---
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
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8715596330275229
    - name: Precision
      type: precision
      value: 0.8725197999744695
    - name: Recall
      type: recall
      value: 0.8715596330275229
    - name: F1
      type: f1
      value: 0.871683140929764
---

<!-- 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: 0.4170
- Accuracy: 0.8716
- Precision: 0.8725
- Recall: 0.8716
- F1: 0.8717

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 0.97  | 8    | 1.0942          | 0.5780   | 0.6102    | 0.5780 | 0.5496 |
| 1.3198        | 1.97  | 16   | 0.6914          | 0.7615   | 0.7498    | 0.7615 | 0.7493 |
| 0.6694        | 2.97  | 24   | 0.4702          | 0.7890   | 0.7808    | 0.7890 | 0.7781 |
| 0.2725        | 3.97  | 32   | 0.3957          | 0.8532   | 0.8514    | 0.8532 | 0.8522 |
| 0.1116        | 4.97  | 40   | 0.3428          | 0.8716   | 0.8697    | 0.8716 | 0.8693 |
| 0.1116        | 5.97  | 48   | 0.3865          | 0.8532   | 0.8514    | 0.8532 | 0.8522 |
| 0.0486        | 6.97  | 56   | 0.3445          | 0.8532   | 0.8495    | 0.8532 | 0.8507 |
| 0.0346        | 7.97  | 64   | 0.3554          | 0.8807   | 0.8921    | 0.8807 | 0.8831 |
| 0.0304        | 8.97  | 72   | 0.3100          | 0.8624   | 0.8592    | 0.8624 | 0.8605 |
| 0.0215        | 9.97  | 80   | 0.3718          | 0.8716   | 0.8700    | 0.8716 | 0.8707 |
| 0.0215        | 10.97 | 88   | 0.3946          | 0.8899   | 0.8901    | 0.8899 | 0.8896 |
| 0.0201        | 11.97 | 96   | 0.4505          | 0.8532   | 0.8558    | 0.8532 | 0.8524 |
| 0.02          | 12.97 | 104  | 0.4543          | 0.8716   | 0.8734    | 0.8716 | 0.8718 |
| 0.0181        | 13.97 | 112  | 0.3837          | 0.8899   | 0.8878    | 0.8899 | 0.8884 |
| 0.0158        | 14.97 | 120  | 0.3904          | 0.8716   | 0.8676    | 0.8716 | 0.8691 |
| 0.0158        | 15.97 | 128  | 0.3881          | 0.9083   | 0.9078    | 0.9083 | 0.9077 |
| 0.0147        | 16.97 | 136  | 0.4233          | 0.8807   | 0.8773    | 0.8807 | 0.8785 |
| 0.0138        | 17.97 | 144  | 0.4335          | 0.8716   | 0.8700    | 0.8716 | 0.8707 |
| 0.0166        | 18.97 | 152  | 0.4492          | 0.8716   | 0.8690    | 0.8716 | 0.8701 |
| 0.016         | 19.97 | 160  | 0.4170          | 0.8716   | 0.8725    | 0.8716 | 0.8717 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.13.1+cu117
- Datasets 2.6.1
- Tokenizers 0.11.0