Human Archive is modeling human sensorimotor intelligence at scale. We collect up to 50,000+ hours of this egocentric dataset per week, making HA-Ego one of the largest and most diverse egocentric datasets available.

We’re backed by Y Combinator and engineers from OpenAI, BAIR, SAIL, Anduril Industries, Mercor, NVIDIA, Jane Street, Google, DoorDash AI Research, Reevo, AfterQuery, and the investors behind AMI Labs.

To purchase the full dataset, find time here (https://cal.com/human-archive-0mw2ab/30min?user=human-archive-0mw2ab)

HA-Ego-Samples

A large-scale egocentric (first-person) video dataset capturing everyday human activities across commercial, residential, and industrial environments. Designed for robotics and manipulation research, the dataset features diverse tasks performed by thousands of unique individuals wearing head-mounted cameras.

Dataset Statistics

Metric	Value
Total Duration	500.0 hours
Total Frames	53,828,712
Video Clips	9,343
Unique Persons	4,651
Task Categories	259
Environment Types	45
Mean Clip Length	192.6 seconds
Storage Size	2.05 TB
Encoding	H.264 (AVC)
Container	MP4
Resolution	1920 x 1080
Frame Rate	30 fps
Camera	Monocular head-mounted
Audio	No

Dataset Structure

HA-Ego-Samples/
├── commercial/
│   ├── factory/
│   │   ├── person1/
│   │   │   ├── person1_segment1.mp4
│   │   │   ├── person1_segment2.mp4
│   │   │   └── person1_segments.json
│   │   ├── person2/
│   │   └── ...
│   └── hospitality/
│       ├── person1373/
│       └── ...
├── residential/
│   ├── person4116/
│   └── ...
└── README.md

The dataset is organized into three top-level categories:

commercial/factory/ — Activities in factory and manufacturing environments (persons 1–1,372)
commercial/hospitality/ — Activities in kitchens, restaurants, and hospitality settings (persons 1,373–4,115)
residential/ — Activities in home and residential environments (persons 4,116–4,651)

Each person folder contains one or more MP4 video segments and a single segments.json metadata file.

Metadata Format (`segments.json`)

Each person directory includes a {person_id}_segments.json file with the following schema:

{
  "person_id": "person1",
  "total_segments": 1,
  "total_duration_sec": 592.0,
  "segments": [
    {
      "person_id": "person1",
      "video_index": "segment1",
      "duration_sec": 592.0,
      "task": "operating_machine",
      "environment": "factory_floor",
      "width": 1920,
      "height": 1080,
      "fps": 30.0,
      "size_bytes": 727529626,
      "codec": "h264"
    }
  ]
}

Field Descriptions

Field	Type	Description
`person_id`	string	Unique person identifier (e.g., `"person1"`)
`total_segments`	int	Number of video segments for this person
`total_duration_sec`	float	Total duration across all segments (seconds)
`segments`	list	Array of per-segment metadata
`segments[].video_index`	string	Segment identifier (e.g., `"segment1"`)
`segments[].duration_sec`	float	Duration of this segment (seconds)
`segments[].task`	string	Activity label (e.g., `"cutting_vegetables"`)
`segments[].environment`	string	Environment label (e.g., `"kitchen"`)
`segments[].width`	int	Video width in pixels
`segments[].height`	int	Video height in pixels
`segments[].fps`	float	Frame rate
`segments[].size_bytes`	int	File size in bytes
`segments[].codec`	string	Video codec (always `"h264"`)

Camera Intrinsics

All videos were recorded with the same head-mounted camera model. The calibrated intrinsic parameters are:

Intrinsic Matrix (K)

K = [[4425.0857,    0.0000,  974.5921],
     [   0.0000, 4384.7678,  522.1587],
     [   0.0000,    0.0000,    1.0000]]

Parameter	Value
fx	4425.0857
fy	4384.7678
cx	974.5921
cy	522.1587

Distortion Coefficients (Brown–Conrady model)

dist = [-6.4654, 130.2946, -0.0033, 0.0356, -1119.5408]

Coefficient	Value
k1	-6.4654
k2	130.2946
p1	-0.0033
p2	0.0356
k3	-1119.5408

To undistort frames using OpenCV:

import cv2
import numpy as np

K = np.array([[4425.0857, 0.0, 974.5921],
              [0.0, 4384.7678, 522.1587],
              [0.0, 0.0, 1.0]])

dist = np.array([-6.4654, 130.2946, -0.0033, 0.0356, -1119.5408])

frame = cv2.imread("frame.png")
undistorted = cv2.undistort(frame, K, dist)

Loading the Dataset

Using Hugging Face `datasets`

from datasets import load_dataset

# Load metadata only (fast — no video download)
ds = load_dataset("humanarchive/HA-Ego-Samples", split="train")

Streaming Individual Videos

from huggingface_hub import hf_hub_download

# Download a specific video clip
path = hf_hub_download(
    repo_id="humanarchive/HA-Ego-Samples",
    filename="commercial/factory/person1/person1_segment1.mp4",
    repo_type="dataset",
)

Loading Metadata for a Person

import json
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="humanarchive/HA-Ego-Samples",
    filename="commercial/factory/person1/person1_segments.json",
    repo_type="dataset",
)

with open(path) as f:
    meta = json.load(f)

print(f"Person: {meta['person_id']}")
print(f"Segments: {meta['total_segments']}")
for seg in meta["segments"]:
    print(f"  {seg['video_index']}: {seg['task']} in {seg['environment']} ({seg['duration_sec']}s)")

Batch Processing with Video Decoding

import cv2
from huggingface_hub import hf_hub_download

video_path = hf_hub_download(
    repo_id="humanarchive/HA-Ego-Samples",
    filename="commercial/hospitality/person2000/person2000_segment1.mp4",
    repo_type="dataset",
)

cap = cv2.VideoCapture(video_path)
while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break
    # Process frame (1920x1080, BGR)
    pass
cap.release()

Cloning the Full Dataset

For bulk access, clone the repository using Git LFS:

# Make sure Git LFS is installed
git lfs install

# Clone (this will download ~2 TB of video data)
git clone https://huggingface.co/datasets/humanarchive/HA-Ego-Samples

License

This dataset is released under the Apache License 2.0.

Citation

@dataset{ha_ego_samples_2026,
  title={HA-Ego-Samples: A Large-Scale Egocentric Video Dataset for Robotics},
  author={Human Archive},
  year={2026},
  url={https://huggingface.co/datasets/humanarchive/HA-Ego-Samples}
}

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Human Archive