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:
- e606a0121c2b6d8cff7e6b247e91d95c15f2e98c21723c888f8dd742f2a690ce
- Size of remote file:
- 885 MB
- SHA256:
- 0986c2881028a2d0ef9b638ab06bc4cfe7c529760d451eaa7098ade2592015f2
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