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https://github.com/DesertsP/SLRNet

🚀 Code for SLRNet: Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation
https://github.com/DesertsP/SLRNet

semi-supervised-segmentation weakly-supervised-segmentation

Last synced: about 2 months ago
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🚀 Code for SLRNet: Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation

Host: GitHub
URL: https://github.com/DesertsP/SLRNet
Owner: DesertsP
License: apache-2.0
Created: 2021-12-23T04:12:47.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2022-05-07T08:27:36.000Z (over 2 years ago)
Last Synced: 2024-08-03T01:11:48.908Z (5 months ago)
Topics: semi-supervised-segmentation, weakly-supervised-segmentation
Language: Python
Homepage:
Size: 793 KB
Stars: 69
Watchers: 1
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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Awesome-Semi-Supervised-Semantic-Segmentation - Code

README

# Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Framework](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?&logo=PyTorch&logoColor=white)](https://pytorch.org/)

This is the official implementation of [Learning Self-Supervised Low-Rank Network for Single-Stage
Weakly and Semi-Supervised Semantic Segmentation](http://dx.doi.org/10.1007/s11263-022-01590-z), [arXiv](https://arxiv.org/abs/2203.10278), IJCV 2022.

This repository contains the code for SLRNet, which is a unified framework that can be well generalized to learn a label-efficient segmentation model in various weakly and semi-supervised settings.
The key component of our approach is the Cross-View Low-Rank (CVLR) module that decompose the multi-view representations via the collective matrix factorization.
We provide scripts for Pascal VOC and COCO datasets.
Moreover, the SLRNet ranked 2nd in the WSSS Track of CVPR 2021 [Learning from Limited and Imperfect Data (L2ID) Challenge](https://l2id.github.io/).

## Setup

0. **Minimum requirements.**
This project was developed with Python 3.7, PyTorch 1.x.
The training requires at least two Titan XP GPUs (12 GB memory each).

1. **Setup your Python environment.**
Install python dependencies in requirements.txt

```
pip install -r requirements.txt
```

2. **Download Datasets.**

**Download Pascal VOC data from:**

- VOC: [Training/Validation (2GB .tar file)](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar)

- SBD: [Training (1.4GB .tgz file)](http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz)

Convert SBD data using `tools/convert_coco.py`.

Link to the data:

```
ln -s /data/voc
ln -s /data/sbd
```

Make sure that the first directory in `data/voc` is `VOCdevkit`; the first directory in `data/sbd` is `benchmark_RELEASE`.

**Download COCO data from:**

- COCO: [Training](http://images.cocodataset.org/zips/train2014.zip), [Validation](http://images.cocodataset.org/zips/val2014.zip), [Annotation](http://images.cocodataset.org/annotations/annotations_trainval2014.zip)

Convert COCO data using `tools/convert_sbd.py`.

**Download L2ID challenge data from:**

- [Learning from Limited and Imperfect Data (L2ID)](https://l2id.github.io/)

3. **Download pre-trained backbones.**

| Backbone | Initial Weights | Comment |
|:------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|
| WideResNet38 | [ilsvrc-cls_rna-a1_cls1000_ep-0001.pth (402M)](https://download.visinf.tu-darmstadt.de/data/2020-cvpr-araslanov-1-stage-wseg/models/ilsvrc-cls_rna-a1_cls1000_ep-0001.pth) | Converted from [mxnet](https://github.com/itijyou/ademxapp) |
| ResNet101 | [resnet101-5d3b4d8f.pth](https://download.pytorch.org/models/resnet101-5d3b4d8f.pth) | PyTorch official |

## Pre-trained Model

For testing, we provide our checkpoints on Pascal VOC dataset:

## Weakly Supervised Semantic Segmentation

#### Training

Train the weakly supervised model:

```bash
python tools/train_voc_wss.py --config experiments/slrnet_voc_wss_v100x2.yaml --run custom_experiment_run_id
```

For the COCO or L2ID dataset, please refer to the relevant scripts/configs with the suffix "_coco".

#### Inference and Evaluation

```bash
python tools/infer_voc.py \
--config experiments/slrnet_voc_wss_v100x2.yaml \
--checkpoint path/to/checkpoint20.pth.tar \
--use_cls_label 0 \
--output outputs/wss_prediction \
--data_list voc12/val.txt \
--data_root path/to/VOCdevkit/VOC2012 \
--fp_cut 0.3 \
--bg_pow 3
```

#### Two-stage Training

Generate pseudo labels with following parameters:

```bash
...
--data_list voc12/train_aug.txt \
--use_cls_label 1 \
...
```

Then we train the DeepLabV3+ (w/ ResNet101 backbone) network implemented by [mmsegmentation](https://github.com/open-mmlab/mmsegmentation)

## Semi-Supervised Semantic Segmentation with Pixel-level and Image-level Labeled Data

#### Training

```bash
python tools/train_voc_semi.py --config experiments/slrnet_voc_semi_w_cls_v100x2.yaml --run custom_experiment_run_id
```

#### Inference and Evaluation

```bash
python tools/infer_voc.py \
--config experiments/slrnet_voc_semi_w_cls_v100x2.yaml \
--checkpoint path/to/checkpoint28.pth.tar \
--use_cls_label 0 \
--output outputs/semi_val_prediction \
--data_list voc12/val.txt \
--data_root ../VOCdevkit/VOC2012 \
--fp_cut 0.3 \
--bg_pow 1 \
--apply_crf 0 \
--verbose 0
```

## Semi-Supervised Semantic Segmentation with Pixel-level Labeled and Unlabeled Data

#### Training

```bash
python tools/train_voc_semi.py --config experiments/slrnet_voc_semi_wo_cls.yaml --run custom_experiment_run_id
```

#### Inference and Evaluation

```bash
python tools/infer_voc.py \
--config experiments/slrnet_voc_semi_wo_cls.yaml \
--checkpoint path/to/checkpoint32.pth.tar \
--use_cls_label 0 \
--output outputs/semi_val_prediction \
--data_list voc12/val.txt \
--data_root ../VOCdevkit/VOC2012 \
--fp_cut 0.3 \
--bg_pow 1 \
--apply_crf 0 \
--verbose 0
```

## Acknowledgements

We thank Nikita Araslanov, and Jiwoon Ahn for their great work that helped in the early stages of this project.

- [https://github.com/visinf/1-stage-wseg](https://github.com/visinf/1-stage-wseg)
- [https://github.com/jiwoon-ahn/irn](https://github.com/jiwoon-ahn/irn)

## Citation

We hope that you find this work useful. If you would like to acknowledge us, please, use the following citation:

```
@article{pan2022learning,
title={Learning Self-supervised Low-Rank Network for Single-Stage Weakly and Semi-supervised Semantic Segmentation},
author={Pan, Junwen and Zhu, Pengfei and Zhang, Kaihua and Cao, Bing and Wang, Yu and Zhang, Dingwen and Han, Junwei and Hu, Qinghua},
journal={International Journal of Computer Vision},
pages={1--15},
year={2022},
publisher={Springer}
}
```

## Contact

Junwen Pan