Image Classification
timm
PyTorch
Safetensors
vision-transformer
pit
gravitational-lensing
strong-lensing
astronomy
astrophysics
Eval Results (legacy)
Instructions to use parlange/pit-gravit-b2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use parlange/pit-gravit-b2 with timm:
import timm model = timm.create_model("hf_hub:parlange/pit-gravit-b2", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Upload PiT model from experiment b2
Browse filesThis view is limited to 50 files because it contains too many changes. See raw diff
- .gitattributes +2 -0
- README.md +161 -0
- config.json +76 -0
- confusion_matrices/PiT_Confusion_Matrix_a.png +0 -0
- confusion_matrices/PiT_Confusion_Matrix_b.png +0 -0
- confusion_matrices/PiT_Confusion_Matrix_c.png +0 -0
- confusion_matrices/PiT_Confusion_Matrix_d.png +0 -0
- confusion_matrices/PiT_Confusion_Matrix_e.png +0 -0
- confusion_matrices/PiT_Confusion_Matrix_f.png +0 -0
- confusion_matrices/PiT_Confusion_Matrix_g.png +0 -0
- confusion_matrices/PiT_Confusion_Matrix_h.png +0 -0
- confusion_matrices/PiT_Confusion_Matrix_i.png +0 -0
- confusion_matrices/PiT_Confusion_Matrix_j.png +0 -0
- confusion_matrices/PiT_Confusion_Matrix_k.png +0 -0
- confusion_matrices/PiT_Confusion_Matrix_l.png +0 -0
- evaluation_results.csv +133 -0
- model.safetensors +3 -0
- pit-gravit-b2.pth +3 -0
- pytorch_model.bin +3 -0
- roc_confusion_matrix/PiT_roc_confusion_matrix_a.png +0 -0
- roc_confusion_matrix/PiT_roc_confusion_matrix_b.png +0 -0
- roc_confusion_matrix/PiT_roc_confusion_matrix_c.png +0 -0
- roc_confusion_matrix/PiT_roc_confusion_matrix_d.png +0 -0
- roc_confusion_matrix/PiT_roc_confusion_matrix_e.png +0 -0
- roc_confusion_matrix/PiT_roc_confusion_matrix_f.png +0 -0
- roc_confusion_matrix/PiT_roc_confusion_matrix_g.png +0 -0
- roc_confusion_matrix/PiT_roc_confusion_matrix_h.png +0 -0
- roc_confusion_matrix/PiT_roc_confusion_matrix_i.png +0 -0
- roc_confusion_matrix/PiT_roc_confusion_matrix_j.png +0 -0
- roc_confusion_matrix/PiT_roc_confusion_matrix_k.png +0 -0
- roc_confusion_matrix/PiT_roc_confusion_matrix_l.png +0 -0
- roc_curves/PiT_ROC_a.png +0 -0
- roc_curves/PiT_ROC_b.png +0 -0
- roc_curves/PiT_ROC_c.png +0 -0
- roc_curves/PiT_ROC_d.png +0 -0
- roc_curves/PiT_ROC_e.png +0 -0
- roc_curves/PiT_ROC_f.png +0 -0
- roc_curves/PiT_ROC_g.png +0 -0
- roc_curves/PiT_ROC_h.png +0 -0
- roc_curves/PiT_ROC_i.png +0 -0
- roc_curves/PiT_ROC_j.png +0 -0
- roc_curves/PiT_ROC_k.png +0 -0
- roc_curves/PiT_ROC_l.png +0 -0
- training_curves/PiT_accuracy.png +0 -0
- training_curves/PiT_auc.png +0 -0
- training_curves/PiT_combined_metrics.png +3 -0
- training_curves/PiT_f1.png +0 -0
- training_curves/PiT_loss.png +0 -0
- training_curves/PiT_metrics.csv +101 -0
- training_metrics.csv +101 -0
.gitattributes
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*.zip 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
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README.md
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| 1 |
+
---
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| 2 |
+
license: apache-2.0
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| 3 |
+
tags:
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| 4 |
+
- vision-transformer
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| 5 |
+
- image-classification
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| 6 |
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- pytorch
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| 7 |
+
- timm
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| 8 |
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- pit
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| 9 |
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- gravitational-lensing
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| 10 |
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- strong-lensing
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| 11 |
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- astronomy
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| 12 |
+
- astrophysics
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| 13 |
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datasets:
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| 14 |
+
- J24
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| 15 |
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metrics:
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| 16 |
+
- accuracy
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| 17 |
+
- auc
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| 18 |
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- f1
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| 19 |
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model-index:
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| 20 |
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- name: PiT-b2
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| 21 |
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results:
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| 22 |
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- task:
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| 23 |
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type: image-classification
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| 24 |
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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 |
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# 🌌 pit-gravit-b2
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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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🔗 **GitHub Repository**: [https://github.com/parlange/gravit](https://github.com/parlange/gravit)
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| 46 |
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## 🛰️ Model Details
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| 47 |
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| 48 |
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- **🤖 Model Type**: PiT
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| 49 |
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- **🧪 Experiment**: B2 - J24-half
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| 50 |
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- **🌌 Dataset**: J24
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| 51 |
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- **🪐 Fine-tuning Strategy**: half
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| 52 |
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| 53 |
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| 54 |
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| 55 |
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## 💻 Quick Start
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| 56 |
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| 57 |
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```python
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| 58 |
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import torch
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| 59 |
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import timm
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| 60 |
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| 61 |
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# Load the model directly from the Hub
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| 62 |
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model = timm.create_model(
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| 63 |
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'hf-hub:parlange/pit-gravit-b2',
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| 64 |
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pretrained=True
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| 65 |
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)
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| 66 |
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model.eval()
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| 67 |
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| 68 |
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# Example inference
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| 69 |
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dummy_input = torch.randn(1, 3, 224, 224)
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| 70 |
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with torch.no_grad():
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| 71 |
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output = model(dummy_input)
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| 72 |
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predictions = torch.softmax(output, dim=1)
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| 73 |
+
print(f"Lens probability: {predictions[0][1]:.4f}")
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| 74 |
+
```
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| 75 |
+
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| 76 |
+
## ⚡️ Training Configuration
|
| 77 |
+
|
| 78 |
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**Training Dataset:** J24 (Jaelani et al. 2024)
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| 79 |
+
**Fine-tuning Strategy:** half
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| 80 |
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| 81 |
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| 82 |
+
| 🔧 Parameter | 📝 Value |
|
| 83 |
+
|--------------|----------|
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| 84 |
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| Batch Size | 192 |
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| 85 |
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| Learning Rate | AdamW with ReduceLROnPlateau |
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| 86 |
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| Epochs | 100 |
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| 87 |
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| Patience | 10 |
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| 88 |
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| Optimizer | AdamW |
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| 89 |
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| Scheduler | ReduceLROnPlateau |
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| 90 |
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| Image Size | 224x224 |
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| 91 |
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| Fine Tune Mode | half |
|
| 92 |
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| Stochastic Depth Probability | 0.1 |
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| 93 |
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| 94 |
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| 95 |
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## 📈 Training Curves
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| 96 |
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| 97 |
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| 98 |
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| 99 |
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| 100 |
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## 🏁 Final Epoch Training Metrics
|
| 101 |
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| 102 |
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| Metric | Training | Validation |
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| 103 |
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|:---------:|:-----------:|:-------------:|
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| 104 |
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| 📉 Loss | 0.1719 | 0.1549 |
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| 105 |
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| 🎯 Accuracy | 0.9337 | 0.9437 |
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| 106 |
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| 📊 AUC-ROC | 0.9806 | 0.9838 |
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| 107 |
+
| ⚖️ F1 Score | 0.9326 | 0.9426 |
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| 108 |
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| 109 |
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| 110 |
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## ☑️ Evaluation Results
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| 111 |
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| 112 |
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### ROC Curves and Confusion Matrices
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| 113 |
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| 114 |
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Performance across all test datasets (a through l) in the Common Test Sample (More et al. 2024):
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| 115 |
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| 117 |
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| 127 |
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| 129 |
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### 📋 Performance Summary
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| 130 |
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Average performance across 12 test datasets from the Common Test Sample (More et al. 2024):
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| 132 |
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| 133 |
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| Metric | Value |
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| 134 |
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|-----------|----------|
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| 135 |
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| 🎯 Average Accuracy | 0.7692 |
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| 136 |
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| 📈 Average AUC-ROC | 0.7233 |
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| 137 |
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| ⚖️ Average F1-Score | 0.4009 |
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| 138 |
+
|
| 139 |
+
|
| 140 |
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## 📘 Citation
|
| 141 |
+
|
| 142 |
+
If you use this model in your research, please cite:
|
| 143 |
+
|
| 144 |
+
```bibtex
|
| 145 |
+
@misc{parlange2025gravit,
|
| 146 |
+
title={GraViT: Transfer Learning with Vision Transformers and MLP-Mixer for Strong Gravitational Lens Discovery},
|
| 147 |
+
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},
|
| 148 |
+
year={2025},
|
| 149 |
+
eprint={2509.00226},
|
| 150 |
+
archivePrefix={arXiv},
|
| 151 |
+
primaryClass={cs.CV},
|
| 152 |
+
url={https://arxiv.org/abs/2509.00226},
|
| 153 |
+
}
|
| 154 |
+
```
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| 155 |
+
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| 156 |
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---
|
| 157 |
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| 158 |
+
|
| 159 |
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## Model Card Contact
|
| 160 |
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| 161 |
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For questions about this model, please contact the author through: https://github.com/parlange/
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config.json
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{
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| 2 |
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"architecture": "vit_base_patch16_224",
|
| 3 |
+
"num_classes": 2,
|
| 4 |
+
"num_features": 1000,
|
| 5 |
+
"global_pool": "avg",
|
| 6 |
+
"crop_pct": 0.875,
|
| 7 |
+
"interpolation": "bicubic",
|
| 8 |
+
"mean": [
|
| 9 |
+
0.485,
|
| 10 |
+
0.456,
|
| 11 |
+
0.406
|
| 12 |
+
],
|
| 13 |
+
"std": [
|
| 14 |
+
0.229,
|
| 15 |
+
0.224,
|
| 16 |
+
0.225
|
| 17 |
+
],
|
| 18 |
+
"first_conv": "conv1",
|
| 19 |
+
"classifier": "fc",
|
| 20 |
+
"input_size": [
|
| 21 |
+
3,
|
| 22 |
+
224,
|
| 23 |
+
224
|
| 24 |
+
],
|
| 25 |
+
"pool_size": [
|
| 26 |
+
7,
|
| 27 |
+
7
|
| 28 |
+
],
|
| 29 |
+
"pretrained_cfg": {
|
| 30 |
+
"tag": "gravit_b2",
|
| 31 |
+
"custom_load": false,
|
| 32 |
+
"input_size": [
|
| 33 |
+
3,
|
| 34 |
+
224,
|
| 35 |
+
224
|
| 36 |
+
],
|
| 37 |
+
"fixed_input_size": true,
|
| 38 |
+
"interpolation": "bicubic",
|
| 39 |
+
"crop_pct": 0.875,
|
| 40 |
+
"crop_mode": "center",
|
| 41 |
+
"mean": [
|
| 42 |
+
0.485,
|
| 43 |
+
0.456,
|
| 44 |
+
0.406
|
| 45 |
+
],
|
| 46 |
+
"std": [
|
| 47 |
+
0.229,
|
| 48 |
+
0.224,
|
| 49 |
+
0.225
|
| 50 |
+
],
|
| 51 |
+
"num_classes": 2,
|
| 52 |
+
"pool_size": [
|
| 53 |
+
7,
|
| 54 |
+
7
|
| 55 |
+
],
|
| 56 |
+
"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 @@
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|
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|
|
|
| 1 |
+
Model,Dataset,Loss,Accuracy,AUCROC,F1
|
| 2 |
+
ViT,a,0.3504258575622333,0.8975165042439485,0.7724705340699815,0.3607843137254902
|
| 3 |
+
ViT,b,0.6139323879378804,0.8443885570575291,0.7591445672191528,0.2709867452135493
|
| 4 |
+
ViT,c,0.2921286642045504,0.9195221628418736,0.7922799263351749,0.41818181818181815
|
| 5 |
+
ViT,d,0.26607740657091966,0.9192077962904747,0.7989585635359117,0.41723356009070295
|
| 6 |
+
ViT,e,0.5051617136084026,0.8562019758507134,0.8035646711571935,0.5841269841269842
|
| 7 |
+
ViT,f,0.29864826543026823,0.9141042521880567,0.7820238007404096,0.1423047177107502
|
| 8 |
+
ViT,g,1.732062186717987,0.5718333333333333,0.6607354444444444,0.3945321706339854
|
| 9 |
+
ViT,h,1.5614525699615478,0.6116666666666667,0.696484388888889,0.4180819180819181
|
| 10 |
+
ViT,i,1.5476410572528838,0.6115,0.7046536666666666,0.41797752808988764
|
| 11 |
+
ViT,j,0.45324009704589846,0.8708333333333333,0.9427406111111111,0.8716249792943515
|
| 12 |
+
ViT,k,0.2688189628124237,0.9105,0.9655912222222223,0.9073978272115882
|
| 13 |
+
ViT,l,0.7289746560802081,0.8074665538575432,0.8200002274918716,0.6616485456741938
|
| 14 |
+
MLP-Mixer,a,0.47016938226997235,0.8553913863564917,0.726208103130755,0.2966360856269113
|
| 15 |
+
MLP-Mixer,b,0.648904620120826,0.8333857277585665,0.7598747697974219,0.26795580110497236
|
| 16 |
+
MLP-Mixer,c,0.3869087039131445,0.8874567745991826,0.733244935543278,0.35144927536231885
|
| 17 |
+
MLP-Mixer,d,0.3633993222574062,0.8997170701037409,0.7682688766114181,0.37816764132553604
|
| 18 |
+
MLP-Mixer,e,0.5312218908486329,0.8474204171240395,0.8186331643078787,0.5825825825825826
|
| 19 |
+
MLP-Mixer,f,0.3886312065722082,0.8861435984819146,0.7510127466766198,0.11658653846153846
|
| 20 |
+
MLP-Mixer,g,1.9292033066749572,0.5541666666666667,0.6170044444444445,0.36566279345506286
|
| 21 |
+
MLP-Mixer,h,1.7903018260002137,0.5828333333333333,0.5599299999999999,0.3812113720642769
|
| 22 |
+
MLP-Mixer,i,1.7778379353284837,0.5893333333333334,0.6098859999999999,0.3849226160758862
|
| 23 |
+
MLP-Mixer,j,0.5891341290473938,0.8386666666666667,0.9153434444444444,0.8365968939905469
|
| 24 |
+
MLP-Mixer,k,0.4377687557935715,0.8738333333333334,0.9274168333333332,0.8674951864169438
|
| 25 |
+
MLP-Mixer,l,0.8833867266855338,0.7767965734228756,0.7550110803792328,0.6132135984605517
|
| 26 |
+
CvT,a,0.710060378377897,0.7095253065073877,0.4610672191528545,0.09941520467836257
|
| 27 |
+
CvT,b,0.6279841153848774,0.7516504243948444,0.5819742173112339,0.11434977578475336
|
| 28 |
+
CvT,c,0.7670251699747135,0.6592266582835586,0.4085635359116022,0.08600337268128162
|
| 29 |
+
CvT,d,0.45408995114606493,0.8179817667400189,0.5189116022099447,0.14977973568281938
|
| 30 |
+
CvT,e,0.6853549914551096,0.756311745334797,0.6546734276848558,0.3148148148148148
|
| 31 |
+
CvT,f,0.5548892200417859,0.7615211834869491,0.5019215495653457,0.03206538824269098
|
| 32 |
+
CvT,g,1.6338303427696228,0.46316666666666667,0.5036535,0.21419858502073677
|
| 33 |
+
CvT,h,1.7075452818870545,0.4141666666666667,0.3043033333333333,0.1998634190758024
|
| 34 |
+
CvT,i,1.5416374638080597,0.49833333333333335,0.4133462222222223,0.22582304526748972
|
| 35 |
+
CvT,j,0.6718657946586609,0.7025,0.7951043333333334,0.6775067750677507
|
| 36 |
+
CvT,k,0.5796729214191437,0.7376666666666667,0.8160571111111111,0.7043576258452291
|
| 37 |
+
CvT,l,0.9414389114699858,0.6422716937232299,0.5856238290999722,0.41148325358851673
|
| 38 |
+
Swin,a,0.38957844183659335,0.9173215969820812,0.7264815837937385,0.30971128608923887
|
| 39 |
+
Swin,b,0.506205921398323,0.8714240804778371,0.7070128913443832,0.2239089184060721
|
| 40 |
+
Swin,c,0.33732365382351015,0.9254951273184533,0.7497605893186002,0.3323943661971831
|
| 41 |
+
Swin,d,0.25108740707354066,0.9465576862621817,0.823268876611418,0.4097222222222222
|
| 42 |
+
Swin,e,0.7929391115167793,0.8419319429198683,0.7817982290168772,0.45038167938931295
|
| 43 |
+
Swin,f,0.21592594718169755,0.9430717992409573,0.7533609214757848,0.13833528722157093
|
| 44 |
+
Swin,g,2.702541620135307,0.5423333333333333,0.5866217222222223,0.2826541274817137
|
| 45 |
+
Swin,h,2.613005870103836,0.571,0.6225122222222222,0.2959518599562363
|
| 46 |
+
Swin,i,2.567286303862929,0.5821666666666667,0.7211619444444445,0.3014767344664252
|
| 47 |
+
Swin,j,0.42502203929424287,0.8855,0.9479796111111113,0.8833021912688975
|
| 48 |
+
Swin,k,0.28976670680940153,0.9253333333333333,0.9796539999999999,0.9206798866855525
|
| 49 |
+
Swin,l,1.03963865723415,0.8099518798582835,0.7976283082751249,0.6403842305383229
|
| 50 |
+
CaiT,a,0.3913202127339292,0.8953159383841559,0.6907965009208104,0.31901840490797545
|
| 51 |
+
CaiT,b,0.5226519536631019,0.8626218170386671,0.7338508287292819,0.2630691399662732
|
| 52 |
+
CaiT,c,0.3735890439830086,0.8984596038981453,0.6786878453038674,0.325678496868476
|
| 53 |
+
CaiT,d,0.2843286003735934,0.9254951273184533,0.76402394106814,0.3969465648854962
|
| 54 |
+
CaiT,e,0.6834587411940687,0.8463227222832053,0.7675773859078181,0.527027027027027
|
| 55 |
+
CaiT,f,0.27040889076227814,0.918054372240725,0.7197493196998433,0.128500823723229
|
| 56 |
+
CaiT,g,2.0468120236396787,0.5761666666666667,0.6781162222222221,0.38351515151515153
|
| 57 |
+
CaiT,h,1.967783824443817,0.5951666666666666,0.5982157777777778,0.3944153577661431
|
| 58 |
+
CaiT,i,1.92046093159914,0.6095,0.7142172222222223,0.40305732484076434
|
| 59 |
+
CaiT,j,0.30098878836631776,0.9125,0.9733297777777777,0.9145368712355526
|
| 60 |
+
CaiT,k,0.1746376877427101,0.9458333333333333,0.9841325555555557,0.9453138145717651
|
| 61 |
+
CaiT,l,0.8100430545029764,0.817143461477447,0.813651736379369,0.6802293323469576
|
| 62 |
+
DeiT,a,0.3698357029348677,0.9119773656082992,0.7087136279926335,0.37777777777777777
|
| 63 |
+
DeiT,b,0.5088012874857205,0.8833700094309965,0.7757348066298342,0.3142329020332717
|
| 64 |
+
DeiT,c,0.3891148048258922,0.9160641307764854,0.7151408839779005,0.3890160183066362
|
| 65 |
+
DeiT,d,0.32573777145838745,0.9352404904118202,0.8077476979742173,0.4521276595744681
|
| 66 |
+
DeiT,e,0.7152948476881215,0.862788144895719,0.814546280178612,0.576271186440678
|
| 67 |
+
DeiT,f,0.2608369113050038,0.9330028657733715,0.7554304661629335,0.1642512077294686
|
| 68 |
+
DeiT,g,2.54885491502285,0.5731666666666667,0.6792770555555556,0.35798445725745803
|
| 69 |
+
DeiT,h,2.485401116847992,0.5905,0.5861576111111111,0.3675675675675676
|
| 70 |
+
DeiT,i,2.451800708413124,0.6006666666666667,0.7191378888888889,0.37343096234309625
|
| 71 |
+
DeiT,j,0.43799715077877044,0.9003333333333333,0.9592409444444444,0.8995295698924731
|
| 72 |
+
DeiT,k,0.3409429641962051,0.9278333333333333,0.9696212777777777,0.9251771211335753
|
| 73 |
+
DeiT,l,1.0167739923843866,0.816297393051663,0.8008547085670262,0.6667945520813351
|
| 74 |
+
DeiT3,a,0.41754333621036777,0.9192077962904747,0.7515147329650091,0.43015521064301554
|
| 75 |
+
DeiT3,b,0.5932955155673173,0.8773970449544168,0.7794677716390424,0.3321917808219178
|
| 76 |
+
DeiT3,c,0.4092358484072567,0.9154353976736875,0.7444696132596684,0.4190064794816415
|
| 77 |
+
DeiT3,d,0.5747989024035925,0.8783401446086136,0.7588406998158379,0.33390705679862304
|
| 78 |
+
DeiT3,e,0.7725568269302764,0.8759604829857299,0.8239688185877545,0.6319218241042345
|
| 79 |
+
DeiT3,f,0.3546846674270619,0.916350398884672,0.7623233064106626,0.152276295133438
|
| 80 |
+
DeiT3,g,2.8653497416973113,0.5903333333333334,0.6676445555555556,0.40828117477130477
|
| 81 |
+
DeiT3,h,2.767767428398132,0.6105,0.5985711666666667,0.4205306223654848
|
| 82 |
+
DeiT3,i,2.8555434824228287,0.5908333333333333,0.6332408888888889,0.40857624668754516
|
| 83 |
+
DeiT3,j,0.407473158121109,0.9088333333333334,0.9701894444444443,0.9098103874690849
|
| 84 |
+
DeiT3,k,0.39766689217090606,0.9093333333333333,0.9684031111111111,0.9102606400527878
|
| 85 |
+
DeiT3,l,1.1462537638319459,0.8163502723282745,0.8007222930468951,0.6808197775939712
|
| 86 |
+
Twins_SVT,a,0.4471702475530552,0.8126375353662371,0.6335423572744014,0.1989247311827957
|
| 87 |
+
Twins_SVT,b,0.4493988096264315,0.8060358377868595,0.6959318600368325,0.1934640522875817
|
| 88 |
+
Twins_SVT,c,0.5063522113864807,0.7780572147123546,0.5922486187845304,0.17330210772833723
|
| 89 |
+
Twins_SVT,d,0.3254203815124425,0.8849418421879912,0.7219650092081031,0.28793774319066145
|
| 90 |
+
Twins_SVT,e,0.5195407480099591,0.7771679473106476,0.7089003254370696,0.42165242165242167
|
| 91 |
+
Twins_SVT,f,0.3887786737239737,0.8404461312059485,0.6636732736434142,0.06702898550724638
|
| 92 |
+
Twins_SVT,g,1.2475184862613677,0.4825,0.532602,0.20689655172413793
|
| 93 |
+
Twins_SVT,h,1.277713261127472,0.4676666666666667,0.36107222222222224,0.2022977022977023
|
| 94 |
+
Twins_SVT,i,1.1817892324924468,0.5243333333333333,0.5369667222222223,0.2210698689956332
|
| 95 |
+
Twins_SVT,j,0.5273123075962066,0.7598333333333334,0.8436975000000001,0.7417099838680767
|
| 96 |
+
Twins_SVT,k,0.46158305954933165,0.8016666666666666,0.8776625,0.7766516516516516
|
| 97 |
+
Twins_SVT,l,0.7025143162813111,0.7046163608481836,0.6522568273932748,0.47706422018348627
|
| 98 |
+
Twins_PCPVT,a,0.45982081515914197,0.7900031436655139,0.6319235727440148,0.17326732673267325
|
| 99 |
+
Twins_PCPVT,b,0.37307003830934016,0.8333857277585665,0.729316758747698,0.208955223880597
|
| 100 |
+
Twins_PCPVT,c,0.5298199271376273,0.7510216912920465,0.5787163904235728,0.15021459227467812
|
| 101 |
+
Twins_PCPVT,d,0.4890483941382786,0.7840301791889343,0.6198968692449357,0.16928657799274485
|
| 102 |
+
Twins_PCPVT,e,0.4528412980515138,0.8068057080131723,0.7623628244910315,0.4430379746835443
|
| 103 |
+
Twins_PCPVT,f,0.41534574994755774,0.8134149175121989,0.6469823751264034,0.05492349941153393
|
| 104 |
+
Twins_PCPVT,g,0.97830464220047,0.5461666666666667,0.6621347777777777,0.33827460510328067
|
| 105 |
+
Twins_PCPVT,h,1.0614082341194153,0.5025,0.47131744444444446,0.31802604523646333
|
| 106 |
+
Twins_PCPVT,i,1.0397925007343292,0.52,0.5257673333333334,0.3258426966292135
|
| 107 |
+
Twins_PCPVT,j,0.36769862127304076,0.8383333333333334,0.9181693333333335,0.834696659850034
|
| 108 |
+
Twins_PCPVT,k,0.42918648648262026,0.8121666666666667,0.8860434444444444,0.8129460580912863
|
| 109 |
+
Twins_PCPVT,l,0.6099785904964774,0.7216434879170853,0.7243182138507473,0.5498546263040875
|
| 110 |
+
PiT,a,0.37776082014932605,0.8651367494498585,0.6834337016574586,0.25906735751295334
|
| 111 |
+
PiT,b,0.44755573657390196,0.8365293932725558,0.7427127071823205,0.22388059701492538
|
| 112 |
+
PiT,c,0.40049510616170875,0.8528764539453002,0.6488581952117863,0.24271844660194175
|
| 113 |
+
PiT,d,0.23405979966281606,0.9214083621502672,0.7678987108655617,0.375
|
| 114 |
+
PiT,e,0.4743333708670739,0.8430296377607025,0.8043820479830468,0.5119453924914675
|
| 115 |
+
PiT,f,0.2892587873664891,0.8926496785686624,0.716096531011705,0.09765625
|
| 116 |
+
PiT,g,1.521324759721756,0.547,0.6473084444444445,0.3386861313868613
|
| 117 |
+
PiT,h,1.4963747837543488,0.5556666666666666,0.5119071111111111,0.3430261212419911
|
| 118 |
+
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roc_confusion_matrix/PiT_roc_confusion_matrix_a.png
ADDED
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roc_confusion_matrix/PiT_roc_confusion_matrix_b.png
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roc_confusion_matrix/PiT_roc_confusion_matrix_c.png
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roc_confusion_matrix/PiT_roc_confusion_matrix_e.png
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roc_confusion_matrix/PiT_roc_confusion_matrix_f.png
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roc_confusion_matrix/PiT_roc_confusion_matrix_g.png
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ADDED
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roc_confusion_matrix/PiT_roc_confusion_matrix_k.png
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ADDED
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roc_curves/PiT_ROC_a.png
ADDED
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ADDED
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ADDED
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ADDED
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ADDED
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ADDED
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ADDED
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ADDED
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ADDED
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training_curves/PiT_accuracy.png
ADDED
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training_curves/PiT_auc.png
ADDED
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training_curves/PiT_combined_metrics.png
ADDED
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Git LFS Details
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training_curves/PiT_f1.png
ADDED
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training_curves/PiT_loss.png
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training_curves/PiT_metrics.csv
ADDED
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|
training_metrics.csv
ADDED
|
@@ -0,0 +1,101 @@
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| 1 |
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epoch,train_loss,val_loss,train_accuracy,val_accuracy,train_auc,val_auc,train_f1,val_f1
|
| 2 |
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