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---
license: apache-2.0
tags:
- vision-transformer
- image-classification
- pytorch
- timm
- vit
- gravitational-lensing
- strong-lensing
- astronomy
- astrophysics
datasets:
- C21
metrics:
- accuracy
- auc
- f1
model-index:
- name: ViT-a2
results:
- task:
type: image-classification
name: Strong Gravitational Lens Discovery
dataset:
type: common-test-sample
name: Common Test Sample (More et al. 2024)
metrics:
- type: accuracy
value: 0.8205
name: Average Accuracy
- type: auc
value: 0.8511
name: Average AUC-ROC
- type: f1
value: 0.5319
name: Average F1-Score
---
# 🌌 vit-gravit-a2
🔭 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](https://github.com/parlange/gravit)
## 🛰️ Model Details
- **🤖 Model Type**: ViT
- **🧪 Experiment**: A2 - C21-half
- **🌌 Dataset**: C21
- **🪐 Fine-tuning Strategy**: half
## 💻 Quick Start
```python
import torch
import timm
# Load the model directly from the Hub
model = timm.create_model(
'hf-hub:parlange/vit-gravit-a2',
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:** 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 |
## 📈 Training Curves
## 🏁 Final Epoch Training Metrics
| Metric | Training | Validation |
|:---------:|:-----------:|:-------------:|
| 📉 Loss | 0.0159 | 0.0354 |
| 🎯 Accuracy | 0.9939 | 0.9870 |
| 📊 AUC-ROC | 0.9998 | 0.9986 |
| ⚖️ F1 Score | 0.9939 | 0.9870 |
## ☑️ 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 | 0.8205 |
| 📈 Average AUC-ROC | 0.8511 |
| ⚖️ Average F1-Score | 0.5319 |
## 📘 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/
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