myYolo is an implementation of YOLOv8 for image classification tasks. It leverages the power of YOLOv8, a state-of-the-art object detection model, adapted specifically for image classification purposes. With myYolo, you can easily perform image classification tasks with high accuracy and efficiency.
- Utilizes YOLOv8 architecture for image classification.
- Easy-to-use interface for training and evaluating classification models.
- Supports various image formats for classification.
- Flexible configuration options for model customization.
- Integration with popular deep learning libraries such as PyTorch.
To install myYolo, simply clone this repository:
git clone https://github.com/vivekbiragoni/GeneticDisorders.gitThen, navigate to the cloned directory and install the required dependencies:
cd myYolo
pip install -r requirements.txtTo start using myYolo for image classification, follow these steps:
- Prepare your dataset: Organize your image dataset into appropriate directories.
- Configure the model: Adjust the configuration file (e.g.,
yolov8-cls.yaml) to suit your classification task requirements. - Train the model: Use the provided training script to train your classification model on the prepared dataset.
- Evaluate the model: Evaluate the trained model's performance on a separate validation dataset.
- Make predictions: Use the trained model to classify new images and make predictions.
For detailed usage instructions and examples, refer to the documentation.