#### NOTE
This is a **Hugging Face dataset**. For large datasets, ensure `huggingface_hub>=1.1.3` to avoid rate limits. Learn more in the Hugging Face integration docs.
# Dataset Card for Egocentric 10K (subset - Factory 51, first 51 videos)
This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 416 samples.
## Installation
If you haven’t already, install FiftyOne:
```bash
pip install -U fiftyone
```
## Usage
```python
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/Egocentric_10K_subset")
# Launch the App
session = fo.launch_app(dataset)
```
Here’s a filled-out dataset card for your Factory 51 subset:
## Dataset Details
### Dataset Description
This is a curated subset of the Egocentric-10K dataset, focusing exclusively on Factory 51 with limited video sequences per worker. The subset contains egocentric video data captured from head-mounted cameras worn by factory workers during their daily tasks, providing first-person perspective footage of real manufacturing environments and hand-object interactions.
The subset includes the first 51 video clips (indices 0-50) from each worker in Factory 51, making it a more manageable dataset for research, development, and prototyping while maintaining the diversity of worker perspectives and temporal coverage.
- **Curated by:** Build AI (original dataset)
- **Funded by:** Build AI (original dataset)
- **Language(s) (NLP):** N/A (video dataset, no speech/text)
- **License:** Apache 2.0
### Dataset Sources
- **Repository:** https://huggingface.co/datasets/builddotai/Egocentric-10K (original dataset)
## Uses
### Direct Use
This dataset subset is suitable for:
- **Egocentric vision research**: Developing and testing algorithms for first-person video understanding
- **Hand detection and tracking**: Training models to detect and track hands in industrial environments
- **Action recognition**: Recognizing manipulation actions and work activities in factory settings
- **Object interaction analysis**: Understanding how workers interact with tools and materials
- **Temporal action segmentation**: Segmenting continuous work activities into discrete actions
- **Prototyping and development**: Testing computer vision pipelines on real-world industrial data with manageable dataset size
- **Educational purposes**: Teaching egocentric vision concepts with authentic factory footage
- **Transfer learning**: Pre-training or fine-tuning models for industrial or egocentric vision tasks
### Out-of-Scope Use
This dataset should not be used for:
- **Worker surveillance or monitoring**: The dataset is intended for research purposes, not for tracking individual worker productivity or behavior
- **Performance evaluation of individual workers**: Videos should not be used to assess or compare worker performance
- **Biometric identification**: The dataset should not be used to develop facial recognition or worker identification systems
- **Safety compliance enforcement**: While useful for safety research, it should not be used punitively
- **Generalization to all factories**: This is data from a single factory (Factory 51) and may not represent all manufacturing environments
- **Real-time production systems without validation**: Models trained on this subset should be thoroughly validated before deployment
## Dataset Structure
The dataset is organized as a FiftyOne video dataset with the following structure:
### Fields
Each video sample contains:
- **filepath**: Path to the MP4 video file
- **metadata**: VideoMetadata object containing:
- `size_bytes`: File size in bytes
- `mime_type`: “video/mp4”
- `frame_width`: 1920 pixels
- `frame_height`: 1080 pixels
- `frame_rate`: 30.0 fps
- `duration`: Video duration in seconds
- `encoding_str`: “h265” (H.265/HEVC codec)
- **worker_id**: Unique identifier for the worker (e.g., “worker_001”, “worker_002”, etc.)
- **video_index**: Sequential index of the video for that worker (0-50)
- **factory_id**: “factory_051” (constant for this subset)
### Statistics
- **Factory**: 1 (Factory 51 only)
- **Workers**: 8 workers (worker_001 through worker_008)
- **Videos per worker**: Up to 51 (indices 0-51)
- **Total videos**: 408 video clips
- **Resolution**: 1080p (1920x1080)
- **Frame rate**: 30 fps
- **Video codec**: H.265/HEVC
- **Format**: MP4
- **Field of view**: 128° horizontal, 67° vertical
- **Camera type**: Monocular head-mounted (Build AI Gen 1)
- **Audio**: No
## Dataset Creation
### Curation Rationale
This subset was created to provide a more manageable version of the Egocentric-10K dataset for researchers and developers who:
- Need a representative sample of factory egocentric video data
- Have limited computational resources or storage capacity
- Want to prototype and test algorithms before scaling to the full dataset
- Require data from a single factory environment for controlled experiments
- Need temporal coverage (51 sequential videos per worker) without the full dataset size
By limiting to Factory 51 and the first 51 videos per worker, this subset maintains:
- **Temporal diversity**: Sequential videos capture different times and activities
- **Worker diversity**: Multiple workers provide varied perspectives and work styles
- **Environmental consistency**: Single factory reduces environmental variability
- **Manageable scale**: Suitable for development and testing workflows
### Source Data
#### Data Collection and Processing
**Original Data Collection** (by Build AI):
- Videos captured using Build AI Gen 1 head-mounted cameras
- Recorded in Factory 51 during normal work operations
- Workers wore monocular cameras with 128° horizontal FOV
- Captured at 1080p resolution, 30 fps
- Encoded in H.265/HEVC for efficient storage
- No audio recorded
**Subset Curation Process**:
1. Downloaded Factory 51 data from Hugging Face: `https://huggingface.co/datasets/builddotai/Egocentric-10K/tree/main/factory_051`
2. Extracted tar archives containing video and metadata pairs
3. Filtered to retain only videos with `video_index` 0-50 (first 51 videos per worker)
4. Deleted videos with `video_index` > 50
5. Organized into FiftyOne dataset structure with metadata preservation
### Recommendations
Users should:
- **Validate on diverse data**: Test models on data from other factories, environments, and contexts before deployment
- **Consider ethical implications**: Use data responsibly and avoid surveillance or punitive applications
- **Acknowledge limitations**: Report the single-factory, limited-temporal nature of the subset in publications
- **Respect privacy**: Implement additional privacy protections if sharing derived data or visualizations
- **Supplement with annotations**: Consider adding task-specific annotations for supervised learning applications
- **Combine with other datasets**: Use alongside other egocentric datasets (Ego4D, EPIC-KITCHENS, etc.) for robustness
- **Monitor for bias**: Evaluate models for fairness across different worker characteristics and conditions
## Citation
```bibtex
@dataset{buildaiegocentric10k2025,
author = {Build AI},
title = {Egocentric-10K},
year = {2025},
publisher = {Hugging Face Datasets},
url = {https://huggingface.co/datasets/builddotai/Egocentric-10K}
}
```
**APA:**
Build AI. (2025). *Egocentric-10K* [Dataset]. Hugging Face Datasets. https://huggingface.co/datasets/builddotai/Egocentric-10K
## More Information
For more information about the original Egocentric-10K dataset:
- **Dataset page**: https://huggingface.co/datasets/builddotai/Egocentric-10K
- **Evaluation set**: https://huggingface.co/datasets/builddotai/Egocentric-10K-Evaluation
- **Build AI**: https://build.ai