Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
remove background
background
background-removal
Pytorch
vision
legal liability
custom_code
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
The effect is not as good as the latest BiRefNet model
#17
by jj11 - opened
The effect is not as good as the latest BiRefNet model
Our model excels on background removal and shows higher success rate over BirefNet model on our benchmark.
origubany changed discussion status to closed
Thank you for your feedback! While no model is perfect across all scenarios, we continually strive to improve performance. To support transparency and evaluation, we’ve created an open and diverse benchmark that you can explore here: https://github.com/Efrat-Taig/RMBG-2.0. We’d love to hear your thoughts or suggestions based on your testing.