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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("ZhengPeng7/BiRefNet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
6f22264
1
Parent(s): 40a7dcf
Ensemble BiRefNet codes to HF to load model with weights in one line.
Browse files
README.md
CHANGED
|
@@ -53,7 +53,7 @@ import torch
|
|
| 53 |
from torchvision import transforms
|
| 54 |
from models.birefnet import BiRefNet
|
| 55 |
|
| 56 |
-
|
| 57 |
torch.set_float32_matmul_precision(['high', 'highest'][0])
|
| 58 |
birefnet.to('cuda')
|
| 59 |
birefnet.eval()
|
|
|
|
| 53 |
from torchvision import transforms
|
| 54 |
from models.birefnet import BiRefNet
|
| 55 |
|
| 56 |
+
birefnet = ... # -- BiRefNet should be loaded with codes above, either way.
|
| 57 |
torch.set_float32_matmul_precision(['high', 'highest'][0])
|
| 58 |
birefnet.to('cuda')
|
| 59 |
birefnet.eval()
|