Update README.md

README.md

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 ---
 license: mit
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+
+
+[![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)
+[![CircleCI](https://circleci.com/gh/AlexeyAB/darknet.svg?style=svg)](https://circleci.com/gh/AlexeyAB/darknet)
+[![Contributors](https://img.shields.io/github/contributors/AlexeyAB/Darknet.svg)](https://github.com/AlexeyAB/darknet/graphs/contributors)
+[![License: Unlicense](https://img.shields.io/badge/license-Unlicense-blue.svg)](https://github.com/AlexeyAB/darknet/blob/master/LICENSE)
+[![DOI](https://zenodo.org/badge/75388965.svg)](https://zenodo.org/badge/latestdoi/75388965)
+[![arxiv.org](http://img.shields.io/badge/cs.CV-arXiv%3A2004.10934-B31B1B.svg)](https://arxiv.org/abs/2004.10934)
+[![arxiv.org](http://img.shields.io/badge/cs.CV-arXiv%3A2011.08036-B31B1B.svg)](https://arxiv.org/abs/2011.08036)
+
+
+## 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}
+}
+```