Instructions to use jaygala223/upernet-swin-tiny-38-cloud-dataset-binarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaygala223/upernet-swin-tiny-38-cloud-dataset-binarization with Transformers:
# Load model directly from transformers import AutoImageProcessor, UperNetForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("jaygala223/upernet-swin-tiny-38-cloud-dataset-binarization") model = UperNetForSemanticSegmentation.from_pretrained("jaygala223/upernet-swin-tiny-38-cloud-dataset-binarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 87759d8c0e0e2e85c3785b420501a2ca2ef1cdfc1e01d2412bab537affdca86e
- Size of remote file:
- 240 MB
- SHA256:
- 8a56b52255465d6a2f9f9fbd3531f0238902963d97c70821d66cb96dc1c06a44
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