Instructions to use openmmlab/upernet-swin-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openmmlab/upernet-swin-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="openmmlab/upernet-swin-small")# Load model directly from transformers import AutoImageProcessor, UperNetForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-swin-small") model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-swin-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4b39b0ce7ade80c1e951d76031e6350965aea3f7732f480e306ca5d785778962
- Size of remote file:
- 326 MB
- SHA256:
- f87459400d8e5f42008e45efaa93a40358814a5a51b20daa2416c1689406357b
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