---
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: 0.1042
- Accuracy: 0.9814
- Precision: 0.9814
- Recall: 0.9814
- F1: 0.9814

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.006         | 0.22  | 100  | 0.0735          | 0.9863   | 0.9863    | 0.9863 | 0.9863 |
| 0.0044        | 0.45  | 200  | 0.0720          | 0.9882   | 0.9882    | 0.9882 | 0.9882 |
| 0.3589        | 0.67  | 300  | 0.5454          | 0.8902   | 0.8902    | 0.8902 | 0.8902 |
| 0.401         | 0.89  | 400  | 0.6406          | 0.8676   | 0.8676    | 0.8676 | 0.8676 |
| 0.1851        | 1.11  | 500  | 0.4838          | 0.8912   | 0.8912    | 0.8912 | 0.8912 |
| 0.1116        | 1.34  | 600  | 0.3375          | 0.9245   | 0.9245    | 0.9245 | 0.9245 |
| 0.2359        | 1.56  | 700  | 0.4032          | 0.9059   | 0.9059    | 0.9059 | 0.9059 |
| 0.062         | 1.78  | 800  | 0.2356          | 0.9549   | 0.9549    | 0.9549 | 0.9549 |
| 0.0221        | 2.0   | 900  | 0.2307          | 0.9559   | 0.9559    | 0.9559 | 0.9559 |
| 0.0052        | 2.23  | 1000 | 0.1620          | 0.9676   | 0.9676    | 0.9676 | 0.9676 |
| 0.0277        | 2.45  | 1100 | 0.1881          | 0.9676   | 0.9676    | 0.9676 | 0.9676 |
| 0.0025        | 2.67  | 1200 | 0.1483          | 0.9735   | 0.9735    | 0.9735 | 0.9735 |
| 0.0078        | 2.9   | 1300 | 0.1199          | 0.9794   | 0.9794    | 0.9794 | 0.9794 |
| 0.002         | 3.12  | 1400 | 0.1343          | 0.9755   | 0.9755    | 0.9755 | 0.9755 |
| 0.0035        | 3.34  | 1500 | 0.1247          | 0.9775   | 0.9775    | 0.9775 | 0.9775 |
| 0.0245        | 3.56  | 1600 | 0.1116          | 0.9775   | 0.9775    | 0.9775 | 0.9775 |
| 0.0015        | 3.79  | 1700 | 0.1099          | 0.9775   | 0.9775    | 0.9775 | 0.9775 |
| 0.0013        | 4.01  | 1800 | 0.1089          | 0.9804   | 0.9804    | 0.9804 | 0.9804 |
| 0.0014        | 4.23  | 1900 | 0.1081          | 0.9804   | 0.9804    | 0.9804 | 0.9804 |
| 0.0013        | 4.45  | 2000 | 0.1076          | 0.9804   | 0.9804    | 0.9804 | 0.9804 |
| 0.0012        | 4.68  | 2100 | 0.1075          | 0.9804   | 0.9804    | 0.9804 | 0.9804 |
| 0.0013        | 4.9   | 2200 | 0.1042          | 0.9814   | 0.9814    | 0.9814 | 0.9814 |


### Framework versions

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