Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
English
segformer
vision
nvidia/mit-b5
Instructions to use jonathandinu/face-parsing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonathandinu/face-parsing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="jonathandinu/face-parsing")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("jonathandinu/face-parsing") model = SegformerForSemanticSegmentation.from_pretrained("jonathandinu/face-parsing", device_map="auto") - Transformers.js
How to use jonathandinu/face-parsing with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'jonathandinu/face-parsing'); - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jonathandinu/face-parsing: direct link, hf CLI and curl.
- Browser
- Download file 339 MB
-
https://huggingface.co/jonathandinu/face-parsing/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jonathandinu/face-parsing/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jonathandinu/face-parsing/resolve/main/pytorch_model.bin
339 MB
- Xet hash:
- 98478fc298f51d952a96302e86b542412241b834dc1ad81ce772a9e6c8bbfc8e
- Size of remote file:
- 339 MB
- SHA256:
- e0139f52e953a00ca01d86faf7363f067a535291a003c096dd9c56b09d8945f1
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