builddotai/Egocentric-100K

Egocentric-100K is the largest egocentric dataset by an order of magnitude. You can visualize the dataset here.

Egocentric-100K is state-of-the-art in hand visibility and active manipulation density compared to previous in-the-wild egocentric datasets. The complete 30,000 frame evaluation set is available at Egocentric-100K-Evaluation.

Dataset Statistics

Attribute	Value
Total Hours	100,405
Total Frames	10.8 billion
Video Clips	2,010,759
Median Clip Length	180.0 seconds
Workers	14,228
Mean Hours per Worker	7.06
Storage Size	24.79 TB
Format	H.265/MP4
Resolution	256p (456x256)
Frame Rate	30 fps
Camera Type	Monocular head-mounted fisheye
Audio	No
Device	Build AI Gen 1

Camera Intrinsics

Each worker folder contains an intrinsics.json file with calibrated camera parameters for that worker's device.

The intrinsics use the OpenCV fisheye model (Kannala-Brandt equidistant projection) with 4 distortion coefficients (k1-k4). All values are scaled appropriately for the 456x256 resolution.

Example intrinsics.json:

{
  "model": "fisheye",
  "image_width": 456,
  "image_height": 256,
  "fx": 137.98,
  "fy": 138.23,
  "cx": 232.17,
  "cy": 125.37,
  "k1": 0.3948,
  "k2": 0.1798,
  "k3": -0.2753,
  "k4": 0.0793
}

Dataset Structure

Egocentric-100K is structured in WebDataset format:

builddotai/Egocentric-100K/
├── factory001/
│   ├── worker001/
│   │   ├── intrinsics.json   # Camera intrinsics for this worker
│   │   ├── part000.tar       # Shard 0 (≤1GB)
│   │   └── part001.tar       # Shard 1 (if needed)
│   ├── worker002/
│   │   ├── intrinsics.json
│   │   └── part000.tar
│   └── ...
│
├── factory002/
│   ├── worker001/
│   │   ├── intrinsics.json
│   │   └── part000.tar
│   └── ...
│
└── ... (85 factories, 14,228 workers)

Each TAR file contains pairs of video and metadata files:

part000.tar
├── factory001_worker001_00001.mp4        # Video 1
├── factory001_worker001_00001.json       # Metadata for video 1
├── factory001_worker001_00002.mp4        # Video 2
├── factory001_worker001_00002.json       # Metadata for video 2
└── ...                                   # Additional video/metadata pairs

Each JSON metadata file has the following fields:

{
  "factory_id": "factory_002",
  "worker_id": "worker_002",
  "video_index": 0,
  "duration_sec": 1200.0,
  "width": 456,
  "height": 256,
  "fps": 30.0,
  "size_bytes": 599697350,
  "codec": "h265"
}

Loading the Dataset

from datasets import load_dataset, Features, Value

# Define features
features = Features({
    'mp4': Value('binary'),
    'json': {
        'factory_id': Value('string'),
        'worker_id': Value('string'),
        'video_index': Value('int64'),
        'duration_sec': Value('float64'),
        'width': Value('int64'),
        'height': Value('int64'),
        'fps': Value('float64'),
        'size_bytes': Value('int64'),
        'codec': Value('string')
    },
    '__key__': Value('string'),
    '__url__': Value('string')
})

# Load entire dataset
dataset = load_dataset(
    "builddotai/Egocentric-100K",
    streaming=True,
    features=features
)

# Load specific factories
dataset = load_dataset(
    "builddotai/Egocentric-100K",
    data_files=["factory001/**/*.tar", "factory002/**/*.tar"],
    streaming=True,
    features=features
)

# Load specific workers
dataset = load_dataset(
    "builddotai/Egocentric-100K",
    data_files=[
        "factory001/worker001/*.tar",
        "factory001/worker002/*.tar"
    ],
    streaming=True,
    features=features
)

Loading Intrinsics

from huggingface_hub import hf_hub_download
import json

# Download intrinsics for a specific worker
intrinsics_path = hf_hub_download(
    repo_id="builddotai/Egocentric-100K",
    filename="factory001/worker001/intrinsics.json",
    repo_type="dataset"
)

with open(intrinsics_path) as f:
    intrinsics = json.load(f)

License

Licensed under the Apache 2.0 License.

Citation

@dataset{buildaiegocentric100k2025,
  author = {Build AI},
  title = {Egocentric-100k},
  year = {2025},
  publisher = {Hugging Face Datasets},
  url = {https://huggingface.co/datasets/builddotai/Egocentric-100K}
}