PyDVL @ PyData 2024 Berlin

These are the materials for the pyDVL presentation held on March 22nd, 2024 at PyData Berlin.

Detecting mislabelled images with pyDVL

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.txt

You 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.py

You 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

Slides

The slides use Slidev. To start the slideshow run:

cd slides
npm install
npm run dev

There'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-clicks

If using VSCode, it is recommended to open the subfolder slides as a workspace to benefit from the Slidev extension.