Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
English
segformer
vision
nvidia/mit-b5
Instructions to use jayson1408/faceparsing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jayson1408/faceparsing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="jayson1408/faceparsing")# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("jayson1408/faceparsing") model = SegformerForSemanticSegmentation.from_pretrained("jayson1408/faceparsing", device_map="auto") - Transformers.js
How to use jayson1408/faceparsing with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'jayson1408/faceparsing'); - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- d1aea355575b3b17768e75c237b0d1642aedba942dd97cb9a922758cc54f4e25
- Size of remote file:
- 645 kB
- SHA256:
- 31c74d29ab9e45f3401f404f7bfc09e2cf9f5825611f07dc20b25d00eb1cac8a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.