TACO-SEG YOLO

Each image in the TACO dataset is manually labeled and segmented according to a comprehensive hierarchical taxonomy, enabling researchers and developers to accurately train AI models for waste detection, environmental monitoring, and sustainability initiatives. By focusing on real-world settings, TACO offers a valuable resource for training object detection models that can recognize and classify different types of waste materials.

Key Features of the TACO Dataset:

• Diverse Environments: Includes images taken in forests, urban streets, and coastal areas.
• Manual Labeling & Segmentation: Every image is precisely labeled and segmented for accurate analysis.
• Hierarchical Taxonomy: Waste objects are categorized using a structured, detailed taxonomy for more robust machine learning models.
• Growing Dataset: Continuously updated with new images and annotations collected from an active server.

The TACO dataset is hosted on Flickr and is regularly expanded to enhance the variety and coverage of the dataset. With its rich annotations and diverse environmental contexts, the dataset offers invaluable insights for AI model training, waste management systems, and environmental conservation efforts.

Whether you’re working on object detection, AI sustainability projects, or environmental research, the TACO dataset provides an essential resource to help tackle the growing issue of litter and pollution through innovative technology.