For a trained model to load into the decoder, use

• VGG19
• ResNet152
• ResNet152 No Teacher Forcing
• VGG19 No Gating Scalar

BLEU scores for VGG19 (Orange) and ResNet152 (Red) Trained With Teacher Forcing.

BLEU Score	Graph	Top-K Accuracy	Graph
BLEU-1		Training Top-1
BLEU-2		Training Top-5
BLEU-3		Validation Top-1
BLEU-4		Validation Top-5

This was written in python3 so may not work for python2. Download the COCO dataset training and validation images. Put them in data/coco/imgs/train2014 and data/coco/imgs/val2014 respectively. Put the COCO dataset split JSON file from Deep Visual-Semantic Alignments in data/coco/. It should be named dataset.json.

Run the preprocessing to create the needed JSON files:

python generate_json_data.py

Start the training by running:

python train.py

The models will be saved in model/ and the training statistics will be saved in runs/. To see the training statistics, use:

tensorboard --logdir runs

python generate_caption.py --img-path <PATH_TO_IMG> --model <PATH_TO_MODEL_PARAMETERS>

• Create image encoder class
• Create decoder class
• Create dataset loader
• Write main function for training and validation
• Implement attention model
• Implement decoder feed forward function
• Write training function
• Write validation function
• Add BLEU evaluation
• Update code to use GPU only when available, otherwise use CPU
• Add performance statistics
• Allow encoder to use resnet-152 and densenet-161

Show, Attend and Tell

Original Theano Implementation

Neural Machine Translation By Jointly Learning to Align And Translate

Karpathy's Data splits