vicss's Introduction

Vocabulary-free Image Classification and Semantic Segmentation

Alessandro Conti, Enrico Fini, Massimiliano Mancini, Paolo Rota, Yiming Wang, Elisa Ricci

Setup

Install dependencies

# clone project
git clone https://github.com/altndrr/vicss
cd vicss

# install requirements
# it will create a .venv folder in the project root
# and install all the dependencies using flit
make install

# activate virtual environment
source .venv/bin/activate

Setup environment variables

# copy .env.example to .env
cp .env.example .env

# edit .env file
vim .env

Usage

The two entry points are train.py and eval.py. Calling them without any argument will use the default configuration.

# train model
python src/train.py

# test model
python src/eval.py

Configuration

The full list of parameters can be found under configs, but the most important ones are:

data: dataset to use, default to caltech101.
experiment: experiment to run, default to baseline/clip.
logger: logger to use, default to null.

Parameters can be overwritten by passing them as command line arguments. You can additionally override any parameter from the config file by using the ++ prefix.

# train model on ucf101 dataset
python src/train.py data=ucf101 experiment=baseline/clip

# train model on ucf101 dataset with RN50 backbone
python src/train.py data=ucf101 experiment=baseline/clip model=clip ++model.model_name=RN50

Note that since all our approaches are training-free, there is virtually no difference between train.py and eval.py. However, we still keep them separate for clarity.

Development

Install pre-commit hooks

# install pre-commit hooks
pre-commit install

Run tests

# run fast tests
make test

# run all tests
make test-full

Format code

# run linters
make format

Clean repository

# remove autogenerated files
make clean

# remove logs
make clean-logs

Acknowledgements

We gratefully acknowledge taap studio for designing the logo of this project and ashleve/lightning-hydra-template for the template used to build this repository.

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