MLMI12 - Computer Vision Mini Project. ( Report )
Training a neural network to perform image similarity matching on the Tiny Imagenet dataset. Given two images identify whether they belong to the same class or different classes.
Classify each image separately and check whether the classes match.
Instead of getting the final class from the network, take the feature embeddings and calculate the cosine similarity between them. If the similarity is above a threshold, then the images are considered to be similar.
Train a siamese network with triplet loss. The network takes in two images and outputs the feature embeddings. The triplet loss is calculated between the anchor, positive and negative images.
Train a network end-to-end to provide binary classes for either similar or dissimal.
Seen-Seen : Both image classes were seen during training
Seen-Unseen : One image class was seen during training and the other was unseen
Unseen-Unseen : Both image classes were unseen during training
