2D Bounding Box Annotation Service for Machine Learning
2D Bounding box annotation service for precise object detection through computer vision to train the AI and machine learning models. Cogito bestow bounding box annotated images as a training data with next level of accuracy for multiple industries developing the object recognition perception models.
Object Detection for Self-driving Cars
The best use of bounding box annotation is to train the AI-enabled autonomous vehicles ADAS-supported cars that can recognize the different types of objects on road. 2D Bounding box annotation techniques create a training data for self-driving model to detect the objects like traffic singles, potholes, lanes, traffic and pedestrians etc. All these objects on road can be labeled with bounding box annotation.
Image Tagging for Ecommerce and Retail
Image bounding box annotation also visualize the items sold on the online store and retail shops. Perceptions models trained with bounding box machine learning able to recognize objects like fashion accessories, furniture and other items with right labeling. Actually, it helps visual search technology to detect the objects picked from the store and generate automatic billing without the help of humans.
Damage Detection for Insurance Claims
Vehicles damaged in accidents or in other mishaps can be easily identified with bounding box deep learning. Vehicle body damage, roof damage, damaged front or tail lights bonnet dented, broken glasses and other accessories of a vehicle can be make recognizable to computer vision. It also helps machine learning to estimate the level of damage making easier to calculate the right insurance claims.
Precise Bounding Box Annotation by Cogito
Apart from autonomous vehicle, ecommerce and insurance claim perception model training, Cogito also provides the high-quality image annotation service for autonomous flaying training, satellite imagery, robotics in agriculture and healthcare sector. Working with a team of well-trained annotators, Cogito is committed to deliver the next level of training data for AI and ML backed with bounding box annotation techniques.