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
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
import torch
import timm
# Load the model directly from the Hub
model = timm.create_model(
'hf-hub:parlange/cait-gravit-a1',
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 (CaΓ±ameras et al. 2021)
Fine-tuning Strategy: classification-head
| 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 | classification_head |
| Stochastic Depth Probability | 0.1 |
| Metric | Training | Validation |
|---|---|---|
| π Loss | 0.2826 | 0.3626 |
| π― Accuracy | 0.8816 | 0.8600 |
| π AUC-ROC | 0.9518 | 0.9309 |
| βοΈ F1 Score | 0.8818 | 0.8625 |
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 |
|---|---|
| π― Average Accuracy | 0.7953 |
| π Average AUC-ROC | 0.8287 |
| βοΈ Average F1-Score | 0.5082 |
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},
}
For questions about this model, please contact the author through: https://github.com/parlange/