FASHN Human Parser A SegFormer B4 model fine tuned for human parsing with 18 semantic classes, optimized for fashion and virtual try on applications. Model Description This model segments human images into 18 semantic categories including body parts (face, hair, arms, hands, legs, feet, torso), clothing items (top, dress, skirt, pants, belt, scarf), and accessories (bag, hat, glasses, jewelry). Architecture : SegFormer B4 (MIT B4 encoder + MLP decoder) Input Size : 384 x 576 (width x height) Output : 18 class semantic segmentation mask Base Model : nvidia/mit b4 Usage Quick Start with Pipeline The pipeline automatically manages GPU/CPU and returns per class masks at the original image resolution. Explicit Usage Production Usage (Recommended) For maximum accuracy, use our Python package which implements the exact preprocessing used during training: The package uses cv2.INTER AREA for resizing (matching training), while the HuggingFace pipeline uses PIL LANCZOS. Label Definitions ID Label 0 background 1 face 2 hair 3 top 4 dress 5 skirt 6 pants 7 belt 8 bag 9 hat 10 scarf 11 glasses 12 arms 13 hands 14 legs 15 feet 16 torso 17 jewelry Category Mappings For virtual try on applications…
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