File size: 5,484 Bytes
8dfbd8b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | ---
license: apache-2.0
tags:
- image-classification
- pytorch
- timm
- pit
- vision-transformer
- transformer
- gravitational-lensing
- strong-lensing
- astronomy
- astrophysics
datasets:
- parlange/gravit-c21-j24
metrics:
- accuracy
- auc
- f1
paper:
- title: "GraViT: A Gravitational Lens Discovery Toolkit with Vision Transformers"
url: "https://arxiv.org/abs/2509.00226"
authors: "Parlange et al."
model-index:
- name: PiT-c2
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: 0.8074
name: Average Accuracy
- type: auc
value: 0.8443
name: Average AUC-ROC
- type: f1
value: 0.5437
name: Average F1-Score
---
# 🌌 pit-gravit-c2
🔭 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**: PiT
- **🧪 Experiment**: C2 - C21+J24-half
- **🌌 Dataset**: C21+J24
- **🪐 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/pit-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 Configuration
**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 |
## 📈 Training Curves
## 🏁 Final Epoch Training Metrics
| Metric | Training | Validation |
|:---------:|:-----------:|:-------------:|
| 📉 Loss | 0.1768 | 0.1241 |
| 🎯 Accuracy | 0.9266 | 0.9534 |
| 📊 AUC-ROC | 0.9810 | 0.9902 |
| ⚖️ F1 Score | 0.9263 | 0.9533 |
## ☑️ 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 | 0.8074 |
| 📈 Average AUC-ROC | 0.8443 |
| ⚖️ Average F1-Score | 0.5437 |
## 📘 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/
|