Image Classification
Transformers
PyTorch
TensorBoard
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use nickmuchi/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 nickmuchi/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="nickmuchi/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("nickmuchi/swin-tiny-patch4-window7-224-finetuned-eurosat") model = AutoModelForImageClassification.from_pretrained("nickmuchi/swin-tiny-patch4-window7-224-finetuned-eurosat") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a95a54614d3228e34fffe22c6ff42b42713663e5d828437aa4d1ddfd9a24885f
- Size of remote file:
- 3.25 kB
- SHA256:
- f21ffebd58be26269ad7cf68f69694d9f529cd64b1cc455050e405f34360ca2f
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