readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - cifar10
  metrics:
  - accuracy
  model-index:
  - name: BEiT-finetuned
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: cifar10
        type: cifar10
        args: plain_text
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9918
  ---
  
  <!-- 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. -->
  
  # BEiT-finetuned
  
  This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the cifar10 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0256
  - Accuracy: 0.9918
  
  ## 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: 3e-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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.3296        | 1.0   | 351  | 0.0492          | 0.9862   |
  | 0.2353        | 2.0   | 702  | 0.0331          | 0.9894   |
  | 0.2127        | 3.0   | 1053 | 0.0256          | 0.9918   |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.1
  - Tokenizers 0.12.1
  