Swin Tiny Patch4 Window7 224 Finetuned Skin Cancer

🚀 swin-tiny-patch4-window7-224-finetuned-skin-cancer

This model is a fine - tuned version of microsoft/swin - tiny - patch4 - window7 - 224 on the imagefolder dataset, used for skin cancer image classification.

🚀 Quick Start

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: 0.7695
• Accuracy: 0.7275

✨ Features

• This model can classify skin diseases in images, with possible classified diseases including 'Actinic - keratoses', 'Basal - cell - carcinoma', 'Benign - keratosis - like - lesions', 'Dermatofibroma', 'Melanocytic - nevi', 'Melanoma', 'Vascular - lesions'.

📚 Documentation

Model description

This model was created by importing the dataset of the photos of skin cancer into Google Colab from kaggle here: https://www.kaggle.com/datasets/kmader/skin - cancer - mnist - ham10000. 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/1bMkXnAvAqjX3J2YJ8wXTNw2Z2pt5KCjy?usp=sharing

The possible classified diseases are: 'Actinic - keratoses', 'Basal - cell - carcinoma', 'Benign - keratosis - like - lesions', 'Dermatofibroma', 'Melanocytic - nevi', 'Melanoma', 'Vascular - lesions'.

Skin example

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

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: 1

Training results

Framework versions

• Transformers 4.20.1
• Pytorch 1.11.0+cu113
• Datasets 2.3.2
• Tokenizers 0.12.1

📄 License

This model is licensed under the Apache - 2.0 license.