Instructions to use openmmlab/upernet-swin-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openmmlab/upernet-swin-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="openmmlab/upernet-swin-base")# Load model directly from transformers import AutoImageProcessor, UperNetForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-swin-base") model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-swin-base") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 378bd9107ae159892b6bdf94ad4fc985453454a15c80c422ef83f64363ea0985
- Size of remote file:
- 490 MB
- SHA256:
- 07121a6f35ca212fff87edae64ad216dd2a9cab671f89b312638d3def3bea47b
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