readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: vit-large-knee
    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-large-knee
  
  This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the knee dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.3621
  - Accuracy: 0.4189
  
  ## 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: 15
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.4679        | 0.28  | 100  | 1.4641          | 0.3995   |
  | 1.4053        | 0.55  | 200  | 1.4261          | 0.2930   |
  | 1.3636        | 0.83  | 300  | 1.4444          | 0.3923   |
  | 1.5026        | 1.1   | 400  | 1.4097          | 0.3898   |
  | 1.3863        | 1.38  | 500  | 1.4074          | 0.3959   |
  | 1.4278        | 1.66  | 600  | 1.4153          | 0.3923   |
  | 1.3142        | 1.93  | 700  | 1.4116          | 0.3923   |
  | 1.3433        | 2.21  | 800  | 1.4222          | 0.3971   |
  | 1.5063        | 2.49  | 900  | 1.4500          | 0.3801   |
  | 1.4077        | 2.76  | 1000 | 1.4082          | 0.3850   |
  | 1.4126        | 3.04  | 1100 | 1.4049          | 0.3886   |
  | 1.416         | 3.31  | 1200 | 1.4224          | 0.3862   |
  | 1.3716        | 3.59  | 1300 | 1.4155          | 0.3838   |
  | 1.4488        | 3.87  | 1400 | 1.3984          | 0.3923   |
  | 1.3114        | 4.14  | 1500 | 1.4174          | 0.3850   |
  | 1.4509        | 4.42  | 1600 | 1.4074          | 0.3862   |
  | 1.4066        | 4.7   | 1700 | 1.4098          | 0.3959   |
  | 1.3377        | 4.97  | 1800 | 1.4119          | 0.3814   |
  | 1.2904        | 5.25  | 1900 | 1.4132          | 0.3971   |
  | 1.3377        | 5.52  | 2000 | 1.4040          | 0.3838   |
  | 1.4686        | 5.8   | 2100 | 1.4128          | 0.3692   |
  | 1.3668        | 6.08  | 2200 | 1.3992          | 0.3826   |
  | 1.3701        | 6.35  | 2300 | 1.4077          | 0.3826   |
  | 1.374         | 6.63  | 2400 | 1.4052          | 0.3814   |
  | 1.3827        | 6.91  | 2500 | 1.4124          | 0.3862   |
  | 1.452         | 7.18  | 2600 | 1.4068          | 0.3910   |
  | 1.3513        | 7.46  | 2700 | 1.3918          | 0.3814   |
  | 1.332         | 7.73  | 2800 | 1.4088          | 0.3826   |
  | 1.3616        | 8.01  | 2900 | 1.4014          | 0.3789   |
  | 1.4113        | 8.29  | 3000 | 1.3948          | 0.3801   |
  | 1.4091        | 8.56  | 3100 | 1.4065          | 0.3717   |
  | 1.3177        | 8.84  | 3200 | 1.3984          | 0.3838   |
  | 1.308         | 9.12  | 3300 | 1.3960          | 0.3898   |
  | 1.3389        | 9.39  | 3400 | 1.3928          | 0.3862   |
  | 1.3127        | 9.67  | 3500 | 1.3894          | 0.3850   |
  | 1.4533        | 9.94  | 3600 | 1.3978          | 0.3729   |
  | 1.4077        | 10.22 | 3700 | 1.4365          | 0.3487   |
  | 1.3918        | 10.5  | 3800 | 1.3821          | 0.3862   |
  | 1.3603        | 10.77 | 3900 | 1.3808          | 0.3898   |
  | 1.3426        | 11.05 | 4000 | 1.3779          | 0.3923   |
  | 1.2512        | 11.33 | 4100 | 1.3772          | 0.3862   |
  | 1.3818        | 11.6  | 4200 | 1.3806          | 0.4019   |
  | 1.2173        | 11.88 | 4300 | 1.4003          | 0.3959   |
  | 1.3086        | 12.15 | 4400 | 1.3812          | 0.3801   |
  | 1.3132        | 12.43 | 4500 | 1.3677          | 0.4056   |
  | 1.2646        | 12.71 | 4600 | 1.3827          | 0.3923   |
  | 1.2597        | 12.98 | 4700 | 1.3689          | 0.4080   |
  | 1.3116        | 13.26 | 4800 | 1.3745          | 0.3923   |
  | 1.1892        | 13.54 | 4900 | 1.3937          | 0.3862   |
  | 1.2782        | 13.81 | 5000 | 1.3579          | 0.4007   |
  | 1.2366        | 14.09 | 5100 | 1.3643          | 0.4056   |
  | 1.2827        | 14.36 | 5200 | 1.3713          | 0.4056   |
  | 1.2082        | 14.64 | 5300 | 1.3577          | 0.4153   |
  | 1.2255        | 14.92 | 5400 | 1.3609          | 0.4189   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  