Instructions to use openmmlab/upernet-swin-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openmmlab/upernet-swin-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="openmmlab/upernet-swin-tiny")# Load model directly from transformers import AutoImageProcessor, UperNetForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-swin-tiny") model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-swin-tiny") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b372c49c0db6967441142876322e43d61852b82a2442ff88d952db721c3dbf13
- Size of remote file:
- 240 MB
- SHA256:
- 70b992f625a36a2edfd9bd176c3bddde3407aae0e95680b63e58305efdaa018d
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