https://github.com/rimo02/revivr
Colorizing black and white Images using Autoencoders
https://github.com/rimo02/revivr
encoder-decoder image-colorization pytoch streamlit-webapp
Last synced: about 2 months ago
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Colorizing black and white Images using Autoencoders
- Host: GitHub
- URL: https://github.com/rimo02/revivr
- Owner: rimo02
- License: mit
- Created: 2023-06-09T15:38:25.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-06-11T21:15:31.000Z (almost 3 years ago)
- Last Synced: 2025-01-16T13:59:03.263Z (over 1 year ago)
- Topics: encoder-decoder, image-colorization, pytoch, streamlit-webapp
- Language: Jupyter Notebook
- Homepage:
- Size: 93.8 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Colorizing Black and White Images
https://github.com/rimo10/Image-Colorization-Project/assets/70977847/5b3fa0c8-9c20-46f6-a518-6180d69f0305
## Tech Stack
* [PyTorch ](https://pytorch.org/) for Model Training
* [Streamlit ](https://streamlit.io/) for making the web Application
## Datasets used
The following datasets were used to train the model
Base
1. [ImageNet 12k ](https://www.kaggle.com/datasets/lijiyu/imagenet)
Landscape
1. [Landscape Recognition | Image Dataset | 12k Images](https://www.kaggle.com/datasets/utkarshsaxenadn/landscape-recognition-image-dataset-12k-images)
2. [Landscape Pictures ](https://www.kaggle.com/datasets/arnaud58/landscape-pictures)
Person
1. [Human Faces ](https://www.kaggle.com/datasets/ashwingupta3012/human-faces)
2. [FFHQ Face Data Set ](https://www.kaggle.com/datasets/greatgamedota/ffhq-face-data-set)
Fruits and Flowers
1. [Fruits 360 ](https://www.kaggle.com/datasets/moltean/fruits)
Animals
1. [Animals Detection Images Dataset ](https://www.kaggle.com/datasets/antoreepjana/animals-detection-images-dataset)
## Train your own model
You can use the pretrained weights avialable under `models` folder. Or if you want you can train on your own custom dataset
1. Clone the repo `https://github.com/rimo10/Image-Colorization-Project.git`
2. Open `Image_Colorizer.ipynb`
3. Copy the model path and run the cell -
```python
def load_checkpoint(model,path):
model.load_state_dict(torch.load(path))
return model
load_checkpoint(model,your_weights_path)
```
## To Run the Web App
1. Clone the repo `git clone https://github.com/rimo10/Image-Colorization-Project.git`
2. If you don't have streamlit installed then `pip install streamlit`
3. Go to the root directory of project and run `streamlit run -main.py` in the terminal . This will open an interactive web application. Now drag and drop and see the Magic
Happy Colorization :hugs: !!
## References
Paper: [Deep Koalarization: Image Colorization using CNNs and Inception-ResNet-v2 ](https://arxiv.org/pdf/1712.03400v1.pdf)
## License
All rights reserved under MIT License