---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-celeba-smiling
  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. -->

# vit-base-patch16-224-celeba-smiling

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1635
- Accuracy: 0.9340

## 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: 5e-05
- train_batch_size: 128
- eval_batch_size: 64
- 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: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|
| 0.1881        | 0.3931 | 500   | 0.1765          | 0.9284   |
| 0.1848        | 0.7862 | 1000  | 0.1680          | 0.9285   |
| 0.1738        | 1.1792 | 1500  | 0.1732          | 0.9266   |
| 0.1729        | 1.5723 | 2000  | 0.1605          | 0.9338   |
| 0.1619        | 1.9654 | 2500  | 0.1623          | 0.9283   |
| 0.1574        | 2.3585 | 3000  | 0.1589          | 0.9334   |
| 0.1500        | 2.7516 | 3500  | 0.1635          | 0.9340   |
| 0.1338        | 3.1447 | 4000  | 0.1616          | 0.9334   |
| 0.1350        | 3.5377 | 4500  | 0.1910          | 0.9228   |
| 0.1370        | 3.9308 | 5000  | 0.1664          | 0.9280   |
| 0.1093        | 4.3239 | 5500  | 0.1803          | 0.9321   |
| 0.1076        | 4.7170 | 6000  | 0.1908          | 0.9312   |
| 0.0677        | 5.1101 | 6500  | 0.2124          | 0.9309   |
| 0.0732        | 5.5031 | 7000  | 0.2236          | 0.9263   |
| 0.0666        | 5.8962 | 7500  | 0.2175          | 0.9280   |
| 0.0430        | 6.2893 | 8000  | 0.2474          | 0.9268   |
| 0.0393        | 6.6824 | 8500  | 0.2578          | 0.9256   |
| 0.0266        | 7.0755 | 9000  | 0.2742          | 0.9280   |
| 0.0243        | 7.4686 | 9500  | 0.2939          | 0.9290   |
| 0.0234        | 7.8616 | 10000 | 0.2994          | 0.9282   |
| 0.0140        | 8.2547 | 10500 | 0.3040          | 0.9287   |
| 0.0137        | 8.6478 | 11000 | 0.3067          | 0.9288   |
| 0.0144        | 9.0409 | 11500 | 0.3179          | 0.9294   |
| 0.0128        | 9.4340 | 12000 | 0.3179          | 0.9292   |
| 0.0110        | 9.8270 | 12500 | 0.3184          | 0.9290   |


### Framework versions

- Transformers 5.3.0
- Pytorch 2.6.0+cu124
- Datasets 4.8.3
- Tokenizers 0.22.2