readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: vit-base-patch16-224-FV2-finetuned-memes
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: imagefolder
        type: imagefolder
        config: default
        split: train
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8647604327666152
      - name: Precision
        type: precision
        value: 0.865115560305398
      - name: Recall
        type: recall
        value: 0.8647604327666152
      - name: F1
        type: f1
        value: 0.8646314523408155
  ---
  
  <!-- 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-base-patch16-224-FV2-finetuned-memes
  
  This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5458
  - Accuracy: 0.8648
  - Precision: 0.8651
  - Recall: 0.8648
  - F1: 0.8646
  
  ## 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: 0.00012
  - train_batch_size: 64
  - eval_batch_size: 64
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 256
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.994         | 0.99  | 20   | 0.7937          | 0.7257   | 0.7148    | 0.7257 | 0.7025 |
  | 0.509         | 1.99  | 40   | 0.4634          | 0.8346   | 0.8461    | 0.8346 | 0.8303 |
  | 0.2698        | 2.99  | 60   | 0.3851          | 0.8594   | 0.8619    | 0.8594 | 0.8586 |
  | 0.1381        | 3.99  | 80   | 0.4186          | 0.8624   | 0.8716    | 0.8624 | 0.8634 |
  | 0.0899        | 4.99  | 100  | 0.4038          | 0.8586   | 0.8624    | 0.8586 | 0.8594 |
  | 0.0708        | 5.99  | 120  | 0.4170          | 0.8563   | 0.8612    | 0.8563 | 0.8580 |
  | 0.0629        | 6.99  | 140  | 0.4414          | 0.8594   | 0.8599    | 0.8594 | 0.8585 |
  | 0.0554        | 7.99  | 160  | 0.4617          | 0.8539   | 0.8563    | 0.8539 | 0.8550 |
  | 0.0582        | 8.99  | 180  | 0.4712          | 0.8648   | 0.8667    | 0.8648 | 0.8651 |
  | 0.0582        | 9.99  | 200  | 0.4753          | 0.8632   | 0.8647    | 0.8632 | 0.8636 |
  | 0.0535        | 10.99 | 220  | 0.4653          | 0.8694   | 0.8690    | 0.8694 | 0.8684 |
  | 0.0516        | 11.99 | 240  | 0.4937          | 0.8679   | 0.8692    | 0.8679 | 0.8681 |
  | 0.0478        | 12.99 | 260  | 0.5109          | 0.8725   | 0.8741    | 0.8725 | 0.8718 |
  | 0.0484        | 13.99 | 280  | 0.5144          | 0.8640   | 0.8660    | 0.8640 | 0.8647 |
  | 0.0472        | 14.99 | 300  | 0.5249          | 0.8679   | 0.8688    | 0.8679 | 0.8678 |
  | 0.043         | 15.99 | 320  | 0.5324          | 0.8709   | 0.8711    | 0.8709 | 0.8704 |
  | 0.0473        | 16.99 | 340  | 0.5352          | 0.8648   | 0.8660    | 0.8648 | 0.8647 |
  | 0.0502        | 17.99 | 360  | 0.5389          | 0.8694   | 0.8692    | 0.8694 | 0.8687 |
  | 0.0489        | 18.99 | 380  | 0.5564          | 0.8648   | 0.8666    | 0.8648 | 0.8651 |
  | 0.04          | 19.99 | 400  | 0.5458          | 0.8648   | 0.8651    | 0.8648 | 0.8646 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0.dev0
  - Pytorch 1.11.0+cu102
  - Datasets 2.6.1.dev0
  - Tokenizers 0.13.1
  