How to use YOLOv3

This post will guide you through detecting objects with the YOLO system using a pre-trained model. If you don't already have Darknet installed, you should do that first. Or instead of reading all that just run:

git clone https://github.com/pjreddie/darknet
cd darknet
make

Easy!

You already have the config file for YOLO in the cfg/ subdirectory. You will have to download the pre-trained weight file here (237 MB). Or just run this:

wget https://pjreddie.com/media/files/yolov3.weights

Sometimes you'll need to copy the libdarknet.so file to some place where the operating system can find it.

sudo cp libdarknet.so /usr/lib/

Then run the detector!

./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg

Darknet

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.

Discord invite link for for communication and questions: https://discord.gg/zSq8rtW

Scaled-YOLOv4:

• paper (CVPR 2021): https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html

• source code - Pytorch (use to reproduce results): https://github.com/WongKinYiu/ScaledYOLOv4

• source code - Darknet: https://github.com/AlexeyAB/darknet

• 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

YOLOv4:

• paper: https://arxiv.org/abs/2004.10934

• source code: https://github.com/AlexeyAB/darknet

• Wiki: https://github.com/AlexeyAB/darknet/wiki

• useful links: https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7

For more information see the Darknet project website.

For questions or issues please use the Google Group.

AP50:95 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2011.08036

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Citation

@misc{bochkovskiy2020yolov4,
      title={YOLOv4: Optimal Speed and Accuracy of Object Detection}, 
      author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao},
      year={2020},
      eprint={2004.10934},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@InProceedings{Wang_2021_CVPR,
    author    = {Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
    title     = {{Scaled-YOLOv4}: Scaling Cross Stage Partial Network},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {13029-13038}
}