Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
SegformerForSemanticSegmentation
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Instructions to use briaai/RMBG-1.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use briaai/RMBG-1.4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="briaai/RMBG-1.4", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("briaai/RMBG-1.4", trust_remote_code=True, device_map="auto") - Transformers.js
How to use briaai/RMBG-1.4 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'briaai/RMBG-1.4'); - Notebooks
- Google Colab
- Kaggle
Download MyConfig.py from briaai/RMBG-1.4: direct link, hf CLI and curl.
- Browser
- Download file 326 Bytes
-
https://huggingface.co/briaai/RMBG-1.4/resolve/main/MyConfig.py
- Command line
-
hf download hf://briaai/RMBG-1.4/MyConfig.py
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curl -L -o MyConfig.py https://huggingface.co/briaai/RMBG-1.4/resolve/main/MyConfig.py
326 Bytes
| from transformers import PretrainedConfig | |
| from typing import List | |
| class RMBGConfig(PretrainedConfig): | |
| model_type = "SegformerForSemanticSegmentation" | |
| def __init__( | |
| self, | |
| in_ch=3, | |
| out_ch=1, | |
| **kwargs): | |
| self.in_ch = in_ch | |
| self.out_ch = out_ch | |
| super().__init__(**kwargs) | |