readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - food101
  metrics:
  - accuracy
  model-index:
  - name: VIT-food101-image-classifier
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: food101
        type: food101
        config: default
        split: train[:5000]
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.933
  ---
  
  <!-- 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-food101-image-classifier
  
  This model was trained from scratch on the food101 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5661
  - Accuracy: 0.933
  
  ## 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
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.1716        | 0.99  | 62   | 1.2149          | 0.896    |
  | 0.7758        | 1.99  | 124  | 0.8727          | 0.906    |
  | 0.6269        | 2.99  | 186  | 0.6833          | 0.928    |
  | 0.5495        | 3.99  | 248  | 0.6041          | 0.931    |
  | 0.4973        | 4.99  | 310  | 0.5661          | 0.933    |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  