Uses a CNN Encoder and a RNN Decoder to generate captions for input images.
The Project has been reviewed by Udacity and graded Meets Specifications.
Here's a sumary of the steps involved.
- Dataset used is the COCO data set by Microsoft.
- Feature vectors for images are generated using a CNN based on the ResNet architecture by Google.
- Word embeddings are generated from captions for training images. NLTK was used for working with processing of captions.
- Implemented an RNN decoder using LSTM cells.
- Trained the network for nearly 3 hrs using GPU to achieve average loss of about 2%.
- Obtained outputs for some test images to understand efficiency of the trained network.
