Image Segmentation
Transformers
Safetensors
English
segformer
human-parsing
semantic-segmentation
fashion
virtual-try-on
Instructions to use fashn-ai/fashn-human-parser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fashn-ai/fashn-human-parser with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="fashn-ai/fashn-human-parser")# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("fashn-ai/fashn-human-parser") model = SegformerForSemanticSegmentation.from_pretrained("fashn-ai/fashn-human-parser") - Inference
- Notebooks
- Google Colab
- Kaggle
Dan Bochman commited on
Commit ·
d1ba17c
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Parent(s): 9e09c4f
docs: add example visualization to model card
Browse filesAdd triplet visualization showing original image, segmentation mask,
and overlay to help users understand model output at a glance.
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A SegFormer-B4 model fine-tuned for human parsing with 18 semantic classes, optimized for fashion and virtual try-on applications.
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## Model Description
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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).
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A SegFormer-B4 model fine-tuned for human parsing with 18 semantic classes, optimized for fashion and virtual try-on applications.
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<p align="center">
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<img src="assets/example.webp" alt="Human Parsing Example" width="800">
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## Model Description
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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).
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