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 quantize_config.json from jonathandinu/face-parsing: direct link, hf CLI and curl.
- Browser
- Download file 749 Bytes
-
https://huggingface.co/jonathandinu/face-parsing/resolve/main/quantize_config.json
- Command line
-
hf download hf://jonathandinu/face-parsing/quantize_config.json
-
curl -L -o quantize_config.json https://huggingface.co/jonathandinu/face-parsing/resolve/main/quantize_config.json
749 Bytes
| { | |
| "per_channel": true, | |
| "reduce_range": true, | |
| "per_model_config": { | |
| "model": { | |
| "op_types": [ | |
| "Unsqueeze", | |
| "Shape", | |
| "Transpose", | |
| "Sqrt", | |
| "Gather", | |
| "Slice", | |
| "Erf", | |
| "Div", | |
| "Reshape", | |
| "Add", | |
| "Cast", | |
| "Sub", | |
| "Concat", | |
| "ReduceMean", | |
| "Mul", | |
| "Conv", | |
| "Constant", | |
| "Resize", | |
| "Softmax", | |
| "Pow", | |
| "Relu", | |
| "MatMul" | |
| ], | |
| "weight_type": "QUInt8" | |
| } | |
| } | |
| } |