Upload ViT model from experiment c1

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+training_curves/ViT_combined_metrics.png filter=lfs diff=lfs merge=lfs -text
+training_notebook_c1.ipynb filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+tags:
+- image-classification
+- pytorch
+- timm
+- vit
+- 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: ViT-c1
+  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: 0.8028
+      name: Average Accuracy
+    - type: auc
+      value: 0.8562
+      name: Average AUC-ROC
+    - type: f1
+      value: 0.5517
+      name: Average F1-Score
+---
+
+# 🌌 vit-gravit-c1
+
+🔭 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**: C1 - C21+J24-classification-head
+- **🌌 Dataset**: C21+J24
+- **🪐 Fine-tuning Strategy**: classification-head
+
+
+
+## 💻 Quick Start
+
+```python
+import torch
+import timm
+
+# Load the model directly from the Hub
+model = timm.create_model(
+    'hf-hub:parlange/vit-gravit-c1',
+    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:** 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
+
+![Combined Training Metrics](https://huggingface.co/parlange/vit-gravit-c1/resolve/main/training_curves/ViT_combined_metrics.png)
+
+
+## 🏁 Final Epoch Training Metrics
+
+| Metric | Training | Validation |
+|:---------:|:-----------:|:-------------:|
+| 📉 Loss | 0.3578 | 0.3397 |
+| 🎯 Accuracy | 0.8453 | 0.8666 |
+| 📊 AUC-ROC | 0.9223 | 0.9388 |
+| ⚖️ F1 Score | 0.8453 | 0.8721 |
+
+
+## ☑️ 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](https://huggingface.co/parlange/vit-gravit-c1/resolve/main/roc_confusion_matrix/ViT_roc_confusion_matrix_a.png)
+![ROC + Confusion Matrix - Dataset B](https://huggingface.co/parlange/vit-gravit-c1/resolve/main/roc_confusion_matrix/ViT_roc_confusion_matrix_b.png)
+![ROC + Confusion Matrix - Dataset C](https://huggingface.co/parlange/vit-gravit-c1/resolve/main/roc_confusion_matrix/ViT_roc_confusion_matrix_c.png)
+![ROC + Confusion Matrix - Dataset D](https://huggingface.co/parlange/vit-gravit-c1/resolve/main/roc_confusion_matrix/ViT_roc_confusion_matrix_d.png)
+![ROC + Confusion Matrix - Dataset E](https://huggingface.co/parlange/vit-gravit-c1/resolve/main/roc_confusion_matrix/ViT_roc_confusion_matrix_e.png)
+![ROC + Confusion Matrix - Dataset F](https://huggingface.co/parlange/vit-gravit-c1/resolve/main/roc_confusion_matrix/ViT_roc_confusion_matrix_f.png)
+![ROC + Confusion Matrix - Dataset G](https://huggingface.co/parlange/vit-gravit-c1/resolve/main/roc_confusion_matrix/ViT_roc_confusion_matrix_g.png)
+![ROC + Confusion Matrix - Dataset H](https://huggingface.co/parlange/vit-gravit-c1/resolve/main/roc_confusion_matrix/ViT_roc_confusion_matrix_h.png)
+![ROC + Confusion Matrix - Dataset I](https://huggingface.co/parlange/vit-gravit-c1/resolve/main/roc_confusion_matrix/ViT_roc_confusion_matrix_i.png)
+![ROC + Confusion Matrix - Dataset J](https://huggingface.co/parlange/vit-gravit-c1/resolve/main/roc_confusion_matrix/ViT_roc_confusion_matrix_j.png)
+![ROC + Confusion Matrix - Dataset K](https://huggingface.co/parlange/vit-gravit-c1/resolve/main/roc_confusion_matrix/ViT_roc_confusion_matrix_k.png)
+![ROC + Confusion Matrix - Dataset L](https://huggingface.co/parlange/vit-gravit-c1/resolve/main/roc_confusion_matrix/ViT_roc_confusion_matrix_l.png)
+
+### 📋 Performance Summary
+
+Average performance across 12 test datasets from the Common Test Sample (More et al. 2024):
+
+| Metric | Value |
+|-----------|----------|
+| 🎯 Average Accuracy | 0.8028 |
+| 📈 Average AUC-ROC | 0.8562 |
+| ⚖️ Average F1-Score | 0.5517 |
+
+
+## 📘 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/

config.json

@@ -0,0 +1,76 @@
+{
+  "architecture": "vit_base_patch16_224",
+  "num_classes": 2,
+  "num_features": 768,
+  "global_pool": "token",
+  "crop_pct": 0.875,
+  "interpolation": "bicubic",
+  "mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "first_conv": "patch_embed.proj",
+  "classifier": "head",
+  "input_size": [
+    3,
+    224,
+    224
+  ],
+  "pool_size": [
+    7,
+    7
+  ],
+  "pretrained_cfg": {
+    "tag": "gravit_c1",
+    "custom_load": false,
+    "input_size": [
+      3,
+      224,
+      224
+    ],
+    "fixed_input_size": true,
+    "interpolation": "bicubic",
+    "crop_pct": 0.875,
+    "crop_mode": "center",
+    "mean": [
+      0.485,
+      0.456,
+      0.406
+    ],
+    "std": [
+      0.229,
+      0.224,
+      0.225
+    ],
+    "num_classes": 2,
+    "pool_size": [
+      7,
+      7
+    ],
+    "first_conv": "patch_embed.proj",
+    "classifier": "head"
+  },
+  "model_name": "vit_gravit_c1",
+  "experiment": "c1",
+  "training_strategy": "classification-head",
+  "dataset": "C21+J24",
+  "hyperparameters": {
+    "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"
+  },
+  "hf_hub_id": "parlange/vit-gravit-c1",
+  "license": "apache-2.0"
+}

evaluation_results.csv

@@ -0,0 +1,133 @@
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