e96bf61a.png)](https://youtu.be/YDFf-TqJOFE "Scaled Yolo v4") |
|---|---|
Others: https://www.youtube.com/user/pjreddie/videos Others: https://www.youtube.com/user/pjreddie/videos
@ -124,6 +131,16 @@ There are weights-file for different cfg-files (trained for MS COCO dataset):
FPS on RTX 2070 (R) and Tesla V100 (V): FPS on RTX 2070 (R) and Tesla V100 (V):
* [yolov4x-mish.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4x-mish.cfg) - **67.9% mAP@0.5 (49.4% AP@0.5:0.95) - 23(R) FPS / 50(V) FPS** - 221 BFlops (110 FMA) - 381 MB: [yolov4x-mish.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.weights)
* pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.conv.166
* [yolov4-csp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-csp.cfg) - 202 MB: [yolov4-csp.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.weights) paper [Scaled Yolo v4](https://arxiv.org/abs/2011.08036)
just change `width=` and `height=` parameters in `yolov4-csp.cfg` file and use the same `yolov4-csp.weights` file for all cases:
* `width=608 height=608` in cfg: **66.2% mAP@0.5 (47.5% AP@0.5:0.95) - 70(V) FPS** - 120 (60 FMA) BFlops
* `width=512 height=512` in cfg: **64.8% mAP@0.5 (46.2% AP@0.5:0.95) - 93(V) FPS** - 77 (39 FMA) BFlops
* pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.conv.142
* [yolov4.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg) - 245 MB: [yolov4.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights) (Google-drive mirror [yolov4.weights](https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT) ) paper [Yolo v4](https://arxiv.org/abs/2004.10934) * [yolov4.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg) - 245 MB: [yolov4.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights) (Google-drive mirror [yolov4.weights](https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT) ) paper [Yolo v4](https://arxiv.org/abs/2004.10934)
just change `width=` and `height=` parameters in `yolov4.cfg` file and use the same `yolov4.weights` file for all cases: just change `width=` and `height=` parameters in `yolov4.cfg` file and use the same `yolov4.weights` file for all cases:
* `width=608 height=608` in cfg: **65.7% mAP@0.5 (43.5% AP@0.5:0.95) - 34(R) FPS / 62(V) FPS** - 128.5 BFlops * `width=608 height=608` in cfg: **65.7% mAP@0.5 (43.5% AP@0.5:0.95) - 34(R) FPS / 62(V) FPS** - 128.5 BFlops