---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exper_batch_32_e4
  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_32_e4

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: 0.3909
- Accuracy: 0.9067

## 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: 32
- 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: Apex, opt level O1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.4295        | 0.31  | 100  | 3.4027          | 0.2837   |
| 2.5035        | 0.62  | 200  | 2.4339          | 0.5247   |
| 1.6542        | 0.94  | 300  | 1.7690          | 0.6388   |
| 1.1589        | 1.25  | 400  | 1.3106          | 0.7460   |
| 0.9363        | 1.56  | 500  | 0.9977          | 0.7803   |
| 0.6946        | 1.88  | 600  | 0.8138          | 0.8207   |
| 0.3488        | 2.19  | 700  | 0.6593          | 0.8489   |
| 0.2935        | 2.5   | 800  | 0.5725          | 0.8662   |
| 0.2557        | 2.81  | 900  | 0.5088          | 0.8855   |
| 0.1509        | 3.12  | 1000 | 0.4572          | 0.8971   |
| 0.1367        | 3.44  | 1100 | 0.4129          | 0.9090   |
| 0.1078        | 3.75  | 1200 | 0.3909          | 0.9067   |


### Framework versions

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