Training input interface

Each registered JSON file contains records with messages, images, and skin_labels:

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.

example_record.json is a synthetic schema illustration. dataset_info.json registers the training-file interface. It contains file registrations rather than case data.

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.

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.

SKINGPT_DATASET_DIR/
  dataset_info.json
  train.json
SKINGPT_IMAGE_ROOT/
  <relative image paths>
SKINGPT_FEATURE_ROOT/
  <corresponding relative .npy paths>