Instructions to use parlange/twins_pcpvt-gravit-s2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use parlange/twins_pcpvt-gravit-s2 with timm:
import timm model = timm.create_model("hf_hub:parlange/twins_pcpvt-gravit-s2", pretrained=True) - Notebooks
- Google Colab
- Kaggle
π twins_pcpvt-gravit-s2
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: Twins_PCPVT
π§ͺ Experiment: S2 - C21-half-18660
π Dataset: C21
πͺ Fine-tuning Strategy: half
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/twins_pcpvt-gravit-s2',
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: half
| 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 | half |
| Stochastic Depth Probability | 0.1 |
π Training Curves
π Final Epoch Training Metrics
| Metric | Training | Validation |
|---|---|---|
| π Loss | 0.2846 | 0.3545 |
| π― Accuracy | 0.8830 | 0.8640 |
| π AUC-ROC | 0.9529 | 0.9341 |
| βοΈ F1 Score | 0.8837 | 0.8682 |
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 | 0.7846 |
| π Average AUC-ROC | 0.8206 |
| βοΈ Average F1-Score | 0.4892 |
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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Paper for parlange/twins_pcpvt-gravit-s2
Evaluation results
- Average Accuracy on Common Test Sample (More et al. 2024)self-reported0.785
- Average AUC-ROC on Common Test Sample (More et al. 2024)self-reported0.821
- Average F1-Score on Common Test Sample (More et al. 2024)self-reported0.489












