import torch
import timm

# Load the model directly from the Hub
model = timm.create_model(
    'hf-hub:parlange/swin-gravit-s3',
    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 (Cañameras et al. 2021)
Fine-tuning Strategy: all-blocks

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

📈 Training Curves

🏁 Final Epoch Training Metrics

Metric	Training	Validation
📉 Loss	0.0058	0.0388
🎯 Accuracy	0.9977	0.9900
📊 AUC-ROC	1.0000	0.9988
⚖️ F1 Score	0.9977	0.9900

☑️ Evaluation Results

ROC Curves and Confusion Matrices

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

📋 Performance Summary

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

Metric	Value
🎯 Average Accuracy	0.7813
📈 Average AUC-ROC	0.8239
⚖️ Average F1-Score	0.5073

📘 Citation

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

bibtex

@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/