---
library_name: transformers
base_model: motheecreator/vit-Facial-Expression-Recognition
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: FER-Facial-Expression-Recognition
  results: []
---

<!-- 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. -->

# FER-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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4710
- Accuracy: 0.8474

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.8868        | 0.8959 | 100  | 1.7638          | 0.5923   |
| 1.2277        | 1.7962 | 200  | 1.1092          | 0.7253   |
| 0.8414        | 2.6965 | 300  | 0.8105          | 0.8041   |
| 0.7076        | 3.5969 | 400  | 0.6746          | 0.8256   |
| 0.6079        | 4.4972 | 500  | 0.6111          | 0.8287   |
| 0.5624        | 5.3975 | 600  | 0.5529          | 0.8379   |
| 0.5254        | 6.2979 | 700  | 0.5266          | 0.8399   |
| 0.4784        | 7.1982 | 800  | 0.4978          | 0.8433   |
| 0.4634        | 8.0985 | 900  | 0.4844          | 0.8458   |
| 0.4305        | 8.9944 | 1000 | 0.4710          | 0.8474   |
| 0.3995        | 9.8947 | 1100 | 0.4381          | 0.8564   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3