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https://github.com/s4mcontext/s4mc

code for s4mc
https://github.com/s4mcontext/s4mc

Last synced: about 1 month ago
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code for s4mc

Host: GitHub
URL: https://github.com/s4mcontext/s4mc
Owner: s4mcontext
License: mit
Created: 2023-03-15T13:36:55.000Z (almost 2 years ago)
Default Branch: main
Last Pushed: 2024-05-25T14:06:04.000Z (7 months ago)
Last Synced: 2024-08-03T01:11:48.437Z (5 months ago)
Language: Python
Size: 3.18 MB
Stars: 8
Watchers: 1
Forks: 1
Open Issues: 1
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

Awesome-Semi-Supervised-Semantic-Segmentation - Code

README

        # Semi-Supervised Semantic Segmentation via Marginal Contextual Information

![](https://github.com/s4mcontext/s4mc/blob/main/imgs/method.png?raw=true)

## Description:information_source:

This is an official PyTorch implementation of the "Semi-Supervised Semantic Segmentation via Marginal Contextual Information" paper submission for NeurIPS 2023.

The method utilize contextual information to produce higher quality and higher quantity of pseudo-labels.

  * [Description](#description-information_source)

  * [Results](#results-bar_chart)

  * [Installation](#Installation-writing_hand)

  * [Preperation](#Preperation-card_index_dividers)

  * [Training](#training-weight_lifting)

  * [License](#License-paperclip:)

  * [Acknowledgement](#Acknowledgement-copyright)

  

  

  ## Results :bar_chart:

Results for PASCAL VOC 12, using additional coarse annotated:

|Method |  1/16  | 1/8 | 1/4 | 1/2 | full | 

|:--- |:---:|:---:|:---:|:---:|:---:|

|Sup only|45.77 |54.92 |65.88 |71.69| 72.50|

|baseline |52.16| 63.47| 69.46| 73.73| 76.54|

|S4MC + CutMix-Seg     |70.96| 71.6*| 75.41| 77.73| 80.58|

|S4MC + FixMatch   |**74.32**| **75.62**| **77.84**| **79.72**| **81.51**|

For all the results, please refer to the paper experiment section

A visual example for the results:

![](https://github.com/s4mcontext/s4mc/blob/main/imgs/res.png?raw=true)

## Installation :writing_hand:

> 

git clone https://github.com/s4mcontext/s4mc.git && cd s4mc

conda create -n s4mc

conda activate s4mc

pip install -r requirements.txt

You also need to download a backbone trained on ImageNet-1k by either:

*  use pretrained pythorch flag and save the path.

* download from [Google drive link].(https://drive.google.com/file/d/1nzSX8bX3zoRREn6WnoEeAPbKYPPOa-3Y/view?usp=sharing "Google drive link") (credit below)

## Preperation :card_index_dividers:

Before training the models please put the datasets in the `data` sub-directory.

For PASCAL VOC 2012:

follow [this instuction](https://github.com/zhixuanli/segmentation-paper-reading-notes/blob/master/others/Summary%20of%20the%20semantic%20segmentation%20datasets.md "this instuction") and download `PASCAL VOC 2012 augmented with SBD dataset.`

For Cityscapes:

Download "leftImg8bit_trainvaltest.zip" and "gtFine_trainvaltest.zip" from: https://www.cityscapes-dataset.com/downloads/

unzip all into data with the following structure:

    data

    ├── cityscapes

    │   ├── gtFine

    │   └── leftImg8bit

    ├── splits

    │   ├── cityscapes

    │   └── pascal

    └── VOC2012

        ├── Annotations

        ├── ImageSets

        ├── JPEGImages

        ├── SegmentationClass

        ├── SegmentationClassAug

        └── SegmentationObject

4. Go over the [dependencies](#dependencies-floppy_disk).

## Training :weight_lifting:

For training a semi-supervised model you need to first set a config.

We've provided an example config for PASCAL. all the splits of data are provided in the data sub-directory as well, so simply change the config with the desired parameters and dataset.

To run the code distributed, go to experiments and run: 

```

python -m torch.distributed.launch --nproc_per_node=<#GPUs> --nnodes=1 ../train_semi.py --config= --seed  --name 

```

where

`<#GPUs>` is the number of cuda devices avalible for distributed training.

`` is the path to your config

`` to set a random seed for reproducability

`` will save the model and tensorboard with the experiment name

## License :paperclip:

This project is released under the [Apache 2.0 ](https://github.com/Haochen-Wang409/U2PL/blob/main/LICENSE "Apache 2.0 ") license.

## Acknowledgement :copyright:

This repository code is heavily based on [U2PL](https://github.com/Haochen-Wang409/U2PL) as well as the link for the pre-trained model provided in here.