Type of Amazon forests satellite image

Multilabel classification NN solution for Amazon forests satellite image. I made accent on "industrial quality" code with next technologies:

• pytorch_lightning
• timm
• ClearML
• linters (black, isort, nbstripout, flake8)
• types with pydantic
• DVC for local usage

Disclaimers:

• the project was originally crated and maintained in GitLab local instance, some repo functionality may be unenviable
• the project was created by me and me only as part of the CVRocket professional development course
• here are a short trained version of NNs (about 15 epochs each)
• this project is my first "industry grade" NN, for more advanced code and features please see car-plate projects

Dataset

Include 40479 tiles of satellite image in jpg and 17 image types. More information about data you can find in notebook.

To download dataset from kaggle into dataset folder:

make download_dataset

How to run

1. Create and activate python environment

   python3 -m venv venv
   . venv/bin/activate

2. Install libraries

    make install

3. Run linters

   make lint

4. Tune config.yaml

5. Train

make train

Additional information

• Inference example in notebook
• Best experiment in ClearML
• History of experiments