---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exper1_mesum5
  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. -->

# exper1_mesum5

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_mesuem5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6401
- Accuracy: 0.8278

## 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: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.9352        | 0.23  | 100  | 3.8550          | 0.1959   |
| 3.1536        | 0.47  | 200  | 3.1755          | 0.2888   |
| 2.6937        | 0.7   | 300  | 2.6332          | 0.4272   |
| 2.3748        | 0.93  | 400  | 2.2833          | 0.4970   |
| 1.5575        | 1.16  | 500  | 1.8712          | 0.5888   |
| 1.4063        | 1.4   | 600  | 1.6048          | 0.6314   |
| 1.1841        | 1.63  | 700  | 1.4109          | 0.6621   |
| 1.0857        | 1.86  | 800  | 1.1832          | 0.7112   |
| 0.582         | 2.09  | 900  | 1.0371          | 0.7479   |
| 0.5971        | 2.33  | 1000 | 0.9839          | 0.7462   |
| 0.4617        | 2.56  | 1100 | 0.9233          | 0.7657   |
| 0.4621        | 2.79  | 1200 | 0.8417          | 0.7828   |
| 0.2128        | 3.02  | 1300 | 0.7644          | 0.7970   |
| 0.1883        | 3.26  | 1400 | 0.7001          | 0.8183   |
| 0.1501        | 3.49  | 1500 | 0.6826          | 0.8201   |
| 0.1626        | 3.72  | 1600 | 0.6568          | 0.8254   |
| 0.1053        | 3.95  | 1700 | 0.6401          | 0.8278   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1