Create README.md

README.md

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+---
+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-s-mish.cfg
+- weights: https://drive.google.com/file/d/1730MvuVhTttVJGk4ftN1zql9z7U4iQ6U/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}
+}
+```