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
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:1970b8c114a49db5a58180de85e323c4d8e4c02729aee9d1398616d79769fa6e
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size 240088956
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