Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
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Pytorch
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custom_code
Instructions to use cocktailpeanut/rm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cocktailpeanut/rm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="cocktailpeanut/rm", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("cocktailpeanut/rm", trust_remote_code=True, device_map="auto") - Transformers.js
How to use cocktailpeanut/rm with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'cocktailpeanut/rm'); - Notebooks
- Google Colab
- Kaggle
| from transformers import PretrainedConfig | |
| class BiRefNetConfig(PretrainedConfig): | |
| model_type = "SegformerForSemanticSegmentation" | |
| def __init__( | |
| self, | |
| bb_pretrained=False, | |
| **kwargs | |
| ): | |
| self.bb_pretrained = bb_pretrained | |
| super().__init__(**kwargs) | |