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Egocentric-10K is the largest egocentric dataset. It is the first dataset collected exclusively in real factories.

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Egocentric-10K 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-10K-Evaluation.

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Dataset Statistics

Attribute	Value
Total Hours	10,000
Total Frames	1.08 billion
Video Clips	192,900
Median Clip Length	180.0 seconds
Mean Hours per Worker	4.68
Storage Size	16.4 TB
Format	H.265/MP4
Resolution	1080p (1920x1080)
Frame Rate	30 fps
Field of View	128° horizontal, 67° vertical
Camera Type	Monocular head-mounted
Audio	No
Device	Build AI Gen 1

Camera Intrinsics

Each worker folder contains an intrinsics.json file with calibrated camera parameters.

The intrinsics use the OpenCV fisheye model (Kannala-Brandt equidistant projection) with 4 distortion coefficients (k1-k4). All values are calibrated for the 1920x1080 resolution.

Example intrinsics.json:

{
  "model": "fisheye",
  "image_width": 1920,
  "image_height": 1080,
  "fx": 1030.59,
  "fy": 1032.82,
  "cx": 966.69,
  "cy": 539.69,
  "k1": -0.1166,
  "k2": -0.0236,
  "k3": 0.0694,
  "k4": -0.0463
}

Dataset Structure

Egocentric-10K is structured in WebDataset format.

builddotai/Egocentric-10K/
├── factory_001/
│   └── workers/
│       ├── worker_001/
│       │   ├── intrinsics.json              # Camera intrinsics for this worker
│       │   ├── factory001_worker001_part00.tar  # Shard 0 (≤1GB)
│       │   └── factory001_worker001_part01.tar  # Shard 1 (if needed)
│       ├── worker_002/
│       │   ├── intrinsics.json
│       │   └── factory001_worker002_part00.tar
│       └── worker_011/
│           ├── intrinsics.json
│           └── factory001_worker011_part00.tar
│
├── factory_002/
│   └── workers/
│       ├── worker_001/
│       │   ├── intrinsics.json
│       │   └── factory002_worker001_part00.tar
│       └── ...
│
├── factory_003/
│   └── workers/
│       └── ...
│
└── ... (factories 001-085)

Each TAR file contains pairs of video and metadata files:

factory001_worker001_part00.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
├── factory001_worker001_00003.mp4        # Video 3
├── factory001_worker001_00003.json       # Metadata for video 3
└── ...                                   # Additional video/metadata pairs

Each JSON metadata file has the following fields:

{
  "factory_id": "factory_002",      // Unique identifier for the factory location
  "worker_id": "worker_002",        // Unique identifier for the worker within factory
  "video_index": 0,                 // Sequential index for videos from this worker
  "duration_sec": 1200.0,           // Video duration in seconds
  "width": 1920,                    // Video width in pixels
  "height": 1080,                   // Video height in pixels
  "fps": 30.0,                      // Frames per second
  "size_bytes": 599697350,          // File size in bytes
  "codec": "h265"                   // Video codec
}

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-10K",
    streaming=True,
    features=features
)

# Load specific factories
dataset = load_dataset(
    "builddotai/Egocentric-10K",
    data_files=["factory_001/**/*.tar", "factory_002/**/*.tar"],
    streaming=True,
    features=features
)

# Load specific workers
dataset = load_dataset(
    "builddotai/Egocentric-10K",
    data_files=[
        "factory_001/workers/worker_001/*.tar",
        "factory_001/workers/worker_002/*.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-10K",
    filename="factory_001/workers/worker_001/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{buildaiegocentric10k2025,
  author = {Build AI},
  title = {Egocentric-10k},
  year = {2025},
  publisher = {Hugging Face Datasets},
  url = {https://huggingface.co/datasets/builddotai/Egocentric-10K}
}

builddotai/Egocentric-10K 已完整同步