license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-skin-cancer
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7275449101796407
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. It achieves the following results on the evaluation set:
- Loss: 0.7695
- Accuracy: 0.7275
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' .
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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6911 | 0.99 | 70 | 0.7695 | 0.7275 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1