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README.md
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@@ -39,21 +39,21 @@ Because paired real damaged/complete crown data is scarce, the model was trained
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### Checkpoints in this repository
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| File | Variant | Notes |
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|---|---|---|
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| `ToothCraft_normal_control_branch.pth` | Normal | Control-branch weights |
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| `ToothCraft_normal_diff_branch.pth` | Normal | Diffusion-branch weights |
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| `ToothCraft_classfree_control_branch.pth` | Classifier-free | Control-branch weights |
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| `ToothCraft_classfree_diff_branch.pth` | Classifier-free | Diffusion-branch weights |
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| `ToothCraft_antag_control_branch.pth` | Antagonist-aware | Control-branch weights |
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| `ToothCraft_antag_diff_branch.pth` | Antagonist-aware | Diffusion-branch weights |
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| `ToothCraft_gingiva-mod_control_branch.pth` | Gingiva-aware ⭐ | Control-branch weights |
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| `ToothCraft_gingiva-mod_diff_branch.pth` | Gingiva-aware ⭐ | Diffusion-branch weights |
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> **Note:** each of the variants ships a *control branch* and a *diffusion branch* checkpoint, which are loaded together at inference time.
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> ⭐ **Recommended for clinical cases:** the gingiva-aware (`gingiva-mod`) variant reports improved results and is the best-suited checkpoint for real/clinical restorations.
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## Usage
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Inference and training code, configuration files, and the data-augmentation pipeline are provided in the companion GitHub repository: [ikarus1211/VISAPP_ToothCraft](https://github.com/ikarus1211/VISAPP_ToothCraft). Download the checkpoint(s) for the variant you need from this repository and point the relevant config (e.g. `configs/test/test_normal.yaml`) at the local file paths, then run:
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### Checkpoints in this repository
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| 41 |
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| 42 |
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| File | Variant | Trained on | Notes |
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| 43 |
+
|---|---|---|---|
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| 44 |
+
| `ToothCraft_normal_control_branch.pth` | Normal | ODD | Control-branch weights |
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| 45 |
+
| `ToothCraft_normal_diff_branch.pth` | Normal | ODD | Diffusion-branch weights |
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| 46 |
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| `ToothCraft_classfree_control_branch.pth` | Classifier-free | ODD | Control-branch weights |
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| 47 |
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| `ToothCraft_classfree_diff_branch.pth` | Classifier-free | ODD | Diffusion-branch weights |
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| 48 |
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| `ToothCraft_antag_control_branch.pth` | Antagonist-aware | ODD | Control-branch weights |
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| 49 |
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| `ToothCraft_antag_diff_branch.pth` | Antagonist-aware | ODD | Diffusion-branch weights |
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| 50 |
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| `ToothCraft_gingiva-mod_control_branch.pth` | Gingiva-aware ⭐ | ODD | Control-branch weights |
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| `ToothCraft_gingiva-mod_diff_branch.pth` | Gingiva-aware ⭐ | ODD | Diffusion-branch weights |
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> **Note:** each of the variants ships a *control branch* and a *diffusion branch* checkpoint, which are loaded together at inference time.
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> ⭐ **Recommended for clinical cases:** the gingiva-aware (`gingiva-mod`) variant reports improved results and is the best-suited checkpoint for real/clinical restorations, where the tooth does not properly connect to gingiva.
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## Usage
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Inference and training code, configuration files, and the data-augmentation pipeline are provided in the companion GitHub repository: [ikarus1211/VISAPP_ToothCraft](https://github.com/ikarus1211/VISAPP_ToothCraft). Download the checkpoint(s) for the variant you need from this repository and point the relevant config (e.g. `configs/test/test_normal.yaml`) at the local file paths, then run:
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