Instructions to use manoghourii/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 manoghourii/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="manoghourii/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("manoghourii/swin-tiny-patch4-window7-224-finetuned-eurosat") model = AutoModelForImageClassification.from_pretrained("manoghourii/swin-tiny-patch4-window7-224-finetuned-eurosat") - Notebooks
- Google Colab
- Kaggle
swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0547
- Accuracy: 0.9957
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.5398 | 1.0 | 17 | 0.1915 | 0.9571 |
| 0.3106 | 2.0 | 34 | 0.0547 | 0.9957 |
| 0.1251 | 3.0 | 51 | 0.0330 | 0.9957 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cpu
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for manoghourii/swin-tiny-patch4-window7-224-finetuned-eurosat
Base model
microsoft/swin-tiny-patch4-window7-224Evaluation results
- Accuracy on imagefolderself-reported0.996