Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
SegformerForSemanticSegmentation
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Pytorch
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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, device_map="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
File size: 326 Bytes
e01e088 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | 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)
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