readme: ---
  license: other
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: mit-b2-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: 0.8323029366306027
      - name: Precision
        type: precision
        value: 0.831217385971583
      - name: Recall
        type: recall
        value: 0.8323029366306027
      - name: F1
        type: f1
        value: 0.831492653119617
  ---
  
  <!-- 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-fv-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: 0.5984
  - Accuracy: 0.8323
  - Precision: 0.8312
  - Recall: 0.8323
  - F1: 0.8315
  
  ## 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 1.3683        | 0.99  | 20   | 1.1798          | 0.5703   | 0.4914    | 0.5703 | 0.4915 |
  | 1.0113        | 1.99  | 40   | 1.0384          | 0.6159   | 0.6813    | 0.6159 | 0.6274 |
  | 0.7581        | 2.99  | 60   | 0.8348          | 0.6808   | 0.7377    | 0.6808 | 0.6840 |
  | 0.6241        | 3.99  | 80   | 0.6034          | 0.7713   | 0.7864    | 0.7713 | 0.7735 |
  | 0.4999        | 4.99  | 100  | 0.5481          | 0.7944   | 0.8000    | 0.7944 | 0.7909 |
  | 0.3981        | 5.99  | 120  | 0.5253          | 0.8022   | 0.8091    | 0.8022 | 0.8000 |
  | 0.3484        | 6.99  | 140  | 0.4688          | 0.8238   | 0.8147    | 0.8238 | 0.8146 |
  | 0.3142        | 7.99  | 160  | 0.6245          | 0.7867   | 0.8209    | 0.7867 | 0.7920 |
  | 0.2339        | 8.99  | 180  | 0.5053          | 0.8362   | 0.8426    | 0.8362 | 0.8355 |
  | 0.2284        | 9.99  | 200  | 0.5070          | 0.8230   | 0.8220    | 0.8230 | 0.8187 |
  | 0.1824        | 10.99 | 220  | 0.5780          | 0.8006   | 0.8138    | 0.8006 | 0.8035 |
  | 0.1561        | 11.99 | 240  | 0.5429          | 0.8253   | 0.8197    | 0.8253 | 0.8218 |
  | 0.1229        | 12.99 | 260  | 0.5325          | 0.8331   | 0.8296    | 0.8331 | 0.8303 |
  | 0.1232        | 13.99 | 280  | 0.5595          | 0.8277   | 0.8290    | 0.8277 | 0.8273 |
  | 0.118         | 14.99 | 300  | 0.5974          | 0.8292   | 0.8345    | 0.8292 | 0.8299 |
  | 0.11          | 15.99 | 320  | 0.5796          | 0.8253   | 0.8228    | 0.8253 | 0.8231 |
  | 0.0948        | 16.99 | 340  | 0.5581          | 0.8346   | 0.8358    | 0.8346 | 0.8349 |
  | 0.0985        | 17.99 | 360  | 0.5700          | 0.8338   | 0.8301    | 0.8338 | 0.8318 |
  | 0.0821        | 18.99 | 380  | 0.5756          | 0.8331   | 0.8343    | 0.8331 | 0.8335 |
  | 0.0813        | 19.99 | 400  | 0.5984          | 0.8323   | 0.8312    | 0.8323 | 0.8315 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0.dev0
  - Pytorch 1.11.0+cu102
  - Datasets 2.6.1.dev0
  - Tokenizers 0.13.1
  