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: 1.0
  ---
  
  <!-- 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: 0.3026
  - Accuracy: 1.0
  
  ## 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: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | No log        | 1.0   | 1    | 1.0940          | 0.25     |
  | No log        | 2.0   | 2    | 0.9836          | 0.25     |
  | No log        | 3.0   | 3    | 0.7624          | 0.25     |
  | No log        | 4.0   | 4    | 0.6527          | 0.5      |
  | No log        | 5.0   | 5    | 0.5697          | 0.75     |
  | No log        | 6.0   | 6    | 0.5167          | 1.0      |
  | No log        | 7.0   | 7    | 0.4898          | 0.75     |
  | No log        | 8.0   | 8    | 0.4572          | 0.75     |
  | No log        | 9.0   | 9    | 0.4286          | 0.75     |
  | 0.299         | 10.0  | 10   | 0.3976          | 0.75     |
  | 0.299         | 11.0  | 11   | 0.3678          | 1.0      |
  | 0.299         | 12.0  | 12   | 0.3531          | 1.0      |
  | 0.299         | 13.0  | 13   | 0.3384          | 1.0      |
  | 0.299         | 14.0  | 14   | 0.3264          | 1.0      |
  | 0.299         | 15.0  | 15   | 0.3188          | 1.0      |
  | 0.299         | 16.0  | 16   | 0.3114          | 1.0      |
  | 0.299         | 17.0  | 17   | 0.3083          | 1.0      |
  | 0.299         | 18.0  | 18   | 0.3071          | 1.0      |
  | 0.299         | 19.0  | 19   | 0.3041          | 1.0      |
  | 0.2051        | 20.0  | 20   | 0.3026          | 1.0      |
  
  
  ### Framework versions
  
  - Transformers 4.21.3
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  