readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: google-vit-base-patch16-224-cartoon-emotion-detection
    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: 0.8807339449541285
      - name: Precision
        type: precision
        value: 0.8768597487153273
      - name: Recall
        type: recall
        value: 0.8807339449541285
      - name: F1
        type: f1
        value: 0.8782945902988435
  ---
  
  <!-- 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. -->
  
  # google-vit-base-patch16-224-cartoon-emotion-detection
  
  This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3706
  - Accuracy: 0.8807
  - Precision: 0.8769
  - Recall: 0.8807
  - F1: 0.8783
  
  ## 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: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | No log        | 0.97  | 8    | 0.9902          | 0.5596   | 0.5506    | 0.5596 | 0.5360 |
  | 1.242         | 1.97  | 16   | 0.5157          | 0.8165   | 0.8195    | 0.8165 | 0.8132 |
  | 0.4438        | 2.97  | 24   | 0.3871          | 0.8440   | 0.8516    | 0.8440 | 0.8446 |
  | 0.1768        | 3.97  | 32   | 0.3531          | 0.8624   | 0.8653    | 0.8624 | 0.8585 |
  | 0.0661        | 4.97  | 40   | 0.3780          | 0.8716   | 0.8693    | 0.8716 | 0.8674 |
  | 0.0661        | 5.97  | 48   | 0.3747          | 0.8624   | 0.8649    | 0.8624 | 0.8632 |
  | 0.0375        | 6.97  | 56   | 0.3760          | 0.8991   | 0.8961    | 0.8991 | 0.8971 |
  | 0.0362        | 7.97  | 64   | 0.4092          | 0.8716   | 0.8684    | 0.8716 | 0.8681 |
  | 0.0322        | 8.97  | 72   | 0.3499          | 0.8899   | 0.8880    | 0.8899 | 0.8888 |
  | 0.029         | 9.97  | 80   | 0.3706          | 0.8807   | 0.8769    | 0.8807 | 0.8783 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu117
  - Datasets 2.8.0
  - Tokenizers 0.11.0
  