---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Emotion_Classifier
  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: 0.6
---

<!-- 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_Classifier

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: 1.1050
- Accuracy: 0.6

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 160  | 1.5776          | 0.4062   |
| No log        | 2.0   | 320  | 1.3785          | 0.45     |
| No log        | 3.0   | 480  | 1.2496          | 0.5437   |
| 1.5301        | 4.0   | 640  | 1.2040          | 0.5312   |
| 1.5301        | 5.0   | 800  | 1.1536          | 0.575    |
| 1.5301        | 6.0   | 960  | 1.1603          | 0.575    |
| 0.9484        | 7.0   | 1120 | 1.1435          | 0.575    |
| 0.9484        | 8.0   | 1280 | 1.1538          | 0.6125   |
| 0.9484        | 9.0   | 1440 | 1.1871          | 0.575    |
| 0.5674        | 10.0  | 1600 | 1.1620          | 0.6125   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1