---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: vit-facial-expression
  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: 0.7378253732509966
    - name: Precision
      type: precision
      value: 0.7302570858866146
    - name: Recall
      type: recall
      value: 0.7378253732509966
    - name: F1
      type: f1
      value: 0.7274264799279716
---

<!-- 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

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: 0.8099
- Accuracy: 0.7378
- Precision: 0.7303
- Recall: 0.7378
- F1: 0.7274

## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use 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: 500
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 2.0154        | 0.4831 | 100  | 1.8473          | 0.3168   | 0.3005    | 0.3168 | 0.1961 |
| 1.569         | 0.9662 | 200  | 1.3372          | 0.5767   | 0.5110    | 0.5767 | 0.4926 |
| 1.185         | 1.4493 | 300  | 1.1047          | 0.6474   | 0.5805    | 0.6474 | 0.5937 |
| 1.044         | 1.9324 | 400  | 0.9638          | 0.6896   | 0.6787    | 0.6896 | 0.6611 |
| 0.8813        | 2.4155 | 500  | 0.8928          | 0.7005   | 0.6822    | 0.7005 | 0.6646 |
| 0.7925        | 2.8986 | 600  | 0.8209          | 0.7301   | 0.7183    | 0.7301 | 0.7186 |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3