The code used for the submissions achieved an 86.85% accuracy on Private Results ranking 2nd in the in-class Kaggle competition.

The goal was to classify chest x-ray images into 3 classes:

• healthy
• bacterial pneumonia
• viral pneumonia

The training dataset consisted of 4672 images out of which:

1227 images belong to healthy subjects 2238 images belong to bacterial pneumonia subjects 1207 images belong to viral pneumonia subjects

The submissions employed:

• Data Transformation
• Data augmentation
• Transfer learning with EfficientNet and ImageNet models, trained on ImageNet
• Softmax ensemble of 16 models for final predictions