We’re releasing FASHN Human Parser, a SegFormer-B4 fine-tuned for human parsing in fashion contexts.

Background: Dataset quality issues

Before training our own model, we spent time analyzing the commonly used datasets for human parsing: ATR, LIP, and iMaterialist. We found consistent quality issues that affect models trained on them:

ATR:

• Annotation “holes” where background pixels appear inside labeled regions
• Label spillage where annotations extend beyond object boundaries

LIP:

• Same issues as ATR (same research group)
• Inconsistent labeling between left/right body parts and clothing
• Aggressive crops from multi-person images causing artifacts
• Ethical concerns (significant portion includes minors)

iMaterialist:

• Higher quality images and annotations overall
• Multi-person images where only one person is labeled (~6% of dataset)
• No body part labels (clothing only)

We documented these findings in detail: Fashion Segmentation Datasets and Their Common Problems

What we did

We curated our own dataset addressing these issues and fine-tuned a SegFormer-B4. The model outputs 18 semantic classes relevant for fashion applications:

• Body parts: face, hair, arms, hands, legs, feet, torso
• Clothing: top, dress, skirt, pants, belt, scarf
• Accessories: bag, hat, glasses, jewelry
• Background

Technical details

The PyPI package uses cv2.INTER_AREA for preprocessing (matching training), while the HuggingFace pipeline uses PIL LANCZOS for broader compatibility.

Links

• PyPI: pip install fashn-human-parser
• HuggingFace: fashn-ai/fashn-human-parser
• Demo: HuggingFace Space
• GitHub: fashn-AI/fashn-human-parser
• Dataset analysis: Blog post

Limitations

• Optimized for fashion/e-commerce images (single person, relatively clean backgrounds)
• Performance may degrade on crowded scenes or unusual poses
• 18-class schema is fashion-focused; may not suit all human parsing use cases

Happy to discuss the dataset curation process, architecture choices, or answer any questions.