Learnt how to write the dataloader, the model and the training code . The following analysis was done:

• With Batch Norm
• Adding new layers
• With Dropout
• Different activation functions at the end
• Different pooling strategies
• Different optimizers
• Basic Augmentation like Rotation, Translation, Color Change

Also experimented with: Experiment with

• Residual Blocks
• Different learning strategies

It is available here.There are approx 9233 images in train and 484 images in test set.

The submission is for this challenge on ai-crowd portal. This was done using fastai and resnet-50 pre-trained model . Using this code I achieved an accuracy of 0.616