nielsr/eurosat-demo
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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")This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the image_folder dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2602 | 1.0 | 190 | 0.1310 | 0.9563 |
| 0.1975 | 2.0 | 380 | 0.1063 | 0.9637 |
| 0.142 | 3.0 | 570 | 0.0642 | 0.9767 |
| 0.1235 | 4.0 | 760 | 0.0560 | 0.9837 |
| 0.1019 | 5.0 | 950 | 0.0536 | 0.9848 |
Base model
microsoft/swin-tiny-patch4-window7-224