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file_name
stringclasses
7 values
clip_id
stringclasses
7 values
activity
stringclasses
1 value
sub_activity
stringclasses
1 value
duration
stringclasses
1 value
duration_seconds
int64
180
180
file_size_mb
float64
103
104
recording_date
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2026-07-20 00:00:00
2026-07-20 00:00:00
resolution
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fps
int64
30
30
view_type
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notes
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videos/metal_industry_operation01.mp4
MTL_001
metal_industry
metalwork_operation
00:03:00
180
103.66
2026-07-20
1080p
30
egocentric
Metal industry operations activity
videos/metal_industry_operation02.mp4
MTL_002
metal_industry
metalwork_operation
00:03:00
180
103.59
2026-07-20
1080p
30
egocentric
Metal industry operations activity
videos/metal_industry_operation03.mp4
MTL_003
metal_industry
metalwork_operation
00:03:00
180
103.47
2026-07-20
1080p
30
egocentric
Metal industry operations activity
videos/metal_industry_operation04.mp4
MTL_004
metal_industry
metalwork_operation
00:03:00
180
103.59
2026-07-20
1080p
30
egocentric
Metal industry operations activity
videos/metal_industry_operation05.mp4
MTL_005
metal_industry
metalwork_operation
00:03:00
180
103.62
2026-07-20
1080p
30
egocentric
Metal industry operations activity
videos/metal_industry_operation06.mp4
MTL_006
metal_industry
metalwork_operation
00:03:00
180
103.88
2026-07-20
1080p
30
egocentric
Metal industry operations activity
videos/metal_industry_operation07.mp4
MTL_007
metal_industry
metalwork_operation
00:03:00
180
103.66
2026-07-20
1080p
30
egocentric
Metal industry operations activity

⚙️ Metal Industry Operations — 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:


Dataset Summary

First-person point-of-view (POV) video recordings from metal industry operations, captured on real factory floors. Videos showcase metalworking activities including welding, grinding, cutting, machining, and finishing operations. This is a sample release showcasing the format and quality of our larger metal industry dataset collection.

Dataset Statistics

Metric Value
Total clips 7
Total duration 21 minutes (7 × 3:00)
Total size ~725 MB
Activity class metal_industry
View type Egocentric (first-person)
Video format MP4
Frame rate 30 fps
Resolution 1080p
Clip length Uniform 3 minutes each

Supported Tasks

  • Video classification — classify metal industry activities
  • Action recognition — recognize metalworking actions
  • Fine-grained metalworking activity classification
  • Hand-object interaction — welding torches, grinders, cutting tools
  • Worker safety monitoring — detect risky actions & PPE compliance
  • Ergonomics research for metal industry workers
  • Assistive robotics for metal fabrication lines
  • Quality control and defect detection AI training
  • Human-robot collaboration in metal manufacturing
  • Industrial AI for smart metal factories

Dataset Structure

Folder Structure

metal-industry-operations-egocentric-sample/
├── videos/
│   ├── metal_industry_operation01.mp4
│   ├── metal_industry_operation02.mp4
│   ├── metal_industry_operation03.mp4
│   ├── metal_industry_operation04.mp4
│   ├── metal_industry_operation05.mp4
│   ├── metal_industry_operation06.mp4
│   └── metal_industry_operation07.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., MTL_001)
activity string Main class: metal_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 Duration Size
MTL_001 metal_industry_operation01.mp4 00:03:00 104 MB
MTL_002 metal_industry_operation02.mp4 00:03:00 104 MB
MTL_003 metal_industry_operation03.mp4 00:03:00 104 MB
MTL_004 metal_industry_operation04.mp4 00:03:00 104 MB
MTL_005 metal_industry_operation05.mp4 00:03:00 104 MB
MTL_006 metal_industry_operation06.mp4 00:03:00 104 MB
MTL_007 metal_industry_operation07.mp4 00:03:00 104 MB

Uniform Clip Length ✨

All 7 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

Activity Coverage

The dataset captures metal industry operations including:

  • 🔥 Welding and joining
  • ⚙️ Grinding and finishing
  • ✂️ Metal cutting operations
  • 🔩 Machining and drilling
  • 🔍 Quality inspection
  • 🛠️ Manual metal fabrication

Usage

Load with 🤗 datasets library

from datasets import load_dataset

dataset = load_dataset("verbosetechlabsllp/metal-industry-operations-egocentric-sample")
print(dataset)

Load metadata directly with Pandas

import pandas as pd

df = pd.read_csv("hf://datasets/verbosetechlabsllp/metal-industry-operations-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/metal-industry-operations-egocentric-sample",
    filename="videos/metal_industry_operation01.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 metal industry / factory floor
  • Lighting: Industrial factory lighting with occasional bright welding/cutting arcs
  • Audio: Included in MP4 (ambient machinery, grinding, and welding 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{metal_industry_operations_egocentric_2026,
  title  = {Metal Industry Operations — Egocentric Video Dataset (Sample)},
  author = {Verbose Tech Labs LLP},
  year   = {2026},
  url    = {https://huggingface.co/datasets/verbosetechlabsllp/metal-industry-operations-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 (sample)
  • ⚙️ Metal industry operations (this — sample)
  • ...and more categories in development

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

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