Recognising classic Hong Kong style dishes using Transfer Learning with Residual Neural Network

Developed By: Mudit Chaudhary and Arjun Rao - The Chinese University of Hong Kong

Dataset Provided :

• Food images classified into 75 "large categories" - each containing up to 1000 images each category.
• 25 small categories - containing 5 images per category

Solution used : Deep Residual Network with Transfer Learning (ResNet-50)

• Data augmenation was used to enhance *Random rotations with range 40 degrees *Height and width shift *Zoom *Shear *Horizontal flip

• Training Methodology:

1. Train the ResNet-50 with images from the large sample
2. Freeze the weights of that model and implement a new output layer for training the images from small sample.
3. Train last few layers of the previous model.
4. Transfer Learning from previous models

• Design methodology

For the first model: We add a Global Average Pooling Layer connecting ResNet. Add a Fully Connected layer with 1024 nodes Add a Dropout Layer with 40% dropout rate Add a Fully Connected output layer with 75 nodes For the second model: We add a Global Average Pooling Layer connecting ResNet. Add a Fully Connected layer with 1024 nodes Add a Dropout Layer with 40% dropout rate Add a Fully Connected output layer with 25 nodes

• Future plans : Future plan include: Tuning the hyperparameters Using Inception ResNet Modify the output layer to reduce overfitting for the small sample