Instructions to use ZhengPeng7/BiRefNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- BiRefNet
How to use ZhengPeng7/BiRefNet with BiRefNet:
# Option 1: use with transformers from transformers import AutoModelForImageSegmentation birefnet = AutoModelForImageSegmentation.from_pretrained("ZhengPeng7/BiRefNet", 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("ZhengPeng7/BiRefNet") - Transformers
How to use ZhengPeng7/BiRefNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="ZhengPeng7/BiRefNet", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("ZhengPeng7/BiRefNet", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
About image repeat processing
That makes sense: you extract the target with an object of which the mask values are from 0 (0% target) to 1 (100% target). By default, the background color changes to 0 (black). Therefore, if you do a second processing, the black pixels might be included in the second result.
BTW, did you add the post-processing for refinement?
That makes sense: you extract the target with an object of which the mask values are from 0 (0% target) to 1 (100% target). By default, the background color changes to 0 (black). Therefore, if you do a second processing, the black pixels might be included in the second result.
BTW, did you add the post-processing for refinement?
After referencing your code, this issue has been resolved. Thank you very much! π

