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https://github.com/NUST-Machine-Intelligence-Laboratory/TorchSemiSeg2

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Host: GitHub
URL: https://github.com/NUST-Machine-Intelligence-Laboratory/TorchSemiSeg2
Owner: NUST-Machine-Intelligence-Laboratory
Created: 2023-04-18T11:33:18.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2023-04-18T11:38:40.000Z (over 1 year ago)
Last Synced: 2024-08-03T01:11:48.691Z (5 months ago)
Language: Python
Size: 5.36 MB
Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

Awesome-Semi-Supervised-Semantic-Segmentation - Code - Machine-Intelligence-Laboratory/TorchSemiSeg2)|[Paper](https://arxiv.org/pdf/2304.11539.pdf)| (2023)

README

# Semi-Supervised Semantic Segmentation With Region Relevance

## Requirements

>📋 The code is developed using Python 3.6 with PyTorch 1.10.1. The code is developed and tested using 2 NVIDIA TITAN RTX GPUs.

## Training && Evaluation

To train and eval the model in the paper, run this command:

```train
cd ./voc/voc8.res50v3+.CPS+CutMix
bash script_mod.sh
```

>📋 In script_mod.sh, you need to specify some variables, such as the path to your data dir, the path to your snapshot dir that stores checkpoints, etc.

## Our Models DownLoad && Test

We have released the weight models on PASCAL VOC in our experiment, you can download here:

- [My voc2 model](https://drive.google.com/file/d/12ub3q4-W_3gBPcUcsoOqbe3fOVBngEDt/view?usp=share_link) trained on PASCAL VOC 2012 at 1/2 partition protocol.
- [My voc4 model](https://drive.google.com/file/d/1GErx-JndoqM1pzuQ-33KUJ21gdatH055/view?usp=share_link) trained on PASCAL VOC 2012 at 1/4 partition protocol.
- [My voc8 model](https://drive.google.com/file/d/1olSbRjWSMFckkhewc6qWM13D-oSeoFG3/view?usp=share_link) trained on PASCAL VOC 2012 at 1/8 partition protocol.
- [My voc16 model](https://drive.google.com/file/d/14WJBhoo1NpPb1PyPzoIwgEUdFWmtadHH/view?usp=share_link) trained on PASCAL VOC 2012 at 1/16 partition protocol.

>📋 The command for test is in script_mod.sh, you only need to annotate the command for training and modify the parameters appropriately.