Added Zero shot instance segmentation

Zero-shot-Instance-Segmentation/README.md

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+
+# Code for CVPR2021 paper
+
+# **Zero-shot Instance Segmentation** 
+
+## Code requirements
++ python: python3.7
++ nvidia GPU
++ pytorch1.1.0
++ GCC >=5.4
++ NCCL 2
++ the other python libs in requirement.txt
+
+## Install 
+
+```
+conda create -n zsi python=3.7 -y
+conda activate zsi
+
+conda install pytorch=1.1.0 torchvision=0.3.0 cudatoolkit=10.0 -c pytorch
+
+pip install cython && pip --no-cache-dir install -r requirements.txt
+
+python setup.py develop
+```
+
+## Dataset prepare
+
+
+- Download the train and test annotations files for zsi from [annotations](https://dagshub.com/f2010126/Zero-shot-Instance-Segmentation/src/main/data/coco/annotations), put all json label file to
+    ```
+    data/coco/annotations/
+    ```
+
+- Download MSCOCO-2014 dataset and unzip the images it to path： 
+    ```
+    data/coco/train2014/
+    data/coco/val2014/
+    ```
+
+
+- **Training**:
+     - 48/17 split:
+       ```
+          chmod +x tools/dist_train.sh
+          ./tools/dist_train.sh configs/zsi/train/zero-shot-mask-rcnn-BARPN-bbox_mask_sync_bg_decoder.py 4
+        ```
+
+    - 65/15 split:
+      ```
+      chmod +x tools/dist_train.sh
+      ./tools/dist_train.sh configs/zsi/train/zero-shot-mask-rcnn-BARPN-bbox_mask_sync_bg_65_15_decoder_notanh.py 4
+      ```
+
+- **Inference & Evaluate**:
+
+    + **ZSI task**:
+
+        - 48/17 split ZSI task:
+            - download [48/17](https://dagshub.com/f2010126/Zero-shot-Instance-Segmentation/src/main/checkpoints) ZSI model, put it in checkpoints/ZSI_48_17.pth
+
+            - inference:
+                ```
+                chmod +x tools/dist_test.sh
+                ./tools/dist_test.sh configs/zsi/48_17/test/zsi/zero-shot-mask-rcnn-BARPN-bbox_mask_sync_bg_decoder.py checkpoints/ZSI_48_17.pth 4 --json_out results/zsi_48_17.json
+                ```
+            - our results zsi_48_17.bbox.json and zsi_48_17.segm.json can also downloaded from [zsi_48_17_reults](https://dagshub.com/f2010126/Zero-shot-Instance-Segmentation/src/main/results).
+            - evaluate:
+                - for zsd performance
+                    ```
+                    python tools/zsi_coco_eval.py results/zsi_48_17.bbox.json --ann data/coco/annotations/instances_val2014_unseen_48_17.json
+                    ```
+                - for zsi performance
+                    ```
+                    python tools/zsi_coco_eval.py results/zsi_48_17.segm.json --ann data/coco/annotations/instances_val2014_unseen_48_17.json --types segm
+                    ```
+        - 65/15 split ZSI task:
+            - download [65/15](https://dagshub.com/f2010126/Zero-shot-Instance-Segmentation/src/main/checkpoints) ZSI model, put it in checkpoints/ZSI_65_15.pth
+
+            - inference:
+                ```
+                chmod +x tools/dist_test.sh
+                ./toools/dist_test.sh configs/zsi/65_15/test/zsi/zero-shot-mask-rcnn-BARPN-bbox_mask_sync_bg_65_15_decoder_notanh.py checkpoints/ZSI_65_15.pth 4 --json_out results/zsi_65_15.json
+                ```
+            - our results zsi_65_15.bbox.json and zsi_65_15.segm.json can also downloaded from [zsi_65_15_reults](https://dagshub.com/f2010126/Zero-shot-Instance-Segmentation/src/main/results).
+            - evaluate:
+                - for zsd performance
+                    ```
+                    python tools/zsi_coco_eval.py results/zsi_65_15.bbox.json --ann data/coco/annotations/instances_val2014_unseen_65_15.json
+                    ```
+                - for zsi performance
+                    ```
+                    python tools/zsi_coco_eval.py results/zsi_65_15.segm.json --ann data/coco/annotations/instances_val2014_unseen_65_15.json --types segm
+                    ```
+
+    + **GZSI task**:
+
+        - 48/17 split GZSI task:
+            - use the same model file ZSI_48_17.pth in ZSI task   
+            - inference:
+                ```
+                chmod +x tools/dist_test.sh
+                ./tools/dist_test.sh configs/zsi/48_17/test/gzsi/zero-shot-mask-rcnn-BARPN-bbox_mask_sync_bg_decoder_gzsi.py checkpoints/ZSI_48_17.pth 4 --json_out results/gzsi_48_17.json
+                ```
+            - our results gzsi_48_17.bbox.json and gzsi_48_17.segm.json can also downloaded from [gzsi_48_17_results](https://dagshub.com/f2010126/Zero-shot-Instance-Segmentation/src/main/results).
+            - evaluate:
+                - for gzsd
+                    ```
+                    python tools/gzsi_coco_eval.py results/gzsi_48_17.bbox.json --ann data/coco/annotations/instances_val2014_gzsi_48_17.json --gzsi --num-seen-classes 48
+                    ```
+                - for gzsi
+                    ```
+                    python tools/gzsi_coco_eval.py results/gzsi_48_17.segm.json --ann data/coco/annotations/instances_val2014_gzsi_48_17.json --gzsi --num-seen-classes 48 --types segm
+                    ```
+        - 65/15 split GZSI task:
+            - use the same model file ZSI_48_17.pth in ZSI task   
+            - inference:
+                ```
+                chmod +x tools/dist_test.sh
+                ./tools/dist_test.sh configs/zsi/65_15/test/gzsi/zero-shot-mask-rcnn-BARPN-bbox_mask_sync_bg_65_15_decoder_notanh_gzsi.py checkpoints/ZSI_65_15.pth 4 --json_out results/gzsi_65_15.json
+                ```
+            - our results gzsi_65_15.bbox.json and gzsi_65_15.segm.json can also downloaded from [gzsi_65_15_results](https://dagshub.com/f2010126/Zero-shot-Instance-Segmentation/src/main/results).
+            - evaluate:
+                - for gzsd
+                    ```
+                    python tools/gzsi_coco_eval.py results/gzsi_65_15.bbox.json --ann data/coco/annotations/instances_val2014_gzsi_65_15.json --gzsd --num-seen-classes 65
+                    ```
+                - for gzsi
+                    ```
+                    python tools/gzsi_coco_eval.py results/gzsi_65_15.segm.json --ann data/coco/annotations/instances_val2014_gzsi_65_15.json --gzsd --num-seen-classes 65 --types segm
+                    ```
+
+
+# License
+
+ZSI is released under MIT License.
+
+
+## Citing
+
+If you use ZSI in your research or wish to refer to the baseline results published here, please use the following BibTeX entries:
+
+```BibTeX
+
+@InProceedings{Zheng_2021_CVPR,
+    author    = {Zheng, Ye and Wu, Jiahong and Qin, Yongqiang and Zhang, Faen and Cui, Li},
+    title     = {Zero-Shot Instance Segmentation},
+    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    month     = {June},
+    year      = {2021},
+    pages     = {2593-2602}
+}
+
+```