The dataset consists of 8,174 images in 13 Kenyan food type classes from which the 20% was used for validation.
The input image size is 224 by 224 pixels and I have used several Data Augmentation techniques, such as Random Vertical/Horizontal Rotation and Color Jitter (brightness, contrast).
I have used pretrained Resnet50 (wide_resnet50_2) and froze 60% of the parameters, leaving the last 40% of the network parameters trainable. The model was trained for 30 epochs and achieved 94% training and 75% validation accuracy. The highest score of my Kaggle submission is currently at 73.5% accuracy.
Initial training Tensorboard logs: https://tensorboard.dev/experiment/VtMb0okWSXCqv3nPSZyHRw/
- Modularize the training pipeline to allow for rapid experimentation.
- Refactor code to increase readability.
- Try ensemble learning to achieve higher accuracy.
- Update tensorboard logs.