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---
license: mit
datasets:
- detection-datasets/coco
---
# Introduction
This repository stores the model for YOLOv4, compatible with Kalray's neural network API. </br>
Please see www.github.com/kalray/kann-model-zoo for details and proper usage [WIKI](https://github.com/kalray/kann-model-zoo/wiki). </br>
# Contents
- ONNX: yolov4.onnx, yolov4-tiny.onnx
# Lecture note reference
+ YOLOv4: Optimal Speed and Accuracy of Object Detection, https://arxiv.org/pdf/2004.10934.pdf
# Repository or links references
- [github: Pytorch YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4): weights: https://github.com/WongKinYiu/PyTorch_YOLOv4/releases/download/weights/yolov4.weights
- [github: Scaled YOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-tiny): weights: https://github.com/WongKinYiu/ScaledYOLOv4/releases/download/weights/yolov4-tiny.weights
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}
}
@inproceedings{wang2020cspnet,
title={{CSPNet}: A New Backbone That Can Enhance Learning Capability of {CNN}},
author={Wang, Chien-Yao and Mark Liao, Hong-Yuan and Wu, Yueh-Hua and Chen, Ping-Yang and Hsieh, Jun-Wei and Yeh, I-Hau},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
pages={390--391},
year={2020}
}
```
Author: Quentin Muller <[email protected]>
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