readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: vit-base-patch16-224-FV-20epochs-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: 0.8632148377125193
      - name: Precision
        type: precision
        value: 0.8617373130509159
      - name: Recall
        type: recall
        value: 0.8632148377125193
      - name: F1
        type: f1
        value: 0.8621436376894498
  ---
  
  <!-- 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-FV-20epochs-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: 0.6532
  - Accuracy: 0.8632
  - Precision: 0.8617
  - Recall: 0.8632
  - F1: 0.8621
  
  ## 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 1.1709        | 0.99  | 20   | 0.9393          | 0.6971   | 0.6896    | 0.6971 | 0.6890 |
  | 0.5295        | 1.99  | 40   | 0.5024          | 0.8091   | 0.8210    | 0.8091 | 0.8133 |
  | 0.2909        | 2.99  | 60   | 0.4070          | 0.8539   | 0.8529    | 0.8539 | 0.8529 |
  | 0.1435        | 3.99  | 80   | 0.4136          | 0.8539   | 0.8522    | 0.8539 | 0.8522 |
  | 0.0928        | 4.99  | 100  | 0.4495          | 0.8478   | 0.8548    | 0.8478 | 0.8507 |
  | 0.0643        | 5.99  | 120  | 0.4897          | 0.8594   | 0.8572    | 0.8594 | 0.8573 |
  | 0.061         | 6.99  | 140  | 0.5040          | 0.8423   | 0.8490    | 0.8423 | 0.8453 |
  | 0.0519        | 7.99  | 160  | 0.5266          | 0.8524   | 0.8502    | 0.8524 | 0.8510 |
  | 0.0546        | 8.99  | 180  | 0.5200          | 0.8586   | 0.8632    | 0.8586 | 0.8605 |
  | 0.0478        | 9.99  | 200  | 0.5654          | 0.8555   | 0.8548    | 0.8555 | 0.8548 |
  | 0.0509        | 10.99 | 220  | 0.5774          | 0.8609   | 0.8626    | 0.8609 | 0.8616 |
  | 0.0467        | 11.99 | 240  | 0.5847          | 0.8594   | 0.8602    | 0.8594 | 0.8594 |
  | 0.0468        | 12.99 | 260  | 0.5909          | 0.8601   | 0.8597    | 0.8601 | 0.8596 |
  | 0.0469        | 13.99 | 280  | 0.5970          | 0.8563   | 0.8560    | 0.8563 | 0.8561 |
  | 0.0438        | 14.99 | 300  | 0.6234          | 0.8594   | 0.8583    | 0.8594 | 0.8586 |
  | 0.0441        | 15.99 | 320  | 0.6190          | 0.8563   | 0.8582    | 0.8563 | 0.8570 |
  | 0.0431        | 16.99 | 340  | 0.6419          | 0.8570   | 0.8584    | 0.8570 | 0.8574 |
  | 0.0454        | 17.99 | 360  | 0.6528          | 0.8563   | 0.8556    | 0.8563 | 0.8558 |
  | 0.0417        | 18.99 | 380  | 0.6688          | 0.8578   | 0.8575    | 0.8578 | 0.8574 |
  | 0.0432        | 19.99 | 400  | 0.6532          | 0.8632   | 0.8617    | 0.8632 | 0.8621 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0.dev0
  - Pytorch 1.11.0+cu102
  - Datasets 2.6.1.dev0
  - Tokenizers 0.13.1
  