---
base_model: motheecreator/vit-Facial-Expression-Recognition
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-Facial-Expression-Recognition
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: None
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9148438153091649
license: apache-2.0
language:
- en
pipeline_tag: image-classification
library_name: transformers
---

<!-- 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-Facial-Expression-Recognition

This model is a fine-tuned version of [motheecreator/vit-Facial-Expression-Recognition](https://huggingface.co./motheecreator/vit-Facial-Expression-Recognition) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2606
- Accuracy: 0.9148

## 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: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6309        | 0.3328 | 100  | 0.2618          | 0.9145   |
| 0.6165        | 0.6656 | 200  | 0.2600          | 0.9150   |
| 0.6283        | 0.9983 | 300  | 0.2659          | 0.9135   |
| 0.6171        | 1.3311 | 400  | 0.2561          | 0.9174   |
| 0.6112        | 1.6639 | 500  | 0.2606          | 0.9148   |
| 0.6081        | 1.9967 | 600  | 0.2624          | 0.9137   |
| 0.5885        | 2.3295 | 700  | 0.2671          | 0.9113   |
| 0.5975        | 2.6622 | 800  | 0.2572          | 0.9156   |
| 0.6067        | 2.9950 | 900  | 0.2683          | 0.9116   |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1