Datasets:

Standout
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open-cortex-fx-v3

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 289, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 237, in _split_generators
                  raise ValueError(
              ValueError: `file_name` or `*_file_name` must be present as dictionary key (with type string) in metadata files
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 343, in get_dataset_split_names
                  info = get_dataset_config_info(
                         ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 294, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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Open Cortex FX v3

A curated dataset of videos depicting human manual labor and physical work, organized by task categories.

Dataset Description

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.

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

Dataset Visualization

Category Distribution

The dataset covers a diverse range of manual labor activities, with cooking being the most represented category:

Video Quality Metrics

All videos in the dataset have been evaluated for quality using three key metrics:

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)

Video Duration Distribution

The dataset contains videos of varying lengths, with most videos being short clips optimized for action recognition:

Frame Count and Frame Rate Distribution

The dataset includes videos with diverse frame counts and frame rates, providing rich temporal information for action recognition tasks:

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

Dataset Structure

The dataset is organized as follows:

final_0.zip
final_1.zip
final_2.zip
...

Each zip file contains:

Category folders: Videos organized by labor type (e.g., Cooking/, Repair/, Construction/)
Metadata CSV: Mapping file with video information

Category Folders

Videos are organized into category folders based on the type of manual labor depicted:

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

Metadata Format

Each zip file includes a metadata.csv file with the following columns:

Column	Description
`name`	Video filename (e.g., `aBc123XyZ789.mp4`)
`category`	Labor category

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

The dataset is available on Hugging Face. You can download it using:

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

Extract zip files: Unzip the downloaded files to access the video content
Read metadata: Load metadata.csv from each zip to get video information
Access by category: Navigate to category folders to find specific types of labor videos

Example: Loading Metadata

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

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:

@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

Apache 2.0

Contact

For questions or issues, please contact [email protected].

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