Instructions to use openmmlab/upernet-convnext-xlarge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openmmlab/upernet-convnext-xlarge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="openmmlab/upernet-convnext-xlarge")# Load model directly from transformers import AutoImageProcessor, UperNetForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-xlarge") model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-xlarge", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f17ae0f0733b5427b9ae1a5ecab37efb5e749c0cea5badbfedbf935d9e590f45
- Size of remote file:
- 1.56 GB
- SHA256:
- 21464a4e7bfaf452501f986baa8ce04ea12ed082eac36fcaf1c704dfa0cc0efa
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