Instructions to use openmmlab/upernet-convnext-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openmmlab/upernet-convnext-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="openmmlab/upernet-convnext-small")# Load model directly from transformers import AutoImageProcessor, UperNetForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small") model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 15b834351fcdc6c72dd4530f9623ca63a4282d5238e2e09f393c13fa3be8be9c
- Size of remote file:
- 328 MB
- SHA256:
- 76c163aa531ab7edfb3a77bbcc039e340645aa0ffe2b0ffcfc68755f550c76ea
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