Instructions to use nobg/BiRefNet-Finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- nobg
How to use nobg/BiRefNet-Finetuned with nobg:
pip install nobg
import torch from loadimg import load_img from nobg import AutoModel, AutoProcessor model = AutoModel.from_pretrained("nobg/BiRefNet-Finetuned").eval() processor = AutoProcessor.from_pretrained("nobg/BiRefNet-Finetuned") image = load_img("input.jpg").convert("RGB") inputs = processor(image, return_tensors="pt") with torch.no_grad(): outputs = model(pixel_values=inputs["pixel_values"]) alpha = processor.post_process_alpha_matting(outputs, target_sizes=[(image.height, image.width)])[0] processor.cutout(image, alpha).save("output.png") - BiRefNet
How to use nobg/BiRefNet-Finetuned with BiRefNet:
# Option 1: use with transformers from transformers import AutoModelForImageSegmentation birefnet = AutoModelForImageSegmentation.from_pretrained("nobg/BiRefNet-Finetuned", 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("nobg/BiRefNet-Finetuned") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d6fea1357dfa3084409993a08265885773e68c6adb616fcfe47b7542d266e4c5
- Size of remote file:
- 881 MB
- SHA256:
- ea77387d72ce19029a3fb808a6d05b86fe05d849c066c68b68fe97b3773cf74e
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