This is in requirement of BT5153 Applied Machine Learning Term Paper. Dataset used : - https://www.kaggle.com/paramaggarwal/fashion-product-images-small

This package includes two solutions:-

• Image Classification using Transfer Learning :-VGG19 and Inception V3
• Image Search using a custom CNN based AutoEncoder and Resnet50

• Three schemes to conduct product classification for e-commerce -by gender+masterCategory -by subCategory -by articleType

• MODEL ARCHITECTURE VGG-19 has 19 deep layers with unified kernel size of 3×3

• LAYERS TUNED Only the last 5 layers are re-trained with newly add-in 3 dense layers in order to predict the correct number of class for current study

• HYPER-PARAMETERS TUNED Optimizer using adam with learning rate of 1e-5 and decay of 1e-6, new add-in layer with 512 hidden units with drop out rate of 0.5

• REGULARIZATION Early stopping, reduce learning rate on plateau, drop-out, data augmentation (ONLY during training) are applied, weight sharing is done implicitly through convolution

• MODEL ARCHITECTURE Inception V3 has 48 dense layers and stacks 11 inception modules, each consisting of pooling layers and convolutional filters

• LAYERS TUNED All the layers are re-trained with newly add-in 3 dense layers in order to predict the correct number of class for current study

• HYPER-PARAMETERS TUNED Optimizer using rmsprop with learning rate of 1e-4 and decay of 1e-6, new add-in layer with 128 hidden units with drop out rate of 0.4

• REGULARIZATION Early stopping, reduce learning rate on plateau, drop-out, data augmentation (ONLY during training) are applied, weight sharing is done implicitly through convolution

  Accuracy,Precision and Recall

• MODEL ARCHITECTURE CNN architecture is used to build AntoEncoder for image search. It comprises of both an Encoder and a Decoder. Embedding features is generated bythe Encoder.

• LAYERS DESIGN Encoder consists of the first 7 layers Decoder consists of the last 7 layers

• HYPER-PARAMETERS TUNED Optimizer using adam with learning rate of 1e-5 and decay of 1e-6

• TRAINING Autoencoders are self-supervised learning model with input and output being the image itself

The names of the creators of these workbooks and their LinkedIn Profiles are given below

• Fine Tune Inception V3
• Improve Image Search

NUS

All authors are graduate students of MSBA program Class of2020 at NUS Business School. .BT5153 Applied Machine Learning Term Paper, MSBA @ NUS,2020. Copyright 2020 by the author(s).