pradeepsinngh / Visual-Recognition-using-CNN

Image classification using ConvNets models for Fashion-MNIST dataset.

Image Classification using CNN models:

Classification of Fashion products using different neural network based models -- Feedforward, CNN and VGG, etc.

Data

Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. You can download it here -- https://github.com/zalandoresearch/fashion-mnist

Target Class	Definition
0	T-shirt
1	Trouser
2	Pullover
3	Dress
4	Coat
5	Sandal
6	Shirt
7	Sneaker
8	Bag
9	Ankle Boot

Models:

1. 2 Layer feedforward Nerual Netwrok
2. CNN (1 Conv layer)
3. CNN (3 Conv layer)
4. CNN (4 Conv layer) + Batch Normalization
5. VGG
6. VGG + Batch Normalization

Data Augmentation:

Since, my data set only had 60,000 test cases, which is quite less for a deep learning model (for eg: VGG). So, I have used data (image) augmentation technique to increase the number of images and also to improve the quality of images.

Overfitting and Underfitting:

• Dropout
• Data Augmentation

Results

Out of all experiments, model with VGG + Batch Normalization performed the best with almost 95% accuracy.

Model	Accuracy
2 Layer Nerual Netwrok	88%
CNN (1 Conv layer)	91%
CNN (3 Conv layer)	91%
CNN (4 Conv layer) + Batch Normalization	92%
VGG	93%
VGG + Batch Normalization	94%