# Detectron-Cascade-RCNN **Repository Path**: srwpf/Detectron-Cascade-RCNN ## Basic Information - **Project Name**: Detectron-Cascade-RCNN - **Description**: Cascade R-CNN in Detectron - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2019-09-24 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Cascade R-CNN: Delving into High Quality Object Detection by Zhaowei Cai and Nuno Vasconcelos This repository is written by Zhaowei Cai at UC San Diego, on the base of [Detectron](https://github.com/facebookresearch/Detectron) @ [e8942c8](https://github.com/facebookresearch/Detectron/tree/e8942c882abf6e28fe68a626ec55028c9bdfe1cf). ## Introduction This repository re-implements Cascade R-CNN on the base of [Detectron](https://github.com/facebookresearch/Detectron). Very consistent gains are available for all tested models, regardless of baseline strength. Please follow [Detectron](https://github.com/facebookresearch/Detectron) on how to install and use Detectron-Cascade-RCNN. It is also recommended to use our original implementation, [cascade-rcnn](https://github.com/zhaoweicai/cascade-rcnn) based on Caffe, and the third-party implementation, [mmdetection](https://github.com/open-mmlab/mmdetection) based on PyTorch and [tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) based on TensorFlow. ## Citation If you use our code/model/data, please cite our paper: ``` @inproceedings{cai18cascadercnn, author = {Zhaowei Cai and Nuno Vasconcelos}, Title = {Cascade R-CNN: Delving into High Quality Object Detection}, booktitle = {CVPR}, Year = {2018} } ``` or its extension: ``` @article{cai2019cascadercnn, author = {Zhaowei Cai and Nuno Vasconcelos}, title = {Cascade R-CNN: High Quality Object Detection and Instance Segmentation}, journal = {arXiv preprint arXiv:1906.09756}, year = {2019} } ``` and Detectron: ``` @misc{Detectron2018, author = {Ross Girshick and Ilija Radosavovic and Georgia Gkioxari and Piotr Doll\'{a}r and Kaiming He}, title = {Detectron}, howpublished = {\url{https://github.com/facebookresearch/detectron}}, year = {2018} } ``` ## Benchmarking ### End-to-End Faster & Mask R-CNN Baselines

^_backbone	^_type	^_lr schd	^_im/ gpu	^_box AP	^_box AP50	^_box AP75	^_mask AP	^_mask AP50	^_mask AP75	^{_{download links}}
^{_{R-50-FPN-baseline}}	^_Faster	^_1x	^₂	^_36.7	^_58.4	^_39.6	^_-	^_-	^_-	^{_{model \| boxes}}
^{_{R-50-FPN-cascade}}	^_Faster	^_1x	^₂	^_40.9	^_59.0	^_44.6	^_-	^_-	^_-	^{_{model \| boxes}}
^{_{R-101-FPN-baseline}}	^_Faster	^_1x	^₂	^_39.4	^_61.2	^_43.4	^_-	^_-	^_-	^{_{model \| boxes}}
^{_{R-101-FPN-cascade}}	^_Faster	^_1x	^₂	^_42.8	^_61.4	^_46.1	^_-	^_-	^_-	^{_{model \| boxes}}
^{_{X-101-64x4d-FPN-baseline}}	^_Faster	^_1x	^₁	^_41.5	^_63.8	^_44.9	^_-	^_-	^_-	^{_{model \| boxes}}
^{_{X-101-64x4d-FPN-cascade}}	^_Faster	^_1x	^₁	^_45.4	^_64.0	^_49.8	^_-	^_-	^_-	^{_{model \| boxes}}
^{_{X-101-32x8d-FPN-baseline}}	^_Faster	^_1x	^₁	^_41.3	^_63.7	^_44.7	^_-	^_-	^_-	^{_{model \| boxes}}
^{_{X-101-32x8d-FPN-cascade}}	^_Faster	^_1x	^₁	^_44.7	^_63.7	^_48.8	^_-	^_-	^_-	^{_{model \| boxes}}
^{_{R-50-FPN-baseline}}	^_Mask	^_1x	^₂	^_37.7	^_59.2	^_40.9	^_33.9	^_55.8	^_35.8	^{_{model \| boxes \| masks}}
^{_{R-50-FPN-cascade}}	^_Mask	^_1x	^₂	^_41.3	^_59.6	^_44.9	^_35.4	^_56.2	^_37.8	^{_{model \| boxes \| masks}}
^{_{R-101-FPN-baseline}}	^_Mask	^_1x	^₂	^_40.0	^_61.8	^_43.7	^_35.9	^_58.3	^_38.0	^{_{model \| boxes \| masks}}
^{_{R-101-FPN-cascade}}	^_Mask	^_1x	^₂	^_43.3	^_61.7	^_47.2	^_37.1	^_58.6	^_39.8	^{_{model \| boxes \| masks}}
^{_{X-101-64x4d-FPN-baseline}}	^_Mask	^_1x	^₁	^_42.4	^_64.3	^_46.4	^_37.5	^_60.6	^_39.9	^{_{model \| boxes \| masks}}
^{_{X-101-64x4d-FPN-cascade}}	^_Mask	^_1x	^₁	^_45.9	^_64.4	^_50.2	^_38.8	^_61.3	^_41.6	^{_{model \| boxes \| masks}}
^{_{X-101-32x8d-FPN-baseline}}	^_Mask	^_1x	^₁	^_42.1	^_64.1	^_45.9	^_37.3	^_60.3	^_39.5	^{_{model \| boxes \| masks}}
^{_{X-101-32x8d-FPN-cascade}}	^_Mask	^_1x	^₁	^_45.8	^_64.1	^_50.3	^_38.6	^_60.6	^_41.5	^{_{model \| boxes \| masks}}

### Mask R-CNN with Bells & Whistles

^_backbone	^_type	^_lr schd	^_im/ gpu	^_box AP	^_box AP50	^_box AP75	^_mask AP	^_mask AP50	^_mask AP75	^{_{download links}}
^{_{X-152-32x8d-FPN-IN5k-baseline}}	^_Mask	^_s1x	^₁	^_48.1	^_68.3	^_52.9	^_41.5	^_65.1	^_44.7	^{_{model \| boxes \| masks}}
^{_{[above without test-time aug.]}}				^_45.2	^_66.9	^_49.7	^_39.7	^_63.5	^_42.4
^{_{X-152-32x8d-FPN-IN5k-cascade}}	^_Mask	^_s1x	^₁	^_50.2	^_68.2	^_55.0	^_42.3	^_65.4	^_45.8	^{_{model \| boxes \| masks}}
^{_{[above without test-time aug.]}}				^_48.1	^_66.7	^_52.6	^_40.7	^_63.7	^_43.8

### Faster & Mask R-CNN with GN

^_backbone	^_type	^_lr schd	^_im/ gpu	^_box AP	^_box AP50	^_box AP75	^_mask AP	^_mask AP50	^_mask AP75	^{_{download links}}
^{_{R-50-FPN-GN-baseline}}	^_Faster	^_1x	^₂	^_38.4	^_59.9	^_41.7	^_-	^_-	^_-	^{_{model \| boxes}}
^{_{R-50-FPN-GN-cascade}}	^_Faster	^_1x	^₂	^_42.2	^_60.6	^_45.8	^_-	^_-	^_-	^{_{model \| boxes}}
^{_{R-101-FPN-GN-baseline}}	^_Faster	^_1x	^₂	^_39.9	^_61.3	^_43.3	^_-	^_-	^_-	^{_{model \| boxes}}
^{_{R-101-FPN-GN-cascade}}	^_Faster	^_1x	^₁	^_43.8	^_62.2	^_47.6	^_-	^_-	^_-	^{_{model \| boxes}}
^{_{R-50-FPN-GN-baseline}}	^_Mask	^_1x	^₂	^_39.2	^_60.5	^_42.9	^_34.9	^_57.1	^_36.9	^{_{model \| boxes}}
^{_{R-50-FPN-GN-cascade}}	^_Mask	^_1x	^₁	^_42.9	^_60.7	^_46.6	^_36.6	^_57.7	^_39.2	^{_{model \| boxes \| masks}}
^{_{R-101-FPN-GN-baseline}}	^_Mask	^_1x	^₂	^_41.1	^_62.1	^_45.1	^_36.3	^_58.9	^_38.5	^{_{model \| boxes \| masks}}
^{_{R-101-FPN-GN-cascade}}	^_Mask	^_1x	^₁	^_44.8	^_62.8	^_48.8	^_38.0	^_59.8	^_40.8	^{_{model \| boxes \| masks}}