readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: vit-classify-manipulations-pretrain
    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-pretrain
  
  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: 0.2361
  - Accuracy: 0.9296
  
  ## 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: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.4813        | 0.66  | 100  | 0.3471          | 0.8963   |
  | 0.2086        | 1.32  | 200  | 0.2774          | 0.9037   |
  | 0.2677        | 1.97  | 300  | 0.3269          | 0.8519   |
  | 0.127         | 2.63  | 400  | 0.2685          | 0.8926   |
  | 0.0794        | 3.29  | 500  | 0.4003          | 0.8852   |
  | 0.1481        | 3.95  | 600  | 0.3396          | 0.8889   |
  | 0.0636        | 4.61  | 700  | 0.3351          | 0.9      |
  | 0.0242        | 5.26  | 800  | 0.3965          | 0.8852   |
  | 0.1552        | 5.92  | 900  | 0.2870          | 0.9111   |
  | 0.0812        | 6.58  | 1000 | 0.2361          | 0.9296   |
  | 0.0545        | 7.24  | 1100 | 0.3029          | 0.9296   |
  | 0.0475        | 7.89  | 1200 | 0.3957          | 0.9148   |
  | 0.0284        | 8.55  | 1300 | 0.3594          | 0.9185   |
  | 0.0074        | 9.21  | 1400 | 0.4176          | 0.9148   |
  | 0.0221        | 9.87  | 1500 | 0.4719          | 0.9185   |
  | 0.0085        | 10.53 | 1600 | 0.4135          | 0.9296   |
  | 0.0024        | 11.18 | 1700 | 0.5279          | 0.9      |
  | 0.0009        | 11.84 | 1800 | 0.4511          | 0.9259   |
  | 0.0287        | 12.5  | 1900 | 0.5679          | 0.9037   |
  | 0.0248        | 13.16 | 2000 | 0.5722          | 0.8889   |
  | 0.0199        | 13.82 | 2100 | 0.4913          | 0.9074   |
  | 0.0403        | 14.47 | 2200 | 0.3112          | 0.9222   |
  | 0.0666        | 15.13 | 2300 | 0.6592          | 0.8667   |
  | 0.0216        | 15.79 | 2400 | 0.4192          | 0.9037   |
  | 0.0175        | 16.45 | 2500 | 0.5231          | 0.9185   |
  | 0.0008        | 17.11 | 2600 | 0.4872          | 0.9      |
  | 0.0662        | 17.76 | 2700 | 0.4426          | 0.9074   |
  | 0.0037        | 18.42 | 2800 | 0.4547          | 0.9111   |
  | 0.0194        | 19.08 | 2900 | 0.5033          | 0.9074   |
  | 0.0201        | 19.74 | 3000 | 0.6102          | 0.9      |
  | 0.0005        | 20.39 | 3100 | 0.5291          | 0.9074   |
  | 0.0003        | 21.05 | 3200 | 0.5601          | 0.9074   |
  | 0.0009        | 21.71 | 3300 | 0.4944          | 0.9148   |
  | 0.0144        | 22.37 | 3400 | 0.4561          | 0.9222   |
  | 0.005         | 23.03 | 3500 | 0.4452          | 0.9185   |
  | 0.0002        | 23.68 | 3600 | 0.4793          | 0.9074   |
  | 0.0002        | 24.34 | 3700 | 0.4803          | 0.9185   |
  | 0.0002        | 25.0  | 3800 | 0.4944          | 0.9111   |
  | 0.0002        | 25.66 | 3900 | 0.5001          | 0.9111   |
  | 0.0002        | 26.32 | 4000 | 0.5097          | 0.9111   |
  | 0.0002        | 26.97 | 4100 | 0.5111          | 0.9111   |
  | 0.0001        | 27.63 | 4200 | 0.5206          | 0.9111   |
  | 0.0001        | 28.29 | 4300 | 0.5225          | 0.9111   |
  | 0.0001        | 28.95 | 4400 | 0.5263          | 0.9111   |
  | 0.0001        | 29.61 | 4500 | 0.5440          | 0.9111   |
  | 0.0064        | 30.26 | 4600 | 0.5357          | 0.9111   |
  | 0.0001        | 30.92 | 4700 | 0.5294          | 0.9111   |
  | 0.0052        | 31.58 | 4800 | 0.5398          | 0.9111   |
  | 0.0052        | 32.24 | 4900 | 0.5410          | 0.9111   |
  | 0.0061        | 32.89 | 5000 | 0.5522          | 0.9074   |
  | 0.0001        | 33.55 | 5100 | 0.5376          | 0.9148   |
  | 0.0043        | 34.21 | 5200 | 0.5574          | 0.9111   |
  | 0.0001        | 34.87 | 5300 | 0.5488          | 0.9111   |
  | 0.0001        | 35.53 | 5400 | 0.5450          | 0.9148   |
  | 0.0001        | 36.18 | 5500 | 0.5490          | 0.9111   |
  | 0.006         | 36.84 | 5600 | 0.5594          | 0.9074   |
  | 0.0001        | 37.5  | 5700 | 0.5747          | 0.9111   |
  | 0.0001        | 38.16 | 5800 | 0.5623          | 0.9074   |
  | 0.0052        | 38.82 | 5900 | 0.5647          | 0.9074   |
  | 0.0001        | 39.47 | 6000