Image Segmentation
Transformers.js
ONNX
BiRefNet
swin
background-removal
dichotomous-image-segmentation
webgpu
onnxruntime-web
Instructions to use CardAnvilModels/BiRefNet-lite-1024-WebGPU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use CardAnvilModels/BiRefNet-lite-1024-WebGPU with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'CardAnvilModels/BiRefNet-lite-1024-WebGPU'); - BiRefNet
How to use CardAnvilModels/BiRefNet-lite-1024-WebGPU with BiRefNet:
# Option 1: use with transformers from transformers import AutoModelForImageSegmentation birefnet = AutoModelForImageSegmentation.from_pretrained("CardAnvilModels/BiRefNet-lite-1024-WebGPU", trust_remote_code=True)# Option 2: use with BiRefNet # Install from https://github.com/ZhengPeng7/BiRefNet from models.birefnet import BiRefNet model = BiRefNet.from_pretrained("CardAnvilModels/BiRefNet-lite-1024-WebGPU") - Notebooks
- Google Colab
- Kaggle
Download onnx/model_fp16.onnx from CardAnvilModels/BiRefNet-lite-1024-WebGPU: direct link, hf CLI and curl.
- Browser
- Download file 115 MB
-
https://huggingface.co/CardAnvilModels/BiRefNet-lite-1024-WebGPU/resolve/main/onnx/model_fp16.onnx
- Command line
-
hf download hf://CardAnvilModels/BiRefNet-lite-1024-WebGPU/onnx/model_fp16.onnx
-
curl -L -o model_fp16.onnx https://huggingface.co/CardAnvilModels/BiRefNet-lite-1024-WebGPU/resolve/main/onnx/model_fp16.onnx
115 MB
- Xet hash:
- b5bf8cee01201e9c2feb1ee323122d148ffc2e84a1343031a83cb5a2718c63d7
- Size of remote file:
- 115 MB
- SHA256:
- 4059896039dfccb0f15b9080ff06d11d90e499449bb045e797055eb8901cf5f4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.