readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  datasets:
  - snacks
  metrics:
  - accuracy
  model-index:
  - name: vit-snacks
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: Matthijs/snacks
        type: snacks
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9392670157068063
  ---
  
  <!-- 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-snacks
  
  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 Matthijs/snacks dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2754
  - Accuracy: 0.9393
  
  ## Model description
  
  upload any image of your fave yummy snack 
  
  ## Intended uses & limitations
  
  there are only 20 different varieties of snacks
  
  ## 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: 16
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.8724        | 0.33  | 100  | 0.9118          | 0.8670   |
  | 0.5628        | 0.66  | 200  | 0.6873          | 0.8471   |
  | 0.4421        | 0.99  | 300  | 0.4995          | 0.8691   |
  | 0.2837        | 1.32  | 400  | 0.4008          | 0.9026   |
  | 0.1645        | 1.65  | 500  | 0.3702          | 0.9058   |
  | 0.1604        | 1.98  | 600  | 0.3981          | 0.8921   |
  | 0.0498        | 2.31  | 700  | 0.3185          | 0.9204   |
  | 0.0406        | 2.64  | 800  | 0.3427          | 0.9141   |
  | 0.1049        | 2.97  | 900  | 0.3444          | 0.9173   |
  | 0.0272        | 3.3   | 1000 | 0.3168          | 0.9246   |
  | 0.0186        | 3.63  | 1100 | 0.3142          | 0.9288   |
  | 0.0203        | 3.96  | 1200 | 0.2931          | 0.9298   |
  | 0.007         | 4.29  | 1300 | 0.2754          | 0.9393   |
  | 0.0072        | 4.62  | 1400 | 0.2778          | 0.9403   |
  | 0.0073        | 4.95  | 1500 | 0.2782          | 0.9393   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  