In this guest feature, Vatsal Ghiya CEO and co-founder of Shaip has discussed some key insights on bounding box annotation and its key importance in training AI/ML models due to similarity in the data that is available in the market.
The Key Takeaway from the Article is-
- For AI/ML models random datasets are just like opaque kitchen containers and only labeling makes them relevant for use. This is the reason why data annotation comes as a major source that allows companies to deed in connected datasets which can make sense to use a case in the hand.
- Bounding box annotation is one of the primary forms of image annotation where object-specific data is fed by outlining the entities in the first place. Bounding box annotation helps model relevant algorithms pick up the insights related to object detection.
- Moreover, bounding box annotation can be used in multiple use cases across industries like- self-driving cars, e-commerce, retail, insurance claims, supply chain management, and a lot more. Hence, bounding box annotation is a must to start creating impactful AI/ML models.
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