README.md

scene-recognition-and-visualization

============

This project involves developing one-shot learning methods for indoor sub-scene classification. Some network visualization techniques will also be implemented.

One-shot learning

Aim is to recognise which floor the robot is currently at.

Methods implemented:

1. Siamese Network:

Uses contrastive loss
~~~
python trainSiamese.py
~~~

![Siamese net](siamese1.png )

2. Modified Siamese network:

Uses identification inaddition to contrastive loss
~~~
python trainModifiedSiamese.py
~~~

![Modified Siamese net](modified_siamese1.png )

trainSiamese.py or trainModifiedSiamese.py

To change the fc8 layer size, train, test and to visualize read the comments in the code

Visualization

Aim is to visualize what parts of the image are important for the classification.

Methods considered:

1. Occulsion heat map
2. ...