---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: emotion_classification
  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: 0.59375
---

<!-- 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. -->

# emotion_classification

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1554
- Accuracy: 0.5938

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2477        | 1.0   | 10   | 1.3618          | 0.5625   |
| 1.2002        | 2.0   | 20   | 1.3367          | 0.5625   |
| 1.111         | 3.0   | 30   | 1.3178          | 0.5312   |
| 1.0286        | 4.0   | 40   | 1.2215          | 0.5625   |
| 0.9376        | 5.0   | 50   | 1.2117          | 0.5437   |
| 0.8948        | 6.0   | 60   | 1.2304          | 0.5625   |
| 0.8234        | 7.0   | 70   | 1.1634          | 0.5563   |
| 0.8069        | 8.0   | 80   | 1.2422          | 0.5563   |
| 0.7146        | 9.0   | 90   | 1.2053          | 0.5563   |
| 0.709         | 10.0  | 100  | 1.1887          | 0.575    |
| 0.6404        | 11.0  | 110  | 1.2208          | 0.5563   |
| 0.6301        | 12.0  | 120  | 1.2319          | 0.5687   |
| 0.6107        | 13.0  | 130  | 1.1684          | 0.6      |
| 0.5825        | 14.0  | 140  | 1.1837          | 0.5813   |
| 0.5454        | 15.0  | 150  | 1.1818          | 0.5687   |
| 0.5517        | 16.0  | 160  | 1.1974          | 0.55     |
| 0.4989        | 17.0  | 170  | 1.1304          | 0.6      |
| 0.4875        | 18.0  | 180  | 1.2277          | 0.5375   |
| 0.4881        | 19.0  | 190  | 1.1363          | 0.5875   |
| 0.4951        | 20.0  | 200  | 1.1540          | 0.6062   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3