Instructions to use Chandanab/beit-base-patch16-224-pt22k-finetuned-eurosat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chandanab/beit-base-patch16-224-pt22k-finetuned-eurosat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Chandanab/beit-base-patch16-224-pt22k-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("Chandanab/beit-base-patch16-224-pt22k-finetuned-eurosat") model = AutoModelForImageClassification.from_pretrained("Chandanab/beit-base-patch16-224-pt22k-finetuned-eurosat") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1ed1a56b8a60e96e62ad10c35910fad5e6d27fcb5db99fe4532c8e5089471480
- Size of remote file:
- 3.06 kB
- SHA256:
- 5c57d8e9ca7a24c5e0a8ae82d5d68dd8dee659afa71f29cc73f62301fbc85c35
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