readme: ---
  license: other
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: mit-b2-VF2-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: 0.8307573415765069
      - name: Precision
        type: precision
        value: 0.8272186656187493
      - name: Recall
        type: recall
        value: 0.8307573415765069
      - name: F1
        type: f1
        value: 0.8286939083150942
  ---
  
  <!-- 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. -->
  
  # mit-b2-VF2-finetuned-memes
  
  This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6547
  - Accuracy: 0.8308
  - Precision: 0.8272
  - Recall: 0.8308
  - F1: 0.8287
  
  ## 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 1.3077        | 0.99  | 20   | 1.1683          | 0.5549   | 0.5621    | 0.5549 | 0.5286 |
  | 0.9359        | 1.99  | 40   | 0.8573          | 0.6731   | 0.6807    | 0.6731 | 0.6535 |
  | 0.7219        | 2.99  | 60   | 0.7106          | 0.7272   | 0.7359    | 0.7272 | 0.7246 |
  | 0.6013        | 3.99  | 80   | 0.6445          | 0.7550   | 0.7686    | 0.7550 | 0.7558 |
  | 0.5243        | 4.99  | 100  | 0.6717          | 0.7573   | 0.8077    | 0.7573 | 0.7584 |
  | 0.4409        | 5.99  | 120  | 0.5315          | 0.8068   | 0.8027    | 0.8068 | 0.7989 |
  | 0.3325        | 6.99  | 140  | 0.5159          | 0.8230   | 0.8236    | 0.8230 | 0.8158 |
  | 0.2719        | 7.99  | 160  | 0.5250          | 0.8215   | 0.8227    | 0.8215 | 0.8202 |
  | 0.242         | 8.99  | 180  | 0.5087          | 0.8277   | 0.8260    | 0.8277 | 0.8268 |
  | 0.2247        | 9.99  | 200  | 0.5313          | 0.8215   | 0.8275    | 0.8215 | 0.8218 |
  | 0.1955        | 10.99 | 220  | 0.6167          | 0.8130   | 0.8062    | 0.8130 | 0.8073 |
  | 0.1567        | 11.99 | 240  | 0.5859          | 0.8168   | 0.8185    | 0.8168 | 0.8173 |
  | 0.1479        | 12.99 | 260  | 0.5938          | 0.8215   | 0.8169    | 0.8215 | 0.8178 |
  | 0.1241        | 13.99 | 280  | 0.6187          | 0.8261   | 0.8234    | 0.8261 | 0.8239 |
  | 0.1114        | 14.99 | 300  | 0.6419          | 0.8261   | 0.8351    | 0.8261 | 0.8293 |
  | 0.1022        | 15.99 | 320  | 0.6322          | 0.8323   | 0.8284    | 0.8323 | 0.8294 |
  | 0.0941        | 16.99 | 340  | 0.6595          | 0.8269   | 0.8266    | 0.8269 | 0.8263 |
  | 0.0935        | 17.99 | 360  | 0.6674          | 0.8269   | 0.8218    | 0.8269 | 0.8237 |
  | 0.089         | 18.99 | 380  | 0.6533          | 0.8253   | 0.8222    | 0.8253 | 0.8235 |
  | 0.0794        | 19.99 | 400  | 0.6547          | 0.8308   | 0.8272    | 0.8308 | 0.8287 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0.dev0
  - Pytorch 1.11.0+cu102
  - Datasets 2.6.1.dev0
  - Tokenizers 0.13.1
  