gravit
Collection
123 fine-tuned models; 10 architectures, 12 experiments, plus 3 baseline ResNet-18
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149 items
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Updated
🔭 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
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 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 |
| 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 |
Performance across all test datasets (a through l) in the Common Test Sample (More et al. 2024):
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 |
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},
}
For questions about this model, please contact the author through: https://github.com/parlange/
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