This repository contains an implementation of a CNN model using a similar ResNet architecture. Namely residual layers coupled with batch normalization and varying kernel sizes for each bottleneck block to create the expansion-maintain-reduce regularization that bottlenecks offer.
Since I haven't messed around with the hyperparameters enough, the best model currently starts overfitting at around 15 epochs which isn't too bad far a smaller model but could definetly be improved with some better data preprocessing and maybe L1/L2 regularization added onto it.
pip install -r requirements.txtSince this is a notebook, I advise you install a jupyter lab server on your own (https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html)
The training and testing is done in a single function train_and_validation_loop with the given number of epochs, device to run on and log writer for Tensorboard display.
The best models (with lowering evaluation loss) are saved in the saved_models dir and Tensorboard logs are stored in the runs dir.
Currently the model achieves around ~78% accuracy without too much overfitting.
Model paramters:
Training Loss:
Train VS Validation loss:
Since I haven't tested this notebook much, some variables like local paths may need to be changed to accomodate your needs.