Instructions to use openmmlab/upernet-swin-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openmmlab/upernet-swin-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="openmmlab/upernet-swin-tiny")# Load model directly from transformers import AutoImageProcessor, UperNetForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-swin-tiny") model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-swin-tiny") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 134 Bytes
e10aae9 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:70b992f625a36a2edfd9bd176c3bddde3407aae0e95680b63e58305efdaa018d
size 240155657
|