---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-FER2013
  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: 0.8732149076976663
---

<!-- 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. -->

# vit-base-patch16-224-in21k-finetuned-FER2013

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: 0.3264
- Accuracy: 0.8732

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4811        | 1.0   | 202  | 0.4315          | 0.8004   |
| 0.4287        | 2.0   | 404  | 0.3579          | 0.8433   |
| 0.4184        | 3.0   | 606  | 0.3517          | 0.8467   |
| 0.3931        | 4.0   | 808  | 0.3308          | 0.8555   |
| 0.3667        | 5.0   | 1010 | 0.3204          | 0.8610   |
| 0.3545        | 6.0   | 1212 | 0.3144          | 0.8659   |
| 0.3137        | 7.0   | 1414 | 0.3308          | 0.8642   |
| 0.3178        | 8.0   | 1616 | 0.3230          | 0.8645   |
| 0.2998        | 9.0   | 1818 | 0.3206          | 0.8708   |
| 0.2773        | 10.0  | 2020 | 0.3264          | 0.8732   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2