---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
model-index:
- name: flower_classification
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9706601466992665
    - name: F1
      type: f1
      value: 0.97382606978311
---

<!-- 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_classification

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1638
- Accuracy: 0.9707
- F1: 0.9738

## 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 63
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.134         | 1.0   | 205  | 0.8454          | 0.8582   | 0.8377 |
| 0.6349        | 2.0   | 410  | 0.7229          | 0.8252   | 0.7947 |
| 0.3946        | 3.0   | 615  | 0.6453          | 0.8521   | 0.8301 |
| 0.2747        | 4.0   | 820  | 0.3665          | 0.9083   | 0.8901 |
| 0.1668        | 5.0   | 1025 | 0.3964          | 0.8998   | 0.8692 |
| 0.0767        | 6.0   | 1230 | 0.2997          | 0.9303   | 0.9282 |
| 0.0205        | 7.0   | 1435 | 0.1774          | 0.9584   | 0.9590 |
| 0.0066        | 8.0   | 1640 | 0.1467          | 0.9719   | 0.9732 |
| 0.0027        | 9.0   | 1845 | 0.1571          | 0.9707   | 0.9716 |
| 0.0026        | 10.0  | 2050 | 0.1603          | 0.9694   | 0.9709 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2