A Vision Transformer encoder pretrained without labels on steel-surface images with a masked autoencoder adapted to texture defects. Frozen features are clustered into defect categories. No class labels, bounding boxes or detection objective are used in pretraining, and no contrastive loss. Encoder: 224×224 grayscale input, 16×16 patches (196 tokens), 6 pre-norm Transformer layers, 12 heads, 768-d. The decoder in the MAE checkpoint has 2 layers and a 36-d output head: Decoder(depth=2) with dec.pred = torch.nn.Linear(256, 36). NEU-DET (hot-rolled steel strip, 6 defect classes, 1,800 images). Pretraining used only the 1,440-image training split. The 360-image validation split was held out…