https://github.com/stupidcucumber/deeplabv3-celebhq

Trained Torch version of the DeepLab on the CelebHQ.
https://github.com/stupidcucumber/deeplabv3-celebhq

deep-neural-networks deeplabv3 face-segmentation python3 segmentation-models torch

Last synced: 5 days ago
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Trained Torch version of the DeepLab on the CelebHQ.

• Host: GitHub
• URL: https://github.com/stupidcucumber/deeplabv3-celebhq
• Owner: stupidcucumber
• Created: 2024-02-17T11:55:53.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-03-08T19:00:37.000Z (11 months ago)
• Last Synced: 2024-11-18T05:37:24.887Z (2 months ago)
• Topics: deep-neural-networks, deeplabv3, face-segmentation, python3, segmentation-models, torch
• Language: Python
• Homepage: https://arxiv.org/pdf/1706.05587.pdf
• Size: 265 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
# DeepLabV3-CelebHQ

![Logotype](./misc/logo.png)

Trained Torch version of the DeepLab on the CelebHQ. Also dataset used in the model training are [here](https://github.com/switchablenorms/CelebAMask-HQ).

# Requirements

This project requires version of Python of at least 3.10.* Other requirements are listed in the [requirements.txt](./requirements.txt) file. To install all requirements:

```

python3 -m pip install -r requirements.txt

```

# Usage

## Training

Application uses the following CLI for training models:

```

options:

  -h, --help            show this help message and exit

  --data DATA           Path to the dataframe that represents data. More in datasets/README.md

  --output-channels OUTPUT_CHANNELS

  --epochs EPOCHS

  --batch-size BATCH_SIZE

  --tv-split TV_SPLIT   Train-validation split, that defines the size of the train part.

  --mapping MAPPING     Path to the mapping of the layer: index in json format

  -d DEVICE, --device DEVICE

                        The device on which model will train.

```

After starting the training CelebAMask-HQ dataset will be automatically downloaded. If there is any troubles with downloading using gdown you can access it on [Google Drive](https://drive.google.com/file/d/17e_IRjSuise59WUDHVrwZKES71KzJ9bU/view?usp=share_link). For usage you must unzip CelebAMask-HQ.zip into datasets/CelebAMask-HQ folder.

## Evaluation

To evaluate you need to download weights of the model from the [Google Drive](https://drive.google.com/file/d/1YR4LTi-CIYl8zr7JmtJj5jcrpdsJx9Nd/view?usp=share_link).

For example inference use the following command:

```

python eval.py --mapping datasets/CelebAMask-HQ/mapping.json --model runs/best_weights.pt -i example/input.png -cmap example/color_mapping.json

```

Example of evaluation is:

![Input](./example/input.png) ![Output](./example/output.png)

In the future weights can be improved.

# :warning: TODO :warning:

This list will be updated throughout the time. Contributions are hugely appreciated!

Task name | Progress |

----------|----------|

Implement Tensorboard logging|:white_check_mark:|

Implement callback for precision|:white_check_mark:|

Implement callback for recall|:white_check_mark:|

Implement script for the evaluation|:white_check_mark:| 

Train weights and make them public|:white_check_mark:|

# Citations

```

@inproceedings{CelebAMask-HQ,

  title={MaskGAN: Towards Diverse and Interactive Facial Image Manipulation},

  author={Lee, Cheng-Han and Liu, Ziwei and Wu, Lingyun and Luo, Ping},

  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

  year={2020}

}

```