• CIFAR-100 Classification with DenseNet
  • Prerequisites
  • Directory Structure
  • Train
    • Visualize Training Loss and Validation Accuracy
  • Infer
    • Pre-trained Network Weights
  • References

Project requirements: https://docs.google.com/document/d/1ZUdQ7c2X7y_KWobwZG0y0uFpwe__K1Hs1I_rv5jR3ws/edit?usp=sharing

• Python 3.8 or later
  • You may remove typing.Final type hints to use this project with earlier Python versions
• Install all requirements in requirements.txt
  • Using pip: pip install -r requirements.txt

• constant.py: Constants
• dataset.py: The CIFAR_SFU dataset, copied from the handout notebook, unmodified
• densenet.py: The DenseNet model
• epoch_logger.py: Logs the train/validation losses/accuracies into 2 files: train_log.csv and validation_log.csv
• epoch_visualizer.py: Generates the training loss & validation plot
  • Copied from the handout notebook
  • Slightly modified to show fewer x labels (showing 400 x labels for 400 epochs is unfeasible)
• infer.py: Generates the Kaggle submission csv using the test set
• train.py: The training script
• Files with filenames ending with _test are Python unit tests

Most scripts are using the argparse module to parse their command line arguments. Use -h on a script to view all available arguments.

Run the train.py script with arguments specifying where to save the checkpoints and logs.

python train.py --checkpoint_save_dir=checkpoints --train_log_filename=checkpoints/train_log.csv --validation_log_filename=checkpoints/validation_log.csv

• Network weights are saved in checkpoint_save_dir
• Training and validation logs (accuracies, losses, etc.) are saved in train_log_filename and validation_log_filename respectively

Training takes about 4 hours on a machine with i7-9700K and RTX 2080.

The epoch_visualizer.py script reads checkpoints/train_log.csv and checkpoints/validation_log.csv to generate a plot of training losses and validation accuracies. It then saves it as checkpoints/plot.png.

Run the infer.py script with arguments specifying the checkpoint location and prediction csv location.

python infer.py --checkpoint_filename=../0.4-128batch/400.pth --csv_filename=predictions.csv

• Loads network weights from checkpoint_filename
• Saves test set predictions in csv_filename

Pre-trained network weights are available at: https://github.com/MacJim/CMPT-762-Assignment-2-Checkpoints

The 0.4-128batch/400.pth weights have the best test accuracy 0.76500 (runner-up in our Kaggle competition).

• SFU CMPT 762 home page: https://www2.cs.sfu.ca/~furukawa/cmpt762-2021-spring/
• CIFAR-100 dataset: https://www.cs.toronto.edu/~kriz/cifar.html
• DenseNet: https://arxiv.org/abs/1608.06993