---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-flower-classifier
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9339263024142312
---

<!-- 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. -->

# swin-tiny-patch4-window7-224-finetuned-flower-classifier

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co./microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2362
- Accuracy: 0.9339

## Model description

This model was created by importing the dataset of the photos of flowers into 
Google Colab from kaggle here: https://www.kaggle.com/datasets/l3llff/flowers. 
I then used the image classification tutorial here: 
https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb

obtaining the following notebook:

https://colab.research.google.com/drive/1bapCEz4vkDd16Ax9jb5oHGa85PeuyZVW?usp=sharing

The possible classified flowers are: 
'common_daisy', 'rose', 'california_poppy', 'iris', 'astilbe', 'carnation', 
'tulip', 'sunflower', 'coreopsis', 'magnolia', 'water_lily', 'bellflower', 
'daffodil', 'calendula', 'dandelion', 'black_eyed_susan' 

## Flower example:

![flower](800px-Magnolia_cylindrica_1zz.jpg)

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.365         | 0.99  | 110  | 0.2362          | 0.9339   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2