🌌 vit-gravit-c2

🔭 This model is part of GraViT: Transfer Learning with Vision Transformers and MLP-Mixer for Strong Gravitational Lens Discovery

🔗 GitHub Repository: https://github.com/parlange/gravit

🛰️ Model Details

  • 🤖 Model Type: ViT
  • 🧪 Experiment: C2 - C21+J24-half
  • 🌌 Dataset: C21+J24
  • 🪐 Fine-tuning Strategy: half

💻 Quick Start

import torch
import timm

# Load the model directly from the Hub
model = timm.create_model(
    'hf-hub:parlange/vit-gravit-c2',
    pretrained=True
)
model.eval()

# Example inference
dummy_input = torch.randn(1, 3, 224, 224)
with torch.no_grad():
    output = model(dummy_input)
    predictions = torch.softmax(output, dim=1)
print(f"Lens probability: {predictions[0][1]:.4f}")

⚡️ Training Configuration

Training Dataset: C21+J24 (Cañameras et al. 2021 + Jaelani et al. 2024)
Fine-tuning Strategy: half

🔧 Parameter 📝 Value
Batch Size 192
Learning Rate AdamW with ReduceLROnPlateau
Epochs 100
Patience 10
Optimizer AdamW
Scheduler ReduceLROnPlateau
Image Size 224x224
Fine Tune Mode half
Stochastic Depth Probability 0.1

📈 Training Curves

Combined Training Metrics

🏁 Final Epoch Training Metrics

Metric Training Validation
📉 Loss 0.0277 0.0615
🎯 Accuracy 0.9898 0.9832
📊 AUC-ROC 0.9995 0.9986
⚖️ F1 Score 0.9898 0.9833

☑️ Evaluation Results

ROC Curves and Confusion Matrices

Performance across all test datasets (a through l) in the Common Test Sample (More et al. 2024):

ROC + Confusion Matrix - Dataset A ROC + Confusion Matrix - Dataset B ROC + Confusion Matrix - Dataset C ROC + Confusion Matrix - Dataset D ROC + Confusion Matrix - Dataset E ROC + Confusion Matrix - Dataset F ROC + Confusion Matrix - Dataset G ROC + Confusion Matrix - Dataset H ROC + Confusion Matrix - Dataset I ROC + Confusion Matrix - Dataset J ROC + Confusion Matrix - Dataset K ROC + Confusion Matrix - Dataset L

📋 Performance Summary

Average performance across 12 test datasets from the Common Test Sample (More et al. 2024):

Metric Value
🎯 Average Accuracy 0.8590
📈 Average AUC-ROC 0.8885
⚖️ Average F1-Score 0.6412

📘 Citation

If you use this model in your research, please cite:

@misc{parlange2025gravit,
      title={GraViT: Transfer Learning with Vision Transformers and MLP-Mixer for Strong Gravitational Lens Discovery}, 
      author={René Parlange and Juan C. Cuevas-Tello and Octavio Valenzuela and Omar de J. Cabrera-Rosas and Tomás Verdugo and Anupreeta More and Anton T. Jaelani},
      year={2025},
      eprint={2509.00226},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2509.00226}, 
}

Model Card Contact

For questions about this model, please contact the author through: https://github.com/parlange/

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Evaluation results

  • Average Accuracy on Common Test Sample (More et al. 2024)
    self-reported
    0.859
  • Average AUC-ROC on Common Test Sample (More et al. 2024)
    self-reported
    0.888
  • Average F1-Score on Common Test Sample (More et al. 2024)
    self-reported
    0.641