readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: swin-base-patch4-window7-224-20epochs-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: 0.847758887171561
    - task:
        type: image-classification
        name: Image Classification
      dataset:
        type: custom
        name: custom
        split: test
      metrics:
      - type: f1
        value: 0.8504084378729573
        name: F1
      - type: precision
        value: 0.8519647060733512
        name: Precision
      - type: recall
        value: 0.8523956723338485
        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. -->
  
  # swin-base-patch4-window7-224-20epochs-finetuned-memes
  
  This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7090
  - Accuracy: 0.8478
  
  ## 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: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.0238        | 0.99  | 40   | 0.9636          | 0.6445   |
  | 0.777         | 1.99  | 80   | 0.6591          | 0.7666   |
  | 0.4763        | 2.99  | 120  | 0.5381          | 0.8130   |
  | 0.3215        | 3.99  | 160  | 0.5244          | 0.8253   |
  | 0.2179        | 4.99  | 200  | 0.5123          | 0.8238   |
  | 0.1868        | 5.99  | 240  | 0.5052          | 0.8308   |
  | 0.154         | 6.99  | 280  | 0.5444          | 0.8338   |
  | 0.1166        | 7.99  | 320  | 0.6318          | 0.8238   |
  | 0.1099        | 8.99  | 360  | 0.5656          | 0.8338   |
  | 0.0925        | 9.99  | 400  | 0.6057          | 0.8338   |
  | 0.0779        | 10.99 | 440  | 0.5942          | 0.8393   |
  | 0.0629        | 11.99 | 480  | 0.6112          | 0.8400   |
  | 0.0742        | 12.99 | 520  | 0.6588          | 0.8331   |
  | 0.0752        | 13.99 | 560  | 0.6143          | 0.8408   |
  | 0.0577        | 14.99 | 600  | 0.6450          | 0.8516   |
  | 0.0589        | 15.99 | 640  | 0.6787          | 0.8400   |
  | 0.0555        | 16.99 | 680  | 0.6641          | 0.8454   |
  | 0.052         | 17.99 | 720  | 0.7213          | 0.8524   |
  | 0.0589        | 18.99 | 760  | 0.6917          | 0.8470   |
  | 0.0506        | 19.99 | 800  | 0.7090          | 0.8478   |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  