---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hq_fer2013notestaugM
  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: 0.6998319625011055
    - name: Precision
      type: precision
      value: 0.7022095243425648
    - name: Recall
      type: recall
      value: 0.6998319625011055
    - name: F1
      type: f1
      value: 0.6999146124635052
---

<!-- 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. -->

# hq_fer2013notestaugM

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: 0.8297
- Accuracy: 0.6998
- Precision: 0.7022
- Recall: 0.6998
- F1: 0.6999

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 17
- 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 | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.2858        | 1.0   | 353  | 1.2814          | 0.5545   | 0.5432    | 0.5545 | 0.5122 |
| 1.0247        | 2.0   | 706  | 1.0343          | 0.6288   | 0.6235    | 0.6288 | 0.6136 |
| 0.9403        | 3.0   | 1059 | 0.9500          | 0.6607   | 0.6592    | 0.6607 | 0.6522 |
| 0.8501        | 4.0   | 1412 | 0.8971          | 0.6803   | 0.6761    | 0.6803 | 0.6760 |
| 0.8148        | 5.0   | 1765 | 0.8733          | 0.6857   | 0.6881    | 0.6857 | 0.6854 |
| 0.7898        | 6.0   | 2118 | 0.8526          | 0.6913   | 0.6911    | 0.6913 | 0.6888 |
| 0.7074        | 7.0   | 2471 | 0.8408          | 0.6959   | 0.6971    | 0.6959 | 0.6953 |
| 0.7273        | 8.0   | 2824 | 0.8361          | 0.6980   | 0.6971    | 0.6980 | 0.6949 |
| 0.6982        | 9.0   | 3177 | 0.8297          | 0.6998   | 0.7022    | 0.6998 | 0.6999 |
| 0.6994        | 10.0  | 3530 | 0.8287          | 0.6998   | 0.7002    | 0.6998 | 0.6991 |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2