This project is an assignment for Deep Learning course. The topic of the project is multimodel problems- specifically, visual question answering (VQA).

In the repo you can find the instructions for the assignment, and a report of what we have done in the project.

Our proposed model is an ensemble of 3 models:

1. no pretrained model with 8 CNN layers
2. pretrained autoEncoder with 4 CNN layers
3. pretrained autoEncoder with 8 CNN layers

main.py is reproducing the train of all 3 models.

evaluate_hw2.py initializes all 3 models, loads the trained model_dicts, creates the dataset and calculates the soft accuracy of the ensemble.

Note 1: main.py and evaluate_hw2.py are running the entire preprocess on creating and preprocessing the images and texts- it takes some time.. Note 2: for convenience, the saved models are inside the folder 'saved models'. evaluate_hw2.py loads the model from this folder