---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: image_folder
      type: image_folder
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7220674282529953
---

<!-- 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. -->

# beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05

This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7881
- Accuracy: 0.7221

## 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: 7e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2307        | 1.0   | 224  | 1.0863          | 0.5874   |
| 1.0893        | 2.0   | 448  | 0.9700          | 0.6362   |
| 1.0244        | 3.0   | 672  | 0.8859          | 0.6757   |
| 1.016         | 4.0   | 896  | 0.8804          | 0.6787   |
| 0.9089        | 5.0   | 1120 | 0.8611          | 0.6897   |
| 0.8935        | 6.0   | 1344 | 0.8283          | 0.7028   |
| 0.8403        | 7.0   | 1568 | 0.8116          | 0.7102   |
| 0.8179        | 8.0   | 1792 | 0.7934          | 0.7166   |
| 0.7764        | 9.0   | 2016 | 0.7865          | 0.7208   |
| 0.771         | 10.0  | 2240 | 0.7881          | 0.7221   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1