Arulkumar03/Fox_Sheep_Detector_Computer_Vision_model

1# MViTv2: Improved Multiscale Vision Transformers for Classification and Detection2 3Yanghao Li*, Chao-Yuan Wu*, Haoqi Fan, Karttikeya Mangalam, Bo Xiong, Jitendra Malik, Christoph Feichtenhofer*4 5[[`arXiv`](https://arxiv.org/abs/2112.01526)] [[`BibTeX`](#CitingMViTv2)]6 7In this repository, we provide detection configs and models for MViTv2 (CVPR 2022) in Detectron2. For image classification tasks, please refer to [MViTv2 repo](https://github.com/facebookresearch/mvit).8 9## Results and Pretrained Models10 11### COCO12 13<table><tbody>14<!-- START TABLE -->15<!-- TABLE HEADER -->16<th valign="bottom">Name</th>17<th valign="bottom">pre-train</th>18<th valign="bottom">Method</th>19<th valign="bottom">epochs</th>20<th valign="bottom">box<br/>AP</th>21<th valign="bottom">mask<br/>AP</th>22<th valign="bottom">#params</th>23<th valign="bottom">FLOPS</th>24<th valign="bottom">model id</th>25<th valign="bottom">download</th>26<!-- TABLE BODY -->27<!-- ROW: mask_rcnn_mvitv2_t_3x -->28 <tr><td align="left"><a href="configs/mask_rcnn_mvitv2_t_3x.py">MViTV2-T</a></td>29<td align="center">IN1K</td>30<td align="center">Mask R-CNN</td>31<td align="center">36</td>32<td align="center">48.3</td>33<td align="center">43.8</td>34<td align="center">44M</td>35<td align="center">279G</td>36<td align="center">307611773</td>37<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/MViTv2/mask_rcnn_mvitv2_t_3x/f307611773/model_final_1a1c30.pkl">model</a></td>38</tr>39<!-- ROW: cascade_mask_rcnn_mvitv2_t_3x -->40 <tr><td align="left"><a href="configs/cascade_mask_rcnn_mvitv2_t_3x.py">MViTV2-T</a></td>41<td align="center">IN1K</td>42<td align="center">Cascade Mask R-CNN</td>43<td align="center">36</td>44<td align="center">52.2</td>45<td align="center">45.0</td>46<td align="center">76M</td>47<td align="center">701G</td>48<td align="center">308344828</td>49<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/MViTv2/cascade_mask_rcnn_mvitv2_t_3x/f308344828/model_final_c6967a.pkl">model</a></td>50</tr>51<!-- ROW: cascade_mask_rcnn_mvitv2_s_3x -->52<tr><td align="left"><a href="configs/cascade_mask_rcnn_mvitv2_s_3x.py">MViTV2-S</a></td>53<td align="center">IN1K</td>54<td align="center">Cascade Mask R-CNN</td>55<td align="center">36</td>56<td align="center">53.2</td>57<td align="center">46.0</td>58<td align="center">87M</td>59<td align="center">748G</td>60<td align="center">308344647</td>61<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/MViTv2/cascade_mask_rcnn_mvitv2_s_3x/f308344647/model_final_279baf.pkl">model</a></td>62</tr>63<!-- ROW: cascade_mask_rcnn_mvitv2_b_3x -->64<tr><td align="left"><a href="configs/cascade_mask_rcnn_mvitv2_b_3x.py">MViTV2-B</a></td>65<td align="center">IN1K</td>66<td align="center">Cascade Mask R-CNN</td>67<td align="center">36</td>68<td align="center">54.1</td>69<td align="center">46.7</td>70<td align="center">103M</td>71<td align="center">814G</td>72<td align="center">308109448</td>73<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/MViTv2/cascade_mask_rcnn_mvitv2_b_3x/f308109448/model_final_421a91.pkl">model</a></td>74</tr>75<!-- ROW: cascade_mask_rcnn_mvitv2_b_in21k_3x -->76<tr><td align="left"><a href="configs/cascade_mask_rcnn_mvitv2_b_in21k_3x.py">MViTV2-B</a></td>77<td align="center">IN21K</td>78<td align="center">Cascade Mask R-CNN</td>79<td align="center">36</td>80<td align="center">54.9</td>81<td align="center">47.4</td>82<td align="center">103M</td>83<td align="center">814G</td>84<td align="center">309003202</td>85<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/MViTv2/cascade_mask_rcnn_mvitv2_b_in12k_3x/f309003202/model_final_be5168.pkl">model</a></td>86</tr>87<!-- ROW: cascade_mask_rcnn_mvitv2_l_in21k_lsj_50ep -->88<tr><td align="left"><a href="configs/cascade_mask_rcnn_mvitv2_l_in21k_lsj_50ep.py">MViTV2-L</a></td>89<td align="center">IN21K</td>90<td align="center">Cascade Mask R-CNN</td>91<td align="center">50</td>92<td align="center">55.8</td>93<td align="center">48.3</td>94<td align="center">270M</td>95<td align="center">1519G</td>96<td align="center">308099658</td>97<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/MViTv2/cascade_mask_rcnn_mvitv2_l_in12k_lsj_50ep/f308099658/model_final_c41c5a.pkl">model</a></td>98</tr>99<!-- ROW: cascade_mask_rcnn_mvitv2_h_in21k_lsj_3x -->100<tr><td align="left"><a href="configs/cascade_mask_rcnn_mvitv2_h_in21k_lsj_3x.py">MViTV2-H</a></td>101<td align="center">IN21K</td>102<td align="center">Cascade Mask R-CNN</td>103<td align="center">36</td>104<td align="center">56.1</td>105<td align="center">48.5</td>106<td align="center">718M</td>107<td align="center">3084G</td>108<td align="center">309013744</td>109<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/MViTv2/cascade_mask_rcnn_mvitv2_h_in12k_lsj_3x/f309013744/model_final_30d36b.pkl">model</a></td>110</tr>111</tbody></table>112 113Note that the above models were trained and measured on 8-node with 64 NVIDIA A100 GPUs in total. The ImageNet pre-trained model weights are obtained from [MViTv2 repo](https://github.com/facebookresearch/mvit).114 115## Training116All configs can be trained with:117 118```119../../tools/lazyconfig_train_net.py --config-file configs/path/to/config.py120```121By default, we use 64 GPUs with batch size as 64 for training.122 123## Evaluation124Model evaluation can be done similarly:125```126../../tools/lazyconfig_train_net.py --config-file configs/path/to/config.py --eval-only train.init_checkpoint=/path/to/model_checkpoint127```128 129 130 131## <a name="CitingMViTv2"></a>Citing MViTv2132 133If you use MViTv2, please use the following BibTeX entry.134 135```BibTeX136@inproceedings{li2021improved,137  title={MViTv2: Improved multiscale vision transformers for classification and detection},138  author={Li, Yanghao and Wu, Chao-Yuan and Fan, Haoqi and Mangalam, Karttikeya and Xiong, Bo and Malik, Jitendra and Feichtenhofer, Christoph},139  booktitle={CVPR},140  year={2022}141}142```143