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Fix configs (open-mmlab#2828)
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ZwwWayne authored May 27, 2020
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4 changes: 0 additions & 4 deletions configs/cascade_rcnn/README.md
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Expand Up @@ -45,12 +45,8 @@
| R-101-FPN | pytorch | 20e | - | - | 43.4 | 37.8 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_bbox_mAP-0.434__segm_mAP-0.378_20200504_174836-005947da.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_20200504_174836.log.json)|
| X-101-32x4d-FPN | pytorch | 1x | 9.2 | 8.6 | 44.3 | 38.3 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201-0f411b1f.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201_052416.log.json) |
| X-101-32x4d-FPN | pytorch | 20e | | | | | |
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| X-101-64x4d-FPN | pytorch | 20e | 12.2 | | 45.6 |39.5 | |
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| X-101-64x4d-FPN | pytorch | 1x | 12.2 | 6.7 | 45.3 | 39.2 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203-9a2db89d.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203_044059.log.json) |
| X-101-64x4d-FPN | pytorch | 20e | 12.2 | | 45.6 |39.5 | |
>>>>>>> 0367dd9ca4456dc701ee79498a5cf733fe8203bf

**Notes:**

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3 changes: 1 addition & 2 deletions configs/instaboost/README.md
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Expand Up @@ -32,7 +32,7 @@ InstaBoost have been already integrated in the data pipeline, thus all you need

- All models were trained on `coco_2017_train` and tested on `coco_2017_val` for conveinience of evaluation and comparison. In the paper, the results are obtained from `test-dev`.
- To balance accuracy and training time when using InstaBoost, models released in this page are all trained for 48 Epochs. Other training and testing configs strictly follow the original framework.
- The results and models are provided by the [authors](https://github.com/GothicAi/Instaboost) (many thanks).
- For results and models in MMDetection V1.x, please refer to [Instaboost](https://github.com/GothicAi/Instaboost).


| Network | Backbone | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Download |
Expand All @@ -41,4 +41,3 @@ InstaBoost have been already integrated in the data pipeline, thus all you need
| Mask R-CNN | R-101-FPN | 4x | | | | | |
| Mask R-CNN | X-101-64x4d-FPN | 4x | 10.7 | | 44.7 | 39.7 | |
| Cascade R-CNN | R-101-FPN | 4x | 6.0 | 12.0 | 43.7 | 38.0 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-c19d98d9.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307_223646.log.json) |
| Cascade R-CNN | X-101-64x4d-FPN | 4x | | | | | |

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