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

# visual_emotion_classification_vit_base_finetunned

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: 1.3576
- 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: 2.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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   | 40   | 1.9700          | 0.2625   |
| 2.0074        | 2.0   | 80   | 1.7194          | 0.3688   |
| 1.726         | 3.0   | 120  | 1.5775          | 0.3875   |
| 1.5202        | 4.0   | 160  | 1.4900          | 0.4875   |
| 1.3857        | 5.0   | 200  | 1.4261          | 0.4938   |
| 1.3857        | 6.0   | 240  | 1.4090          | 0.5      |
| 1.2831        | 7.0   | 280  | 1.3510          | 0.475    |
| 1.2007        | 8.0   | 320  | 1.3788          | 0.475    |
| 1.1256        | 9.0   | 360  | 1.3183          | 0.5      |
| 1.0345        | 10.0  | 400  | 1.2937          | 0.4813   |
| 1.0345        | 11.0  | 440  | 1.2384          | 0.5437   |
| 0.9438        | 12.0  | 480  | 1.2100          | 0.525    |
| 0.872         | 13.0  | 520  | 1.2450          | 0.525    |
| 0.8193        | 14.0  | 560  | 1.2264          | 0.5312   |
| 0.763         | 15.0  | 600  | 1.2296          | 0.5125   |
| 0.763         | 16.0  | 640  | 1.2539          | 0.5188   |
| 0.7166        | 17.0  | 680  | 1.2253          | 0.5563   |
| 0.6328        | 18.0  | 720  | 1.2723          | 0.5312   |
| 0.5917        | 19.0  | 760  | 1.2870          | 0.4875   |
| 0.5841        | 20.0  | 800  | 1.3011          | 0.5375   |
| 0.5841        | 21.0  | 840  | 1.2071          | 0.575    |
| 0.559         | 22.0  | 880  | 1.2555          | 0.575    |
| 0.5109        | 23.0  | 920  | 1.3140          | 0.5      |
| 0.5036        | 24.0  | 960  | 1.2593          | 0.5      |
| 0.4787        | 25.0  | 1000 | 1.2573          | 0.5813   |
| 0.4787        | 26.0  | 1040 | 1.2163          | 0.55     |
| 0.4583        | 27.0  | 1080 | 1.2349          | 0.5563   |
| 0.4766        | 28.0  | 1120 | 1.3349          | 0.5188   |
| 0.4709        | 29.0  | 1160 | 1.3284          | 0.5312   |
| 0.4442        | 30.0  | 1200 | 1.2684          | 0.5625   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1