Please note VQ test annotations (for the challenge) were recently released. If needed, please download the annotations dataset again, e.g. python -m ego4d.cli.cli --output_directory="~/ego4d_data" --datasets annotations
EGO4D is the world's largest egocentric (first person) video ML dataset and benchmark suite, with 3,600 hrs (and counting) of densely narrated video and a wide range of annotations across five new benchmark tasks. It covers hundreds of scenarios (household, outdoor, workplace, leisure, etc.) of daily life activity captured in-the-wild by 926 unique camera wearers from 74 worldwide locations and 9 different countries. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. The approach to data collection was designed to uphold rigorous privacy and ethics standards with consenting participants and robust de-identification procedures where relevant.
- To access the data, please refer to the Documentation's Getting Started page.
- To download the data, refer to the CLI README
- Explore the dataset here (you'll need a license): Ego4D Visualizer
- For a demo notebook: Annotation Notebook
- For the visualization engine: Viz README
The repository contains multiple directories covering a specific theme. Each
theme contains an associated README.md file, please refer to them.
All python code is located in the ego4d and associated subdirectories. The
goal for each subdirectory is to cover one specific theme.
ego4d: theego4dpython module existscli: The Ego4D CLI for downloading the datasetfeatures: Feature extraction across the datasetresearch: Everything related to research and usage of the dataset (dataloaders, etc).research/clep: Contrastive Language Ego-centric video Pre-training
viz: visualization engine
Ego4D is released under the MIT License.