readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  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: 0.9341978866474544
  ---
  
  <!-- 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: 0.1507
  - Accuracy: 0.9342
  
  ## 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: 12
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.2891        | 1.0   | 146  | 0.2322          | 0.9068   |
  | 0.2609        | 2.0   | 292  | 0.1710          | 0.9227   |
  | 0.2417        | 3.0   | 438  | 0.1830          | 0.9251   |
  | 0.2406        | 4.0   | 584  | 0.1809          | 0.9198   |
  | 0.2113        | 5.0   | 730  | 0.1631          | 0.9289   |
  | 0.1812        | 6.0   | 876  | 0.1561          | 0.9308   |
  | 0.2082        | 7.0   | 1022 | 0.1507          | 0.9342   |
  | 0.1922        | 8.0   | 1168 | 0.1611          | 0.9294   |
  | 0.1715        | 9.0   | 1314 | 0.1536          | 0.9308   |
  | 0.1675        | 10.0  | 1460 | 0.1609          | 0.9289   |
  | 0.194         | 11.0  | 1606 | 0.1499          | 0.9337   |
  | 0.1706        | 12.0  | 1752 | 0.1514          | 0.9323   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  