---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: emotion_classification_v1.2
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train[:5000]
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.625
    - name: Precision
      type: precision
      value: 0.620708259363687
    - name: Recall
      type: recall
      value: 0.625
    - name: F1
      type: f1
      value: 0.6034583857987293
---

<!-- 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_v1.2

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: 1.2401
- Accuracy: 0.625
- Precision: 0.6207
- Recall: 0.625
- F1: 0.6035

## Model description

A slightly more accurate model compared to previous 1.1 version. More information needed

## Intended uses & limitations

This model is fined tune solely for face emotion recognition. 

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 20   | 1.9487          | 0.3312   | 0.3554    | 0.3312 | 0.2830 |
| No log        | 2.0   | 40   | 1.6735          | 0.4437   | 0.4238    | 0.4437 | 0.4232 |
| No log        | 3.0   | 60   | 1.5359          | 0.4813   | 0.3990    | 0.4813 | 0.4272 |
| No log        | 4.0   | 80   | 1.4249          | 0.5      | 0.4178    | 0.5    | 0.4443 |
| No log        | 5.0   | 100  | 1.3733          | 0.5062   | 0.4753    | 0.5062 | 0.4653 |
| No log        | 6.0   | 120  | 1.3513          | 0.5188   | 0.5076    | 0.5188 | 0.4908 |
| No log        | 7.0   | 140  | 1.2377          | 0.6125   | 0.6163    | 0.6125 | 0.5976 |
| No log        | 8.0   | 160  | 1.2354          | 0.6062   | 0.6131    | 0.6062 | 0.5961 |
| No log        | 9.0   | 180  | 1.2574          | 0.575    | 0.5847    | 0.575  | 0.5728 |
| No log        | 10.0  | 200  | 1.2493          | 0.5813   | 0.5912    | 0.5813 | 0.5776 |
| No log        | 11.0  | 220  | 1.1954          | 0.5813   | 0.5795    | 0.5813 | 0.5730 |
| No log        | 12.0  | 240  | 1.2283          | 0.5625   | 0.5651    | 0.5625 | 0.5598 |
| No log        | 13.0  | 260  | 1.1984          | 0.5625   | 0.5800    | 0.5625 | 0.5643 |
| No log        | 14.0  | 280  | 1.2308          | 0.5437   | 0.5523    | 0.5437 | 0.5414 |
| No log        | 15.0  | 300  | 1.1665          | 0.5938   | 0.6005    | 0.5938 | 0.5935 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1