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_dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- BiRefNet
How to use ZhengPeng7/BiRefNet_dynamic with BiRefNet:
# Option 1: use with transformers from transformers import AutoModelForImageSegmentation birefnet = AutoModelForImageSegmentation.from_pretrained("ZhengPeng7/BiRefNet_dynamic", 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_dynamic") - Transformers
How to use ZhengPeng7/BiRefNet_dynamic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="ZhengPeng7/BiRefNet_dynamic", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("ZhengPeng7/BiRefNet_dynamic", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| torch>=2.5.1 | |
| torchvision | |
| numpy<2 | |
| opencv-python | |
| timm | |
| scipy | |
| scikit-image | |
| kornia | |
| einops | |
| tqdm | |
| prettytable | |
| transformers | |
| huggingface-hub>0.25 | |
| accelerate | |