https://github.com/ashryaagr/image-captioning
Image Captioning
https://github.com/ashryaagr/image-captioning
Last synced: about 1 month ago
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Image Captioning
- Host: GitHub
- URL: https://github.com/ashryaagr/image-captioning
- Owner: ashryaagr
- Created: 2023-02-24T09:23:34.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-03-09T20:13:17.000Z (about 2 years ago)
- Last Synced: 2025-02-05T16:48:10.486Z (3 months ago)
- Language: Jupyter Notebook
- Size: 289 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 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