readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: dresses
    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: 0.9013840830449827
  ---
  
  <!-- 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. -->
  
  # dresses
  
  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4588
  - Accuracy: 0.9014
  
  ## 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: 0.0002
  - train_batch_size: 64
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 15
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.2458        | 1.23  | 100  | 0.4519          | 0.8633   |
  | 0.0937        | 2.47  | 200  | 0.4285          | 0.8754   |
  | 0.0802        | 3.7   | 300  | 0.4683          | 0.8754   |
  | 0.041         | 4.94  | 400  | 0.4088          | 0.9031   |
  | 0.0277        | 6.17  | 500  | 0.3979          | 0.8945   |
  | 0.0459        | 7.41  | 600  | 0.4253          | 0.9014   |
  | 0.024         | 8.64  | 700  | 0.4680          | 0.8893   |
  | 0.0267        | 9.88  | 800  | 0.4575          | 0.8945   |
  | 0.019         | 11.11 | 900  | 0.4470          | 0.8893   |
  | 0.0235        | 12.35 | 1000 | 0.4380          | 0.9066   |
  | 0.0129        | 13.58 | 1100 | 0.4557          | 0.9048   |
  | 0.0211        | 14.81 | 1200 | 0.4588          | 0.9014   |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  