readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - vision
  - generated_from_trainer
  datasets:
  - cifar10
  metrics:
  - accuracy
  model-index:
  - name: vit-base-cifar10
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: cifar10
        type: cifar10
        config: plain_text
        split: train
        args: plain_text
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.106
  ---
  
  <!-- 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. -->
  
  # vit-base-cifar10
  
  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 cifar10 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.3302
  - Accuracy: 0.106
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 2.3324        | 1.0   | 664  | 2.3352          | 0.0967   |
  | 2.3489        | 2.0   | 1328 | 2.3288          | 0.1049   |
  | 2.4899        | 3.0   | 1992 | 2.4473          | 0.0989   |
  | 2.479         | 4.0   | 2656 | 2.4894          | 0.1      |
  | 2.4179        | 5.0   | 3320 | 2.4404          | 0.0947   |
  | 2.3881        | 6.0   | 3984 | 2.3931          | 0.102    |
  | 2.3597        | 7.0   | 4648 | 2.3744          | 0.0967   |
  | 2.3721        | 8.0   | 5312 | 2.3667          | 0.0935   |
  | 2.3456        | 9.0   | 5976 | 2.3495          | 0.1036   |
  | 2.3361        | 10.0  | 6640 | 2.3473          | 0.1025   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.1+cu117
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  