autoannotate-vision 0.1.3

SOTA unsupervised auto-annotation SDK for image classification

AutoAnnotate-Vision 🎯

State-of-the-art unsupervised auto-annotation SDK for image classification with GUI

AutoAnnotate-Vision automatically clusters and organizes unlabeled image datasets using cutting-edge vision models (CLIP, DINOv2, SigLIP2). It features a GUI and interactive HTML preview with Plotly for visual cluster inspection, as well as a CLI tool.

✨ Features

• 🎨 Graphical User Interface: Easy folder browsers and visual controls
• 🖼️ HTML Image Preview: View cluster samples in browse before labeling
• 🤖 SOTA Vision Models: CLIP, DINOv2, DINOv2-Large, SigLIP2
• 🔬 Multiple Clustering: K-means, Spectral, DBSCAN, HDBSCAN (optional)
• 📁 Smart Organization: Preserves original filenames
• ✂️ Auto Splits: Train/val/test dataset splitting
• 💾 Export: CSV, JSON formats
• 🔌 Python API: Full programmatic control

🚀 Installation

pip install autoannotate-vision

🎨 Quick Start - GUI

The easiest and most simplified way to use AutoAnnotate-Vision:

autoannotate-images

Note: Windows users need to have the latest C++ Redistributable installed which can be found here

Workflow:

1. 📁 Select input folder with images
2. 📂 Select output folder
3. 🔢 Set number of classes
4. 🤖 Choose model (SigLIP2 or DINOv2 recommended)
5. ▶️ Click "Start Auto-Annotation"

The app will cluster images and open HTML previews in your browser showing sample images from each cluster for easy labeling!

💻 CLI Usage

For extra commands and utilities.

autoannotate-images-cli annotate /path/to/images /path/to/output \
    --n-clusters 10 \
    --method kmeans \
    --model siglip2 \
    --create-splits

Available models: clip, dinov2, dinov2-large, siglip2

Command Arguments

The autoannotate-images-cli annotate command accepts the following arguments:

Required Arguments:

• INPUT_DIR - Path to the directory containing images to annotate
• OUTPUT_DIR - Path where annotated images and metadata will be saved

Optional Arguments:

• -n, --n-clusters INTEGER - Number of clusters to create (required for kmeans/spectral methods)
• -m, --method [kmeans|hdbscan|spectral|dbscan] - Clustering algorithm to use (default: kmeans)
• --model [clip|dinov2|dinov2-large|siglip2] - Vision model for embeddings (default: siglip2)
• -b, --batch-size INTEGER - Batch size for embedding extraction (default: 32)
• -r, --recursive - Search for images in subdirectories recursively
• --reduce-dims / --no-reduce-dims - Apply dimensionality reduction before clustering
• --n-samples INTEGER - Number of representative samples per cluster for preview
• --copy / --symlink - Copy image files or create symbolic links (default: copy)
• --create-splits - Automatically create train/val/test dataset splits
• --export-format [csv|json] - Format for exporting labels (default: csv)

Examples:

# Basic usage with 5 clusters
autoannotate-images-cli annotate ./my_images ./output --n-clusters 5

# Use DBSCAN
autoannotate-images-cli annotate ./my_images ./output --method dbscan

# Use larger batch size with dimensionality reduction
autoannotate-images-cli annotate ./my_images ./output \
    --n-clusters 10 \
    --batch-size 64 \
    --reduce-dims

🐍 Python API

from autoannotate import AutoAnnotator

annotator = AutoAnnotator(
    input_dir="./images",
    output_dir="./output",
    model="siglip2",  # or "dinov2", "dinov2-large", "clip"
    clustering_method="kmeans",
    n_clusters=5,
    batch_size=32
)

result = annotator.run_full_pipeline(create_splits=True)
print(f"Processed {result['n_images']} images into {result['n_clusters']} classes")

Available models: clip, dinov2, dinov2-large, siglip2 Available clustering methods: kmeans, hdbscan, spectral, dbscan

📁 Output Structure

output/
├── metadata.json
├── labels.csv
├── cats/              # Your class names
│   ├── IMG_001.jpg   # Original filenames preserved!
│   └── ...
├── dogs/
└── splits/            # train/val/test. Availabe only through CLI --create-splits
    ├── train/
    ├── val/
    └── test/

🧠 Model Comparison

Model	Speed	Quality	Notes
CLIP	⚡⚡	⭐⭐⭐	General-purpose, good for diverse datasets
DINOv2	⚡⚡⚡	⭐⭐⭐⭐	Fast, self-supervised, excellent for objects
DINOv2-Large	⚡	⭐⭐⭐⭐⭐	Best quality, slower, great for fine details
SigLIP2	⚡⚡	⭐⭐⭐⭐⭐	Latest Google model - Recommended 🌟

Recommendation: Start with SigLIP2 for best results, or DINOv2 for faster processing.

🔧 Features

• ✅ Fast Image Processing: All models use optimized processors (use_fast=True) for better performance
• ✅ Normalized Embeddings: All embeddings are L2-normalized for consistent similarity measurements
• ✅ Batch Processing: Efficient batch processing with configurable batch sizes
• ✅ GPU Support: Automatic GPU detection and usage when available
• ✅ Progress Tracking: Real-time progress bars for all operations
• ✅ HTML Previews: Interactive HTML preview for visual cluster inspection before labeling

🤝 Contributing

1. Fork the repository
2. Create feature branch
3. All actions from tests.yml should pass
4. Push and create PR

📄 License

MIT License - see LICENSE file.

🙏 Acknowledgments

Built with PyTorch, Transformers, scikit-learn and more. Vision models: CLIP, DINOv2, SigLIP2.

Made for the RAIDO Project, from MetaMind Innovations

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Sister Project: AutoAnnotate-Timeseries - For time series auto-annotation