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

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

This model is a fine-tuned version of 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:

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

Framework versions

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