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KhangTruong
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Testing-model

Image Segmentation
PyTorch
sid-unet
synthetic-image-detection
diffusion-detection
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KhangTruong commited on
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feat(release): final training report and checkpoints for KhangTruong/Testing-model (v1)

Browse files
Files changed (28) hide show
  1. README.md +1 -1
  2. checkpoints/checkpoint_latest.pt +2 -2
  3. checkpoints/checkpoint_latest_config.yaml +1 -1
  4. checkpoints/effective_config.yaml +1 -1
  5. config.yaml +1 -1
  6. manifest.json +3 -3
  7. reports/test_evaluation_report.json +14 -14
  8. reports/test_evaluation_report.md +10 -10
  9. reports/training_curves.jpg +2 -2
  10. reports/training_curves.pdf +0 -0
  11. reports/training_curves.png +2 -2
  12. reports/training_final_report.json +112 -66
  13. reports/training_final_report.md +19 -18
  14. reports/training_history.csv +2 -1
  15. reports/training_history.json +78 -32
  16. versions/v1/checkpoint_latest_config.yaml +1 -1
  17. versions/v1/checkpoints/checkpoint_latest.pt +2 -2
  18. versions/v1/effective_config.yaml +1 -1
  19. versions/v1/manifest.json +3 -3
  20. versions/v1/reports/test_evaluation_report.json +14 -14
  21. versions/v1/reports/test_evaluation_report.md +10 -10
  22. versions/v1/reports/training_curves.jpg +2 -2
  23. versions/v1/reports/training_curves.pdf +0 -0
  24. versions/v1/reports/training_curves.png +2 -2
  25. versions/v1/reports/training_final_report.json +112 -66
  26. versions/v1/reports/training_final_report.md +19 -18
  27. versions/v1/reports/training_history.csv +2 -1
  28. versions/v1/reports/training_history.json +78 -32
README.md CHANGED
@@ -22,7 +22,7 @@ Official repository for **Testing-model** checkpoints, evaluated on AI-generated
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  - **Active Version:** `v1`
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  - **Best Validation Score:** `0.6` (Epoch `1`)
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- - **Total Training Epochs:** `12`
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  ---
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  - **Active Version:** `v1`
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  - **Best Validation Score:** `0.6` (Epoch `1`)
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+ - **Total Training Epochs:** `13`
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  ---
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  | Metric | Value |
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  |------------------|---------|
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- | Test Bce Loss | 0.6948 |
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- | Test Dice Loss | 0.6765 |
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- | Test Focal Loss | 0.0806 |
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- | Test Mask Loss | 0.7259 |
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- | Test Total Loss | 0.7259 |
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  | Test Iou | 0.001 |
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- | Test Auroc | 0.7378 |
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- | Test Pixel Acc | 0.4868 |
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  | Test Precision | 0.5 |
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  | Test Recall | 0.001 |
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  ### Per-Subset Metrics Breakdown
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  | Subset | Mean IoU | Dice / F1 | Pixel Accuracy | Sample Count |
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  |-----------------------------------|------------|-------------|------------------|----------------|
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- | Label 0 (Real / Black Mask) | 0 | 0 | 0.9717 | 1 |
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  | Metric | Value |
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  |------------------|---------|
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+ | Test Bce Loss | 0.6953 |
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+ | Test Pixel Acc | 0.4878 |
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  | Test Precision | 0.5 |
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  | Test Recall | 0.001 |
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+ | Test Specificity | 0.9868 |
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44
  ### Per-Subset Metrics Breakdown
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46
  | Subset | Mean IoU | Dice / F1 | Pixel Accuracy | Sample Count |
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  |-----------------------------------|------------|-------------|------------------|----------------|
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+ | Label 0 (Real / Black Mask) | 0 | 0 | 0.9736 | 1 |
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  | Label 1 (Synthetic / White Mask) | 0.002 | 0.0039 | 0.002 | 1 |
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reports/training_final_report.md CHANGED
@@ -25,21 +25,21 @@
25
 
26
  | Metric | Value |
27
  |-----------------|---------|
28
- | Val Bce Loss | 0.7021 |
29
- | Val Dice Loss | 0.6108 |
30
- | Val Focal Loss | 0.0746 |
31
- | Val Mask Loss | 0.6937 |
32
- | Val Total Loss | 0.6937 |
33
- | Val Iou | 0.0152 |
34
- | Val Dice | 0.0296 |
35
- | Val F1 | 0.0296 |
36
- | Val Pixel F1 | 0.0296 |
37
- | Val Auroc | 0.687 |
38
- | Val Pixel Auroc | 0.687 |
39
- | Val Pixel Acc | 0.4031 |
40
  | Val Precision | 0.6 |
41
- | Val Recall | 0.0152 |
42
- | Val Specificity | 0.9879 |
43
 
44
  ### Training & Validation Curves
45
 
@@ -60,13 +60,14 @@
60
  | 9 | 0.668 | 0.6899 | 0.015 | 0.0292 | 0.6933 | 0.4035 | 2.54e-05 |
61
  | 10 | 0.6664 | 0.6917 | 0.0142 | 0.0275 | 0.6906 | 0.4023 | 2.12e-05 |
62
  | 11 | 0.6651 | 0.6929 | 0.0151 | 0.0294 | 0.6882 | 0.403 | 1.8e-05 |
63
- | 12 | 0.664 | 0.6937 | 0.0152 | 0.0296 | 0.687 | 0.4031 | 1e-06 |
 
64
 
65
  ### Per-Subset Metrics Breakdown
66
 
67
  | Subset | Mean IoU | Dice / F1 | Pixel Accuracy | Sample Count |
68
  |-----------------------------------|------------|-------------|------------------|----------------|
69
- | Label 0 (Real / Black Mask) | 0 | 0 | 0.9697 | 4 |
70
- | Label 1 (Synthetic / White Mask) | 0.0212 | 0.0411 | 0.0212 | 3 |
71
- | Label 2 (Tampered / Partial Mask) | 0.0296 | 0.0575 | 0.0296 | 3 |
72
 
 
25
 
26
  | Metric | Value |
27
  |-----------------|---------|
28
+ | Val Bce Loss | 0.7056 |
29
+ | Val Dice Loss | 0.6131 |
30
+ | Val Focal Loss | 0.0747 |
31
+ | Val Mask Loss | 0.6967 |
32
+ | Val Total Loss | 0.6967 |
33
+ | Val Iou | 0.0158 |
34
+ | Val Dice | 0.0307 |
35
+ | Val F1 | 0.0307 |
36
+ | Val Pixel F1 | 0.0307 |
37
+ | Val Auroc | 0.6844 |
38
+ | Val Pixel Auroc | 0.6844 |
39
+ | Val Pixel Acc | 0.3995 |
40
  | Val Precision | 0.6 |
41
+ | Val Recall | 0.0158 |
42
+ | Val Specificity | 0.9837 |
43
 
44
  ### Training & Validation Curves
45
 
 
60
  | 9 | 0.668 | 0.6899 | 0.015 | 0.0292 | 0.6933 | 0.4035 | 2.54e-05 |
61
  | 10 | 0.6664 | 0.6917 | 0.0142 | 0.0275 | 0.6906 | 0.4023 | 2.12e-05 |
62
  | 11 | 0.6651 | 0.6929 | 0.0151 | 0.0294 | 0.6882 | 0.403 | 1.8e-05 |
63
+ | 12 | 0.664 | 0.6951 | 0.0158 | 0.0307 | 0.6857 | 0.4033 | 1.55e-05 |
64
+ | 13 | 0.663 | 0.6967 | 0.0158 | 0.0307 | 0.6844 | 0.3995 | 1e-06 |
65
 
66
  ### Per-Subset Metrics Breakdown
67
 
68
  | Subset | Mean IoU | Dice / F1 | Pixel Accuracy | Sample Count |
69
  |-----------------------------------|------------|-------------|------------------|----------------|
70
+ | Label 0 (Real / Black Mask) | 0 | 0 | 0.9592 | 4 |
71
+ | Label 1 (Synthetic / White Mask) | 0.0221 | 0.0429 | 0.0221 | 3 |
72
+ | Label 2 (Tampered / Partial Mask) | 0.0306 | 0.0594 | 0.0306 | 3 |
73
 
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  | Metric | Value |
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versions/v1/reports/training_final_report.md CHANGED
@@ -25,21 +25,21 @@
25
 
26
  | Metric | Value |
27
  |-----------------|---------|
28
- | Val Bce Loss | 0.7021 |
29
- | Val Dice Loss | 0.6108 |
30
- | Val Focal Loss | 0.0746 |
31
- | Val Mask Loss | 0.6937 |
32
- | Val Total Loss | 0.6937 |
33
- | Val Iou | 0.0152 |
34
- | Val Dice | 0.0296 |
35
- | Val F1 | 0.0296 |
36
- | Val Pixel F1 | 0.0296 |
37
- | Val Auroc | 0.687 |
38
- | Val Pixel Auroc | 0.687 |
39
- | Val Pixel Acc | 0.4031 |
40
  | Val Precision | 0.6 |
41
- | Val Recall | 0.0152 |
42
- | Val Specificity | 0.9879 |
43
 
44
  ### Training & Validation Curves
45
 
@@ -60,13 +60,14 @@
60
  | 9 | 0.668 | 0.6899 | 0.015 | 0.0292 | 0.6933 | 0.4035 | 2.54e-05 |
61
  | 10 | 0.6664 | 0.6917 | 0.0142 | 0.0275 | 0.6906 | 0.4023 | 2.12e-05 |
62
  | 11 | 0.6651 | 0.6929 | 0.0151 | 0.0294 | 0.6882 | 0.403 | 1.8e-05 |
63
- | 12 | 0.664 | 0.6937 | 0.0152 | 0.0296 | 0.687 | 0.4031 | 1e-06 |
 
64
 
65
  ### Per-Subset Metrics Breakdown
66
 
67
  | Subset | Mean IoU | Dice / F1 | Pixel Accuracy | Sample Count |
68
  |-----------------------------------|------------|-------------|------------------|----------------|
69
- | Label 0 (Real / Black Mask) | 0 | 0 | 0.9697 | 4 |
70
- | Label 1 (Synthetic / White Mask) | 0.0212 | 0.0411 | 0.0212 | 3 |
71
- | Label 2 (Tampered / Partial Mask) | 0.0296 | 0.0575 | 0.0296 | 3 |
72
 
 
25
 
26
  | Metric | Value |
27
  |-----------------|---------|
28
+ | Val Bce Loss | 0.7056 |
29
+ | Val Dice Loss | 0.6131 |
30
+ | Val Focal Loss | 0.0747 |
31
+ | Val Mask Loss | 0.6967 |
32
+ | Val Total Loss | 0.6967 |
33
+ | Val Iou | 0.0158 |
34
+ | Val Dice | 0.0307 |
35
+ | Val F1 | 0.0307 |
36
+ | Val Pixel F1 | 0.0307 |
37
+ | Val Auroc | 0.6844 |
38
+ | Val Pixel Auroc | 0.6844 |
39
+ | Val Pixel Acc | 0.3995 |
40
  | Val Precision | 0.6 |
41
+ | Val Recall | 0.0158 |
42
+ | Val Specificity | 0.9837 |
43
 
44
  ### Training & Validation Curves
45
 
 
60
  | 9 | 0.668 | 0.6899 | 0.015 | 0.0292 | 0.6933 | 0.4035 | 2.54e-05 |
61
  | 10 | 0.6664 | 0.6917 | 0.0142 | 0.0275 | 0.6906 | 0.4023 | 2.12e-05 |
62
  | 11 | 0.6651 | 0.6929 | 0.0151 | 0.0294 | 0.6882 | 0.403 | 1.8e-05 |
63
+ | 12 | 0.664 | 0.6951 | 0.0158 | 0.0307 | 0.6857 | 0.4033 | 1.55e-05 |
64
+ | 13 | 0.663 | 0.6967 | 0.0158 | 0.0307 | 0.6844 | 0.3995 | 1e-06 |
65
 
66
  ### Per-Subset Metrics Breakdown
67
 
68
  | Subset | Mean IoU | Dice / F1 | Pixel Accuracy | Sample Count |
69
  |-----------------------------------|------------|-------------|------------------|----------------|
70
+ | Label 0 (Real / Black Mask) | 0 | 0 | 0.9592 | 4 |
71
+ | Label 1 (Synthetic / White Mask) | 0.0221 | 0.0429 | 0.0221 | 3 |
72
+ | Label 2 (Tampered / Partial Mask) | 0.0306 | 0.0594 | 0.0306 | 3 |
73
 
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  }
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  ]