readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: deit-base-patch16-224-FV-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: 0.848531684698609
      - name: Precision
        type: precision
        value: 0.8458069264500935
      - name: Recall
        type: recall
        value: 0.848531684698609
      - name: F1
        type: f1
        value: 0.8463625265241504
  ---
  
  <!-- 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. -->
  
  # deit-base-patch16-224-FV-finetuned-memes
  
  This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6769
  - Accuracy: 0.8485
  - Precision: 0.8458
  - Recall: 0.8485
  - F1: 0.8464
  
  ## 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 1.2733        | 0.99  | 20   | 1.0893          | 0.5811   | 0.5790    | 0.5811 | 0.5293 |
  | 0.7284        | 1.99  | 40   | 0.7351          | 0.7210   | 0.7642    | 0.7210 | 0.7271 |
  | 0.4267        | 2.99  | 60   | 0.5202          | 0.7991   | 0.8104    | 0.7991 | 0.8033 |
  | 0.2181        | 3.99  | 80   | 0.4605          | 0.8346   | 0.8351    | 0.8346 | 0.8334 |
  | 0.1504        | 4.99  | 100  | 0.5281          | 0.8253   | 0.8281    | 0.8253 | 0.8266 |
  | 0.1001        | 5.99  | 120  | 0.4945          | 0.8369   | 0.8336    | 0.8369 | 0.8347 |
  | 0.0874        | 6.99  | 140  | 0.5902          | 0.8338   | 0.8370    | 0.8338 | 0.8348 |
  | 0.0634        | 7.99  | 160  | 0.6088          | 0.8253   | 0.8221    | 0.8253 | 0.8234 |
  | 0.0699        | 8.99  | 180  | 0.6210          | 0.8207   | 0.8202    | 0.8207 | 0.8186 |
  | 0.0661        | 9.99  | 200  | 0.5675          | 0.8385   | 0.8417    | 0.8385 | 0.8393 |
  | 0.0592        | 10.99 | 220  | 0.6550          | 0.8253   | 0.8324    | 0.8253 | 0.8275 |
  | 0.0559        | 11.99 | 240  | 0.6400          | 0.8416   | 0.8370    | 0.8416 | 0.8387 |
  | 0.0501        | 12.99 | 260  | 0.6726          | 0.8393   | 0.8353    | 0.8393 | 0.8350 |
  | 0.0529        | 13.99 | 280  | 0.6285          | 0.8408   | 0.8399    | 0.8408 | 0.8401 |
  | 0.0478        | 14.99 | 300  | 0.6423          | 0.8400   | 0.8380    | 0.8400 | 0.8384 |
  | 0.0458        | 15.99 | 320  | 0.6632          | 0.8369   | 0.8337    | 0.8369 | 0.8348 |
  | 0.048         | 16.99 | 340  | 0.6719          | 0.8423   | 0.8401    | 0.8423 | 0.8404 |
  | 0.0417        | 17.99 | 360  | 0.6807          | 0.8423   | 0.8415    | 0.8423 | 0.8408 |
  | 0.0461        | 18.99 | 380  | 0.6732          | 0.8454   | 0.8440    | 0.8454 | 0.8438 |
  | 0.044         | 19.99 | 400  | 0.6769          | 0.8485   | 0.8458    | 0.8485 | 0.8464 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0.dev0
  - Pytorch 1.11.0+cu102
  - Datasets 2.6.1.dev0
  - Tokenizers 0.13.1
  