Pixel Perfect 2D Bounding Boxes for Object Detection AI Model Training

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Object Detection

2D Bounding Boxes enable Object Detection

Among the most prevalent fields in Computer Vision, is Object Detection.  The goal is to enable machine learning algorithms to detect the presence, or absence, of certain objects of interest.

For instance, you may want to be alerted if a burglar is detected on your security camera.  An Autonomous car may want to plan its next move based on whether it can detect a traffic light, stop sign, or pedestrian in the front image.  In AI for Agriculture, a farmer may want to be alerted when his crop cameras detect certain pests on his crop, or when his animal husbandry cameras detect ticks on his livestock. 

These applications are all possible with a subfield of Computer Vision Artificial Intelligence,  known as Object Detection. 

2D Bounding Box Image Annotation for Object Detection


Train Machine Learning Algorithms to identify objects

How do Artificial Intelligence Models learn to Detect Objects in Images? Machine Learning Algorithms (or AI Models) must be provided with large data sets of images, with the objects clearly marked on them with 2D Bounding Boxes.  Given a large enough dataset, and accurately marked bonding boxes, a Machine Learning Algorithm can be trained – that is begin to identify patterns in the bounding boxes.   Once adequately trained, the AI model can detect the given object in future images automatically, without any human assistance. 

Tools for 2D Bounding Box

2D Bounding Box Annotation tools are very simple pieces of software that enable annotators to use a mouse to draw rectangular bounding boxes around objects of interest.  The coordinates for the bounding boxes drawn are then stored in PASCAL VOC, YOLO, COCO, or another format that machine learning algorithms read for training.

ScaleOps offers a Data Labeling tool bundled with our Data Annotation Services.  Depending on client preference, we are also open to using any other Annotation Tool that clients recommend.   Listed below are some tools we have used in the past.

In the past, we have used CVAT, VOTT, Labelmg, AWS SageMaker Ground Truth along with some client proprietary tools for our Bounding Box Annotation projects.

Learn More about Image Annotation Tools


Output Formats – PASCAL VOC, YOLO, or COCO  

.. or any other proprietary format you want

PASCAL VOC, YOLO and COCO are the primary output formats we deliver results of our Annotation Services. 

 PASCAL is  an acronym for Pattern Analysis, Statistical Modelling, and Computational Learning.  They ran a Visual Object Challenge (VOC), in 2005.  The project is famous and the output XML format they used, PASCAL VOC, has now become an industry standard for AI Model training.   

COCO (acronym for Common Object in Context), by contrast is a newer JSON based output format that is also commonly used for ML Model training.  

YOLO (acronym for You Only Look Once), is an object detection system whose output format has also become commonly used for training Deep Learning Models.   





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