vision-data-curation 0.0.1.dev6

A pipeline for curating and sanitizing large-scale image datasets.

Vision Data Curation (VDC)

A lightweight framework for cleaning, filtering, and sampling large-scale image datasets. Built for computer vision researchers and practitioners who want higher-quality data with less manual effort.

Status

This project is in early development. Most features are functional, but APIs may still change.

• Implemented Features: Input validation, Example-based filtering, Aesthetic filtering, NSFW filtering, Hierarchical sampling
• Features in Progress: Duplicate removal, Rotation correction

Feedback and contributions are welcome.

Features

VDC provides modular tools for dataset cleanup:

• Input validation - detect corrupt files, invalid formats, low resolution, or extreme aspect ratios
• Example-based filtering - remove images similar to a set of unwanted examples
• Image Quality Filtering - remove images based on aesthetic score or NSFW classification
• Duplicate removal - identify and remove near-duplicate images from your dataset
• Hierarchical K-Means sampling - select diverse, representative subsets from large datasets

Coming soon:

• Rotation correction (correct 90°/180°/270° orientation errors)

The Curation Pipeline

flowchart LR
    A[Raw<br/>Dataset] --> V[Validation]
    V --> R[Rotation*]
    R --> D[Dedup]
    D --> E[Example<br/>Filter]
    E --> Q[Quality Filter<br/>Aesthetic/NSFW]
    Q --> S[Cluster-based<br/>Sampling]
    S --> F[Curated<br/>Dataset]

    U[Unwanted<br/>Examples] --> E

Note: * = WIP

Installation

From PyPI

pip install vision-data-curation

From Source

git clone https://gitlab.com/birder/vision-data-curation.git
cd vision-data-curation
pip install -e .

Developing directly from the project root allows for script and configuration execution as if fully installed.

Usage

Each step is a script under vdc.scripts.

Examples:

# Remove corrupt/invalid images
python -m vdc.scripts.sanitize_images data/raw_images/

# Filter based on "Unwanted examples"
python -m vdc.scripts.filter_by_examples data/embeddings.csv --examples bad_examples.csv

• Run python -m vdc.scripts to see available scripts
• Run python -m vdc.scripts.<script> --help for options

Configuration:

• Default settings live in vdc/conf/config.json
• A config.json in your project root will take precedence (or pass --config to any script)

License

This project is licensed under the Apache-2.0 License - see the LICENSE file for details.