Image Similarity using PyTorch

Auto-encoder based Image-Similarity Engine

Blog Post is here

Dataset is available over Kaggle

Published in Official PyTorch Medium Channel. Thank you PyTorch team !

• Builds a simple Convolutional Auto-encoder based Image similarity engine.
• This solves the problem of finding similar images using unsupervised learning. There are no labels for images.

Tasks that it can do.

• Similar images using Auto-encoders.
• Similar image search from image features.
• Clustering similar images.

Repostory Structure.

=== data                        Read data from here. It is in gitignore so it won't appear here.
=== docs                        Documentation using mkdocs.
=== image_similarity
====== cluster_images.py        Clusters the embeddings learnt using PCA and T-SNE.
====== torch_data.py            Contains Dataset class to create PyTorch dataset from folder.
====== torch_model.py           Convolutional Auto-enocder Model.
====== torch_engine.py          Train_step and validation_step for training.
====== torch_train.py           Trainng script. Trains Auto-enocder and saves the embeddings.
====== torch_infer.py           Contains inference code
====== config.py                Configurations of models and paths.
====== torch_inference.ipynb    Inference code in .ipynb to play with.
====== torch_train.ipynb        Stand-alone code to train in Jupyter.
=== tests                       Contains tests for CI

Documentation

Please visit here for documentation.

It contains detailed description of work.

Image similarity Outputs

Some sample outputs of recommendations.

More outputs can be found in outputs folder.

Models

• Some models trained using a sample dataset are given in this link.
• It contains encoder, decoder and numpy embeddings generated from auto-enocoders.
• These are obtained by running torch_train.py script.