Dataset Card for Egocentric_10K_Evaluation

This is a FiftyOne dataset with 30000 samples.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

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_Evaluation")

# Launch the App
session = fo.launch_app(dataset)

Dataset Details

Dataset Description

Egocentric-10K-Evaluation is a benchmark evaluation set and analysis protocol for large-scale egocentric (first-person) video datasets, focused on measuring hand visibility and active manipulation in real-world, in-the-wild scenarios, especially relevant for robotics, computer vision, and AI agent training on manipulation tasks.[1][2][3]

• Curated by: builddotai
• Shared by : builddotai
• License: Apache 2.0

Dataset Sources

• Repository: https://huggingface.co/datasets/builddotai/Egocentric-10K-Evaluation

Uses

Direct Use

This dataset is intended for benchmarking egocentric video data with respect to hand presence and active object manipulation, enabling standardized analysis, dataset comparison, and the development/evaluation of perception and robotics models centered on real-world human skill tasks.

Dataset Structure

Egocentric-10K-Evaluation consists of 10,000 sampled frames from factory egocentric video and comparable samples from other major datasets (Ego4D, EPIC-KITCHENS); each sample includes JSON metadata, hand label annotations (count 0, 1, or 2), and a binary label for presence/absence of active manipulation. The splits are standardized; additional metadata includes dataset, worker, and video index references.

Dataset Creation

Curation Rationale

To create a standardized benchmark for hand visibility and manipulation, facilitating research on manipulation-heavy tasks in robotics and AI using real industrial and skill-focused footage.

Source Data

Data Collection and Processing

The evaluation set comprises frames drawn from the primary Egocentric-10K dataset (real-world factory footage collected via head-mounted cameras), as well as standardized samples from open egocentric datasets Ego4D and EPIC-KITCHENS for comparison. Data is provided in 1080p, 30 FPS H.265 MP4 format, with structured JSON metadata and hand/manipulation annotations.

Who are the source data producers?

Egocentric-10K’s original video data was produced by real factory workers wearing head-mounted cameras, performing natural work-line activities. Annotation was performed following strict guidelines as described in the evaluation schema.

Annotations

Annotation process

Each sampled frame is annotated for number of visible hands (0/1/2; detailed rules provided) and whether the hands are engaged in active manipulation (“yes”/“no” per explicit definition). The annotation schema and rules are detailed in the benchmark documentation.

Citation