readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: swin-base-patch4-window7-224-in22k-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: 0.8562596599690881
      - name: Precision
        type: precision
        value: 0.8545652818321074
      - name: Recall
        type: recall
        value: 0.8562596599690881
      - name: F1
        type: f1
        value: 0.8552274649509984
  ---
  
  <!-- 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-in22k-finetuned-memes
  
  This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7094
  - Accuracy: 0.8563
  - Precision: 0.8546
  - Recall: 0.8563
  - F1: 0.8552
  
  ## 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 1.1655        | 0.99  | 20   | 0.8573          | 0.6955   | 0.6953    | 0.6955 | 0.6683 |
  | 0.5506        | 1.99  | 40   | 0.5327          | 0.8083   | 0.8050    | 0.8083 | 0.7963 |
  | 0.3573        | 2.99  | 60   | 0.4497          | 0.8338   | 0.8339    | 0.8338 | 0.8317 |
  | 0.2083        | 3.99  | 80   | 0.4561          | 0.8354   | 0.8450    | 0.8354 | 0.8368 |
  | 0.1545        | 4.99  | 100  | 0.4605          | 0.8423   | 0.8458    | 0.8423 | 0.8430 |
  | 0.1014        | 5.99  | 120  | 0.4924          | 0.8524   | 0.8571    | 0.8524 | 0.8538 |
  | 0.0854        | 6.99  | 140  | 0.5759          | 0.8393   | 0.8452    | 0.8393 | 0.8400 |
  | 0.1012        | 7.99  | 160  | 0.5142          | 0.8362   | 0.8378    | 0.8362 | 0.8361 |
  | 0.077         | 8.99  | 180  | 0.5647          | 0.8331   | 0.8538    | 0.8331 | 0.8407 |
  | 0.0667        | 9.99  | 200  | 0.5294          | 0.8462   | 0.8509    | 0.8462 | 0.8483 |
  | 0.0666        | 10.99 | 220  | 0.6038          | 0.8385   | 0.8415    | 0.8385 | 0.8396 |
  | 0.0574        | 11.99 | 240  | 0.6384          | 0.8408   | 0.8431    | 0.8408 | 0.8411 |
  | 0.0488        | 12.99 | 260  | 0.6305          | 0.8516   | 0.8561    | 0.8516 | 0.8532 |
  | 0.0524        | 13.99 | 280  | 0.6411          | 0.8509   | 0.8526    | 0.8509 | 0.8510 |
  | 0.0511        | 14.99 | 300  | 0.6462          | 0.8547   | 0.8542    | 0.8547 | 0.8543 |
  | 0.0495        | 15.99 | 320  | 0.6869          | 0.8532   | 0.8534    | 0.8532 | 0.8527 |
  | 0.0412        | 16.99 | 340  | 0.6643          | 0.8578   | 0.8554    | 0.8578 | 0.8564 |
  | 0.0411        | 17.99 | 360  | 0.7214          | 0.8570   | 0.8539    | 0.8570 | 0.8552 |
  | 0.0434        | 18.99 | 380  | 0.7037          | 0.8524   | 0.8507    | 0.8524 | 0.8514 |
  | 0.0394        | 19.99 | 400  | 0.7094          | 0.8563   | 0.8546    | 0.8563 | 0.8552 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0.dev0
  - Pytorch 1.11.0+cu102
  - Datasets 2.6.1.dev0
  - Tokenizers 0.13.1
  