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")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, UperNetForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-tiny") model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-tiny", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from openmmlab/upernet-convnext-tiny: direct link, hf CLI and curl.
- Browser
- Download file 241 MB
-
https://huggingface.co/openmmlab/upernet-convnext-tiny/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://openmmlab/upernet-convnext-tiny/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/openmmlab/upernet-convnext-tiny/resolve/main/pytorch_model.bin
241 MB
- Xet hash:
- 5b6b867d8cf4c17d32a999f24cc6d55cd241d629b5aa1c5e8e9bb40c4f07811f
- Size of remote file:
- 241 MB
- SHA256:
- 4f5d3b46a4fcbfa119208913aaf6c924f2051d22a54fb2e9b8d0772d1289abaa
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