Image Captioning

TODO: Details specific to the student's implementation to be added by the student

Usage

Define the configuration for your experiment. See default.json to see the structure and available options. You are free to modify and restructure the configuration as per your needs.
Implement factories to return project specific models, datasets based on config. Add more flags as per requirement in the config.
Implement experiment.py based on the project requirements.
After defining the configuration (say my_exp.json) - simply run python3 main.py my_exp to start the experiment
The logs, stats, plots and saved models would be stored in ./experiment_data/my_exp dir. This can be configured in contants.py
To resume an ongoing experiment, simply run the same command again. It will load the latest stats and models and resume training pr evaluate performance.

main.py: Main driver class
experiment.py: Main experiment class. Initialized based on config - takes care of training, saving stats and plots, logging and resuming experiments.
dataset_factory: Factory to build datasets based on config
model_factory.py: Factory to build models based on config
constants.py: constants used across the project
file_utils.py: utility functions for handling files
caption_utils.py: utility functions to generate bleu scores
vocab.py: A simple Vocabulary wrapper
coco_dataset: A simple implementation of torch.utils.data.Dataset the Coco Dataset
get_datasets.ipynb: A helper notebook to set up the dataset in your workspace