# SID-UNet Evaluation Report

### Run Configuration & Hyperparameters

| Hyperparameter / Setting   | Configured Value                                         |
|----------------------------|----------------------------------------------------------|
| Project Name               | test_smoke                                               |
| Dataset                    | KhangTruong/IMD2020                                      |
| Splits                     | Train: 'train' | Val: 'validation' | Test: 'test'        |
| Streaming                  | False                                                    |
| Image Size                 | [32, 32]                                                 |
| Batch Size                 | 16                                                       |
| Epoch Limit / Steps        | 2 samples/epoch                                          |
| Model                      | unet (features=[8, 16], bilinear=True)                   |
| Aux Classifier             | False (classes=3)                                        |
| Mask Loss                  | Combined (BCE: 0.5, Dice: 0.5, Focal: 0.5)               |
| Aux Loss                   | cross_entropy (wt=0.2)                                   |
| Post-Processing            | Enabled (min_area=64, fill_holes=True, morph=open_close) |
| Optimizer                  | adamw (lr=0.001, wd=0.0001)                              |
| Scheduler                  | cosine                                                   |
| Epochs                     | 1                                                        |
| AMP                        | False                                                    |

### Overall Segmentation & Classification Metrics

| Metric          |   Value |
|-----------------|---------|
| Val Bce Loss    |  0.6957 |
| Val Dice Loss   |  0.608  |
| Val Focal Loss  |  0.0759 |
| Val Mask Loss   |  0.6898 |
| Val Total Loss  |  0.6898 |
| Val Iou         |  0.1676 |
| Val Dice        |  0.2576 |
| Val F1          |  0.2576 |
| Val Pixel F1    |  0.2576 |
| Val Auroc       |  0.69   |
| Val Pixel Auroc |  0.69   |
| Val Pixel Acc   |  0.485  |
| Val Precision   |  0.6    |
| Val Recall      |  0.1676 |
| Val Specificity |  0.9174 |

### Training & Validation Curves

![Training Curves](training_curves.png)

### Epoch-by-Epoch Training & Validation Progression

|   Epoch |   Train Loss |   Val Loss |   Val IoU |   Val F1 |   Val AUROC |   Val Pixel Acc |   Learning Rate |
|---------|--------------|------------|-----------|----------|-------------|-----------------|-----------------|
|       1 |       0.6793 |     0.6806 |    0.6    |   0.6    |      0.7947 |          0.6    |        0.001    |
|       2 |       0.8813 |     0.9076 |    0.0129 |   0.0254 |      0.5057 |          0.0143 |        0.000251 |
|       3 |       0.6155 |     0.6859 |    0.1782 |   0.2147 |      0.6987 |          0.416  |        0.000147 |
|       4 |       0.7411 |     0.6793 |    0.6    |   0.6    |      0.7932 |          0.6    |        0.001    |
|       5 |       0.6793 |     0.6816 |    0.6    |   0.6    |      0.7959 |          0.6    |        6.79e-05 |
|       6 |       0.6755 |     0.6836 |    0.5813 |   0.5904 |      0.7982 |          0.591  |        5.05e-05 |
|       7 |       0.6724 |     0.6858 |    0.0902 |   0.1493 |      0.6492 |          0.3758 |        3.9e-05  |
|       8 |       0.67   |     0.6878 |    0.0186 |   0.036  |      0.6962 |          0.4041 |        3.11e-05 |
|       9 |       0.668  |     0.6899 |    0.015  |   0.0292 |      0.6933 |          0.4035 |        2.54e-05 |
|      10 |       0.6664 |     0.6917 |    0.0142 |   0.0275 |      0.6906 |          0.4023 |        2.12e-05 |
|      11 |       0.6651 |     0.6929 |    0.0151 |   0.0294 |      0.6882 |          0.403  |        1.8e-05  |
|      12 |       0.664  |     0.6951 |    0.0158 |   0.0307 |      0.6857 |          0.4033 |        1.55e-05 |
|      13 |       0.663  |     0.6981 |    0.0176 |   0.0341 |      0.6831 |          0.3997 |        1.35e-05 |
|      14 |       0.6621 |     0.7005 |    0.0287 |   0.0545 |      0.6809 |          0.4055 |        1.19e-05 |
|      15 |       0.6614 |     0.7018 |    0.0394 |   0.0728 |      0.6801 |          0.4048 |        1.06e-05 |
|      16 |       0.6607 |     0.6998 |    0.0573 |   0.1017 |      0.6816 |          0.4112 |        9.5e-06  |
|      17 |       0.6601 |     0.6964 |    0.0835 |   0.1404 |      0.6843 |          0.423  |        8.59e-06 |
|      18 |       0.6596 |     0.6927 |    0.1124 |   0.1821 |      0.687  |          0.4475 |        7.81e-06 |
|      19 |       0.6591 |     0.69   |    0.1313 |   0.2095 |      0.6889 |          0.4612 |        7.15e-06 |
|      20 |       0.6586 |     0.6888 |    0.1456 |   0.2294 |      0.6899 |          0.4709 |        6.58e-06 |
|      21 |       0.6582 |     0.6893 |    0.1593 |   0.2474 |      0.69   |          0.479  |        6.08e-06 |
|      22 |       0.6578 |     0.6898 |    0.1676 |   0.2576 |      0.69   |          0.485  |        1e-06    |

### Per-Subset Metrics Breakdown

| Subset                            |   Mean IoU |   Dice / F1 |   Pixel Accuracy |   Sample Count |
|-----------------------------------|------------|-------------|------------------|----------------|
| Label 0 (Real / Black Mask)       |     0      |      0      |           0.7935 |              4 |
| Label 1 (Synthetic / White Mask)  |     0.3099 |      0.4658 |           0.3099 |              3 |
| Label 2 (Tampered / Partial Mask) |     0.2487 |      0.3929 |           0.2487 |              3 |