Awesome
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
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Download the train and test annotations files for zsi from annotations, put all json label file to
data/coco/annotations/
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Download MSCOCO-2014 dataset and unzip the images it to path:
data/coco/train2014/ data/coco/val2014/
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Training:
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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
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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
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Inference & Evaluate:
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ZSI task:
- 48/17 split ZSI task:
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download 48/17 ZSI model, put it in checkpoints/ZSI_48_17.pth
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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
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our results zsi_48_17.bbox.json and zsi_48_17.segm.json can also downloaded from zsi_48_17_reults.
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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
- for zsd performance
-
- 65/15 split ZSI task:
-
download 65/15 ZSI model, put it in checkpoints/ZSI_65_15.pth
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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
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our results zsi_65_15.bbox.json and zsi_65_15.segm.json can also downloaded from zsi_65_15_reults.
-
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
- for zsd performance
-
- 48/17 split ZSI task:
-
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.
- 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
- for gzsd
- 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.
- 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
- for gzsd
- 48/17 split GZSI task:
-
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:
@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}
}