Built four different neural network models for visual question answering using Tensorflow 2.0. Trained the model together on images of MS Coco and the VQA 2.0 dataset.
URL: https://www.youtube.com/watch?v=5wNP7VoB4tM
We have used the VQA v2 dataset for training the models.
Experimented by implementing 4 different models. The four models are as follows:
- Model 1: Append Image as Word
- Model 2: Prepend Image as word
- Model 3: Question through LSTM with image
- Model 4: Attention Based Model
- Trained the above models with 30K examples and started with 30 epochs.
| Train Accuracy | Train Loss | Test Accuracy | Test Loss | |
|---|---|---|---|---|
| Model 1 | 19.47 % | 8.10 % | 19.43 % | 8.09 % |
| Model 2 | 19.40 % | 8.11 % | 19.43 % | 8.09 % |
| Model 3 | 18.31 % | 8.11 % | 18.35 % | 8.11 % |
| Model 4 | 22.49 % | 4.07 % | 24.57 % | 4.09 % |
