Image Segmentation
BiRefNet
Safetensors
Transformers
background-removal
mask-generation
Dichotomous Image Segmentation
Camouflaged Object Detection
Salient Object Detection
pytorch_model_hub_mixin
model_hub_mixin
custom_code
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
Update README.md
#13
by artemtumch - opened
Added half precision.
Hi, @artemtumch , thanks for your contribution. But this model was trained in FP32. So, though it can be inferenced in FP16 with very limited difference in performance, I think it's important to mention it in the README instead of setting it as the default one quietly.
Still many thanks. Days ago, I made comprehensive experiments on FP16 inference. Even the previously trained FP32 weights can be loaded and work perfectly (~0 difference) in FP16 mode. Therefore, I set FP16 as the default setting for all occurances.
This PR has conflicts and cannot be merged. But still many thanks :)
ZhengPeng7 changed pull request status to closed
No problem, thanks for the model :)