---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exper_batch_16_e8
  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. -->

# exper_batch_16_e8

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 sudo-s/herbier_mesuem1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3951
- Accuracy: 0.9129

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Apex, opt level O1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.8115        | 0.16  | 100  | 3.7948          | 0.1862   |
| 3.1194        | 0.31  | 200  | 3.0120          | 0.3281   |
| 2.3703        | 0.47  | 300  | 2.4791          | 0.4426   |
| 2.07          | 0.63  | 400  | 2.1720          | 0.5      |
| 1.6847        | 0.78  | 500  | 1.7291          | 0.5956   |
| 1.3821        | 0.94  | 600  | 1.4777          | 0.6299   |
| 0.9498        | 1.1   | 700  | 1.2935          | 0.6681   |
| 0.8741        | 1.25  | 800  | 1.1353          | 0.7051   |
| 0.8875        | 1.41  | 900  | 0.9951          | 0.7448   |
| 0.7233        | 1.56  | 1000 | 0.9265          | 0.7487   |
| 0.6696        | 1.72  | 1100 | 0.8660          | 0.7625   |
| 0.7364        | 1.88  | 1200 | 0.8710          | 0.7579   |
| 0.3933        | 2.03  | 1300 | 0.7162          | 0.8038   |
| 0.3443        | 2.19  | 1400 | 0.6305          | 0.8300   |
| 0.3376        | 2.35  | 1500 | 0.6273          | 0.8315   |
| 0.3071        | 2.5   | 1600 | 0.5988          | 0.8319   |
| 0.2863        | 2.66  | 1700 | 0.6731          | 0.8153   |
| 0.3017        | 2.82  | 1800 | 0.6042          | 0.8315   |
| 0.2382        | 2.97  | 1900 | 0.5118          | 0.8712   |
| 0.1578        | 3.13  | 2000 | 0.4917          | 0.8736   |
| 0.1794        | 3.29  | 2100 | 0.5302          | 0.8631   |
| 0.1093        | 3.44  | 2200 | 0.5035          | 0.8635   |
| 0.1076        | 3.6   | 2300 | 0.5186          | 0.8674   |
| 0.1219        | 3.76  | 2400 | 0.4723          | 0.8801   |
| 0.1017        | 3.91  | 2500 | 0.5132          | 0.8712   |
| 0.0351        | 4.07  | 2600 | 0.4709          | 0.8728   |
| 0.0295        | 4.23  | 2700 | 0.4674          | 0.8824   |
| 0.0416        | 4.38  | 2800 | 0.4836          | 0.8805   |
| 0.0386        | 4.54  | 2900 | 0.4663          | 0.8828   |
| 0.0392        | 4.69  | 3000 | 0.4003          | 0.8990   |
| 0.0383        | 4.85  | 3100 | 0.4187          | 0.8948   |
| 0.0624        | 5.01  | 3200 | 0.4460          | 0.8874   |
| 0.0188        | 5.16  | 3300 | 0.4169          | 0.9029   |
| 0.0174        | 5.32  | 3400 | 0.4098          | 0.8951   |
| 0.0257        | 5.48  | 3500 | 0.4289          | 0.8951   |
| 0.0123        | 5.63  | 3600 | 0.4295          | 0.9029   |
| 0.0052        | 5.79  | 3700 | 0.4395          | 0.8994   |
| 0.0081        | 5.95  | 3800 | 0.4217          | 0.9082   |
| 0.0032        | 6.1   | 3900 | 0.4216          | 0.9056   |
| 0.0033        | 6.26  | 4000 | 0.4113          | 0.9082   |
| 0.0024        | 6.42  | 4100 | 0.4060          | 0.9102   |
| 0.0022        | 6.57  | 4200 | 0.4067          | 0.9090   |
| 0.0031        | 6.73  | 4300 | 0.4005          | 0.9113   |
| 0.0021        | 6.89  | 4400 | 0.4008          | 0.9129   |
| 0.0021        | 7.04  | 4500 | 0.3967          | 0.9113   |
| 0.0043        | 7.2   | 4600 | 0.3960          | 0.9121   |
| 0.0022        | 7.36  | 4700 | 0.3962          | 0.9125   |
| 0.0021        | 7.51  | 4800 | 0.3992          | 0.9121   |
| 0.002         | 7.67  | 4900 | 0.3951          | 0.9129   |
| 0.0023        | 7.82  | 5000 | 0.3952          | 0.9125   |
| 0.0021        | 7.98  | 5100 | 0.3952          | 0.9129   |


### Framework versions

- Transformers 4.19.4
- Pytorch 1.5.1
- Datasets 2.3.2
- Tokenizers 0.12.1