Image Classification
timm
PyTorch
Safetensors
vision-transformer
deit3
gravitational-lensing
strong-lensing
astronomy
astrophysics
Eval Results (legacy)
Instructions to use parlange/deit3-gravit-a1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use parlange/deit3-gravit-a1 with timm:
import timm model = timm.create_model("hf_hub:parlange/deit3-gravit-a1", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Upload DeiT3 model from experiment a1
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/DeiT3_Confusion_Matrix_a.png +0 -0
- confusion_matrices/DeiT3_Confusion_Matrix_b.png +0 -0
- confusion_matrices/DeiT3_Confusion_Matrix_c.png +0 -0
- confusion_matrices/DeiT3_Confusion_Matrix_d.png +0 -0
- confusion_matrices/DeiT3_Confusion_Matrix_e.png +0 -0
- confusion_matrices/DeiT3_Confusion_Matrix_f.png +0 -0
- confusion_matrices/DeiT3_Confusion_Matrix_g.png +0 -0
- confusion_matrices/DeiT3_Confusion_Matrix_h.png +0 -0
- confusion_matrices/DeiT3_Confusion_Matrix_i.png +0 -0
- confusion_matrices/DeiT3_Confusion_Matrix_j.png +0 -0
- confusion_matrices/DeiT3_Confusion_Matrix_k.png +0 -0
- confusion_matrices/DeiT3_Confusion_Matrix_l.png +0 -0
- deit3-gravit-a1.pth +3 -0
- evaluation_results.csv +133 -0
- model.safetensors +3 -0
- pytorch_model.bin +3 -0
- roc_confusion_matrix/DeiT3_roc_confusion_matrix_a.png +0 -0
- roc_confusion_matrix/DeiT3_roc_confusion_matrix_b.png +0 -0
- roc_confusion_matrix/DeiT3_roc_confusion_matrix_c.png +0 -0
- roc_confusion_matrix/DeiT3_roc_confusion_matrix_d.png +0 -0
- roc_confusion_matrix/DeiT3_roc_confusion_matrix_e.png +0 -0
- roc_confusion_matrix/DeiT3_roc_confusion_matrix_f.png +0 -0
- roc_confusion_matrix/DeiT3_roc_confusion_matrix_g.png +0 -0
- roc_confusion_matrix/DeiT3_roc_confusion_matrix_h.png +0 -0
- roc_confusion_matrix/DeiT3_roc_confusion_matrix_i.png +0 -0
- roc_confusion_matrix/DeiT3_roc_confusion_matrix_j.png +0 -0
- roc_confusion_matrix/DeiT3_roc_confusion_matrix_k.png +0 -0
- roc_confusion_matrix/DeiT3_roc_confusion_matrix_l.png +0 -0
- roc_curves/DeiT3_ROC_a.png +0 -0
- roc_curves/DeiT3_ROC_b.png +0 -0
- roc_curves/DeiT3_ROC_c.png +0 -0
- roc_curves/DeiT3_ROC_d.png +0 -0
- roc_curves/DeiT3_ROC_e.png +0 -0
- roc_curves/DeiT3_ROC_f.png +0 -0
- roc_curves/DeiT3_ROC_g.png +0 -0
- roc_curves/DeiT3_ROC_h.png +0 -0
- roc_curves/DeiT3_ROC_i.png +0 -0
- roc_curves/DeiT3_ROC_j.png +0 -0
- roc_curves/DeiT3_ROC_k.png +0 -0
- roc_curves/DeiT3_ROC_l.png +0 -0
- training_curves/DeiT3_accuracy.png +0 -0
- training_curves/DeiT3_auc.png +0 -0
- training_curves/DeiT3_combined_metrics.png +3 -0
- training_curves/DeiT3_f1.png +0 -0
- training_curves/DeiT3_loss.png +0 -0
- training_curves/DeiT3_metrics.csv +101 -0
- training_metrics.csv +101 -0
.gitattributes
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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training_curves/DeiT3_combined_metrics.png filter=lfs diff=lfs merge=lfs -text
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training_notebook_a1.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 |
+
- pytorch
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| 7 |
+
- timm
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| 8 |
+
- deit3
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| 9 |
+
- gravitational-lensing
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| 10 |
+
- strong-lensing
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| 11 |
+
- astronomy
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| 12 |
+
- astrophysics
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| 13 |
+
datasets:
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| 14 |
+
- C21
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| 15 |
+
metrics:
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| 16 |
+
- accuracy
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| 17 |
+
- auc
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| 18 |
+
- f1
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| 19 |
+
model-index:
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| 20 |
+
- name: DeiT3-a1
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| 21 |
+
results:
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| 22 |
+
- 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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| 25 |
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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: 0.8213
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| 31 |
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name: Average Accuracy
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| 32 |
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- type: auc
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value: 0.8238
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| 34 |
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name: Average AUC-ROC
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- type: f1
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value: 0.5164
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| 37 |
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name: Average F1-Score
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| 38 |
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---
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| 39 |
+
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| 40 |
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# 🌌 deit3-gravit-a1
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| 41 |
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| 42 |
+
🔭 This model is part of **GraViT**: Transfer Learning with Vision Transformers and MLP-Mixer for Strong Gravitational Lens Discovery
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| 43 |
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| 44 |
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🔗 **GitHub Repository**: [https://github.com/parlange/gravit](https://github.com/parlange/gravit)
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| 45 |
+
|
| 46 |
+
## 🛰️ Model Details
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| 47 |
+
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| 48 |
+
- **🤖 Model Type**: DeiT3
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| 49 |
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- **🧪 Experiment**: A1 - C21-classification-head
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| 50 |
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- **🌌 Dataset**: C21
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| 51 |
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- **🪐 Fine-tuning Strategy**: classification-head
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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/deit3-gravit-a1',
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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 |
+
# Example inference
|
| 69 |
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dummy_input = torch.randn(1, 3, 224, 224)
|
| 70 |
+
with torch.no_grad():
|
| 71 |
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output = model(dummy_input)
|
| 72 |
+
predictions = torch.softmax(output, dim=1)
|
| 73 |
+
print(f"Lens probability: {predictions[0][1]:.4f}")
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| 74 |
+
```
|
| 75 |
+
|
| 76 |
+
## ⚡️ Training Configuration
|
| 77 |
+
|
| 78 |
+
**Training Dataset:** C21 (Cañameras et al. 2021)
|
| 79 |
+
**Fine-tuning Strategy:** classification-head
|
| 80 |
+
|
| 81 |
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|
| 82 |
+
| 🔧 Parameter | 📝 Value |
|
| 83 |
+
|--------------|----------|
|
| 84 |
+
| Batch Size | 192 |
|
| 85 |
+
| Learning Rate | AdamW with ReduceLROnPlateau |
|
| 86 |
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| Epochs | 100 |
|
| 87 |
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| Patience | 10 |
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| 88 |
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| Optimizer | AdamW |
|
| 89 |
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| Scheduler | ReduceLROnPlateau |
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| 90 |
+
| Image Size | 224x224 |
|
| 91 |
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| Fine Tune Mode | classification_head |
|
| 92 |
+
| Stochastic Depth Probability | 0.1 |
|
| 93 |
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|
| 94 |
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|
| 95 |
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## 📈 Training Curves
|
| 96 |
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|
| 97 |
+

|
| 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 | 0.2031 | 0.2811 |
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| 105 |
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| 🎯 Accuracy | 0.9194 | 0.9070 |
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| 106 |
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| 📊 AUC-ROC | 0.9759 | 0.9564 |
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| 107 |
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| ⚖️ F1 Score | 0.9194 | 0.9071 |
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| 108 |
+
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| 109 |
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| 110 |
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## ☑️ Evaluation Results
|
| 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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| 116 |
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| 117 |
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| 118 |
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| 119 |
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| 126 |
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| 127 |
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| 128 |
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| 129 |
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### 📋 Performance Summary
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| 130 |
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| 131 |
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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 |
+
|-----------|----------|
|
| 135 |
+
| 🎯 Average Accuracy | 0.8213 |
|
| 136 |
+
| 📈 Average AUC-ROC | 0.8238 |
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| 137 |
+
| ⚖️ Average F1-Score | 0.5164 |
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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 |
+
```
|
| 155 |
+
|
| 156 |
+
---
|
| 157 |
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| 158 |
+
|
| 159 |
+
## Model Card Contact
|
| 160 |
+
|
| 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": "deit3_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_a1",
|
| 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": "deit3_gravit_a1",
|
| 60 |
+
"experiment": "a1",
|
| 61 |
+
"training_strategy": "classification-head",
|
| 62 |
+
"dataset": "C21",
|
| 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": "classification_head",
|
| 72 |
+
"stochastic_depth_probability": "0.1"
|
| 73 |
+
},
|
| 74 |
+
"hf_hub_id": "parlange/deit3-gravit-a1",
|
| 75 |
+
"license": "apache-2.0"
|
| 76 |
+
}
|
confusion_matrices/DeiT3_Confusion_Matrix_a.png
ADDED
|
confusion_matrices/DeiT3_Confusion_Matrix_b.png
ADDED
|
confusion_matrices/DeiT3_Confusion_Matrix_c.png
ADDED
|
confusion_matrices/DeiT3_Confusion_Matrix_d.png
ADDED
|
confusion_matrices/DeiT3_Confusion_Matrix_e.png
ADDED
|
confusion_matrices/DeiT3_Confusion_Matrix_f.png
ADDED
|
confusion_matrices/DeiT3_Confusion_Matrix_g.png
ADDED
|
confusion_matrices/DeiT3_Confusion_Matrix_h.png
ADDED
|
confusion_matrices/DeiT3_Confusion_Matrix_i.png
ADDED
|
confusion_matrices/DeiT3_Confusion_Matrix_j.png
ADDED
|
confusion_matrices/DeiT3_Confusion_Matrix_k.png
ADDED
|
confusion_matrices/DeiT3_Confusion_Matrix_l.png
ADDED
|
deit3-gravit-a1.pth
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:40c54c3c6d7bc946e7b20ade559330bb0c0e87e1a25101c0a457ffd93f6ff9d2
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size 343337390
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evaluation_results.csv
ADDED
|
@@ -0,0 +1,133 @@
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
Model,Dataset,Loss,Accuracy,AUCROC,F1
|
| 2 |
+
ViT,a,0.2907674585365717,0.8663942156554543,0.8790764272559853,0.36281859070464767
|
| 3 |
+
ViT,b,0.3363787103321372,0.8409305249921408,0.8598268876611419,0.3235294117647059
|
| 4 |
+
ViT,c,0.3624865817540249,0.8145237346746306,0.8460092081031308,0.29086538461538464
|
| 5 |
+
ViT,d,0.20147570416317237,0.9100911662999057,0.914537753222836,0.4583333333333333
|
| 6 |
+
ViT,e,0.38351454359247183,0.8397365532381997,0.8551653674411563,0.6237113402061856
|
| 7 |
+
ViT,f,0.28049477608445245,0.8673224382309659,0.873733035896412,0.12378516624040921
|
| 8 |
+
ViT,g,0.20559095215797424,0.9115,0.982819,0.9165225593460148
|
| 9 |
+
ViT,h,0.21943247628211976,0.8975,0.9817622222222222,0.9045771916214119
|
| 10 |
+
ViT,i,0.13406987351179123,0.9481666666666667,0.9929653333333334,0.9493567822830158
|
| 11 |
+
ViT,j,1.5079676084518432,0.4855,0.5106800555555556,0.18869908015768724
|
| 12 |
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ViT,k,1.4364465267062188,0.5221666666666667,0.6366698888888889,0.200278940027894
|
| 13 |
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ViT,l,0.6356719980429736,0.7652688911215695,0.7653929261211886,0.6046842995814409
|
| 14 |
+
MLP-Mixer,a,0.23711907013393504,0.904118201823326,0.8973158379373849,0.40545808966861596
|
| 15 |
+
MLP-Mixer,b,0.32535140213071456,0.8626218170386671,0.8485451197053407,0.32248062015503876
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| 16 |
+
MLP-Mixer,c,0.26777226502923085,0.8937441056271612,0.8793204419889503,0.38095238095238093
|
| 17 |
+
MLP-Mixer,d,0.24414238826503773,0.9034894687205282,0.8925340699815838,0.40388349514563104
|
| 18 |
+
MLP-Mixer,e,0.4684625132219982,0.7826564215148188,0.7941497010519943,0.5123152709359606
|
| 19 |
+
MLP-Mixer,f,0.25652938471803655,0.9010920920145612,0.8745385460021788,0.14006734006734006
|
| 20 |
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MLP-Mixer,g,0.21773884654045106,0.9175,0.981478,0.9204819277108434
|
| 21 |
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MLP-Mixer,h,0.18721229648590088,0.934,0.987164111111111,0.9353574926542605
|
| 22 |
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MLP-Mixer,i,0.17468452334403992,0.9391666666666667,0.9881612222222221,0.9401148482362592
|
| 23 |
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MLP-Mixer,j,1.2574891588687898,0.499,0.5052343333333333,0.19063004846526657
|
| 24 |
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MLP-Mixer,k,1.2144348313808442,0.5206666666666667,0.6023342222222222,0.19754464285714285
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| 25 |
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MLP-Mixer,l,0.5478135314681644,0.7854158955105495,0.7718881621999252,0.6208893871449925
|
| 26 |
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CvT,a,0.48862357091431496,0.7661112857591952,0.7645202578268876,0.23613963039014374
|
| 27 |
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CvT,b,0.606147515758783,0.6821754165356806,0.7114585635359115,0.185334407735697
|
| 28 |
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CvT,c,0.5599785684152655,0.7161270040867652,0.7296648250460406,0.2030008826125331
|
| 29 |
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CvT,d,0.2567151319639595,0.8965734045897517,0.8801289134438306,0.41144901610017887
|
| 30 |
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CvT,e,0.634658475738718,0.677277716794731,0.7027472943313403,0.4389312977099237
|
| 31 |
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CvT,f,0.4719573438800043,0.7663232902176439,0.767503786678703,0.07083461656914075
|
| 32 |
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CvT,g,0.37052693724632263,0.824,0.9481419444444444,0.845478489903424
|
| 33 |
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CvT,h,0.34604967188835145,0.842,0.9550463333333333,0.8590544157002676
|
| 34 |
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CvT,i,0.18526951444149017,0.9376666666666666,0.9862493333333333,0.9392067620286085
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| 35 |
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CvT,j,1.7297569365501404,0.37716666666666665,0.23870022222222223,0.10016855285335902
|
| 36 |
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CvT,k,1.5444995085000992,0.49083333333333334,0.47829777777777777,0.1198501872659176
|
| 37 |
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CvT,l,0.7996079008316138,0.686954682459944,0.637911609367734,0.5204147764095917
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| 38 |
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Swin,a,0.41406949968579354,0.8063502043382584,0.9315,0.346072186836518
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Swin,b,0.46100307634250803,0.7742848160955674,0.9219889502762431,0.31226053639846746
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Swin,i,0.08781581997871399,0.9746666666666667,0.9978362222222223,0.974950560316414
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Swin,j,2.0428302302360533,0.42033333333333334,0.3025185555555555,0.11320754716981132
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Swin,l,0.8495750360742048,0.7241816931944371,0.6664912098538803,0.5617543270038649
|
| 50 |
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CaiT,a,0.40013275007637966,0.8390443256837472,0.9108057090239411,0.3694581280788177
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| 51 |
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CaiT,b,0.36606653323716165,0.8506758880855076,0.9150920810313077,0.3870967741935484
|
| 52 |
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CaiT,c,0.45299853780328836,0.8003772398616787,0.8942338858195211,0.32085561497326204
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| 53 |
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CaiT,d,0.1995404364271233,0.9261238604212512,0.9594475138121547,0.5607476635514018
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CaiT,e,0.4928394595302159,0.7771679473106476,0.8707636418678574,0.5964214711729622
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CaiT,f,0.36103595997415405,0.850050344667338,0.9170773784465286,0.13416815742397137
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CaiT,g,0.2526822371482849,0.9083333333333333,0.976861222222222,0.9132218365414957
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CaiT,h,0.2987706809043884,0.8816666666666667,0.9732373333333334,0.8907356109572176
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CaiT,i,0.16439563977718352,0.9483333333333334,0.9884212222222222,0.9491636602164644
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CaiT,j,1.6938938024044037,0.48483333333333334,0.34579655555555555,0.18593626547274164
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| 60 |
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CaiT,k,1.6056072112321853,0.5248333333333334,0.5246175555555556,0.1984818667416362
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CaiT,l,0.7488868967172212,0.7520490719686954,0.6676479234122561,0.5916572324305495
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DeiT,a,0.4765459399334255,0.7749135491983653,0.850939226519337,0.28112449799196787
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DeiT,b,0.29641423940058964,0.8814838101226029,0.9148747697974217,0.426179604261796
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DeiT,c,0.4700488794977955,0.7830870795347376,0.8519852670349909,0.28865979381443296
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DeiT,d,0.11101193318283434,0.9657340458975165,0.9766685082872928,0.7197943444730077
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DeiT,e,0.4436056611305011,0.8035126234906695,0.8648906380080225,0.6100217864923747
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DeiT,f,0.337409115804358,0.8522964913639532,0.8966829128564795,0.12802926383173296
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DeiT,g,0.1696254106760025,0.9383333333333334,0.9919374444444445,0.9412884798476674
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DeiT,h,0.26168069899082186,0.8861666666666667,0.9872772222222223,0.8967498110355253
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DeiT,i,0.07133128488063813,0.983,0.998361111111111,0.9830957905203845
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DeiT,j,1.940858449459076,0.4736666666666667,0.36180238888888894,0.10130904951622083
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DeiT,k,1.8425643298625947,0.5183333333333333,0.5667426111111111,0.10967344423906346
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DeiT,l,0.8096289309307544,0.7481360054994448,0.6678239118866791,0.5796487512134851
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DeiT3,a,0.3153771912384543,0.8786545111600126,0.8877375690607736,0.396875
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DeiT3,b,0.27676928310377796,0.8972021376925495,0.9046813996316758,0.43717728055077454
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DeiT3,c,0.3605959014448869,0.8566488525620874,0.867316758747698,0.35774647887323946
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DeiT3,e,0.42457847907305024,0.8111964873765093,0.853205176719897,0.596244131455399
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DeiT3,h,0.2210533607006073,0.9213333333333333,0.9848492222222223,0.9254579911560329
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model.safetensors
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roc_confusion_matrix/DeiT3_roc_confusion_matrix_c.png
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roc_confusion_matrix/DeiT3_roc_confusion_matrix_d.png
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roc_confusion_matrix/DeiT3_roc_confusion_matrix_e.png
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roc_confusion_matrix/DeiT3_roc_confusion_matrix_f.png
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roc_confusion_matrix/DeiT3_roc_confusion_matrix_g.png
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roc_confusion_matrix/DeiT3_roc_confusion_matrix_h.png
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roc_confusion_matrix/DeiT3_roc_confusion_matrix_j.png
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roc_confusion_matrix/DeiT3_roc_confusion_matrix_k.png
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roc_confusion_matrix/DeiT3_roc_confusion_matrix_l.png
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roc_curves/DeiT3_ROC_a.png
ADDED
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roc_curves/DeiT3_ROC_b.png
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roc_curves/DeiT3_ROC_c.png
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roc_curves/DeiT3_ROC_d.png
ADDED
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roc_curves/DeiT3_ROC_e.png
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roc_curves/DeiT3_ROC_f.png
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roc_curves/DeiT3_ROC_g.png
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roc_curves/DeiT3_ROC_h.png
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roc_curves/DeiT3_ROC_i.png
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roc_curves/DeiT3_ROC_j.png
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ADDED
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training_curves/DeiT3_accuracy.png
ADDED
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training_curves/DeiT3_auc.png
ADDED
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training_curves/DeiT3_combined_metrics.png
ADDED
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Git LFS Details
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training_curves/DeiT3_f1.png
ADDED
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training_curves/DeiT3_loss.png
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training_curves/DeiT3_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
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| 2 |
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