---
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_4
  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_4

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: 0.0651
- Accuracy: 0.9873
- Precision: 0.9873
- Recall: 0.9873
- F1: 0.9873

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 2.5217        | 0.22  | 100  | 2.8392          | 0.8363   | 0.8363    | 0.8363 | 0.8363 |
| 1.2157        | 0.45  | 200  | 1.5467          | 0.9304   | 0.9304    | 0.9304 | 0.9304 |
| 0.5702        | 0.67  | 300  | 0.7642          | 0.9598   | 0.9598    | 0.9598 | 0.9598 |
| 0.367         | 0.89  | 400  | 0.4966          | 0.9637   | 0.9637    | 0.9637 | 0.9637 |
| 0.1299        | 1.11  | 500  | 0.2458          | 0.9784   | 0.9784    | 0.9784 | 0.9784 |
| 0.1142        | 1.34  | 600  | 0.1678          | 0.9833   | 0.9833    | 0.9833 | 0.9833 |
| 0.043         | 1.56  | 700  | 0.1746          | 0.9706   | 0.9706    | 0.9706 | 0.9706 |
| 0.0683        | 1.78  | 800  | 0.1554          | 0.9745   | 0.9745    | 0.9745 | 0.9745 |
| 0.0364        | 2.0   | 900  | 0.1132          | 0.9843   | 0.9843    | 0.9843 | 0.9843 |
| 0.019         | 2.23  | 1000 | 0.0939          | 0.9843   | 0.9843    | 0.9843 | 0.9843 |
| 0.0535        | 2.45  | 1100 | 0.1033          | 0.9833   | 0.9833    | 0.9833 | 0.9833 |
| 0.0164        | 2.67  | 1200 | 0.0698          | 0.9902   | 0.9902    | 0.9902 | 0.9902 |
| 0.1128        | 2.9   | 1300 | 0.0810          | 0.9853   | 0.9853    | 0.9853 | 0.9853 |
| 0.0127        | 3.12  | 1400 | 0.0725          | 0.9873   | 0.9873    | 0.9873 | 0.9873 |
| 0.0112        | 3.34  | 1500 | 0.0702          | 0.9902   | 0.9902    | 0.9902 | 0.9902 |
| 0.0383        | 3.56  | 1600 | 0.0860          | 0.9843   | 0.9843    | 0.9843 | 0.9843 |
| 0.0091        | 3.79  | 1700 | 0.0750          | 0.9843   | 0.9843    | 0.9843 | 0.9843 |
| 0.0079        | 4.01  | 1800 | 0.0674          | 0.9882   | 0.9882    | 0.9882 | 0.9882 |
| 0.0083        | 4.23  | 1900 | 0.0659          | 0.9873   | 0.9873    | 0.9873 | 0.9873 |
| 0.0078        | 4.45  | 2000 | 0.0652          | 0.9882   | 0.9882    | 0.9882 | 0.9882 |
| 0.0073        | 4.68  | 2100 | 0.0652          | 0.9873   | 0.9873    | 0.9873 | 0.9873 |
| 0.0076        | 4.9   | 2200 | 0.0651          | 0.9873   | 0.9873    | 0.9873 | 0.9873 |


### Framework versions

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