license: apache-2.0
task_categories:
- video-classification
- image-to-video
language:
- en
tags:
- video
- egocentric
- first-person
- household
- webdataset
size_categories:
- 10K<n<100K
configs:
- config_name: browse
default: true
data_files:
- split: train
path: metadata/browse.parquet
- config_name: metadata
data_files:
- split: train
path: metadata/index.parquet
RekaDaily-10k (raw)
Raw, unscripted, first-person daily-life video, collected through Claru, Reka's data collection marketplace — recorded by paid collectors in their own homes and workplaces on head-mounted and handheld phones, across multiple regions.
This dataset is being released incrementally and is growing toward the full
10,312-hour release.
Current contents: ~1,883 hours / 82,244 videos / 4,015 shards. Videos
are exactly as collected — no re-encoding, no cuts, no filtering beyond basic
integrity checks. A processed tier (short clips with machine captions) is
released separately under the same RekaDaily-10k prefix.
Structure
Videos are packed into WebDataset tar archives (~5-8 GB), organized by collection project:
data/<project>/shard-NNNNN.tar # pairs of <video_id>.<mp4|mov> + <video_id>.json
metadata/browse.parquet # one row per video: thumbnail + every metadata field
metadata/index.parquet # the same metadata fields, without thumbnails
sample/ # ~400 loose videos for quick browsing
Load the video shards with any WebDataset reader, e.g.:
import webdataset as wds
url = "https://huggingface.co/datasets/RekaAI/RekaDaily-10k-raw/resolve/main/data/egocentric_household_tasks/shard-00000.tar"
ds = wds.WebDataset(url)
The Dataset Viewer above opens on the browse table: every video as a thumbnail
next to its full metadata row, so the collection can be skimmed without
downloading anything. The metadata config is the same fields without the
images, for lighter programmatic reads. The tar shards are not previewed in the viewer
(the Hub's WebDataset preview is currently broken platform-wide for video
archives); they download and stream normally.
Projects: egocentric_household_tasks, egocentric_household_tasks_usa,
egocentric_commercial_environments, residential_egocentric_latam_upload_via_claru,
video_capture_activities, video_capture, video_capture_first_person_videos_phone.
Metadata fields
Each .json sidecar carries the fields below; metadata/index.parquet and
metadata/browse.parquet carry the same set, one row per video (browse adds
the thumbnail image column):
| field | description |
|---|---|
video_id |
unique id, matches the media file name |
project |
Claru collection project |
flow, activities |
activity taxonomy (activities-type projects): session scenario + performed actions |
category, subcategory |
category taxonomy (video-capture-type projects) |
lighting |
lighting condition, where captured |
duration_s, fps, width, height, num_frames, codec |
probe stats |
collector |
salted-hash collector id — distinct values ≈ distinct environments |
Each video populates one taxonomy family (flow/activities or category/subcategory) depending on its project type.
Consent, privacy & takedown
This dataset was collected through Claru, Reka's data collection marketplace, as described in the release announcement. Collectors are paid contractors who opt in, and every session is recorded with the wearer's knowledge and agreement. Collectors are instructed to record only with the agreement of other adults present and to keep others out of frame where that is not possible.
Every video in this release has been processed to remove container metadata — GPS coordinates, device identifiers, and capture timestamps — and verified clean before upload, in addition to the automated PII screening described in the announcement. Screening is not perfect. If you find something in this release that should not be there, tell us and we will remove it: contact contact@reka.ai.
License
Apache 2.0 — use, redistribute, and build on this data, including commercially, with attribution per the license terms.