pictsure/pictsure-vit

PictSure: Few-Shot Image Classification with In-Context Learning

Model Description

PictSure (link to project website) is a novel few-shot learning model for image classification that leverages context images to make predictions on new, unseen images. The model combines pre-trained image encoders with transformer architecture to enable effective few-shot learning with minimal examples. More details can be found on our paper page.

Key Features

• Few-shot Learning: Classify images with only a few examples per class
• Context-Aware: Uses context images and labels to inform predictions
• Flexible Architecture: Supports both ResNet and Vision Transformer (ViT) backbones as well as other custom backbones
• Transformer-Based: Employs transformer encoders for sequence processing
• Easy Integration: Simple API for setting context and making predictions

How to Use

Installation

pip install torch torchvision
pip install huggingface_hub
pip install PictSure

Basic Usage

This example can also be found (including the mentioned images) in our GitHub Repository.

from PictSure import PictSure
from PIL import Image

# Load pre-trained model
model = PictSure.from_pretrained("pictsure/pictsure-vit")

# Prepare context images and labels
context_images = [
    Image.open("cat1.jpg"),
    Image.open("cat2.jpg"),
    Image.open("dog1.jpg"),
    Image.open("dog2.jpg")
]
context_labels = [0, 0, 1, 1]  # 0 for cat, 1 for dog

# Set context
model.set_context_images(context_images, context_labels)

# Make prediction on new image
test_image = Image.open("unknown_animal.jpg")
prediction = model.predict(test_image)
print(f"Predicted class: {prediction}")

Model Architecture

PictSure consists of several key components:

1. Embedding Network: Pre-trained ResNet18 or custom Vision Transformer for feature extraction
2. Projection Layers: Linear projections for image features and label embeddings
3. Transformer Encoder: Multi-head attention mechanism with configurable heads and layers
4. Classification Head: Final linear layer for class prediction

Architecture Details

• Image Resolution: 224 × 224 pixels
• Feature Dimension: 1024D (concatenated image + label projections)
• Default Configuration:
  • ResNet model: 8 attention heads, 4 transformer layers, ~53M parameters
  • ViT model: 8 attention heads, 4 transformer layers, ~128M parameters

Intended Use

Primary Use Cases

• Few-shot image classification in scenarios with limited labeled data
• Meta-learning applications where rapid adaptation to new classes is required
• Educational and research purposes in computer vision and machine learning
• Prototyping classification systems with minimal training data

Limitations

• Requires context images to be set before making predictions
• Performance depends on the quality and representativeness of context examples
• Limited to classification tasks (not suitable for detection or segmentation)
• Input images must be resized to 224×224 pixels

Training Data

The pre-trained models were trained on curated datasets for few-shot learning evaluation:

• Encoder models: ImageNet pre-trained features (ResNet18/ViT)
• Meta-Training: ImageNet-21k
• Validation: Standard few-shot learning benchmarks, e.g. mini-ImageNet, PlantDoc and BoneBreak

Data Preprocessing

• Images resized to 224×224 pixels
• Normalized with ImageNet statistics:
  • Mean: [0.4914, 0.4822, 0.4465]
  • Std: [0.2023, 0.1994, 0.2010]

Evaluation

Performance Metrics

The model is evaluated using standard few-shot learning metrics:

• Accuracy: Overall classification accuracy
• Few-shot Accuracy: Performance with 1, 5 shot scenarios

Model Variants

Model	Backbone	Parameters	Model Size	Performance
ResPreAll	ResNet18	53M	~200MB	Balanced speed/accuracy
ViTPreAll	ViT-Base	128M	~500MB	Higher accuracy

Ethical Considerations

Potential Biases

• The model inherits biases from ImageNet and Imagenet-21k pre-training
• Performance may vary across different demographic groups or geographic regions
• Context examples significantly influence predictions and may introduce bias

Responsible Use

• Validate performance on your specific use case and demographic groups
• Be aware of potential biases in context image selection
• Consider fairness implications when deploying in production systems
• Ensure diverse and representative context examples

Limitations and Risks

Technical Limitations

• Context Dependency: Requires good context examples for optimal performance
• Computational Requirements: Transformer architecture requires significant memory
• Fixed Architecture: Pre-trained models have fixed class numbers and architecture
• Image Size: Limited to 224×224 input resolution

Potential Risks

• Misclassification: Incorrect predictions in critical applications
• Bias Amplification: May amplify biases present in context images
• Overfitting to Context: May not generalize beyond provided examples

Citation

@misc{schiesser2025pictsure,
      title={PictSure: Pretraining Embeddings Matters for In-Context Learning Image Classifiers}, 
      author={Lukas Schiesser and Cornelius Wolff and Sophie Haas and Simon Pukrop},
      year={2025},
      eprint={2506.14842},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2506.14842}, 
}

Model Card Contact

For questions about this model card or the PictSure model, open an issue in the GitHub repository.