---
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](https://claru.ai), 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](https://reka.ai/news/rekadaily-10k-egocentric-household-manipulation-data).**
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.:

```python
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](https://claru.ai), Reka's data
collection marketplace, as described in the
[release announcement](https://reka.ai/news/rekadaily-10k-egocentric-household-manipulation-data).
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.