Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
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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
- Xet hash:
- 111c50d96ab8c4c65f7a5ece67596dc19043645701dff45a7b1d15bb446eb26e
- Size of remote file:
- 366 MB
- SHA256:
- fcea23951a378f92634834888896cc1eec54655366ae6e949282646ce17c5420
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