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 preprocessor_config.json from jonathandinu/face-parsing: direct link, hf CLI and curl.
- Browser
- Download file 374 Bytes
-
https://huggingface.co/jonathandinu/face-parsing/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://jonathandinu/face-parsing/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/jonathandinu/face-parsing/resolve/main/preprocessor_config.json
374 Bytes
| { | |
| "do_normalize": true, | |
| "do_reduce_labels": false, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_processor_type": "SegformerFeatureExtractor", | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 512, | |
| "width": 512 | |
| } | |
| } | |