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---
title: Image Colorization
emoji: 🐢
colorFrom: purple
colorTo: yellow
sdk: docker
pinned: false
license: apache-2.0
app_port: 5000
---
hugging face config
## Image Colorization
==============================
An deep learning based Image Colorization project.
## FINDINGS
- the task we want to learn is `image-colorization` but we can accompolish that by doing different types of tasks, I call these **sub-task**, in our content they could be like `regression based image colorization`, `classification(by binning) based colorization`, `GAN based colorization`, `image colorization + scene classication(Let there be colors research paper did this)`.
- based on analysis and while I was trying to come up with a project file structure I came to know that the data, model, loss, metrics, dataloader all these are very coupled while dealing with a particular task(`image-colorization`) but when we talk about a **sub-task** we have much more freedom.
- within a sub-task(e.g., regression-unet-learner) we already made a set of rules and now we can use different models without changing the data, or we can change different datasets while using the same model, **so it is important to fix the sub-task we want to do first.**
- so making a folder for each sub-task seems right as a sub-task has high cohesion and no coupling with any other sub-task.
## RULES
- use **lower_snake_case** for **functions**
- use **lower_snake_case** for **file_name & folder names**
- use **UpperCamelCase** for **class names**
- **sub-task** name should be in **lower-kebab-case**
## Project File Structure
------------
.
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data/
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
├── models/ <- Trained models
├── notebooks/ <- Jupyter notebooks
├── configs/
│ ├── experiment1.yaml
│ ├── experiment2.yaml
│ ├── experiment3.yaml
│ └── ...
└── src/
├── sub_task_1/
│ ├── validate_config.py
│ ├── data/
│ │ ├── register_datasets.py
│ │ ├── datasets/
│ │ │ ├── dataset1.py
│ │ │ └── dataset2.py
│ ├── model/
│ │ ├── base_model_interface.py
│ │ ├── register_models.py
│ │ ├── models/
│ │ │ ├── simple_model.py
│ │ │ └── complex_model.py
│ │ ├── losses.py
│ │ ├── metrics.py
│ │ ├── callbacks.py
│ │ └── dataloader.py
│ └── scripts/
│ ├── create_dataset.py
│ └── create_model.py
├── sub_task_2/
│ └── ...
├── sub_task_3/
│ └── ...
├── scripts/
│ ├── create_sub_task.py
│ ├── prepare_dataset.py
│ ├── visualize_dataset.py
│ ├── visualize_results.py
│ ├── train.py
│ ├── evaluate.py
│ └── inference.py
└── utils/
├── data_utils.py
└── model_utils.py
--------
<p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>
Kaggle API docs:- https://github.com/Kaggle/kaggle-api/blob/main/docs/README.md
## Kaggle Commands:-
- kaggle kernels pull anujpanthri/training-image-colorization-model -p kaggle/
- kaggle kernels push -p kaggle/
- echo "{\"username\":\"$KAGGLE_USERNAME\",\"key\":\"$KAGGLE_KEY\"}" > kaggle.json
## Docker Commands:-
- docker buildx build --secret id=COMET_API_KEY,env=COMET_API_KEY -t testcontainer
- docker run -it -p 5000:5000 -e COMET_API_KEY=$COMET_API_KEY testcontainer
## Git Commands:-
- git lfs migrate info --everything --include="*.zip,*.png,*.jpg"
- git lfs migrate import --everything --include="*.zip,*.png,*.jpg"
### Version 1:
- im gonna skip logging for now and rather use print statements
## Dataset
## Result
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