readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: swinv2-tiny-patch4-window8-256-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: 0.9825925925925926
      - name: F1
        type: f1
        value: 0.9825868474705166
      - name: Precision
        type: precision
        value: 0.9828193476192771
      - name: Recall
        type: recall
        value: 0.9825925925925926
  ---
  
  <!-- 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. -->
  
  # swinv2-tiny-patch4-window8-256-finetuned-eurosat
  
  This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0510
  - Accuracy: 0.9826
  - F1: 0.9826
  - Precision: 0.9828
  - Recall: 0.9826
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | 0.4479        | 1.0   | 95   | 0.1592          | 0.9478   | 0.9478 | 0.9500    | 0.9478 |
  | 0.3078        | 2.0   | 190  | 0.0914          | 0.9685   | 0.9686 | 0.9695    | 0.9685 |
  | 0.2307        | 3.0   | 285  | 0.0603          | 0.9785   | 0.9785 | 0.9790    | 0.9785 |
  | 0.227         | 4.0   | 380  | 0.0531          | 0.9811   | 0.9811 | 0.9814    | 0.9811 |
  | 0.1674        | 5.0   | 475  | 0.0510          | 0.9826   | 0.9826 | 0.9828    | 0.9826 |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  