readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: swin-tiny-patch4-window7-224-finetuned-eurosat
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: imagefolder
        type: imagefolder
        config: default
        split: train
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9844444444444445
      - name: F1
        type: f1
        value: 0.9844678306487884
      - name: Precision
        type: precision
        value: 0.9846508141836958
      - name: Recall
        type: recall
        value: 0.9844444444444445
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # swin-tiny-patch4-window7-224-finetuned-eurosat
  
  This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0393
  - Accuracy: 0.9844
  - F1: 0.9845
  - Precision: 0.9847
  - Recall: 0.9844
  
  ## 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: 0.0001
  - train_batch_size: 64
  - eval_batch_size: 64
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 256
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.2
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | 0.3039        | 1.0   | 95   | 0.1300          | 0.9607   | 0.9609 | 0.9619    | 0.9607 |
  | 0.2357        | 2.0   | 190  | 0.0815          | 0.9678   | 0.9678 | 0.9685    | 0.9678 |
  | 0.163         | 3.0   | 285  | 0.0559          | 0.9807   | 0.9807 | 0.9809    | 0.9807 |
  | 0.1267        | 4.0   | 380  | 0.0492          | 0.9837   | 0.9837 | 0.9839    | 0.9837 |
  | 0.1059        | 5.0   | 475  | 0.0393          | 0.9844   | 0.9845 | 0.9847    | 0.9844 |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  