readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: swinv2-small-patch4-window16-256-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: 0.9892592592592593
      - name: F1
        type: f1
        value: 0.9892542163878574
      - name: Precision
        type: precision
        value: 0.9892896521886161
      - name: Recall
        type: recall
        value: 0.9892592592592593
  ---
  
  <!-- 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-small-patch4-window16-256-finetuned-eurosat
  
  This model is a fine-tuned version of [microsoft/swinv2-small-patch4-window16-256](https://huggingface.co/microsoft/swinv2-small-patch4-window16-256) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0328
  - Accuracy: 0.9893
  - F1: 0.9893
  - Precision: 0.9893
  - Recall: 0.9893
  
  ## 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: 24
  - eval_batch_size: 24
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 96
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | 0.2326        | 1.0   | 253  | 0.0870          | 0.9715   | 0.9716 | 0.9720    | 0.9715 |
  | 0.1955        | 2.0   | 506  | 0.0576          | 0.9789   | 0.9788 | 0.9794    | 0.9789 |
  | 0.1229        | 3.0   | 759  | 0.0450          | 0.9837   | 0.9837 | 0.9839    | 0.9837 |
  | 0.0797        | 4.0   | 1012 | 0.0332          | 0.9889   | 0.9889 | 0.9889    | 0.9889 |
  | 0.0826        | 5.0   | 1265 | 0.0328          | 0.9893   | 0.9893 | 0.9893    | 0.9893 |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  