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 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2798
  - Accuracy: 0.8694
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.2882        | 0.66  | 100   | 0.2873          | 0.8806   |
  | 0.3869        | 1.32  | 200   | 0.2798          | 0.8694   |
  | 0.2345        | 1.99  | 300   | 0.3074          | 0.8843   |
  | 0.081         | 2.65  | 400   | 0.3563          | 0.8470   |
  | 0.129         | 3.31  | 500   | 0.3736          | 0.8918   |
  | 0.0823        | 3.97  | 600   | 0.2815          | 0.9104   |
  | 0.1213        | 4.64  | 700   | 0.2902          | 0.9216   |
  | 0.0422        | 5.3   | 800   | 0.3492          | 0.8993   |
  | 0.0753        | 5.96  | 900   | 0.4281          | 0.8657   |
  | 0.2148        | 6.62  | 1000  | 0.4206          | 0.8955   |
  | 0.0213        | 7.28  | 1100  | 0.4723          | 0.8843   |
  | 0.0376        | 7.95  | 1200  | 0.3787          | 0.8918   |
  | 0.0226        | 8.61  | 1300  | 0.4501          | 0.9030   |
  | 0.1102        | 9.27  | 1400  | 0.7684          | 0.8396   |
  | 0.0444        | 9.93  | 1500  | 0.3732          | 0.9142   |
  | 0.0329        | 10.6  | 1600  | 0.4021          | 0.9030   |
  | 0.0379        | 11.26 | 1700  | 0.4920          | 0.8918   |
  | 0.1129        | 11.92 | 1800  | 0.3605          | 0.9216   |
  | 0.0271        | 12.58 | 1900  | 0.4282          | 0.9030   |
  | 0.0247        | 13.25 | 2000  | 0.5272          | 0.8993   |
  | 0.0205        | 13.91 | 2100  | 0.3310          | 0.8881   |
  | 0.0128        | 14.57 | 2200  | 0.5010          | 0.8955   |
  | 0.02          | 15.23 | 2300  | 0.4055          | 0.9142   |
  | 0.0004        | 15.89 | 2400  | 0.4748          | 0.9179   |
  | 0.0004        | 16.56 | 2500  | 0.4993          | 0.9179   |
  | 0.0031        | 17.22 | 2600  | 0.6183          | 0.9030   |
  | 0.0017        | 17.88 | 2700  | 0.4281          | 0.8993   |
  | 0.0525        | 18.54 | 2800  | 0.5069          | 0.8955   |
  | 0.0585        | 19.21 | 2900  | 0.4555          | 0.9067   |
  | 0.0739        | 19.87 | 3000  | 0.7661          | 0.8470   |
  | 0.0948        | 20.53 | 3100  | 0.5650          | 0.8769   |
  | 0.0017        | 21.19 | 3200  | 0.5727          | 0.8881   |
  | 0.0058        | 21.85 | 3300  | 0.4706          | 0.8993   |
  | 0.0005        | 22.52 | 3400  | 0.7194          | 0.8806   |
  | 0.0149        | 23.18 | 3500  | 0.5310          | 0.9030   |
  | 0.0502        | 23.84 | 3600  | 0.5698          | 0.8843   |
  | 0.001         | 24.5  | 3700  | 0.5458          | 0.8881   |
  | 0.0089        | 25.17 | 3800  | 0.7860          | 0.8769   |
  | 0.0402        | 25.83 | 3900  | 0.6264          | 0.8843   |
  | 0.0004        | 26.49 | 4000  | 0.6089          | 0.8955   |
  | 0.001         | 27.15 | 4100  | 0.7378          | 0.8731   |
  | 0.0788        | 27.81 | 4200  | 0.4598          | 0.9142   |
  | 0.0022        | 28.48 | 4300  | 0.5228          | 0.8843   |
  | 0.1316        | 29.14 | 4400  | 0.4931          | 0.8843   |
  | 0.0015        | 29.8  | 4500  | 0.4824          | 0.9179   |
  | 0.0786        | 30.46 | 4600  | 0.5039          | 0.9067   |
  | 0.0003        | 31.13 | 4700  | 0.5460          | 0.9179   |
  | 0.0457        | 31.79 | 4800  | 0.5526          | 0.9104   |
  | 0.0005        | 32.45 | 4900  | 0.6904          | 0.8694   |
  | 0.02          | 33.11 | 5000  | 0.4874          | 0.8955   |
  | 0.0084        | 33.77 | 5100  | 0.4371          | 0.9067   |
  | 0.1005        | 34.44 | 5200  | 0.4710          | 0.8993   |
  | 0.0006        | 35.1  | 5300  | 0.6772          | 0.8843   |
  | 0.0004        | 35.76 | 5400  | 0.5969          | 0.9030   |
  | 0.0002        | 36.42 | 5500  | 0.6072          | 0.8955   |
  | 0.0571        | 37.09 | 5600  | 0.5104          | 0.8993   |
  | 0.0017        | 37.75 | 5700  | 0.6352          | 0.8769   |
  | 0.0015        | 38.41 | 5800  | 0.6217          | 0.8993   |
  | 0.0002        | 39.07 | 5900  | 0.7594   