
<!-- 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-finetuned-barkley

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: 0.0036
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0
- Top1 Accuracy: 1.0
- Error Rate: 0.0

## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy | Top1 Accuracy | Error Rate |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|
| 1.6093        | 1.0   | 38   | 1.4340          | 0.4769    | 0.4342 | 0.4066 | 0.4149   | 0.4342        | 0.5851     |
| 1.2908        | 2.0   | 76   | 1.1747          | 0.6587    | 0.6118 | 0.6160 | 0.6161   | 0.6118        | 0.3839     |
| 1.0409        | 3.0   | 114  | 0.9174          | 0.7382    | 0.7303 | 0.7293 | 0.7425   | 0.7303        | 0.2575     |
| 0.781         | 4.0   | 152  | 0.6528          | 0.8632    | 0.8618 | 0.8622 | 0.8650   | 0.8618        | 0.1350     |
| 0.5429        | 5.0   | 190  | 0.4112          | 0.9417    | 0.9408 | 0.9405 | 0.9443   | 0.9408        | 0.0557     |
| 0.328         | 6.0   | 228  | 0.2229          | 0.9809    | 0.9803 | 0.9802 | 0.9811   | 0.9803        | 0.0189     |
| 0.1837        | 7.0   | 266  | 0.1181          | 0.9871    | 0.9868 | 0.9868 | 0.9878   | 0.9868        | 0.0122     |
| 0.1131        | 8.0   | 304  | 0.0680          | 0.9937    | 0.9934 | 0.9934 | 0.9944   | 0.9934        | 0.0056     |
| 0.0526        | 9.0   | 342  | 0.0387          | 0.9937    | 0.9934 | 0.9934 | 0.9944   | 0.9934        | 0.0056     |
| 0.0283        | 10.0  | 380  | 0.0328          | 0.9873    | 0.9868 | 0.9869 | 0.9878   | 0.9868        | 0.0122     |
| 0.019         | 11.0  | 418  | 0.0224          | 0.9873    | 0.9868 | 0.9868 | 0.9889   | 0.9868        | 0.0111     |
| 0.0148        | 12.0  | 456  | 0.0201          | 0.9873    | 0.9868 | 0.9868 | 0.9889   | 0.9868        | 0.0111     |
| 0.0095        | 13.0  | 494  | 0.0396          | 0.9871    | 0.9868 | 0.9868 | 0.9878   | 0.9868        | 0.0122     |
| 0.007         | 14.0  | 532  | 0.0048          | 1.0       | 1.0    | 1.0    | 1.0      | 1.0           | 0.0        |
| 0.011         | 15.0  | 570  | 0.0036          | 1.0       | 1.0    | 1.0    | 1.0      | 1.0           | 0.0        |
| 0.0071        | 16.0  | 608  | 0.0092          | 0.9936    | 0.9934 | 0.9934 | 0.9941   | 0.9934        | 0.0059     |
| 0.0103        | 17.0  | 646  | 0.0148          | 0.9936    | 0.9934 | 0.9934 | 0.9944   | 0.9934        | 0.0056     |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1

iinstia bijuga mangifera indica pterocarpus indicus roystonea regia tabebuia