swin-tiny-patch4-window7-224-finetuned-leukemia-08-2024

<!-- 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-leukemia-08-2024

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: 0.1282
• Accuracy: 0.9527

Model description

This model was developed to aid in the diagnosis of Leukemia. Leukemia is the cancer that most affects children between 4 and 10 years of age.

Intended uses & limitations

In this version, the images used for testing correspond to 10% of the images in the image dataset. Consider that the images in the dataset are originally:

• All: 7,272
• Hem: 3,389

Applying Data augmentation, we arrive at: 20,000 images for each segment. Totaling 36,000 images for training and 4,000 for testing.

Important Note:

The images used in the test may be similar to those used in training, which may cause overfit..

Training and evaluation data

DataSet: Dataset ISBI-2019 (ISIC 2019 Challenge)

Training procedure

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: 10

Training results

Framework versions

• Transformers 4.44.2
• Pytorch 2.3.0+cpu
• Datasets 2.21.0
• Tokenizers 0.19.1

Author

Douglas Braga https://www.linkedin.com/in/douglas-braga-891a701/