readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: swin-large-patch4-window7-224-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: 0.8601236476043277
      - name: Precision
        type: precision
        value: 0.8582306285016578
      - name: Recall
        type: recall
        value: 0.8601236476043277
      - name: F1
        type: f1
        value: 0.8582797853944862
  ---
  
  <!-- 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-large-patch4-window7-224-fv-finetuned-memes
  
  This model is a fine-tuned version of [microsoft/swin-large-patch4-window7-224](https://huggingface.co/microsoft/swin-large-patch4-window7-224) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6502
  - Accuracy: 0.8601
  - Precision: 0.8582
  - Recall: 0.8601
  - F1: 0.8583
  
  ## 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 1.2077        | 0.99  | 20   | 0.9499          | 0.6461   | 0.6764    | 0.6461 | 0.5863 |
  | 0.5687        | 1.99  | 40   | 0.5365          | 0.7975   | 0.8018    | 0.7975 | 0.7924 |
  | 0.3607        | 2.99  | 60   | 0.4007          | 0.8423   | 0.8419    | 0.8423 | 0.8398 |
  | 0.203         | 3.99  | 80   | 0.3751          | 0.8509   | 0.8502    | 0.8509 | 0.8503 |
  | 0.1728        | 4.99  | 100  | 0.4168          | 0.8509   | 0.8519    | 0.8509 | 0.8506 |
  | 0.0963        | 5.99  | 120  | 0.4351          | 0.8586   | 0.8573    | 0.8586 | 0.8555 |
  | 0.0956        | 6.99  | 140  | 0.4415          | 0.8547   | 0.8542    | 0.8547 | 0.8541 |
  | 0.079         | 7.99  | 160  | 0.5312          | 0.8501   | 0.8475    | 0.8501 | 0.8459 |
  | 0.0635        | 8.99  | 180  | 0.5376          | 0.8601   | 0.8578    | 0.8601 | 0.8577 |
  | 0.0593        | 9.99  | 200  | 0.5060          | 0.8609   | 0.8615    | 0.8609 | 0.8604 |
  | 0.0656        | 10.99 | 220  | 0.4997          | 0.8617   | 0.8573    | 0.8617 | 0.8587 |
  | 0.0561        | 11.99 | 240  | 0.5430          | 0.8586   | 0.8604    | 0.8586 | 0.8589 |
  | 0.0523        | 12.99 | 260  | 0.5354          | 0.8624   | 0.8643    | 0.8624 | 0.8626 |
  | 0.0489        | 13.99 | 280  | 0.5539          | 0.8609   | 0.8572    | 0.8609 | 0.8577 |
  | 0.0487        | 14.99 | 300  | 0.5785          | 0.8609   | 0.8591    | 0.8609 | 0.8591 |
  | 0.0485        | 15.99 | 320  | 0.6186          | 0.8601   | 0.8578    | 0.8601 | 0.8573 |
  | 0.0518        | 16.99 | 340  | 0.6342          | 0.8624   | 0.8612    | 0.8624 | 0.8606 |
  | 0.0432        | 17.99 | 360  | 0.6302          | 0.8586   | 0.8598    | 0.8586 | 0.8580 |
  | 0.0469        | 18.99 | 380  | 0.6323          | 0.8617   | 0.8606    | 0.8617 | 0.8604 |
  | 0.0426        | 19.99 | 400  | 0.6502          | 0.8601   | 0.8582    | 0.8601 | 0.8583 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0.dev0
  - Pytorch 1.11.0+cu102
  - Datasets 2.6.1.dev0
  - Tokenizers 0.13.1
  