To create a labeled image dataset, start by collecting or sourcing images relevant to your task. Use tools like cameras, web scraping, or open datasets (e.g., ImageNet or COCO) to build your dataset.
Annotate the images using tools like LabelImg or CVAT. Define the annotation format based on your task, such as labels for classification, bounding boxes for detection, or masks for segmentation. Ensure class labels are consistent and representative of the task’s objectives.
Organize the dataset into training, validation, and test sets, ensuring balanced representation of all classes. Store metadata (e.g., file paths, labels) in a structured format like CSV or JSON for seamless integration into training workflows.