tensorflow / tensorflow/datasets
[data request] Holistic Video Understanding Dataset (HVU)
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dataset request
- Dominant language
- Python
- Stars
- 4.6k
- Forks
- 1.6k
- Avg merge
- 3h 54m
- Merged PRs (30d)
- 1
Description
- Name of dataset: Holistic Video Understanding Dataset
- URL of dataset: https://holistic-video-understanding.github.io/
- License of dataset:
- Short description of dataset and use case(s): HVU is organized hierarchically in a semantic taxonomy that focuses on multi-label and multi-task video understanding as a comprehensive problem that encompasses the recognition of multiple semantic aspects in the dynamic scene. HVU contains approx.~577k videos in total with ~13M annotations for training and validation set spanning over ~3k classes. HVU encompasses semantic aspects defined on categories of scenes, objects, actions, events, attributes and concepts, which naturally captures the real-world scenarios.
Folks who would also like to see this dataset in tensorflow/datasets, please thumbs-up so the developers can know which requests to prioritize.
And if you'd like to contribute the dataset (thank you!), see our guide to adding a dataset.
/cc @alidiba67 @MohsenFayyaz89
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with docs/add_dataset.md and the HVU project URL to verify the dataset details, especially its license and available files. Follow the guide's contribution requirements and confirm that the completed dataset definition covers the stated videos, annotations, and taxonomy and is ready for review.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100