The project's features enable the partitioning of the dataset into training, testing, and validation sets based on user-provided values. Users can specify parameters such as batch size, epochs, and early stopping criteria. Additionally, the project applies user preferences for data augmentation operations and parameters, allowing for the creation of a model checkpoint based on user-defined names.
This project specifically focuses on training a model for eye diseases. Users can choose one of the following models for desired model training: CNN hold-out, CNN k-fold, Xception, MobileNetV2, or ResNet101. After model training, users can view the training results and confusion matrix. They can also inspect images from the test, training, and validation folders. Based on the selected image, users can compare different models and view prediction results.
- This application requires Dataset.
- Please click the link below to download and install Dataset: Dataset
- Clone the repo
git clone https://github.com/harunsefainan/eye-diseases
- Open the project in Spyder or any suitable IDE of your choice.
- Copy the downloaded dataset into a folder named "dataset" in the project directory.
- Run the project.
