---
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
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: 0.65
    - name: F1
      type: f1
      value: 0.6231481481481482
---

<!-- 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: 1.1136
- Accuracy: 0.65
- F1: 0.6231

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 45
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.9172        | 1.0   | 43   | 1.5751          | 0.4333   | 0.3263 |
| 1.4505        | 2.0   | 86   | 1.3041          | 0.5333   | 0.4651 |
| 1.1121        | 3.0   | 129  | 1.2902          | 0.4833   | 0.4684 |
| 0.8491        | 4.0   | 172  | 1.2309          | 0.5167   | 0.4916 |
| 0.6168        | 5.0   | 215  | 1.2573          | 0.5583   | 0.5310 |
| 0.3953        | 6.0   | 258  | 1.1502          | 0.575    | 0.5401 |
| 0.3048        | 7.0   | 301  | 1.1136          | 0.65     | 0.6231 |
| 0.1875        | 8.0   | 344  | 1.4224          | 0.5667   | 0.5598 |
| 0.1277        | 9.0   | 387  | 1.3467          | 0.6167   | 0.6011 |
| 0.1123        | 10.0  | 430  | 1.5838          | 0.5833   | 0.5657 |
| 0.1123        | 11.0  | 473  | 1.5063          | 0.5833   | 0.5550 |
| 0.0694        | 12.0  | 516  | 1.7733          | 0.55     | 0.5320 |
| 0.0499        | 13.0  | 559  | 1.6329          | 0.5833   | 0.5536 |
| 0.0367        | 14.0  | 602  | 1.6878          | 0.5833   | 0.5685 |
| 0.0291        | 15.0  | 645  | 1.6855          | 0.575    | 0.5392 |
| 0.0284        | 16.0  | 688  | 1.7869          | 0.6083   | 0.5880 |
| 0.0316        | 17.0  | 731  | 1.5831          | 0.5917   | 0.5670 |
| 0.0273        | 18.0  | 774  | 1.5933          | 0.625    | 0.5984 |
| 0.0234        | 19.0  | 817  | 1.7830          | 0.5833   | 0.5652 |
| 0.0194        | 20.0  | 860  | 1.6804          | 0.6083   | 0.5878 |
| 0.0214        | 21.0  | 903  | 1.5962          | 0.6      | 0.5701 |
| 0.0204        | 22.0  | 946  | 1.5684          | 0.625    | 0.5992 |
| 0.0178        | 23.0  | 989  | 1.5924          | 0.625    | 0.5992 |
| 0.0173        | 24.0  | 1032 | 1.6228          | 0.6167   | 0.5933 |
| 0.016         | 25.0  | 1075 | 1.6177          | 0.6333   | 0.6073 |
| 0.016         | 26.0  | 1118 | 1.6268          | 0.625    | 0.6009 |
| 0.016         | 27.0  | 1161 | 1.6387          | 0.625    | 0.6009 |
| 0.0159        | 28.0  | 1204 | 1.6403          | 0.625    | 0.6009 |
| 0.0162        | 29.0  | 1247 | 1.6409          | 0.625    | 0.6009 |
| 0.018         | 30.0  | 1290 | 1.6412          | 0.625    | 0.6009 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1