| --- |
| license: mit |
| task_categories: |
| - video-classification |
| tags: |
| - manual-labor |
| - human-work |
| - video-dataset |
| - action-classification |
| - physical-work |
| size_categories: |
| - 1K<n<10K |
| language: |
| - en |
| --- |
| |
| # Open Cortex FX v3 |
|
|
| A curated dataset of videos depicting human manual labor and physical work, organized by task categories. |
|
|
| ## Dataset Description |
|
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| Standout's Cortex FX v3 is a video dataset focusing on human manual labor and physical work activities. Each video has been carefully annotated to identify work-related content and categorized into specific labor types. |
|
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| ### Dataset Statistics |
|
|
| - **Total Videos**: 708 videos showing human manual labor |
| - **Categories**: 28 distinct labor categories |
| - **Total Frames**: 204,329 frames across all videos |
| - **Average Duration**: 10.5 seconds (median: 6.4s) |
| - **Average Frame Count**: 289 frames per video (median: 170) |
| - **Frame Rate**: Most common FPS is 30.0 (388 videos), range: 15.0-30.0 FPS |
| - **Average Aesthetic Score**: 5.89 (out of 10) |
| - **Average Motion Score**: 7.64 (out of 10) |
| - **Format**: MP4 video files organized by category |
| - **Structure**: Videos are split into multiple zip files for easy distribution |
|
|
| #### Top Categories by Video Count: |
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|
| - **Cooking**: 363 videos (51.3%) |
| - **Repair**: 100 videos (14.1%) |
| - **Automotive**: 58 videos (8.2%) |
| - **Gardening**: 28 videos (4.0%) |
| - **Construction**: 22 videos (3.1%) |
| - **Serving**: 19 videos (2.7%) |
| - **Assembly**: 18 videos (2.5%) |
| - **Crafting**: 17 videos (2.4%) |
| - **General Labor**: 14 videos (2.0%) |
| - **Sewing**: 12 videos (1.7%) |
|
|
| ## Dataset Visualization |
|
|
| ### Category Distribution |
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| The dataset covers a diverse range of manual labor activities, with cooking being the most represented category: |
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|  |
|
|
| ### Video Quality Metrics |
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| All videos in the dataset have been evaluated for quality using three key metrics: |
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|  |
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| - **Aesthetic Score**: Measures visual quality and composition (0-10 scale) |
| - **Motion Score**: Quantifies the amount and quality of motion in the video (0-10 scale) |
| - **Temporal Consistency**: Evaluates frame-to-frame coherence and stability (0-1 scale) |
|
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| ### Video Duration Distribution |
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| The dataset contains videos of varying lengths, with most videos being short clips optimized for action recognition: |
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|  |
|
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| ### Frame Count and Frame Rate Distribution |
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| The dataset includes videos with diverse frame counts and frame rates, providing rich temporal information for action recognition tasks: |
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|  |
|
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| - **Frame Count**: Videos range from short clips (~100 frames) to longer sequences (1000+ frames), with an average of 289 frames per video |
| - **Frame Rate**: Most videos are captured at 30 FPS (standard video rate), with some at 25 FPS and 23.976 FPS (cinematic rates) |
| - **Temporal Coverage**: The dataset provides over 200,000 total frames, offering substantial data for temporal modeling and action understanding |
|
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| ## Dataset Structure |
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| The dataset is organized as follows: |
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| ``` |
| final_0.zip |
| final_1.zip |
| final_2.zip |
| ... |
| ``` |
|
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| Each zip file contains: |
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| - **Category folders**: Videos organized by labor type (e.g., `Cooking/`, `Repair/`, `Construction/`) |
| - **Metadata CSV**: Mapping file with video information |
|
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| ### Category Folders |
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| Videos are organized into category folders based on the type of manual labor depicted: |
|
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| - **Construction** - Building, construction work, using construction tools |
| - **Cooking** - Preparing food, cooking, chopping, mixing ingredients |
| - **Repair** - Fixing, repairing, maintenance work |
| - **Cleaning** - Cleaning, mopping, scrubbing, organizing spaces |
| - **Assembly** - Assembling products, putting things together |
| - **Gardening** - Gardening, landscaping, outdoor manual work |
| - **Painting** - Painting surfaces, applying paint or finishes |
| - **Sewing** - Sewing, textile work, fabric manipulation |
| - **Woodworking** - Working with wood, carpentry, wood crafting |
| - **Metalworking** - Working with metal, welding, metal fabrication |
| - **Moving** - Lifting, carrying, moving heavy objects |
| - **Serving** - Serving food or drinks, manual service work |
| - **Organizing** - Arranging, organizing physical items |
| - **Crafting** - General crafting, artisan work, making things by hand |
| - **Electrical** - Electrical work, wiring, electrical installation |
| - **Plumbing** - Plumbing work, pipe installation, water systems |
| - **Automotive** - Automotive repair, working on vehicles |
| - **Farming** - Farming, agriculture, working with crops/animals |
| - **Welding** - Welding, joining metals with heat |
| - **General Labor** - Other manual labor that doesn't fit above categories |
|
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| ## Metadata Format |
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| Each zip file includes a `metadata.csv` file with the following columns: |
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| | Column | Description | |
| | ---------- | ----------------------------------------- | |
| | `name` | Video filename (e.g., `aBc123XyZ789.mp4`) | |
| | `category` | Labor category | |
|
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| ## Usage |
|
|
| **Note:** The dataset viewer may not be available due to the zip file structure. However, the dataset can be loaded programmatically using the code examples below. |
|
|
| ### Downloading the Dataset |
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| The dataset is available on Hugging Face. You can download it using: |
|
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| ```python |
| from huggingface_hub import snapshot_download |
| |
| snapshot_download( |
| repo_id="Standout/open-cortex-fx-v3", |
| repo_type="dataset", |
| local_dir="./open-cortex-fx-v3" |
| ) |
| ``` |
|
|
| ### Extracting and Using the Data |
|
|
| 1. **Extract zip files**: Unzip the downloaded files to access the video content |
| 2. **Read metadata**: Load `metadata.csv` from each zip to get video information |
| 3. **Access by category**: Navigate to category folders to find specific types of labor videos |
|
|
| ### Example: Loading Metadata |
|
|
| ```python |
| import zipfile |
| import pandas as pd |
| import io |
| |
| # Extract and read metadata from a zip file |
| with zipfile.ZipFile('final_0.zip', 'r') as z: |
| with z.open('metadata.csv') as f: |
| metadata = pd.read_csv(io.BytesIO(f.read())) |
| |
| # Filter by category |
| cooking_videos = metadata[metadata['category'] == 'Cooking'] |
| print(f"Found {len(cooking_videos)} cooking videos") |
| |
| # Access video names |
| for video_name in cooking_videos['name']: |
| print(f"Video: {video_name}") |
| ``` |
|
|
| ### Example: Accessing Videos |
|
|
| ```python |
| import zipfile |
| |
| # Extract a specific video |
| with zipfile.ZipFile('final_0.zip', 'r') as z: |
| # List all videos in Cooking category |
| cooking_videos = [name for name in z.namelist() if name.startswith('Cooking/')] |
| |
| # Extract a video |
| z.extract('Cooking/example_video.mp4', './output/') |
| ``` |
|
|
| ## Dataset Characteristics |
|
|
| - **Focus**: Human manual labor and physical work activities |
| - **Quality**: Videos are filtered to show clear human action performing manual tasks |
| - **Diversity**: Multiple categories covering various types of physical work |
| - **Organization**: Structured by labor type for easy access and filtering |
|
|
| ## Citation |
|
|
| If you use this dataset in your research, please cite: |
|
|
| ```bibtex |
| @dataset{open_cortex_fx_v3, |
| title={Open Cortex FX v3: A Classified Dataset of Human Manual Labor}, |
| author={Standout}, |
| year={2024}, |
| url={https://huggingface.co/datasets/Standout/open-cortex-fx-v3} |
| } |
| ``` |
|
|
| ## License |
|
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| Apache 2.0 |
|
|
| ## Contact |
|
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| For questions or issues, please contact standout@standout.work. |
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|
