---
library_name: transformers
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: 0.5125
---

<!-- 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: 1.3578
- Accuracy: 0.5125

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0796        | 1.0   | 10   | 2.0709          | 0.1562   |
| 2.0631        | 2.0   | 20   | 2.0496          | 0.225    |
| 2.0242        | 3.0   | 30   | 2.0148          | 0.2875   |
| 1.9387        | 4.0   | 40   | 1.9268          | 0.325    |
| 1.789         | 5.0   | 50   | 1.7454          | 0.3812   |
| 1.6216        | 6.0   | 60   | 1.5996          | 0.3937   |
| 1.4795        | 7.0   | 70   | 1.5577          | 0.375    |
| 1.3735        | 8.0   | 80   | 1.5090          | 0.4062   |
| 1.2889        | 9.0   | 90   | 1.4418          | 0.4313   |
| 1.2092        | 10.0  | 100  | 1.4209          | 0.425    |
| 1.1127        | 11.0  | 110  | 1.3828          | 0.4437   |
| 1.032         | 12.0  | 120  | 1.3507          | 0.4562   |
| 0.9616        | 13.0  | 130  | 1.3556          | 0.4875   |
| 0.9099        | 14.0  | 140  | 1.3204          | 0.5188   |
| 0.8425        | 15.0  | 150  | 1.3490          | 0.4688   |
| 0.806         | 16.0  | 160  | 1.3690          | 0.5062   |
| 0.7377        | 17.0  | 170  | 1.3344          | 0.5563   |
| 0.677         | 18.0  | 180  | 1.4178          | 0.4625   |
| 0.6071        | 19.0  | 190  | 1.3305          | 0.4875   |
| 0.5581        | 20.0  | 200  | 1.3070          | 0.5      |
| 0.5599        | 21.0  | 210  | 1.3245          | 0.4938   |
| 0.5222        | 22.0  | 220  | 1.3765          | 0.4562   |
| 0.4856        | 23.0  | 230  | 1.3345          | 0.5      |
| 0.458         | 24.0  | 240  | 1.2938          | 0.5188   |
| 0.4393        | 25.0  | 250  | 1.3380          | 0.5062   |
| 0.4239        | 26.0  | 260  | 1.3756          | 0.525    |
| 0.4443        | 27.0  | 270  | 1.4586          | 0.4813   |
| 0.4374        | 28.0  | 280  | 1.2996          | 0.55     |
| 0.3917        | 29.0  | 290  | 1.3222          | 0.5062   |
| 0.3986        | 30.0  | 300  | 1.4486          | 0.4813   |
| 0.353         | 31.0  | 310  | 1.5204          | 0.4562   |
| 0.3598        | 32.0  | 320  | 1.3027          | 0.5625   |
| 0.3538        | 33.0  | 330  | 1.6122          | 0.4313   |
| 0.3246        | 34.0  | 340  | 1.5237          | 0.4437   |
| 0.3089        | 35.0  | 350  | 1.4717          | 0.5125   |
| 0.3278        | 36.0  | 360  | 1.5666          | 0.45     |
| 0.2865        | 37.0  | 370  | 1.4377          | 0.5      |
| 0.2958        | 38.0  | 380  | 1.4766          | 0.4938   |
| 0.3036        | 39.0  | 390  | 1.5345          | 0.4375   |
| 0.286         | 40.0  | 400  | 1.4174          | 0.5062   |
| 0.3099        | 41.0  | 410  | 1.4087          | 0.4625   |
| 0.2801        | 42.0  | 420  | 1.4439          | 0.4813   |
| 0.2973        | 43.0  | 430  | 1.4712          | 0.4938   |
| 0.2892        | 44.0  | 440  | 1.4099          | 0.5188   |
| 0.2835        | 45.0  | 450  | 1.3011          | 0.5563   |
| 0.261         | 46.0  | 460  | 1.6512          | 0.4188   |
| 0.2589        | 47.0  | 470  | 1.5651          | 0.4375   |
| 0.2806        | 48.0  | 480  | 1.5194          | 0.4938   |
| 0.2749        | 49.0  | 490  | 1.4519          | 0.525    |
| 0.2482        | 50.0  | 500  | 1.4127          | 0.5188   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1