---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Validated_Balanced_Raw_Data_model_vit
  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. -->

# Validated_Balanced_Raw_Data_model_vit

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the Logiroad/Validated_Balanced_Raw_Dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2154
- Accuracy: 0.5094

## 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: 8
- eval_batch_size: 8
- seed: 1337
- 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_ratio: 0.05
- num_epochs: 25.0
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.05

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4452        | 1.0   | 80   | 1.4537          | 0.2406   |
| 1.3534        | 2.0   | 160  | 1.4004          | 0.3538   |
| 1.2977        | 3.0   | 240  | 1.3417          | 0.3774   |
| 1.2604        | 4.0   | 320  | 1.3132          | 0.3774   |
| 1.2428        | 5.0   | 400  | 1.2443          | 0.4009   |
| 1.213         | 6.0   | 480  | 1.2148          | 0.4198   |
| 1.1426        | 7.0   | 560  | 1.2096          | 0.4670   |
| 1.1657        | 8.0   | 640  | 1.2066          | 0.4670   |
| 1.1249        | 9.0   | 720  | 1.2209          | 0.4387   |
| 1.1622        | 10.0  | 800  | 1.1446          | 0.4811   |
| 1.0625        | 11.0  | 880  | 1.1742          | 0.4670   |
| 1.1157        | 12.0  | 960  | 1.2200          | 0.4434   |
| 1.0807        | 13.0  | 1040 | 1.2117          | 0.4670   |
| 1.0629        | 14.0  | 1120 | 1.2296          | 0.4811   |
| 1.0323        | 15.0  | 1200 | 1.1887          | 0.4906   |
| 1.0128        | 16.0  | 1280 | 1.2075          | 0.4953   |
| 1.0266        | 17.0  | 1360 | 1.2082          | 0.5      |
| 1.004         | 18.0  | 1440 | 1.2154          | 0.5094   |
| 0.9543        | 19.0  | 1520 | 1.2048          | 0.5047   |
| 0.9439        | 20.0  | 1600 | 1.2218          | 0.4906   |
| 0.9891        | 21.0  | 1680 | 1.2136          | 0.4906   |
| 0.9801        | 22.0  | 1760 | 1.2166          | 0.4858   |
| 0.9632        | 23.0  | 1840 | 1.2149          | 0.4906   |
| 0.9584        | 24.0  | 1920 | 1.2135          | 0.4906   |
| 0.9561        | 25.0  | 2000 | 1.2136          | 0.4906   |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.3.0
- Datasets 3.1.0
- Tokenizers 0.20.3