---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- vision-transformer
- flowers
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: flower-vit
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: custom flower dataset
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9636363636363636
    - name: Precision
      type: precision
      value: 0.9632702640149449
    - name: Recall
      type: recall
      value: 0.9636363636363636
    - name: F1
      type: f1
      value: 0.9632142875960066
---

<!-- 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. -->

# flower-vit

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the custom flower dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1623
- Accuracy: 0.9636
- Precision: 0.9633
- Recall: 0.9636
- F1: 0.9632

## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.1765        | 1.0   | 138  | 0.1646          | 0.9673   | 0.9679    | 0.9673 | 0.9673 |
| 0.1386        | 2.0   | 276  | 0.1291          | 0.9673   | 0.9681    | 0.9673 | 0.9673 |
| 0.0889        | 3.0   | 414  | 0.1214          | 0.9673   | 0.9681    | 0.9673 | 0.9673 |
| 0.0857        | 4.0   | 552  | 0.1183          | 0.9673   | 0.9681    | 0.9673 | 0.9673 |
| 0.0942        | 5.0   | 690  | 0.1177          | 0.9673   | 0.9681    | 0.9673 | 0.9673 |


### Framework versions

- Transformers 5.5.4
- Pytorch 2.11.0+cpu
- Datasets 4.8.4
- Tokenizers 0.22.2