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

python

from transformers import pipeline

pipe = pipeline("image-segmentation", model="fashn-ai/fashn-human-parser")
result = pipe("image.jpg")
# result is a list of dicts with 'label', 'score', 'mask' for each detected class

The pipeline automatically manages GPU/CPU and returns per-class masks at the original image resolution.

Explicit Usage

python

from transformers import SegformerForSemanticSegmentation, SegformerImageProcessor
from PIL import Image
import torch

# Load model and processor
processor = SegformerImageProcessor.from_pretrained("fashn-ai/fashn-human-parser")
model = SegformerForSemanticSegmentation.from_pretrained("fashn-ai/fashn-human-parser")

# Load and preprocess image
image = Image.open("path/to/image.jpg")
inputs = processor(images=image, return_tensors="pt")

# Inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits  # (1, 18, H/4, W/4)

# Upsample to original size and get predictions
upsampled = torch.nn.functional.interpolate(
    logits, size=image.size[::-1], mode="bilinear", align_corners=False
)
predictions = upsampled.argmax(dim=1).squeeze().numpy()

Production Usage (Recommended)

For maximum accuracy, use our Python package which implements the exact preprocessing used during training:

pip install fashn-human-parser

python

from fashn_human_parser import FashnHumanParser

parser = FashnHumanParser()  # auto-detects GPU
segmentation = parser.predict("image.jpg")
# segmentation is a numpy array of shape (H, W) with class IDs 0-17

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:

Category	Body Coverage	Relevant Labels
Tops	Upper body	top, dress, scarf
Bottoms	Lower body	skirt, pants, belt
One-pieces	Full body	top, dress, scarf, skirt, pants, belt

Identity Labels

Labels typically preserved during virtual try-on: face, hair, jewelry, bag, glasses, hat

Training

This model was fine-tuned on a proprietary dataset curated and annotated by FASHN AI, specifically designed for virtual try-on applications. The 18-class label schema was developed to capture the semantic regions most relevant for clothing transfer and human body understanding in fashion contexts.

Limitations

Optimized for single-person images with clear visibility
Best results on fashion/e-commerce style photography
Input images are resized to 384x576; very small subjects may lose detail

Citation

bibtex

@misc{fashn-human-parser,
  author = {FASHN AI},
  title = {FASHN Human Parser: SegFormer for Fashion Human Parsing},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/fashn-ai/fashn-human-parser}
}

License

This model inherits the NVIDIA Source Code License for SegFormer. Please review the license terms before use.

fashn-ai/fashn-human-parser

FASHN Human Parser

Model Description

Usage

Quick Start with Pipeline

Explicit Usage

Production Usage (Recommended)

Label Definitions

Category Mappings

Identity Labels

Training

Limitations

Citation

License

Links

fashn-ai/
fashn-human-parser