Instructions to use openmmlab/upernet-swin-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openmmlab/upernet-swin-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="openmmlab/upernet-swin-large")# Load model directly from transformers import AutoImageProcessor, UperNetForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-swin-large") model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-swin-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c878eacc68fad15a815db02438eab67aa714f7f4e082d53201911b8415136771
- Size of remote file:
- 940 MB
- SHA256:
- 78069b1c568d6bfc8df0e8b668cc3840c2c37d56fc5b4c568261d13d819e61cc
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