Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
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Instructions to use briaai/RMBG-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use briaai/RMBG-2.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="briaai/RMBG-2.0", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("briaai/RMBG-2.0", trust_remote_code=True, dtype="auto") - Transformers.js
How to use briaai/RMBG-2.0 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'briaai/RMBG-2.0'); - Inference
- Notebooks
- Google Colab
- Kaggle
about infer image size
#25
by liu00 - opened
Thanks to the author for the open source model, I want to ask what is the resolution of the image input to the model?
As 1024*1024 is given in the example, whether other resolutions will cause the effect to be reduced, should we resize to 1024*1024 to test or any resolution is ok
it is recommended to run the model with image size (1024,1024) and resize the image back to original dimension after inference.
