---
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