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.

Files

• 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