Legiblate

In 2020 I started the course “Deep Learning for Coders” from fast.ai. As one of my projects, I had to train an image classifier (fine-tune a Resnet model with fastai’s wrapper around PyTorch, specifically).

I wanted to use a dataset that no one had tried before, so I acquired the book cover images from the paper Judging a Book by its Cover (2017). Citing the paper here:

@misc{iwana2017judging,
      title={Judging a Book By its Cover}, 
      author={Brian Kenji Iwana and Syed Tahseen Raza Rizvi and Sheraz Ahmed and Andreas Dengel and Seiichi Uchida},
      year={2017},
      eprint={1610.09204},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

The authors achieved 24.7% accuracy. As we’ll see below, mine achieved 33.7% accuracy — not amazing, but SOTA for the time. I called this book cover judger “../creations/Legiblate”, for unknown reasons.

I later decided to upload Legiblate to Heroku as a Streamlit app. I had to retrain a smaller resnet34 model for that, as the resnet50 version wouldn’t fit on a free dynamo.

That model had 32.8% accuracy and you can find it here: Legiblate app.

Note that if the site has not been accessed recently it will take a few seconds to load, as the free dynamo boots up.