readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  - image-classification
  - pytorch
  datasets:
  - food101
  metrics:
  - accuracy
  model-index:
  - name: food101_outputs
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: food-101
        type: food101
        args: default
      metrics:
        - name: Accuracy
          type: accuracy
          value: 0.8912871287128713
  ---
  
  <!-- 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. -->
  
  # nateraw/food
  
  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 nateraw/food101 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4501
  - Accuracy: 0.8913
  
  ## 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: 128
  - eval_batch_size: 128
  - seed: 1337
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.8271        | 1.0   | 592  | 0.6070          | 0.8562   |
  | 0.4376        | 2.0   | 1184 | 0.4947          | 0.8691   |
  | 0.2089        | 3.0   | 1776 | 0.4876          | 0.8747   |
  | 0.0882        | 4.0   | 2368 | 0.4639          | 0.8857   |
  | 0.0452        | 5.0   | 2960 | 0.4501          | 0.8913   |
  
  
  ### Framework versions
  
  - Transformers 4.9.0.dev0
  - Pytorch 1.9.0+cu102
  - Datasets 1.9.1.dev0
  - Tokenizers 0.10.3
  