Trash Annotations in Context (TACO)

Taco is an open image dataset of waste in the wild. It contains photos of litter taken under diverse environments, from tropical beaches to London streets. These images are manually labeled and segmented according to a hierarchical taxonomy to train and evaluate object detection algorithms. The best way to know TACO is to explore our dataset.For convenience, annotations are provided in COCO format. TACO is still a baby, but it is growing and you can help it!Humans have been trashing planet Earth from the bottom of Mariana trench to Mount Everest. Every minute, at least 15 tonnes of plastic waste leak into the ocean, that is equivalent to the capacity of one garbage truck. We have all seen the impact of this behaviour to wildlife on images of turtles choking on plastic bags and birds filled with bottle caps. Recent studies have also found microplastics in human stools. These should be kept in the recycling chain not in our food chain. It is time for a revolution.We believe AI has an important role to play. Think of drones surveying trash, robots picking up litter, anti-littering video surveillance and AR to educate and help humans to separate trash. That is our vision. All of this is now possible with the recent advances of deep learning. However, to learn accurate trash detectors, deep learning needs many annotated images. Enters TACO. While there are a few other trash datasets, we believe these are not enough. Our goal is to take TACO to the next level with the following features: Object segmentation. Typically used bounding boxes are not enough for certain tasks, e.g., robotic grasping Images under licence CC BY. You can do whatever you want with TACO as long as you cite us. Background annotation. TACO covers many environments which are tagged for convenience. Object context tag. Not all objects in TACO are strictly litter. Some objects are handheld or not even trash yet. Thus, objects are tagged based on context