Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
remove background
background
background-removal
Pytorch
vision
legal liability
custom_code
Instructions to use hiepnd11/rm_back2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hiepnd11/rm_back2.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="hiepnd11/rm_back2.0", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("hiepnd11/rm_back2.0", trust_remote_code=True, dtype="auto") - Transformers.js
How to use hiepnd11/rm_back2.0 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'hiepnd11/rm_back2.0'); - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- ffe81cfe637c246db2b9b7bd4351b2a5be5fdc00114e06cf5e34e57bf3ca8522
- Size of remote file:
- 4.52 MB
- SHA256:
- f9f802564aa1e3a7c90762c7e65b77007f081cb179cdd9b42607bad3b1fdaf16
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