readme: ---
  license: apache-2.0
  tags:
  - huggingpics
  - image-classification
  - generated_from_trainer
  metrics:
  - accuracy
  model_index:
  - name: planes-trains-automobiles
    results:
    - task:
        name: Image Classification
        type: image-classification
      metric:
        name: Accuracy
        type: accuracy
        value: 0.9850746268656716
  ---
  
  <!-- 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. -->
  
  # planes-trains-automobiles
  
  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 huggingpics dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0534
  - Accuracy: 0.9851
  
  
  ## Model description
  
  Autogenerated by HuggingPics🤗🖼️
  
  Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
  
  Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
  ## Example Images
  
  
  #### automobiles
  
  ![automobiles](images/automobiles.jpg)
  
  #### planes
  
  ![planes](images/planes.jpg)
  
  #### trains
  
  ![trains](images/trains.jpg)
  
  
  ## 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: 2e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 1337
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.0283        | 1.0   | 48   | 0.0434          | 0.9851   |
  | 0.0224        | 2.0   | 96   | 0.0548          | 0.9851   |
  | 0.0203        | 3.0   | 144  | 0.0445          | 0.9851   |
  | 0.0195        | 4.0   | 192  | 0.0534          | 0.9851   |
  
  
  ### Framework versions
  
  - Transformers 4.9.2
  - Pytorch 1.9.0+cu102
  - Datasets 1.11.0
  - Tokenizers 0.10.3
  