readme: ---
  license: mit
  ---
  
  
  [![Darknet Continuous Integration](https://github.com/AlexeyAB/darknet/workflows/Darknet%20Continuous%20Integration/badge.svg)](https://github.com/AlexeyAB/darknet/actions?query=workflow%3A%22Darknet+Continuous+Integration%22)
  
  
  ## Model
  
  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.
  
  ## How to use:
  
  ```
  # clone the repo
  git clone https://huggingface.co/hashb/darknet-yolov4-object-detection
  
  # open file darknet-yolov4-object-detection.ipynb and run in colab
  ```
  
  ## 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}
  }
  ```
  