---
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: 1.2453
- 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: 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 20   | 1.9465          | 0.325    |
| No log        | 2.0   | 40   | 1.7314          | 0.4375   |
| No log        | 3.0   | 60   | 1.5249          | 0.5375   |
| No log        | 4.0   | 80   | 1.4166          | 0.4875   |
| No log        | 5.0   | 100  | 1.3605          | 0.55     |
| No log        | 6.0   | 120  | 1.3204          | 0.5563   |
| No log        | 7.0   | 140  | 1.2074          | 0.6      |
| No log        | 8.0   | 160  | 1.2138          | 0.6      |
| No log        | 9.0   | 180  | 1.2600          | 0.5625   |
| No log        | 10.0  | 200  | 1.2103          | 0.5563   |
| No log        | 11.0  | 220  | 1.1736          | 0.5687   |
| No log        | 12.0  | 240  | 1.2462          | 0.5687   |
| No log        | 13.0  | 260  | 1.2009          | 0.5813   |
| No log        | 14.0  | 280  | 1.2105          | 0.5437   |
| No log        | 15.0  | 300  | 1.2705          | 0.5125   |
| No log        | 16.0  | 320  | 1.2135          | 0.5938   |
| No log        | 17.0  | 340  | 1.2089          | 0.5563   |
| No log        | 18.0  | 360  | 1.2818          | 0.5375   |
| No log        | 19.0  | 380  | 1.3076          | 0.5062   |
| No log        | 20.0  | 400  | 1.2479          | 0.55     |
| No log        | 21.0  | 420  | 1.2218          | 0.55     |
| No log        | 22.0  | 440  | 1.0957          | 0.6188   |
| No log        | 23.0  | 460  | 1.2437          | 0.5875   |
| No log        | 24.0  | 480  | 1.3598          | 0.5125   |
| 0.8126        | 25.0  | 500  | 1.2759          | 0.55     |
| 0.8126        | 26.0  | 520  | 1.1474          | 0.6      |
| 0.8126        | 27.0  | 540  | 1.1115          | 0.6375   |
| 0.8126        | 28.0  | 560  | 1.1715          | 0.5687   |
| 0.8126        | 29.0  | 580  | 1.3133          | 0.5625   |
| 0.8126        | 30.0  | 600  | 1.2526          | 0.5437   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1