---
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: 0.46875
---

<!-- 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: 1.4050
- Accuracy: 0.4688

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8187        | 1.0   | 10   | 1.8406          | 0.3063   |
| 1.6795        | 2.0   | 20   | 1.6701          | 0.3688   |
| 1.5506        | 3.0   | 30   | 1.5578          | 0.45     |
| 1.4417        | 4.0   | 40   | 1.5077          | 0.4875   |
| 1.3707        | 5.0   | 50   | 1.4297          | 0.5062   |
| 1.3167        | 6.0   | 60   | 1.4157          | 0.4938   |
| 1.267         | 7.0   | 70   | 1.3779          | 0.525    |
| 1.2197        | 8.0   | 80   | 1.3784          | 0.5      |
| 1.191         | 9.0   | 90   | 1.3701          | 0.5188   |
| 1.1649        | 10.0  | 100  | 1.3611          | 0.4938   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3