readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: beit-base-patch16-224-pt22k-ft22k-rim_one-new
    results:
    - task:
        type: image-classification
        name: Image Classification
      dataset:
        type: rimonedl
        name: RIM ONE DL
        split: test
      metrics:
      - type: f1
        value: 0.9197860962566845
        name: F1
    - task:
        type: image-classification
        name: Image Classification
      dataset:
        type: rim one
        name: RIMONEDL
        split: test
      metrics:
      - type: precision
        value: 0.9247311827956989
        name: precision
      - type: recall
        value: 0.9148936170212766
        name: Recall
      - type: accuracy
        value: 0.8972602739726028
        name: Accuracy
      - type: roc_auc
        value: 0.8901391162029461
        name: AUC
  ---
  
  <!-- 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. -->
  
  # beit-base-patch16-224-pt22k-ft22k-rim_one-new
  
  This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4550
  - Accuracy: 0.8767
  
  ## 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: 5e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 30
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | No log        | 0.73  | 2    | 0.2411          | 0.9178   |
  | No log        | 1.73  | 4    | 0.2182          | 0.8973   |
  | No log        | 2.73  | 6    | 0.3085          | 0.8973   |
  | No log        | 3.73  | 8    | 0.2794          | 0.8973   |
  | 0.1392        | 4.73  | 10   | 0.2398          | 0.9110   |
  | 0.1392        | 5.73  | 12   | 0.2925          | 0.8973   |
  | 0.1392        | 6.73  | 14   | 0.2798          | 0.9110   |
  | 0.1392        | 7.73  | 16   | 0.2184          | 0.9178   |
  | 0.1392        | 8.73  | 18   | 0.3007          | 0.9110   |
  | 0.0416        | 9.73  | 20   | 0.3344          | 0.9041   |
  | 0.0416        | 10.73 | 22   | 0.3626          | 0.9110   |
  | 0.0416        | 11.73 | 24   | 0.4842          | 0.8904   |
  | 0.0416        | 12.73 | 26   | 0.3664          | 0.8973   |
  | 0.0416        | 13.73 | 28   | 0.3458          | 0.9110   |
  | 0.0263        | 14.73 | 30   | 0.2810          | 0.9110   |
  | 0.0263        | 15.73 | 32   | 0.4695          | 0.8699   |
  | 0.0263        | 16.73 | 34   | 0.3723          | 0.9041   |
  | 0.0263        | 17.73 | 36   | 0.3447          | 0.9041   |
  | 0.0263        | 18.73 | 38   | 0.3708          | 0.8904   |
  | 0.0264        | 19.73 | 40   | 0.4052          | 0.9110   |
  | 0.0264        | 20.73 | 42   | 0.4492          | 0.9041   |
  | 0.0264        | 21.73 | 44   | 0.4649          | 0.8904   |
  | 0.0264        | 22.73 | 46   | 0.4061          | 0.9178   |
  | 0.0264        | 23.73 | 48   | 0.4136          | 0.9110   |
  | 0.0139        | 24.73 | 50   | 0.4183          | 0.8973   |
  | 0.0139        | 25.73 | 52   | 0.4504          | 0.8904   |
  | 0.0139        | 26.73 | 54   | 0.4368          | 0.8973   |
  | 0.0139        | 27.73 | 56   | 0.4711          | 0.9110   |
  | 0.0139        | 28.73 | 58   | 0.3928          | 0.9110   |
  | 0.005         | 29.73 | 60   | 0.4550          | 0.8767   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  