Arulkumar03/Fox_Sheep_Detector_Computer_Vision_model

1<img src=".github/Detectron2-Logo-Horz.svg" width="300" >2 3<a href="https://opensource.facebook.com/support-ukraine">4  <img src="https://img.shields.io/badge/Support-Ukraine-FFD500?style=flat&labelColor=005BBB" alt="Support Ukraine - Help Provide Humanitarian Aid to Ukraine." />5</a>6 7Detectron2 is Facebook AI Research's next generation library8that provides state-of-the-art detection and segmentation algorithms.9It is the successor of10[Detectron](https://github.com/facebookresearch/Detectron/)11and [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/).12It supports a number of computer vision research projects and production applications in Facebook.13 14<div align="center">15  <img src="https://user-images.githubusercontent.com/1381301/66535560-d3422200-eace-11e9-9123-5535d469db19.png"/>16</div>17<br>18 19## Learn More about Detectron220 21Explain Like I’m 5: Detectron2            |  Using Machine Learning with Detectron222:-------------------------:|:-------------------------:23[![Explain Like I’m 5: Detectron2](https://img.youtube.com/vi/1oq1Ye7dFqc/0.jpg)](https://www.youtube.com/watch?v=1oq1Ye7dFqc)  |  [![Using Machine Learning with Detectron2](https://img.youtube.com/vi/eUSgtfK4ivk/0.jpg)](https://www.youtube.com/watch?v=eUSgtfK4ivk)24 25## What's New26* Includes new capabilities such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend,27  DeepLab, ViTDet, MViTv2 etc.28* Used as a library to support building [research projects](projects/) on top of it.29* Models can be exported to TorchScript format or Caffe2 format for deployment.30* It [trains much faster](https://detectron2.readthedocs.io/notes/benchmarks.html).31 32See our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/)33to see more demos and learn about detectron2.34 35## Installation36 37See [installation instructions](https://detectron2.readthedocs.io/tutorials/install.html).38 39## Getting Started40 41See [Getting Started with Detectron2](https://detectron2.readthedocs.io/tutorials/getting_started.html),42and the [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)43to learn about basic usage.44 45Learn more at our [documentation](https://detectron2.readthedocs.org).46And see [projects/](projects/) for some projects that are built on top of detectron2.47 48## Model Zoo and Baselines49 50We provide a large set of baseline results and trained models available for download in the [Detectron2 Model Zoo](MODEL_ZOO.md).51 52## License53 54Detectron2 is released under the [Apache 2.0 license](LICENSE).55 56## Citing Detectron257 58If you use Detectron2 in your research or wish to refer to the baseline results published in the [Model Zoo](MODEL_ZOO.md), please use the following BibTeX entry.59 60```BibTeX61@misc{wu2019detectron2,62  author =       {Yuxin Wu and Alexander Kirillov and Francisco Massa and63                  Wan-Yen Lo and Ross Girshick},64  title =        {Detectron2},65  howpublished = {\url{https://github.com/facebookresearch/detectron2}},66  year =         {2019}67}68```69