---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hq_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: 0.7022445455972375
    - name: Precision
      type: precision
      value: 0.7038651811268685
    - name: Recall
      type: recall
      value: 0.7022445455972375
    - name: F1
      type: f1
      value: 0.702185081437324
---

<!-- 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_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: 0.8438
- Accuracy: 0.7022
- Precision: 0.7039
- Recall: 0.7022
- F1: 0.7022

## 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: 13

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.3081        | 1.0   | 398  | 1.3132          | 0.5555   | 0.5079    | 0.5555 | 0.5137 |
| 0.991         | 2.0   | 796  | 1.0141          | 0.6332   | 0.6356    | 0.6332 | 0.6153 |
| 0.9099        | 3.0   | 1194 | 0.9257          | 0.6682   | 0.6677    | 0.6682 | 0.6631 |
| 0.8306        | 4.0   | 1592 | 0.8832          | 0.6765   | 0.6838    | 0.6765 | 0.6747 |
| 0.7755        | 5.0   | 1990 | 0.8583          | 0.6892   | 0.6896    | 0.6892 | 0.6876 |
| 0.7129        | 6.0   | 2388 | 0.8442          | 0.6931   | 0.6951    | 0.6931 | 0.6922 |
| 0.6549        | 7.0   | 2786 | 0.8494          | 0.6952   | 0.7054    | 0.6952 | 0.6978 |
| 0.6246        | 8.0   | 3184 | 0.8394          | 0.6963   | 0.7023    | 0.6963 | 0.6977 |
| 0.6138        | 9.0   | 3582 | 0.8421          | 0.6996   | 0.7080    | 0.6996 | 0.7013 |
| 0.5824        | 10.0  | 3980 | 0.8438          | 0.7022   | 0.7039    | 0.7022 | 0.7022 |
| 0.5517        | 11.0  | 4378 | 0.8497          | 0.7002   | 0.7034    | 0.7002 | 0.7005 |
| 0.5154        | 12.0  | 4776 | 0.8508          | 0.7021   | 0.7030    | 0.7021 | 0.7018 |
| 0.5318        | 13.0  | 5174 | 0.8540          | 0.7010   | 0.7029    | 0.7010 | 0.7013 |


### Framework versions

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