Instructions to use DawidTobolski/swin-tiny-patch4-window7-224-fine-eurosat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DawidTobolski/swin-tiny-patch4-window7-224-fine-eurosat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DawidTobolski/swin-tiny-patch4-window7-224-fine-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("DawidTobolski/swin-tiny-patch4-window7-224-fine-eurosat") model = AutoModelForImageClassification.from_pretrained("DawidTobolski/swin-tiny-patch4-window7-224-fine-eurosat", device_map="auto") - Notebooks
- Google Colab
- Kaggle
swin-tiny-patch4-window7-224-fine-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: 0.0501
- Accuracy: 0.9841
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2682 | 1.0 | 190 | 0.1162 | 0.9626 |
| 0.1964 | 2.0 | 380 | 0.0754 | 0.9763 |
| 0.1559 | 3.0 | 570 | 0.0650 | 0.9785 |
| 0.1403 | 4.0 | 760 | 0.0771 | 0.9726 |
| 0.1104 | 5.0 | 950 | 0.0501 | 0.9841 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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Model tree for DawidTobolski/swin-tiny-patch4-window7-224-fine-eurosat
Base model
microsoft/swin-tiny-patch4-window7-224Evaluation results
- Accuracy on imagefolderself-reported0.984