readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - image_folder
  metrics:
  - accuracy
  model-index:
  - name: orchid219_ft_vit-large-patch16-224-in21k-finetuned-eurosat
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: image_folder
        type: image_folder
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9230769230769231
  ---
  
  <!-- 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. -->
  
  # orchid219_ft_vit-large-patch16-224-in21k-finetuned-eurosat
  
  This model is a fine-tuned version of [gary109/orchid219_ft_vit-large-patch16-224-in21k](https://huggingface.co/gary109/orchid219_ft_vit-large-patch16-224-in21k) on the image_folder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9545
  - Accuracy: 0.9231
  
  ## 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: 10
  - eval_batch_size: 10
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 40
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 3.5728        | 0.96  | 17   | 2.1936          | 0.8718   |
  | 1.6005        | 1.96  | 34   | 1.2044          | 0.9359   |
  | 0.9764        | 2.96  | 51   | 0.9545          | 0.9231   |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  