Used pre-trained VGG16, ResNet50, and InceptionV3 networks to extract bottleneck features and build a model on top of each of them to evaluate and compare the model performances. (Model performances include classification report, confusion matrices, plots of Loss Vs Epochs) Optimized models using TF-Lite (Post Training Dynamic range quantization)