Instructions to use openmmlab/upernet-convnext-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openmmlab/upernet-convnext-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="openmmlab/upernet-convnext-tiny")# Load model directly from transformers import AutoImageProcessor, UperNetForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-tiny") model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-tiny") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 46df12c4ce7f3e015b9c5b5bca34cc515ba80b989980de81a35388f66e427e1c
- Size of remote file:
- 241 MB
- SHA256:
- cd55b1d36c6602d34f8cd300d7f788ea0fa5c3ed928e9ac98f353ecb31c7fa1d
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