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:
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 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
