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
File size: 374 Bytes
184cbad 1f7e152 184cbad 1f7e152 184cbad 1f7e152 184cbad | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"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
}
}
|