https://github.com/msminhas93/deeplabv3finetuning

Tutorial on fine tuning DeepLabv3 segmentation network for your own segmentation task in PyTorch.
https://github.com/msminhas93/deeplabv3finetuning

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Tutorial on fine tuning DeepLabv3 segmentation network for your own segmentation task in PyTorch.

• Host: GitHub
• URL: https://github.com/msminhas93/deeplabv3finetuning
• Owner: msminhas93
• License: mit
• Created: 2019-08-29T20:54:16.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-07-06T22:11:25.000Z (over 1 year ago)
• Last Synced: 2025-01-15T14:32:11.762Z (10 days ago)
• Language: Jupyter Notebook
• Size: 3.99 MB
• Stars: 167
• Watchers: 6
• Forks: 100
• Open Issues: 11
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
## Transfer Learning for Semantic Segmentation using PyTorch DeepLab v3

This repository contains code for Fine Tuning [DeepLabV3 ResNet101](https://arxiv.org/abs/1706.05587) in PyTorch. The model is from the [torchvision module](https://pytorch.org/docs/stable/torchvision/models.html#semantic-segmentation). The tutorial can be found here: [https://towardsdatascience.com/transfer-learning-for-segmentation-using-deeplabv3-in-pytorch-f770863d6a42?sk=b403331a7b30c02bff165a93823a5524](https://towardsdatascience.com/transfer-learning-for-segmentation-using-deeplabv3-in-pytorch-f770863d6a42?sk=b403331a7b30c02bff165a93823a5524)

I've fine tuned the model for the [CrackForest](https://github.com/cuilimeng/CrackForest-dataset) data-set. 

The model was fine tuned for 25 epochs and achieves an testing AUROC value of 0.842.

The segmentation output of the model on a sample image are shown below.

![Sample segmentation output](./CFExp/SegmentationOutput.png)

### Installing dependencies

#### Using pip

```

pip install -r requirements.txt

```

#### Using conda

```

conda env create -f environment.yml

```

### Usage of the module

```

Usage: main.py [OPTIONS]

Options:

  --data-directory TEXT  Specify the data directory.  [required]

  --exp_directory TEXT   Specify the experiment directory.  [required]

  --epochs INTEGER       Specify the number of epochs you want to run the

                         experiment for. Default is 25.

  --batch-size INTEGER   Specify the batch size for the dataloader. Default is 4.

  --help                 Show this message and exit.

```

To run the code with the CrackForest dataset and store the results in folder called CFExp use the following command.

```

python main.py --data-directory CrackForest --exp_directory CFExp

```

The datahandler module has two functions for creating datasets fron single and different folders.

1. ```

   def get_dataloader_sep_folder(data_dir: str,

                              image_folder: str = 'Image',

                              mask_folder: str = 'Mask',

                              batch_size: int = 4)

    ```

    Create Train and Test dataloaders from two separate Train and Test folders. The directory structure should be as follows.

    ```

    data_dir

    --Train

    ------Image

    ---------Image1

    ---------ImageN

    ------Mask

    ---------Mask1

    ---------MaskN

    --Test

    ------Image

    ---------Image1

    ---------ImageM

    ------Mask

    ---------Mask1

    ---------MaskM

    ```

2. ```

   def get_dataloader_single_folder(data_dir: str,

                                 image_folder: str = 'Images',

                                 mask_folder: str = 'Masks',

                                 fraction: float = 0.2,

                                 batch_size: int = 4)

   ```

    Create from a single folder. The structure should be as follows.

    ```

    --data_dir

    ------Image

    ---------Image1

    ---------ImageN

    ------Mask

    ---------Mask1

    ---------MaskN

    ```

The repository also contains a JupyterLab file with the loss and metric plots as well as the sample prediction code.

## Citation

If you found this repository to be useful and use it in your work, please consider citing it:

Bibtex Entry:

@misc{minhas_2019, title={Transfer Learning for Semantic Segmentation using PyTorch DeepLab v3}, url={https://github.com/msminhas93/DeepLabv3FineTuning}, journal={GitHub.com/msminhas93}, author={Minhas, Manpreet Singh}, year={2019}, month={Sep}}

IEEE Format Citation:

M. S. Minhas, “Transfer Learning for Semantic Segmentation using PyTorch DeepLab v3,” GitHub.com/msminhas93, 12-Sep-2019. [Online]. Available: https://github.com/msminhas93/DeepLabv3FineTuning.