readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: vit-base-DogSick
    results: []
  ---
  
  <!-- 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-base-DogSick
  
  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.3041
  - Acc: {'accuracy': 0.6102564102564103}
  - F1: {'f1': 0.5980148081337936}
  
  ## 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: 4e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 8
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Acc                               | F1                         |
  |:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:--------------------------:|
  | 2.4055        | 0.61  | 50   | 2.2086          | {'accuracy': 0.41794871794871796} | {'f1': 0.3246788612052483} |
  | 2.0379        | 1.22  | 100  | 1.9233          | {'accuracy': 0.4846153846153846}  | {'f1': 0.4386383497855148} |
  | 1.7287        | 1.83  | 150  | 1.7200          | {'accuracy': 0.5256410256410257}  | {'f1': 0.4806042289317683} |
  | 1.4667        | 2.44  | 200  | 1.6021          | {'accuracy': 0.5692307692307692}  | {'f1': 0.533374137436958}  |
  | 1.3444        | 3.05  | 250  | 1.5410          | {'accuracy': 0.5333333333333333}  | {'f1': 0.4846134797922835} |
  | 1.1334        | 3.66  | 300  | 1.4674          | {'accuracy': 0.5743589743589743}  | {'f1': 0.5533432367508125} |
  | 1.007         | 4.27  | 350  | 1.4096          | {'accuracy': 0.5923076923076923}  | {'f1': 0.5801847507206119} |
  | 0.897         | 4.88  | 400  | 1.3674          | {'accuracy': 0.6}                 | {'f1': 0.5903283954748092} |
  | 0.7326        | 5.49  | 450  | 1.3359          | {'accuracy': 0.5923076923076923}  | {'f1': 0.5793036546532927} |
  | 0.7105        | 6.1   | 500  | 1.3259          | {'accuracy': 0.6153846153846154}  | {'f1': 0.6064330281486513} |
  | 0.6164        | 6.71  | 550  | 1.3183          | {'accuracy': 0.6102564102564103}  | {'f1': 0.6014695572651212} |
  | 0.5804        | 7.32  | 600  | 1.3103          | {'accuracy': 0.6025641025641025}  | {'f1': 0.5965366941171513} |
  | 0.5313        | 7.93  | 650  | 1.3041          | {'accuracy': 0.6102564102564103}  | {'f1': 0.5980148081337936} |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  