readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: mit-b2-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: 0.8523956723338485
    - task:
        type: image-classification
        name: Image Classification
      dataset:
        type: custom
        name: custom
        split: test
      metrics:
      - type: f1
        value: 0.8580847578266328
        name: F1
      - type: precision
        value: 0.8587893412503379
        name: Precision
      - type: recall
        value: 0.8593508500772797
        name: Recall
  ---
  
  <!-- 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-finetuned-memes
  
  This model is a fine-tuned version of [aaraki/vit-base-patch16-224-in21k-finetuned-cifar10](https://huggingface.co/aaraki/vit-base-patch16-224-in21k-finetuned-cifar10) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4137
  - Accuracy: 0.8524
  
  ## 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: 32
  - eval_batch_size: 32
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.9727        | 0.99  | 40   | 0.8400          | 0.7334   |
  | 0.5305        | 1.99  | 80   | 0.5147          | 0.8284   |
  | 0.3124        | 2.99  | 120  | 0.4698          | 0.8145   |
  | 0.2263        | 3.99  | 160  | 0.3892          | 0.8563   |
  | 0.1453        | 4.99  | 200  | 0.3874          | 0.8570   |
  | 0.1255        | 5.99  | 240  | 0.4097          | 0.8470   |
  | 0.0989        | 6.99  | 280  | 0.3860          | 0.8570   |
  | 0.0755        | 7.99  | 320  | 0.4141          | 0.8539   |
  | 0.08          | 8.99  | 360  | 0.4049          | 0.8594   |
  | 0.0639        | 9.99  | 400  | 0.4137          | 0.8524   |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  