PictSure 0.1.0

A package for generalized image classification using In-Context-Learning with PyTorch.

PictSure: In-Context Learning for Image Classification

PictSure is a deep learning library designed for in-context learning using images and labels. It allows users to provide a set of labeled reference images and then predict labels for new images based on those references. This approach eliminates the need for traditional training, making it highly adaptable for various classification tasks.

Features

• In-Context Learning: Predict labels for new images using a set of reference images without traditional model training.
• Multiple Model Architectures: Choose between ResNet and ViT-based models for your specific needs.
• Pretrained Models: Use our pretrained models or train your own.
• Torch Compatibility: Fully integrated with PyTorch, supporting CPU and GPU.
• Easy-to-use CLI: Manage models and weights through a simple command-line interface.

Installation

1. Clone this repository

git clone https://git.ni.dfki.de/pictsure/pictsure-library

2. Navigate into the folder

cd pictsure-library

3. Install the pip package

pip install .

Quick Start

from PictSure import PictSure
import torch

# Initialize the model (using ViT as an example)
model = PictSure(
    embedding='vit',  # or 'resnet'
    pretrained=True,  # use pretrained weights
    device='cuda'     # or 'cpu'
)

# you can also pull our pre-trained models from Huggingface
model = PictSure.from_pretrained("pictsure/pictsure-vit")

# Set your reference images and labels
model.set_context_images(reference_images, reference_labels)

# Make predictions on new images
predictions = model.predict(new_images)

Command Line Interface

PictSure comes with a command-line interface to manage models and weights:

List Available Models

pictsure list-models

This command shows all available models, their status (downloaded/not downloaded), and detailed information about each model.

Remove Model Weights

pictsure remove <model_name> [--force]

Remove the weights of a specific model. Available models are:

• ViTPreAll: ViT-based model
• ResPreAll: ResNet-based model

Use the --force or -f flag to skip the confirmation prompt.

Examples

For a complete working example, check out the Jupyter notebook in the Examples directory:

Examples/example.ipynb

This notebook demonstrates:

• Model initialization
• Loading and preprocessing images
• Setting up reference images
• Making predictions
• Visualizing results

Citation

If you use this work, please cite it using the following BibTeX entry:

@article{schiesser2025pictsure,
  title={PictSure: Pretraining Embeddings Matters for In-Context Learning Image Classifiers},
  author={Schiesser, Lukas and Wolff, Cornelius and Haas, Sophie and Pukrop, Simon},
  journal={arXiv preprint arXiv:2506.14842},
  year={2025}
}

License

This project is open-source under the MIT License.

Contributing

Contributions and suggestions are welcome! Open an issue or submit a pull request.

Contact

For questions or support, open an issue on GitHub.