---
license: mit
datasets:
- detection-datasets/coco
---

# Introduction

This repository stores the model for YOLOv4-CSP-Mish, compatible with Kalray's neural network API. </br>
Please see www.github.com/kalray/kann-models-zoo for details and proper usage. </br>

# Contents

- ONNX:       yolov4-csp-mish_608x608.optimized.onnx

# Lecture note reference

+ YOLOv4: Optimal Speed and Accuracy of Object Detection, https://arxiv.org/pdf/2004.10934.pdf

# Repository or links references

- repository: https://github.com/WongKinYiu/PyTorch_YOLOv4
- cfg: https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/cfg/yolov4-csp-mish.cfg
- weights: https://drive.google.com/file/d/17pQoMfJYbroYqxb6grem2SDY7pZIJPrN/view?usp=sharing

BibTeX entry and citation info
```
@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}
}
```