Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
SegformerForSemanticSegmentation
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background
background-removal
Pytorch
vision
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custom_code
Instructions to use wide-video/rmbg-v1.0.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wide-video/rmbg-v1.0.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="wide-video/rmbg-v1.0.0", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("wide-video/rmbg-v1.0.0", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 11b4080f109e2358866a55b58973dc0c6cc64d73edb8c218dff1b006ce03aecb
- Size of remote file:
- 44.4 MB
- SHA256:
- a6648479275dfd0ede0f3a8abc20aa5c437b394681b05e5af6d268250aaf40f3
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