---
library_name: transformers
base_model: motheecreator/vit-Facial-Expression-Recognition
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: trainer_output
  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: 0.755896023411947
---

<!-- 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. -->

# trainer_output

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: 0.8323
- Accuracy: 0.7559

## 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: 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log        | 0.9890 | 45   | 2.0569          | 0.1918   |
| 2.0911        | 1.9890 | 90   | 1.9314          | 0.4433   |
| 1.9842        | 2.9890 | 135  | 1.6798          | 0.6192   |
| 1.7415        | 3.9890 | 180  | 1.3741          | 0.6504   |
| 1.413         | 4.9890 | 225  | 1.1637          | 0.6953   |
| 1.1803        | 5.9890 | 270  | 1.0347          | 0.7172   |
| 1.0364        | 6.9890 | 315  | 0.9433          | 0.7356   |
| 0.9529        | 7.9890 | 360  | 0.8781          | 0.7478   |
| 0.8837        | 8.9890 | 405  | 0.8323          | 0.7559   |
| 0.8339        | 9.9890 | 450  | 0.8072          | 0.7531   |


### Framework versions

- Transformers 4.51.0
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0