readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: vit-classify-manipulations-ft
    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-classify-manipulations-ft
  
  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 vit-classify-manipulations-v2-2 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2064
  - Accuracy: 0.9067
  
  ## 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: 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: 100
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.2543        | 0.66  | 100   | 0.3142          | 0.8657   |
  | 0.1982        | 1.32  | 200   | 0.3589          | 0.8694   |
  | 0.3137        | 1.99  | 300   | 0.2064          | 0.9067   |
  | 0.1667        | 2.65  | 400   | 0.2949          | 0.8955   |
  | 0.1258        | 3.31  | 500   | 0.2742          | 0.9104   |
  | 0.1316        | 3.97  | 600   | 0.3565          | 0.8843   |
  | 0.099         | 4.64  | 700   | 0.2812          | 0.9216   |
  | 0.0222        | 5.3   | 800   | 0.4249          | 0.8881   |
  | 0.0728        | 5.96  | 900   | 0.3928          | 0.8955   |
  | 0.0797        | 6.62  | 1000  | 0.3776          | 0.8955   |
  | 0.0035        | 7.28  | 1100  | 0.4553          | 0.9030   |
  | 0.0543        | 7.95  | 1200  | 0.4734          | 0.8843   |
  | 0.0118        | 8.61  | 1300  | 0.3744          | 0.9104   |
  | 0.0117        | 9.27  | 1400  | 0.5624          | 0.8843   |
  | 0.0304        | 9.93  | 1500  | 0.4978          | 0.8881   |
  | 0.0465        | 10.6  | 1600  | 0.4483          | 0.9104   |
  | 0.0527        | 11.26 | 1700  | 0.5409          | 0.8806   |
  | 0.0164        | 11.92 | 1800  | 0.5570          | 0.8955   |
  | 0.0367        | 12.58 | 1900  | 0.5798          | 0.8955   |
  | 0.0109        | 13.25 | 2000  | 0.5195          | 0.8843   |
  | 0.0725        | 13.91 | 2100  | 0.3585          | 0.9067   |
  | 0.0536        | 14.57 | 2200  | 0.4510          | 0.8918   |
  | 0.0234        | 15.23 | 2300  | 0.4542          | 0.8843   |
  | 0.1024        | 15.89 | 2400  | 0.4382          | 0.8955   |
  | 0.0359        | 16.56 | 2500  | 0.5638          | 0.8918   |
  | 0.0028        | 17.22 | 2600  | 0.4226          | 0.9216   |
  | 0.0359        | 17.88 | 2700  | 0.4038          | 0.9216   |
  | 0.0286        | 18.54 | 2800  | 0.3682          | 0.9030   |
  | 0.0007        | 19.21 | 2900  | 0.4494          | 0.9291   |
  | 0.0011        | 19.87 | 3000  | 0.5247          | 0.8993   |
  | 0.0012        | 20.53 | 3100  | 0.4359          | 0.9142   |
  | 0.0137        | 21.19 | 3200  | 0.4726          | 0.9179   |
  | 0.0007        | 21.85 | 3300  | 0.6007          | 0.8955   |
  | 0.0269        | 22.52 | 3400  | 0.3105          | 0.9216   |
  | 0.0241        | 23.18 | 3500  | 0.3852          | 0.9030   |
  | 0.1016        | 23.84 | 3600  | 0.4785          | 0.9142   |
  | 0.0015        | 24.5  | 3700  | 0.5085          | 0.9104   |
  | 0.0454        | 25.17 | 3800  | 0.6256          | 0.8731   |
  | 0.0005        | 25.83 | 3900  | 0.5935          | 0.8993   |
  | 0.0535        | 26.49 | 4000  | 0.5092          | 0.8918   |
  | 0.0156        | 27.15 | 4100  | 0.7119          | 0.8806   |
  | 0.0983        | 27.81 | 4200  | 0.4342          | 0.9104   |
  | 0.0008        | 28.48 | 4300  | 0.5630          | 0.8955   |
  | 0.0337        | 29.14 | 4400  | 0.4331          | 0.9216   |
  | 0.0076        | 29.8  | 4500  | 0.5523          | 0.9067   |
  | 0.0002        | 30.46 | 4600  | 0.5822          | 0.9104   |
  | 0.0447        | 31.13 | 4700  | 0.4997          | 0.9142   |
  | 0.0038        | 31.79 | 4800  | 0.4663          | 0.9179   |
  | 0.001         | 32.45 | 4900  | 0.5102          | 0.8955   |
  | 0.0037        | 33.11 | 5000  | 0.6439          | 0.8955   |
  | 0.0009        | 33.77 | 5100  | 1.0129          | 0.8507   |
  | 0.032         | 34.44 | 5200  | 0.5261          | 0.9030   |
  | 0.0002        | 35.1  | 5300  | 0.6782          | 0.8993   |
  | 0.0002        | 35.76 | 5400  | 0.6949          | 0.8918   |
  | 0.0002        | 36.42 | 5500  | 0.6965          | 0.8955   |
  | 0.0001        | 37.09 | 5600  | 0.6989          | 0.8993   |
  | 0.0001        | 37.75 | 5700  | 0.7056          | 0.8955   |
  | 0.0063        | 38.41 | 5800  | 0.7139          | 0.8955   |
  | 0.0001        | 39.07 | 5900  | 0.7213 