VerboseTechLabs/textile-industry-manufacturing-egocentric-sample

🧵 Textile Industry Manufacturing — 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 from textile industry manufacturing operations, captured on real mill floors across multiple sessions and time periods. Videos showcase textile production activities including spinning, weaving, knitting, dyeing, finishing, and quality control. This is a sample release showcasing the format and quality of our larger textile industry dataset collection.

Dataset Statistics

Metric	Value
Total clips	15
Total duration	45 minutes (15 × 3:00)
Total size	~1.5 GB
Activity class	textile_industry
View type	Egocentric (first-person)
Video format	MP4
Frame rate	30 fps
Resolution	1080p
Clip length	Uniform 3 minutes each
Recording period	February 2025 – July 2026
Recording sessions	6 (spanning 17 months)

Supported Tasks

• Video classification — classify textile industry activities
• Action recognition — recognize textile industry actions
• Fine-grained textile activity classification
• Worker productivity and time-motion analysis
• Machine operation understanding (looms, knitting machines, dyeing units)
• Ergonomics research in textile industry
• Assistive robotics for textile factories
• Quality control AI training
• Longitudinal studies — spanning 17+ months of recording
• Industrial AI for textile automation

Dataset Structure

Folder Structure

textile-industry-manufacturing-egocentric-sample/
├── videos/
│   ├── textile_industry01.mp4
│   ├── textile_industry02.mp4
│   ├── textile_industry03.mp4
│   ├── textile_industry04.mp4
│   ├── textile_industry05.mp4
│   ├── textile_industry06.mp4
│   ├── textile_industry07.mp4
│   ├── textile_industry08.mp4
│   ├── textile_industry09.mp4
│   ├── textile_industry10.mp4
│   ├── textile_industry11.mp4
│   ├── textile_industry12.mp4
│   ├── textile_industry13.mp4
│   ├── textile_industry14.mp4
│   └── textile_industry15.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., TIN_001)
activity	string	Main class: textile_industry
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	Recording Date	Duration	Size
TIN_001	textile_industry01.mp4	2025-02-27	00:03:00	104 MB
TIN_002	textile_industry02.mp4	2025-02-27	00:03:00	104 MB
TIN_003	textile_industry03.mp4	2025-02-27	00:03:00	104 MB
TIN_004	textile_industry04.mp4	2025-04-06	00:03:00	104 MB
TIN_005	textile_industry05.mp4	2025-04-06	00:03:00	104 MB
TIN_006	textile_industry06.mp4	2025-04-06	00:03:00	104 MB
TIN_007	textile_industry07.mp4	2025-04-12	00:03:00	103 MB
TIN_008	textile_industry08.mp4	2025-04-12	00:03:00	104 MB
TIN_009	textile_industry09.mp4	2025-04-12	00:03:00	103 MB
TIN_010	textile_industry10.mp4	2025-04-12	00:03:00	103 MB
TIN_011	textile_industry11.mp4	2025-04-12	00:03:00	104 MB
TIN_012	textile_industry12.mp4	2026-03-07	00:03:00	103 MB
TIN_013	textile_industry13.mp4	2026-07-20	00:03:00	101 MB
TIN_014	textile_industry14.mp4	2026-07-20	00:03:00	101 MB
TIN_015	textile_industry15.mp4	2026-07-20	00:03:00	101 MB

Uniform Clip Length ✨

All 15 clips have identical 3-minute duration, making this dataset:

• ✅ Ideal for balanced batch training — no padding or trimming needed
• ✅ Perfect for temporal comparisons between clips
• ✅ Easy to work with for model benchmarking

Multi-Session Recording 📅

Clips captured across 6 distinct recording sessions spanning 17 months, providing valuable diversity for training robust models that generalize across time and conditions.

Activity Coverage

The dataset captures diverse textile industry activities from real mill floors, spanning operations across the textile production pipeline — spinning, weaving, knitting, dyeing, printing, finishing, and quality control.

Usage

Load with 🤗 datasets library

from datasets import load_dataset

dataset = load_dataset("verbosetechlabsllp/textile-industry-manufacturing-egocentric-sample")
print(dataset)

Load metadata directly with Pandas

import pandas as pd

df = pd.read_csv("hf://datasets/verbosetechlabsllp/textile-industry-manufacturing-egocentric-sample/metadata.csv")
print(df.head())
print(f"Total duration: {df['duration_seconds'].sum() / 60:.1f} minutes")
print(f"Recording sessions: {df['recording_date'].nunique()}")

Download a specific video

from huggingface_hub import hf_hub_download

video_path = hf_hub_download(
    repo_id="verbosetechlabsllp/textile-industry-manufacturing-egocentric-sample",
    filename="videos/textile_industry01.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=5):
    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 textile mill / factory floor
• Lighting: Industrial factory lighting
• Audio: Included in MP4 (ambient loom, machine, and worker sounds — usable for multimodal research)
• Recording period: February 2025 – July 2026 (spanning 17 months, 6 sessions)

Licensing Information

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

Citation

@dataset{textile_industry_manufacturing_egocentric_2026,
  title  = {Textile Industry Manufacturing — Egocentric Video Dataset (Sample)},
  author = {Verbose Tech Labs LLP},
  year   = {2026},
  url    = {https://huggingface.co/datasets/verbosetechlabsllp/textile-industry-manufacturing-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 industry manufacturing (this — sample)
• 🔌 Electronics assembly (sample)
• ⚙️ Metal industry operations (sample)
• ...and more categories in development

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