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: train_new
        split: train
        args: train_new
      metrics:
      - name: Accuracy
        type: accuracy
        value: 1.0
  widget:
  - text: "Is this review positive or negative? Review: Best cast iron skillet you will ever buy."
    example_title: "Sentiment analysis"
  ---
  
  <!-- 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: 0.0066
  - 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: 15
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | No log        | 1.0   | 6    | 1.5267          | 0.6071   |
  | 1.5949        | 2.0   | 12   | 0.8291          | 0.7262   |
  | 1.5949        | 3.0   | 18   | 0.3461          | 0.8929   |
  | 0.5682        | 4.0   | 24   | 0.0966          | 0.9881   |
  | 0.1066        | 5.0   | 30   | 0.0329          | 0.9762   |
  | 0.1066        | 6.0   | 36   | 0.0066          | 1.0      |
  | 0.0342        | 7.0   | 42   | 0.0031          | 1.0      |
  | 0.0342        | 8.0   | 48   | 0.0022          | 1.0      |
  | 0.0198        | 9.0   | 54   | 0.0013          | 1.0      |
  | 0.0125        | 10.0  | 60   | 0.0012          | 1.0      |
  | 0.0125        | 11.0  | 66   | 0.0015          | 1.0      |
  | 0.0111        | 12.0  | 72   | 0.0012          | 1.0      |
  | 0.0111        | 13.0  | 78   | 0.0011          | 1.0      |
  | 0.0102        | 14.0  | 84   | 0.0010          | 1.0      |
  | 0.0093        | 15.0  | 90   | 0.0009          | 1.0      |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  