---
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-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: 0.5259515570934256
---

<!-- 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-finetuned-FER2013-7e-05

This model is a fine-tuned version of [lixiqi/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05](https://huggingface.co/lixiqi/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5659
- Accuracy: 0.5260

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6537        | 0.97  | 14   | 1.4980          | 0.4683   |
| 1.4325        | 1.97  | 28   | 1.4777          | 0.5040   |
| 1.1532        | 2.97  | 42   | 1.5007          | 0.4960   |
| 1.0428        | 3.97  | 56   | 1.5480          | 0.4890   |
| 0.8716        | 4.97  | 70   | 1.5659          | 0.5260   |
| 0.892         | 5.97  | 84   | 1.6132          | 0.4960   |
| 0.8109        | 6.97  | 98   | 1.5895          | 0.5167   |
| 0.7413        | 7.97  | 112  | 1.6271          | 0.5202   |
| 0.765         | 8.97  | 126  | 1.5991          | 0.5040   |
| 0.6575        | 9.97  | 140  | 1.6041          | 0.4960   |


### Framework versions

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