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https://github.com/dvlab-research/GFS-Seg
The official implementation of Generalized Few-shot Semantic Segmentation (CVPR 2022)
https://github.com/dvlab-research/GFS-Seg
Last synced: 3 months ago
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The official implementation of Generalized Few-shot Semantic Segmentation (CVPR 2022)
- Host: GitHub
- URL: https://github.com/dvlab-research/GFS-Seg
- Owner: dvlab-research
- Created: 2022-03-28T09:40:07.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2022-08-26T07:26:57.000Z (about 2 years ago)
- Last Synced: 2024-04-17T00:18:45.702Z (7 months ago)
- Language: Python
- Size: 59.6 KB
- Stars: 64
- Watchers: 1
- Forks: 11
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
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README
# GFS-Seg
This is the implementation of [**Generalized Few-shot Semantic Segmentation**](https://arxiv.org/abs/2010.05210) (CVPR 2022).
# Get Started
### Environment
+ Python 3.7.9
+ Torch 1.5.1
+ cv2 4.4.0
+ numpy 1.21.0
+ CUDA 10.1
### Datasets and Data Preparation
Different from [**PFENet**](https://github.com/dvlab-research/PFENet) (5953 images), in GFS-Seg, the training set of Pascal-VOC is used with augmented data (10582 images), following the original [**PSPNet**](https://github.com/hszhao/semseg), as done in most of works in normal semantic segmentation. But our experiments in FS-Seg follow the setting of PFENet.
The preparation of [**COCO 2014**](https://cocodataset.org/#download) follows [**PFENet**](https://github.com/dvlab-research/PFENet). Please download the original COCO 2014 annotations and images from the official website of [**COCO**](https://cocodataset.org/#download). Then the labels can be generated by using [**this script**](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155122171_link_cuhk_edu_hk/ETkX1jKVuJpAjpS2dk8XRhgBxkOUregKv4C7zigqFXiEtQ).
The specific data storage structure is not necessary, since this code directly reads data from .txt files where each line contains the paths for image and the correcponding label respectively. Image and label paths are seperated by a space. Example is as follows:
image_path_1 label_path_1
image_path_2 label_path_2
image_path_3 label_path_3
...
image_path_n label_path_n
Then update the train/val list paths in the config files.
### Run Demo / Test with Pretrained Models
+ Execute `mkdir initmodel` at the root directory.
+ Download the ImageNet pretrained [**backbones**](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155122171_link_cuhk_edu_hk/EQEY0JxITwVHisdVzusEqNUBNsf1CT8MsALdahUhaHrhlw?e=4%3a2o3XTL&at=9) and put them into the `initmodel` directory.
+ Please download the pretrained models.
+ We provide **16 pre-trained** [**models**](https://mycuhk-my.sharepoint.com/:f:/g/personal/1155122171_link_cuhk_edu_hk/Ej4c5aUV1RxDpXuSjK-9BZYBKF23mgq2zR8bNYWTkIJtkA?e=Rhb1xi):
8 for 1/5 shot results on PASCAL-5i and 8 for COCO.
+ Update the config files by speficifying the target **split**, **weights** and **shot** for loading different checkpoints.
Note: The pre-trained models are re-trained by this repo, and you should be able to get generally comparable or slightly better performance than the ones reported in our paper.
### Train / Evaluate
+ For training, please set the option **only_evaluate** to **False** in the configuration file. Then execute this command at the root directory:
sh train.sh {*dataset*} {*model_config*}
+ For evaluation only, please set the option **only_evaluate** to **True** in the corresponding configuration file.
Example: Train / Evaluate CAPL with 1-shot on the split 0 of PASCAL-5i:
sh train.sh pascal split0_1shot
### Implementation in FS-Seg
We have provided our implementation of CAPL in the setting of classic few-shot segmentation:
https://github.com/tianzhuotao/CAPL-FSSeg
## Related Assets \& Acknowledgement
Our work is closely related to the following assets that inspire our implementation. We gratefully thank the authors.
+ RePRI: https://github.com/mboudiaf/RePRI-for-Few-Shot-Segmentation
+ HSNet: https://github.com/juhongm999/hsnet
+ SCL: https://github.com/zbf1991/SCL
+ ASGNet: https://github.com/Reagan1311/ASGNet
+ CANet: https://github.com/icoz69/CaNet
+ PANet: https://github.com/kaixin96/PANet
+ PPNet: https://github.com/Xiangyi1996/PPNet-PyTorch
+ PFENet: https://github.com/dvlab-research/PFENet
+ SemSeg: https://github.com/hszhao/semseg
# Citation
If you find this project useful, please consider citing:
```
@InProceedings{tian2022gfsseg,
title={Generalized Few-shot Semantic Segmentation},
author={Zhuotao Tian and Xin Lai and Li Jiang and Shu Liu and Michelle Shu and Hengshuang Zhao and Jiaya Jia},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2022}
}
```