Image Classification
Transformers
PyTorch
TensorBoard
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use eric1993/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 eric1993/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="eric1993/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("eric1993/swin-tiny-patch4-window7-224-finetuned-eurosat") model = AutoModelForImageClassification.from_pretrained("eric1993/swin-tiny-patch4-window7-224-finetuned-eurosat", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f3d3548bccf4dfbe84f1dfdeae72ce61536eea77c7cf9d08d3a0895a5364849
- Size of remote file:
- 110 MB
- SHA256:
- 4c0186cad1591c071f1dcf6f96dd05659cb3465b5eae56dbfa95bff772c43013
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