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
Performance comparison
#7
by felfri - opened
Thanks for the great release. How does this model perform compared to its predecessor, rmbg-1.4?
Hello ! Thanks for the release, I'm curious about the dataset on which you benchmarked the model. What are the characteristics of the dataset and the methodology you used to do the benchmark?
origubany changed discussion status to closed