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")# pip install -U transformers accelerate # 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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
-
docs: add link to HuggingFace Space demo
Dan BochmanAdd official "Open in Spaces" badge and demo link to make the interactive demo more discoverable from the model card.1f80c34 -
docs: add FASHN AI website link
Dan Bochman2e4b7cf -
docs: use static image hosting
Dan Bochman- Switch example image to static.fashn.ai URL - Remove local assets folder (no longer needed)41a1224 -
docs: add example visualization to model card
Dan BochmanAdd triplet visualization showing original image, segmentation mask, and overlay to help users understand model output at a glance.d1ba17c -
docs: improve usage examples and clarify output formats
Dan Bochman- Add output format comments to pipeline example - Note that pipeline auto-manages GPU and returns original resolution - Update production example with auto GPU detection - Clarify preprocessing difference (cv2.INTER_AREA vs PIL LANCZOS)9e09c4f
-
Initial release: FASHN Human Parser - SegFormer-B4 for human parsing
Dan Bochmana92eba4