---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-finetuned-cephalometric
  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-finetuned-cephalometric

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the cepha-cutoutCLAHE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7340
- Accuracy: 0.6528

## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- 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: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 16   | 0.9458          | 0.5486   |
| 0.9879        | 2.0   | 32   | 0.6947          | 0.6597   |
| 0.4628        | 3.0   | 48   | 0.6375          | 0.6597   |
| 0.135         | 4.0   | 64   | 0.7060          | 0.6944   |
| 0.0339        | 5.0   | 80   | 0.7301          | 0.6597   |
| 0.0339        | 6.0   | 96   | 0.9236          | 0.6875   |
| 0.0059        | 7.0   | 112  | 0.9261          | 0.6806   |
| 0.0024        | 8.0   | 128  | 0.9961          | 0.6875   |
| 0.0012        | 9.0   | 144  | 1.0060          | 0.6736   |
| 0.0008        | 10.0  | 160  | 1.0329          | 0.6875   |
| 0.0008        | 11.0  | 176  | 1.0575          | 0.6944   |
| 0.0006        | 12.0  | 192  | 1.0768          | 0.6944   |
| 0.0006        | 13.0  | 208  | 1.1002          | 0.6944   |
| 0.0005        | 14.0  | 224  | 1.1220          | 0.6875   |
| 0.0004        | 15.0  | 240  | 1.1367          | 0.6875   |
| 0.0004        | 16.0  | 256  | 1.1538          | 0.6875   |
| 0.0004        | 17.0  | 272  | 1.1707          | 0.6875   |
| 0.0003        | 18.0  | 288  | 1.1855          | 0.6875   |
| 0.0003        | 19.0  | 304  | 1.2007          | 0.6875   |
| 0.0003        | 20.0  | 320  | 1.2066          | 0.6806   |
| 0.0003        | 21.0  | 336  | 1.2211          | 0.6806   |
| 0.0003        | 22.0  | 352  | 1.2291          | 0.6875   |
| 0.0002        | 23.0  | 368  | 1.2385          | 0.6875   |
| 0.0002        | 24.0  | 384  | 1.2508          | 0.6875   |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0