VerboseTechLabs/electronics-assembly-egocentric-sample

🔌 Electronics Assembly — Egocentric Video Dataset (Sample)

This dataset is part of a larger collection of egocentric activity datasets by Verbose Tech Labs LLP. If you want the full dataset, or want access to more categories? Get in touch with us:

• 📞 Phone: +91 7672 000 500
• 💬 WhatsApp: +91 7672 000 500
• 📧 Email: Hello@VerboseTechLabs.com
• 🌐 Website: VerboseTechLabs.com
• 🔗 More datasets: kaggle.com/verbosetechlabsllp

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Dataset Summary

First-person point-of-view (POV) video recordings of electronics assembly work, captured on real electronics manufacturing floors. Videos showcase PCB assembly, component placement, soldering, and related electronics production tasks. This is a sample release showcasing the format and quality of our larger electronics manufacturing dataset collection.

Dataset Statistics

Metric	Value
Total clips	7
Total duration	83 minutes (1 hour 23 minutes)
Total size	~7.6 GB
Activity class	electronics_assembly
View type	Egocentric (first-person)
Video format	MP4
Frame rate	30 fps
Resolution	1080p

Supported Tasks

• Video classification — classify electronics assembly activities
• Action recognition — recognize electronics manufacturing actions
• Fine-grained assembly step detection (component placement, soldering, testing)
• Hand-object interaction — tweezers, soldering irons, PCBs, components
• Worker productivity and time-motion analysis
• Ergonomics research for electronics assembly workers
• Assistive robotics for electronics manufacturing lines
• Quality control and defect detection AI training
• Human-robot collaboration in electronics assembly
• Industrial AI for smart electronics factories

Dataset Structure

Folder Structure

electronics-assembly-egocentric-sample/
├── videos/
│   ├── electronics_assembly_01.mp4
│   ├── electronics_assembly_02.mp4
│   ├── electronics_assembly_03.mp4
│   ├── electronics_assembly_04.mp4
│   ├── electronics_assembly_05.mp4
│   ├── electronics_assembly_06.mp4
│   └── electronics_assembly_07.mp4
├── metadata.csv
└── README.md

Data Fields

The metadata.csv file contains the following columns:

Column	Type	Description
file_name	string	Relative path to the video file
clip_id	string	Unique identifier (e.g., ELE_001)
activity	string	Main class: electronics_assembly
sub_activity	string	Fine-grained label
duration	string	Human-readable duration (HH:MM:SS)
duration_seconds	integer	Duration in seconds
file_size_mb	float	File size in megabytes
recording_date	date	Recording date (YYYY-MM-DD)
resolution	string	Video resolution
fps	integer	Frames per second
view_type	string	Camera view type (egocentric)
notes	string	Additional context

Clip Overview

Clip ID	File	Duration	Size
ELE_001	electronics_assembly_01.mp4	00:01:52	171 MB
ELE_002	electronics_assembly_02.mp4	00:16:10	1.77 GB
ELE_003	electronics_assembly_03.mp4	00:11:10	859 MB
ELE_004	electronics_assembly_04.mp4	00:16:30	1.37 GB
ELE_005	electronics_assembly_05.mp4	00:15:35	1.50 GB
ELE_006	electronics_assembly_06.mp4	00:21:52	1.87 GB
ELE_007	electronics_assembly_07.mp4	00:00:10	99 MB

Activity Coverage

The dataset captures electronics assembly workflows including:

• 🔌 PCB (Printed Circuit Board) assembly
• 🧲 Component placement and soldering
• 🔧 Manual assembly operations
• 🔍 Quality inspection during assembly
• 🛠️ Tool usage — tweezers, soldering irons, testers

Usage

Load with 🤗 datasets library

from datasets import load_dataset

dataset = load_dataset("verbosetechlabsllp/electronics-assembly-egocentric-sample")
print(dataset)

Load metadata directly with Pandas

import pandas as pd

df = pd.read_csv("hf://datasets/verbosetechlabsllp/electronics-assembly-egocentric-sample/metadata.csv")
print(df.head())
print(f"Total duration: {df['duration_seconds'].sum() / 60:.1f} minutes")

Download a specific video

from huggingface_hub import hf_hub_download

video_path = hf_hub_download(
    repo_id="verbosetechlabsllp/electronics-assembly-egocentric-sample",
    filename="videos/electronics_assembly_02.mp4",
    repo_type="dataset"
)
print(f"Video downloaded to: {video_path}")

Extract sample frames

import cv2, os

def extract_frames(video_path, out_dir, every_n_seconds=10):
    os.makedirs(out_dir, exist_ok=True)
    cap = cv2.VideoCapture(video_path)
    fps = cap.get(cv2.CAP_PROP_FPS)
    frame_interval = int(fps * every_n_seconds)
    count, saved = 0, 0
    while True:
        ret, frame = cap.read()
        if not ret: break
        if count % frame_interval == 0:
            cv2.imwrite(f"{out_dir}/frame_{saved:04d}.jpg", frame)
            saved += 1
        count += 1
    cap.release()
    return saved

Data Collection

• Camera view: First-person / egocentric (head-mounted or chest-mounted)
• Environment: Real electronics manufacturing / assembly floor
• Lighting: Industrial workshop lighting with task illumination
• Audio: Included in MP4 (ambient soldering, tool, and machine sounds — usable for multimodal research)
• Recording date: July 2026

Licensing Information

CC BY 4.0 — Free for research and commercial use with attribution.

Citation

@dataset{electronics_assembly_egocentric_2026,
  title  = {Electronics Assembly — Egocentric Video Dataset (Sample)},
  author = {Verbose Tech Labs LLP},
  year   = {2026},
  url    = {https://huggingface.co/datasets/verbosetechlabsllp/electronics-assembly-egocentric-sample}
}

More Datasets from Verbose Tech Labs

This dataset is part of a larger collection of egocentric activity datasets covering:

• 👕 Clothing industry manufacturing
• 🍳 Cooking & food preparation
• 🧹 Household cleaning tasks
• 🏭 Manufacturing unit workflows (sample)
• 🛠️ Skilled commercial work (sample)
• 🧵 Textile manufacturing (sample)
• 🔌 Electronics assembly (this — sample)
• ...and more categories in development

🔗 Browse all our datasets: kaggle.com/verbosetechlabsllp | huggingface.co/verbosetechlabsllp