Image Segmentation
Transformers.js
ONNX
BiRefNet
swin
background-removal
matting
webgpu
alpha-matting
image-matting
foreground-extraction
salient-object-detection
dichotomous-image-segmentation
client-side
transformers-js
fp16
Instructions to use studioludens/birefnet-lite-512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use studioludens/birefnet-lite-512 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'studioludens/birefnet-lite-512'); - BiRefNet
How to use studioludens/birefnet-lite-512 with BiRefNet:
# Option 1: use with transformers from transformers import AutoModelForImageSegmentation birefnet = AutoModelForImageSegmentation.from_pretrained("studioludens/birefnet-lite-512", 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("studioludens/birefnet-lite-512") - Notebooks
- Google Colab
- Kaggle
Add MIT LICENSE file and License section
#3
by alexludens - opened
Adds an explicit LICENSE file covering the ONNX weights, config files and model card, carrying the upstream BiRefNet copyright notice (MIT, ZhengPeng) alongside ours for the 512x512 export. Adds a License section to the model card. Addresses discussion #1.
alexludens changed pull request status to merged