Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
SegformerForSemanticSegmentation
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background
background-removal
Pytorch
vision
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custom_code
Instructions to use SolonD/RMBG-1.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SolonD/RMBG-1.4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="SolonD/RMBG-1.4", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("SolonD/RMBG-1.4", trust_remote_code=True, dtype="auto") - Transformers.js
How to use SolonD/RMBG-1.4 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'SolonD/RMBG-1.4'); - Notebooks
- Google Colab
- Kaggle
| { | |
| "do_normalize": true, | |
| "do_pad": false, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "feature_extractor_type": "ImageFeatureExtractor", | |
| "image_std": [ | |
| 1, | |
| 1, | |
| 1 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "width": 1024, | |
| "height": 1024 | |
| } | |
| } |