Your dataset folder should be structured correctly with images and labels for effective training and validation of YOLO models within ComfyUI. This tool provides a streamlined process for configuring, training, and detecting objects using the YOLO (You Only Look Once) framework.
- Facilitates the setup of datasets, including images and corresponding labels, for training models.
- Offers three distinct nodes: configuration, training, and detection, each with specific inputs and outputs for a comprehensive workflow.
- Allows users to customize training parameters such as model size, epochs, and batch size, enhancing flexibility in model training.
Context
This tool consists of GIS Processing Nodes designed for use within ComfyUI, enabling users to train YOLO models for object detection tasks. Its primary purpose is to simplify the process of preparing datasets, configuring training settings, and executing detection workflows.
Key Features & Benefits
The tool includes three main nodes: YOLO Train Config, YOLO Train, and YOLO Detect. The YOLO Train Config node allows users to specify training parameters, while the YOLO Train node executes the training process based on these configurations. The YOLO Detect node enables users to apply their trained models to images for object detection, providing outputs that include both annotated images and a list of detected objects.
Advanced Functionalities
The tool supports advanced features such as resuming training from previous sessions and utilizing pretrained weights, which can significantly reduce the time required to achieve effective model performance. Users can also adjust confidence and IOU (Intersection over Union) thresholds to fine-tune detection accuracy based on their specific requirements.
Practical Benefits
By utilizing this tool, users can enhance their workflow efficiency in ComfyUI, allowing for better control over the training process and improved quality of object detection results. The structured dataset preparation and customizable training parameters lead to more effective model training and easier integration into various applications.
Credits/Acknowledgments
This repository is developed and maintained by contributors dedicated to enhancing the capabilities of ComfyUI, with the code being open-source for community use and improvement.