This is an implementation and application of the SENet-154 neural network architecture (https://arxiv.org/pdf/1709.01507.pdf) to the Human Protein Atlas Challenge dataset on Kaggle.
Performance on the leaderboard: Top 12% (234/2172)
This is a record of the various iterations and experiments I conducted along the way to create a convolutional neural network that to recognise proteins found on microscopy staining images.
Used the FastAI library as well as the pretrained models from: https://github.com/Cadene/pretrained-models.pytorch.
If you are considering using some of this code, things to note:
- I used tabbed views to retain results of previous experiments - this means that trying to read the notebooks in order may be confusing
- The code using the FastAI library is likely to be out of date (I used v0.7 whereas v1.0 has been launched)
- The one-cycle policy did not seem to work well and I had trouble tuning the hyperparameters as recommended (https://sgugger.github.io/the-1cycle-policy.html)