π pit-gravit-s3
π This model is part of GraViT: Transfer Learning with Vision Transformers and MLP-Mixer for Strong Gravitational Lens Discovery
π GitHub Repository: https://github.com/parlange/gravit
π°οΈ Model Details
π€ Model Type: PiT
π§ͺ Experiment: S3 - C21-all-blocks-ResNet18-18660
π Dataset: C21
πͺ Fine-tuning Strategy: all-blocks
π² Random Seed: 18660
π» Quick Start
import torch
import timm
# Load the model directly from the Hub
model = timm.create_model(
    'hf-hub:parlange/pit-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}")
β‘οΈ Training Configuration
Training Dataset: C21 (CaΓ±ameras et al. 2021)
Fine-tuning Strategy: all-blocks
| π§ Parameter | π 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 | 
π Training Curves
π Final Epoch Training Metrics
| Metric | Training | Validation | 
|---|---|---|
| π Loss | 0.0071 | 0.0399 | 
| π― Accuracy | 0.9973 | 0.9910 | 
| π AUC-ROC | 1.0000 | 0.9995 | 
| βοΈ F1 Score | 0.9973 | 0.9910 | 
βοΈ Evaluation Results
ROC Curves and Confusion Matrices
Performance across all test datasets (a through l) in the Common Test Sample (More et al. 2024):
π Performance Summary
Average performance across 12 test datasets from the Common Test Sample (More et al. 2024):
| Metric | Value | 
|---|---|
| π― Average Accuracy | 0.6007 | 
| π Average AUC-ROC | 0.8019 | 
| βοΈ Average F1-Score | 0.4270 | 
π 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={RenΓ© Parlange and Juan C. Cuevas-Tello and Octavio Valenzuela and Omar de J. Cabrera-Rosas and TomΓ‘s 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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Collection including parlange/pit-gravit-s3
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123 fine-tuned models; 10 architectures,  12  experiments, plus 3 baseline ResNet-18
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Evaluation results
- Average Accuracy on Common Test Sample (More et al. 2024)self-reported0.601
 - Average AUC-ROC on Common Test Sample (More et al. 2024)self-reported0.802
 - Average F1-Score on Common Test Sample (More et al. 2024)self-reported0.427
 












