readme: ---
  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' 
  
  ### 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
  