This project aims to classify images from CIFAR-10 dataset by using various CNN architectures with Pytorch Lightning.
- Clone the repo
- Setup a virtual env by using environment.yaml
- Change directory to src/models/
- Run the train_model.py file
- Modify model name according to your choice
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── 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 and serialized models, model predictions, or model summaries
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── arguments.py <- Hyperparameters and arguments for model.
│ │ ├── datamodule.py <- Reads, transform and loads the data.
│ │ ├── logger.py <- Logger to track the run.
│ │ ├── model.py <- Script for image classifier.
│ │ ├── alexnet.py <- Implementation of AlexNet.
│ │ └── train_model.py <- Script to train models.
│ │
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience