Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
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Pytorch
vision
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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, dtype="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

- Xet hash:
- 7d0c5778e8063678221504b3bc1c62256684907048b417bc3bc3d18f4fc1e6a2
- Size of remote file:
- 4.52 MB
- SHA256:
- f9f802564aa1e3a7c90762c7e65b77007f081cb179cdd9b42607bad3b1fdaf16
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