Instructions to use MatrixYao/swin-tiny-patch4-window7-224-finetuned-eurosat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatrixYao/swin-tiny-patch4-window7-224-finetuned-eurosat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MatrixYao/swin-tiny-patch4-window7-224-finetuned-eurosat") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("MatrixYao/swin-tiny-patch4-window7-224-finetuned-eurosat") model = AutoModelForImageClassification.from_pretrained("MatrixYao/swin-tiny-patch4-window7-224-finetuned-eurosat") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 026c7fc9bf6a5b7a0f46a0ffc1b4b33c890755e675176af951253be3953d4817
- Size of remote file:
- 5.37 kB
- SHA256:
- e6da000adf0ebce8febba7f41590a0ee3f8107c8660916d4f110dd388e759a6c
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