These are the materials for the pyDVL presentation held on March 22nd, 2024 at PyData Berlin.
In this example we use Influence Functions with pyDVL to detect mislabelled images in a NIH dataset with ~28K images for malaria screening.
To run the code, create a virtual environment and install the requirements:
python -m venv venv
source venv/bin/activate
pip install -r requirements.txtYou will also need to create a kaggle API token to be benefit from the automatic
download of the dataset. You can create a token in your Kaggle account settings
and save it in a file called kaggle.json under ~/.kaggle/.
If you would like to download the data manually, make sure the result looks like this:
You can the run the experiment with:
python run_evaluation_malaria.pyYou might need to change the batch sizes to fit in your GPU RAM, both for the
training of the model in the main section of run_evaluation_malaria.py, as
well as for the computation of the influence functions in compute_infuences()
in the same file.
The script trains a pytorch lightning model, computes corresponding
influence values and generates histograms and plots. By default, the results
are persisted in the folder results on the top-level of the repository, which
can be configured via config.py
The slides use Slidev. To start the slideshow run:
cd slides
npm install
npm run devThere's a live reload server running at http://localhost:3030. Edit slides.md to see the changes.
The slides are deployed with Netlify to pydata2024.pydvl.org upon every push to master.
To export them as PDF run (from the slides folder):
npm run export -- --with-clicksIf using VSCode, it is recommended to open the subfolder slides as a workspace
to benefit from the Slidev extension.