---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: ViT_Flower102_2
  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. -->

# ViT_Flower102_2

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1533
- Accuracy: 0.9716
- Precision: 0.9716
- Recall: 0.9716
- F1: 0.9716

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.053         | 0.22  | 100  | 0.3198          | 0.9235   | 0.9235    | 0.9235 | 0.9235 |
| 0.1225        | 0.45  | 200  | 0.4087          | 0.9167   | 0.9167    | 0.9167 | 0.9167 |
| 0.1985        | 0.67  | 300  | 0.2068          | 0.9569   | 0.9569    | 0.9569 | 0.9569 |
| 0.0804        | 0.89  | 400  | 0.3181          | 0.9333   | 0.9333    | 0.9333 | 0.9333 |
| 0.1672        | 1.11  | 500  | 0.3582          | 0.9275   | 0.9275    | 0.9275 | 0.9275 |
| 0.1287        | 1.34  | 600  | 0.2700          | 0.9451   | 0.9451    | 0.9451 | 0.9451 |
| 0.0147        | 1.56  | 700  | 0.3691          | 0.9206   | 0.9206    | 0.9206 | 0.9206 |
| 0.0416        | 1.78  | 800  | 0.2535          | 0.9471   | 0.9471    | 0.9471 | 0.9471 |
| 0.0211        | 2.0   | 900  | 0.2575          | 0.9471   | 0.9471    | 0.9471 | 0.9471 |
| 0.088         | 2.23  | 1000 | 0.1908          | 0.9529   | 0.9529    | 0.9529 | 0.9529 |
| 0.1849        | 2.45  | 1100 | 0.2201          | 0.9529   | 0.9529    | 0.9529 | 0.9529 |
| 0.0009        | 2.67  | 1200 | 0.2229          | 0.9549   | 0.9549    | 0.9549 | 0.9549 |
| 0.0599        | 2.9   | 1300 | 0.1781          | 0.9608   | 0.9608    | 0.9608 | 0.9608 |
| 0.0004        | 3.12  | 1400 | 0.1751          | 0.9667   | 0.9667    | 0.9667 | 0.9667 |
| 0.0004        | 3.34  | 1500 | 0.1684          | 0.9686   | 0.9686    | 0.9686 | 0.9686 |
| 0.0352        | 3.56  | 1600 | 0.1502          | 0.9755   | 0.9755    | 0.9755 | 0.9755 |
| 0.0003        | 3.79  | 1700 | 0.1597          | 0.9745   | 0.9745    | 0.9745 | 0.9745 |
| 0.0003        | 4.01  | 1800 | 0.2573          | 0.9559   | 0.9559    | 0.9559 | 0.9559 |
| 0.0005        | 4.23  | 1900 | 0.1907          | 0.9667   | 0.9667    | 0.9667 | 0.9667 |
| 0.0741        | 4.45  | 2000 | 0.2038          | 0.9637   | 0.9637    | 0.9637 | 0.9637 |
| 0.0025        | 4.68  | 2100 | 0.1929          | 0.9647   | 0.9647    | 0.9647 | 0.9647 |
| 0.0293        | 4.9   | 2200 | 0.1740          | 0.9608   | 0.9608    | 0.9608 | 0.9608 |
| 0.0003        | 5.12  | 2300 | 0.2598          | 0.9569   | 0.9569    | 0.9569 | 0.9569 |
| 0.0037        | 5.35  | 2400 | 0.1772          | 0.9618   | 0.9618    | 0.9618 | 0.9618 |
| 0.0213        | 5.57  | 2500 | 0.2911          | 0.9520   | 0.9520    | 0.9520 | 0.9520 |
| 0.027         | 5.79  | 2600 | 0.2540          | 0.9520   | 0.9520    | 0.9520 | 0.9520 |
| 0.0155        | 6.01  | 2700 | 0.2252          | 0.9549   | 0.9549    | 0.9549 | 0.9549 |
| 0.0002        | 6.24  | 2800 | 0.3040          | 0.9431   | 0.9431    | 0.9431 | 0.9431 |
| 0.011         | 6.46  | 2900 | 0.1923          | 0.9598   | 0.9598    | 0.9598 | 0.9598 |
| 0.0006        | 6.68  | 3000 | 0.2089          | 0.9637   | 0.9637    | 0.9637 | 0.9637 |
| 0.0002        | 6.9   | 3100 | 0.2206          | 0.9578   | 0.9578    | 0.9578 | 0.9578 |
| 0.0006        | 7.13  | 3200 | 0.2267          | 0.9627   | 0.9627    | 0.9627 | 0.9627 |
| 0.0001        | 7.35  | 3300 | 0.1735          | 0.9637   | 0.9637    | 0.9637 | 0.9637 |
| 0.0001        | 7.57  | 3400 | 0.1611          | 0.9686   | 0.9686    | 0.9686 | 0.9686 |
| 0.0003        | 7.8   | 3500 | 0.1584          | 0.9676   | 0.9676    | 0.9676 | 0.9676 |
| 0.0001        | 8.02  | 3600 | 0.1591          | 0.9716   | 0.9716    | 0.9716 | 0.9716 |
| 0.0005        | 8.24  | 3700 | 0.1596          | 0.9706   | 0.9706    | 0.9706 | 0.9706 |
| 0.0002        | 8.46  | 3800 | 0.1563          | 0.9716   | 0.9716    | 0.9716 | 0.9716 |
| 0.0002        | 8.69  | 3900 | 0.1550          | 0.9716   | 0.9716    | 0.9716 | 0.9716 |
| 0.0001        | 8.91  | 4000 | 0.1542          | 0.9706   | 0.9706    | 0.9706 | 0.9706 |
| 0.0001        | 9.13  | 4100 | 0.1538          | 0.9716   | 0.9716    | 0.9716 | 0.9716 |
| 0.0001        | 9.35  | 4200 | 0.1536          | 0.9716   | 0.9716    | 0.9716 | 0.9716 |
| 0.0001        | 9.58  | 4300 | 0.1534          | 0.9716   | 0.9716    | 0.9716 | 0.9716 |
| 0.0001        | 9.8   | 4400 | 0.1533          | 0.9716   | 0.9716    | 0.9716 | 0.9716 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2