readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - vision
  - generated_from_trainer
  datasets:
  - gtsrb
  metrics:
  - accuracy
  model-index:
  - name: gtsrb-model
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: bazyl/GTSRB
        type: gtsrb
        args: gtsrb
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9993199591975519
  ---
  
  <!-- 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. -->
  
  # gtsrb-model
  
  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 bazyl/GTSRB dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0034
  - Accuracy: 0.9993
  
  ## 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: 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: 10.0
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.2593        | 1.0   | 4166  | 0.1585          | 0.9697   |
  | 0.2659        | 2.0   | 8332  | 0.0472          | 0.9900   |
  | 0.2825        | 3.0   | 12498 | 0.0155          | 0.9971   |
  | 0.0953        | 4.0   | 16664 | 0.0113          | 0.9983   |
  | 0.1277        | 5.0   | 20830 | 0.0076          | 0.9985   |
  | 0.0816        | 6.0   | 24996 | 0.0047          | 0.9988   |
  | 0.0382        | 7.0   | 29162 | 0.0041          | 0.9990   |
  | 0.0983        | 8.0   | 33328 | 0.0059          | 0.9990   |
  | 0.1746        | 9.0   | 37494 | 0.0034          | 0.9993   |
  | 0.1153        | 10.0  | 41660 | 0.0038          | 0.9990   |
  
  
  ### Framework versions
  
  - Transformers 4.21.0.dev0
  - Pytorch 1.12.0
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  