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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 | |
|  | |
|  | |