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Upload PiT model from experiment b2

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  1. .gitattributes +2 -0
  2. README.md +161 -0
  3. config.json +76 -0
  4. confusion_matrices/PiT_Confusion_Matrix_a.png +0 -0
  5. confusion_matrices/PiT_Confusion_Matrix_b.png +0 -0
  6. confusion_matrices/PiT_Confusion_Matrix_c.png +0 -0
  7. confusion_matrices/PiT_Confusion_Matrix_d.png +0 -0
  8. confusion_matrices/PiT_Confusion_Matrix_e.png +0 -0
  9. confusion_matrices/PiT_Confusion_Matrix_f.png +0 -0
  10. confusion_matrices/PiT_Confusion_Matrix_g.png +0 -0
  11. confusion_matrices/PiT_Confusion_Matrix_h.png +0 -0
  12. confusion_matrices/PiT_Confusion_Matrix_i.png +0 -0
  13. confusion_matrices/PiT_Confusion_Matrix_j.png +0 -0
  14. confusion_matrices/PiT_Confusion_Matrix_k.png +0 -0
  15. confusion_matrices/PiT_Confusion_Matrix_l.png +0 -0
  16. evaluation_results.csv +133 -0
  17. model.safetensors +3 -0
  18. pit-gravit-b2.pth +3 -0
  19. pytorch_model.bin +3 -0
  20. roc_confusion_matrix/PiT_roc_confusion_matrix_a.png +0 -0
  21. roc_confusion_matrix/PiT_roc_confusion_matrix_b.png +0 -0
  22. roc_confusion_matrix/PiT_roc_confusion_matrix_c.png +0 -0
  23. roc_confusion_matrix/PiT_roc_confusion_matrix_d.png +0 -0
  24. roc_confusion_matrix/PiT_roc_confusion_matrix_e.png +0 -0
  25. roc_confusion_matrix/PiT_roc_confusion_matrix_f.png +0 -0
  26. roc_confusion_matrix/PiT_roc_confusion_matrix_g.png +0 -0
  27. roc_confusion_matrix/PiT_roc_confusion_matrix_h.png +0 -0
  28. roc_confusion_matrix/PiT_roc_confusion_matrix_i.png +0 -0
  29. roc_confusion_matrix/PiT_roc_confusion_matrix_j.png +0 -0
  30. roc_confusion_matrix/PiT_roc_confusion_matrix_k.png +0 -0
  31. roc_confusion_matrix/PiT_roc_confusion_matrix_l.png +0 -0
  32. roc_curves/PiT_ROC_a.png +0 -0
  33. roc_curves/PiT_ROC_b.png +0 -0
  34. roc_curves/PiT_ROC_c.png +0 -0
  35. roc_curves/PiT_ROC_d.png +0 -0
  36. roc_curves/PiT_ROC_e.png +0 -0
  37. roc_curves/PiT_ROC_f.png +0 -0
  38. roc_curves/PiT_ROC_g.png +0 -0
  39. roc_curves/PiT_ROC_h.png +0 -0
  40. roc_curves/PiT_ROC_i.png +0 -0
  41. roc_curves/PiT_ROC_j.png +0 -0
  42. roc_curves/PiT_ROC_k.png +0 -0
  43. roc_curves/PiT_ROC_l.png +0 -0
  44. training_curves/PiT_accuracy.png +0 -0
  45. training_curves/PiT_auc.png +0 -0
  46. training_curves/PiT_combined_metrics.png +3 -0
  47. training_curves/PiT_f1.png +0 -0
  48. training_curves/PiT_loss.png +0 -0
  49. training_curves/PiT_metrics.csv +101 -0
  50. training_metrics.csv +101 -0
.gitattributes CHANGED
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ training_curves/PiT_combined_metrics.png filter=lfs diff=lfs merge=lfs -text
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+ training_notebook_b2.ipynb filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,161 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
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+ license: apache-2.0
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+ tags:
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+ - vision-transformer
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+ - image-classification
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+ - pytorch
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+ - timm
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+ - pit
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+ - gravitational-lensing
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+ - strong-lensing
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+ - astronomy
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+ - astrophysics
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+ datasets:
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+ - J24
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+ metrics:
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+ - accuracy
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+ - auc
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+ - f1
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+ model-index:
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+ - name: PiT-b2
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+ results:
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+ - task:
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+ type: image-classification
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+ name: Strong Gravitational Lens Discovery
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+ dataset:
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+ type: common-test-sample
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+ name: Common Test Sample (More et al. 2024)
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+ metrics:
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+ - type: accuracy
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+ value: 0.7692
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+ name: Average Accuracy
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+ - type: auc
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+ value: 0.7233
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+ name: Average AUC-ROC
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+ - type: f1
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+ value: 0.4009
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+ name: Average F1-Score
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+ ---
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+
40
+ # 🌌 pit-gravit-b2
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+
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+ 🔭 This model is part of **GraViT**: Transfer Learning with Vision Transformers and MLP-Mixer for Strong Gravitational Lens Discovery
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+
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+ 🔗 **GitHub Repository**: [https://github.com/parlange/gravit](https://github.com/parlange/gravit)
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+
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+ ## 🛰️ Model Details
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+
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+ - **🤖 Model Type**: PiT
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+ - **🧪 Experiment**: B2 - J24-half
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+ - **🌌 Dataset**: J24
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+ - **🪐 Fine-tuning Strategy**: half
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+
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+
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+
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+ ## 💻 Quick Start
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+
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+ ```python
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+ import torch
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+ import timm
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+
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+ # Load the model directly from the Hub
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+ model = timm.create_model(
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+ 'hf-hub:parlange/pit-gravit-b2',
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+ pretrained=True
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+ )
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+ model.eval()
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+
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+ # Example inference
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+ dummy_input = torch.randn(1, 3, 224, 224)
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+ with torch.no_grad():
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+ output = model(dummy_input)
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+ predictions = torch.softmax(output, dim=1)
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+ print(f"Lens probability: {predictions[0][1]:.4f}")
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+ ```
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+
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+ ## ⚡️ Training Configuration
77
+
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+ **Training Dataset:** J24 (Jaelani et al. 2024)
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+ **Fine-tuning Strategy:** half
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+
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+
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+ | 🔧 Parameter | 📝 Value |
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+ |--------------|----------|
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+ | Batch Size | 192 |
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+ | Learning Rate | AdamW with ReduceLROnPlateau |
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+ | Epochs | 100 |
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+ | Patience | 10 |
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+ | Optimizer | AdamW |
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+ | Scheduler | ReduceLROnPlateau |
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+ | Image Size | 224x224 |
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+ | Fine Tune Mode | half |
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+ | Stochastic Depth Probability | 0.1 |
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+
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+
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+ ## 📈 Training Curves
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+
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+ ![Combined Training Metrics](https://huggingface.co/parlange/pit-gravit-b2/resolve/main/training_curves/PiT_combined_metrics.png)
98
+
99
+
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+ ## 🏁 Final Epoch Training Metrics
101
+
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+ | Metric | Training | Validation |
103
+ |:---------:|:-----------:|:-------------:|
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+ | 📉 Loss | 0.1719 | 0.1549 |
105
+ | 🎯 Accuracy | 0.9337 | 0.9437 |
106
+ | 📊 AUC-ROC | 0.9806 | 0.9838 |
107
+ | ⚖️ F1 Score | 0.9326 | 0.9426 |
108
+
109
+
110
+ ## ☑️ Evaluation Results
111
+
112
+ ### ROC Curves and Confusion Matrices
113
+
114
+ Performance across all test datasets (a through l) in the Common Test Sample (More et al. 2024):
115
+
116
+ ![ROC + Confusion Matrix - Dataset A](https://huggingface.co/parlange/pit-gravit-b2/resolve/main/roc_confusion_matrix/PiT_roc_confusion_matrix_a.png)
117
+ ![ROC + Confusion Matrix - Dataset B](https://huggingface.co/parlange/pit-gravit-b2/resolve/main/roc_confusion_matrix/PiT_roc_confusion_matrix_b.png)
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+ ![ROC + Confusion Matrix - Dataset C](https://huggingface.co/parlange/pit-gravit-b2/resolve/main/roc_confusion_matrix/PiT_roc_confusion_matrix_c.png)
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+ ![ROC + Confusion Matrix - Dataset D](https://huggingface.co/parlange/pit-gravit-b2/resolve/main/roc_confusion_matrix/PiT_roc_confusion_matrix_d.png)
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+ ![ROC + Confusion Matrix - Dataset E](https://huggingface.co/parlange/pit-gravit-b2/resolve/main/roc_confusion_matrix/PiT_roc_confusion_matrix_e.png)
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+ ![ROC + Confusion Matrix - Dataset F](https://huggingface.co/parlange/pit-gravit-b2/resolve/main/roc_confusion_matrix/PiT_roc_confusion_matrix_f.png)
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+ ![ROC + Confusion Matrix - Dataset G](https://huggingface.co/parlange/pit-gravit-b2/resolve/main/roc_confusion_matrix/PiT_roc_confusion_matrix_g.png)
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+ ![ROC + Confusion Matrix - Dataset H](https://huggingface.co/parlange/pit-gravit-b2/resolve/main/roc_confusion_matrix/PiT_roc_confusion_matrix_h.png)
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+ ![ROC + Confusion Matrix - Dataset I](https://huggingface.co/parlange/pit-gravit-b2/resolve/main/roc_confusion_matrix/PiT_roc_confusion_matrix_i.png)
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+ ![ROC + Confusion Matrix - Dataset J](https://huggingface.co/parlange/pit-gravit-b2/resolve/main/roc_confusion_matrix/PiT_roc_confusion_matrix_j.png)
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+ ![ROC + Confusion Matrix - Dataset K](https://huggingface.co/parlange/pit-gravit-b2/resolve/main/roc_confusion_matrix/PiT_roc_confusion_matrix_k.png)
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+ ![ROC + Confusion Matrix - Dataset L](https://huggingface.co/parlange/pit-gravit-b2/resolve/main/roc_confusion_matrix/PiT_roc_confusion_matrix_l.png)
128
+
129
+ ### 📋 Performance Summary
130
+
131
+ Average performance across 12 test datasets from the Common Test Sample (More et al. 2024):
132
+
133
+ | Metric | Value |
134
+ |-----------|----------|
135
+ | 🎯 Average Accuracy | 0.7692 |
136
+ | 📈 Average AUC-ROC | 0.7233 |
137
+ | ⚖️ Average F1-Score | 0.4009 |
138
+
139
+
140
+ ## 📘 Citation
141
+
142
+ If you use this model in your research, please cite:
143
+
144
+ ```bibtex
145
+ @misc{parlange2025gravit,
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+ title={GraViT: Transfer Learning with Vision Transformers and MLP-Mixer for Strong Gravitational Lens Discovery},
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+ 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},
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+ year={2025},
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+ eprint={2509.00226},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CV},
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+ url={https://arxiv.org/abs/2509.00226},
153
+ }
154
+ ```
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+
156
+ ---
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+
158
+
159
+ ## Model Card Contact
160
+
161
+ For questions about this model, please contact the author through: https://github.com/parlange/
config.json ADDED
@@ -0,0 +1,76 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architecture": "vit_base_patch16_224",
3
+ "num_classes": 2,
4
+ "num_features": 1000,
5
+ "global_pool": "avg",
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+ "crop_pct": 0.875,
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+ "interpolation": "bicubic",
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+ "mean": [
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+ 0.485,
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+ 0.456,
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+ 0.406
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+ ],
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+ "std": [
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+ 0.229,
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+ 0.224,
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+ 0.225
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+ ],
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+ "first_conv": "conv1",
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+ "classifier": "fc",
20
+ "input_size": [
21
+ 3,
22
+ 224,
23
+ 224
24
+ ],
25
+ "pool_size": [
26
+ 7,
27
+ 7
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+ ],
29
+ "pretrained_cfg": {
30
+ "tag": "gravit_b2",
31
+ "custom_load": false,
32
+ "input_size": [
33
+ 3,
34
+ 224,
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+ 224
36
+ ],
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+ "fixed_input_size": true,
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+ "interpolation": "bicubic",
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+ "crop_pct": 0.875,
40
+ "crop_mode": "center",
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+ "mean": [
42
+ 0.485,
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+ 0.456,
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+ 0.406
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+ ],
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+ "std": [
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+ 0.229,
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+ 0.224,
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+ 0.225
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+ ],
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+ "num_classes": 2,
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+ "pool_size": [
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+ 7,
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+ 7
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+ ],
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+ "first_conv": "conv1",
57
+ "classifier": "fc"
58
+ },
59
+ "model_name": "pit_gravit_b2",
60
+ "experiment": "b2",
61
+ "training_strategy": "half",
62
+ "dataset": "J24",
63
+ "hyperparameters": {
64
+ "batch_size": "192",
65
+ "learning_rate": "AdamW with ReduceLROnPlateau",
66
+ "epochs": "100",
67
+ "patience": "10",
68
+ "optimizer": "AdamW",
69
+ "scheduler": "ReduceLROnPlateau",
70
+ "image_size": "224x224",
71
+ "fine_tune_mode": "half",
72
+ "stochastic_depth_probability": "0.1"
73
+ },
74
+ "hf_hub_id": "parlange/pit-gravit-b2",
75
+ "license": "apache-2.0"
76
+ }
confusion_matrices/PiT_Confusion_Matrix_a.png ADDED
confusion_matrices/PiT_Confusion_Matrix_b.png ADDED
confusion_matrices/PiT_Confusion_Matrix_c.png ADDED
confusion_matrices/PiT_Confusion_Matrix_d.png ADDED
confusion_matrices/PiT_Confusion_Matrix_e.png ADDED
confusion_matrices/PiT_Confusion_Matrix_f.png ADDED
confusion_matrices/PiT_Confusion_Matrix_g.png ADDED
confusion_matrices/PiT_Confusion_Matrix_h.png ADDED
confusion_matrices/PiT_Confusion_Matrix_i.png ADDED
confusion_matrices/PiT_Confusion_Matrix_j.png ADDED
confusion_matrices/PiT_Confusion_Matrix_k.png ADDED
confusion_matrices/PiT_Confusion_Matrix_l.png ADDED
evaluation_results.csv ADDED
@@ -0,0 +1,133 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Model,Dataset,Loss,Accuracy,AUCROC,F1
2
+ ViT,a,0.3504258575622333,0.8975165042439485,0.7724705340699815,0.3607843137254902
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+ ViT,b,0.6139323879378804,0.8443885570575291,0.7591445672191528,0.2709867452135493
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+ ViT,c,0.2921286642045504,0.9195221628418736,0.7922799263351749,0.41818181818181815
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+ ViT,d,0.26607740657091966,0.9192077962904747,0.7989585635359117,0.41723356009070295
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+ ViT,e,0.5051617136084026,0.8562019758507134,0.8035646711571935,0.5841269841269842
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+ ViT,f,0.29864826543026823,0.9141042521880567,0.7820238007404096,0.1423047177107502
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+ ViT,g,1.732062186717987,0.5718333333333333,0.6607354444444444,0.3945321706339854
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+ ViT,h,1.5614525699615478,0.6116666666666667,0.696484388888889,0.4180819180819181
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+ ViT,i,1.5476410572528838,0.6115,0.7046536666666666,0.41797752808988764
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+ ViT,j,0.45324009704589846,0.8708333333333333,0.9427406111111111,0.8716249792943515
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+ ViT,k,0.2688189628124237,0.9105,0.9655912222222223,0.9073978272115882
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+ ViT,l,0.7289746560802081,0.8074665538575432,0.8200002274918716,0.6616485456741938
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+ MLP-Mixer,a,0.47016938226997235,0.8553913863564917,0.726208103130755,0.2966360856269113
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+ MLP-Mixer,b,0.648904620120826,0.8333857277585665,0.7598747697974219,0.26795580110497236
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+ MLP-Mixer,c,0.3869087039131445,0.8874567745991826,0.733244935543278,0.35144927536231885
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+ MLP-Mixer,d,0.3633993222574062,0.8997170701037409,0.7682688766114181,0.37816764132553604
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+ MLP-Mixer,e,0.5312218908486329,0.8474204171240395,0.8186331643078787,0.5825825825825826
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+ MLP-Mixer,f,0.3886312065722082,0.8861435984819146,0.7510127466766198,0.11658653846153846
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+ MLP-Mixer,g,1.9292033066749572,0.5541666666666667,0.6170044444444445,0.36566279345506286
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+ MLP-Mixer,h,1.7903018260002137,0.5828333333333333,0.5599299999999999,0.3812113720642769
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+ MLP-Mixer,i,1.7778379353284837,0.5893333333333334,0.6098859999999999,0.3849226160758862
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+ MLP-Mixer,j,0.5891341290473938,0.8386666666666667,0.9153434444444444,0.8365968939905469
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+ MLP-Mixer,k,0.4377687557935715,0.8738333333333334,0.9274168333333332,0.8674951864169438
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+ MLP-Mixer,l,0.8833867266855338,0.7767965734228756,0.7550110803792328,0.6132135984605517
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+ CvT,a,0.710060378377897,0.7095253065073877,0.4610672191528545,0.09941520467836257
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+ CvT,b,0.6279841153848774,0.7516504243948444,0.5819742173112339,0.11434977578475336
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+ CvT,c,0.7670251699747135,0.6592266582835586,0.4085635359116022,0.08600337268128162
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+ CvT,d,0.45408995114606493,0.8179817667400189,0.5189116022099447,0.14977973568281938
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+ CvT,e,0.6853549914551096,0.756311745334797,0.6546734276848558,0.3148148148148148
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+ CvT,f,0.5548892200417859,0.7615211834869491,0.5019215495653457,0.03206538824269098
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+ CvT,g,1.6338303427696228,0.46316666666666667,0.5036535,0.21419858502073677
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+ CvT,h,1.7075452818870545,0.4141666666666667,0.3043033333333333,0.1998634190758024
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+ CvT,i,1.5416374638080597,0.49833333333333335,0.4133462222222223,0.22582304526748972
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+ CvT,j,0.6718657946586609,0.7025,0.7951043333333334,0.6775067750677507
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+ CvT,k,0.5796729214191437,0.7376666666666667,0.8160571111111111,0.7043576258452291
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+ CvT,l,0.9414389114699858,0.6422716937232299,0.5856238290999722,0.41148325358851673
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+ Swin,a,0.38957844183659335,0.9173215969820812,0.7264815837937385,0.30971128608923887
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+ Swin,b,0.506205921398323,0.8714240804778371,0.7070128913443832,0.2239089184060721
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+ Swin,c,0.33732365382351015,0.9254951273184533,0.7497605893186002,0.3323943661971831
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+ Swin,d,0.25108740707354066,0.9465576862621817,0.823268876611418,0.4097222222222222
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+ Swin,e,0.7929391115167793,0.8419319429198683,0.7817982290168772,0.45038167938931295
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+ Swin,f,0.21592594718169755,0.9430717992409573,0.7533609214757848,0.13833528722157093
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+ Swin,g,2.702541620135307,0.5423333333333333,0.5866217222222223,0.2826541274817137
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+ Swin,h,2.613005870103836,0.571,0.6225122222222222,0.2959518599562363
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+ Swin,i,2.567286303862929,0.5821666666666667,0.7211619444444445,0.3014767344664252
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+ Swin,j,0.42502203929424287,0.8855,0.9479796111111113,0.8833021912688975
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+ Swin,k,0.28976670680940153,0.9253333333333333,0.9796539999999999,0.9206798866855525
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