README.md

metadata

license: apache-2.0
tags:
  - vision-transformer
  - image-classification
  - pytorch
  - timm
  - pit
  - gravitational-lensing
  - strong-lensing
  - astronomy
  - astrophysics
datasets:
  - J24
metrics:
  - accuracy
  - auc
  - f1
model-index:
  - name: PiT-b1
    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: 0.7504
            name: Average Accuracy
          - type: auc
            value: 0.7049
            name: Average AUC-ROC
          - type: f1
            value: 0.4053
            name: Average F1-Score

🌌 pit-gravit-b1

🔭 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

🛰️ Model Details

• 🤖 Model Type: PiT
• 🧪 Experiment: B1 - J24-classification-head
• 🌌 Dataset: J24
• 🪐 Fine-tuning Strategy: classification-head

💻 Quick Start

import torch
import timm

# Load the model directly from the Hub
model = timm.create_model(
    'hf-hub:parlange/pit-gravit-b1',
    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: J24 (Jaelani et al. 2024)
Fine-tuning Strategy: classification-head

🔧 Parameter	📝 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

📈 Training Curves

🏁 Final Epoch Training Metrics

Metric	Training	Validation
📉 Loss	0.2301	0.2265
🎯 Accuracy	0.9086	0.9105
📊 AUC-ROC	0.9665	0.9668
⚖️ F1 Score	0.9071	0.9094

☑️ 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	0.7504
📈 Average AUC-ROC	0.7049
⚖️ Average F1-Score	0.4053

📘 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={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/