| # Training input interface |
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| Each registered JSON file contains records with `messages`, `images`, and `skin_labels`: |
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| - `messages`: a list of `role` and `content` pairs, with image placeholders where applicable. |
| - `images`: relative paths resolved beneath `SKINGPT_IMAGE_ROOT`. |
| - `skin_labels`: an integer, with `0 = Light`, `1 = Medium`, `2 = Dark`. |
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| `example_record.json` is a synthetic schema illustration. `dataset_info.json` registers the training-file interface. It contains file registrations rather than case data. |
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| For each image, the loader resolves the teacher feature under `SKINGPT_FEATURE_ROOT` using the same relative path with the image suffix changed to `.npy`. The stored feature is flattened to `teacher_feat`. The SFT interface uses 1,024-dimensional teacher features. Check that each image has the intended teacher feature before training; the supplied loader uses a zero vector when a feature cannot be read. |
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| The converter and collator preserve `skin_labels` and `teacher_feat`. The integer `skin_labels` value supervises the skin classifier. The classes describe apparent skin colour in the image. Predicted skin-colour probabilities condition expert routing. |
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| ```text |
| SKINGPT_DATASET_DIR/ |
| dataset_info.json |
| train.json |
| SKINGPT_IMAGE_ROOT/ |
| <relative image paths> |
| SKINGPT_FEATURE_ROOT/ |
| <corresponding relative .npy paths> |
| ``` |
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