🌌 mlp-mixer-gravit-a2

πŸ”­ This model is part of GraViT: Transfer Learning with Vision Transformers and MLP-Mixer for Strong Gravitational Lens Discovery

πŸ”— GitHub Repository: https://github.com/parlange/gravit

πŸ›°οΈ Model Details

  • πŸ€– Model Type: MLP-Mixer
  • πŸ§ͺ Experiment: A2 - C21-half
  • 🌌 Dataset: C21
  • πŸͺ Fine-tuning Strategy: half

πŸ’» Quick Start

import torch
import timm

# Load the model directly from the Hub
model = timm.create_model(
    'hf-hub:parlange/mlp-mixer-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 (CaΓ±ameras et al. 2021)
Fine-tuning Strategy: half

πŸ”§ Parameter πŸ“ 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

πŸ“ˆ Training Curves

Combined Training Metrics

🏁 Final Epoch Training Metrics

Metric Training Validation
πŸ“‰ Loss 0.0473 0.0293
🎯 Accuracy 0.9807 0.9880
πŸ“Š AUC-ROC 0.9986 0.9996
βš–οΈ F1 Score 0.9807 0.9881

β˜‘οΈ Evaluation Results

ROC Curves and Confusion Matrices

Performance across all test datasets (a through l) in the Common Test Sample (More et al. 2024):

ROC + Confusion Matrix - Dataset A ROC + Confusion Matrix - Dataset B ROC + Confusion Matrix - Dataset C ROC + Confusion Matrix - Dataset D ROC + Confusion Matrix - Dataset E ROC + Confusion Matrix - Dataset F ROC + Confusion Matrix - Dataset G ROC + Confusion Matrix - Dataset H ROC + Confusion Matrix - Dataset I ROC + Confusion Matrix - Dataset J ROC + Confusion Matrix - Dataset K ROC + Confusion Matrix - Dataset L

πŸ“‹ Performance Summary

Average performance across 12 test datasets from the Common Test Sample (More et al. 2024):

Metric Value
🎯 Average Accuracy 0.7375
πŸ“ˆ Average AUC-ROC 0.8349
βš–οΈ Average F1-Score 0.4737

πŸ“˜ 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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Evaluation results

  • Average Accuracy on Common Test Sample (More et al. 2024)
    self-reported
    0.738
  • Average AUC-ROC on Common Test Sample (More et al. 2024)
    self-reported
    0.835
  • Average F1-Score on Common Test Sample (More et al. 2024)
    self-reported
    0.474