Glossary

Active learning is a machine learning algorithm that gives users the ability to actively to label data points with the intended outputs. The algorithm randomly selects the data points to be labeled next from the unlabeled data pool.

Anchor box is the predefined bounding boxes which is usually seen in object detection models.

Annotation is the process of labeling your data to teach your deep learning model the outcome your want to predict. Generally, bounding boxes are used to train for object detection and polygons are used to train for instance segmentation.

Annotation format is the specific method to encode the annotation and to describe the bounding box’s size and position (COCO, YOLO, TXT, etc).

An application programming interface is a mechanism that provides components to convey with other software within databases or applications. Companies can use it to assist digital transformation or an ecosystem. We use REST API to allow users to easily import their models into our platform.

An attribute is the item of data that is utilized in machine learning, and the attribute groups define clusters of attributes to create the product’s additional information.

Augmentations are good for dataset robustness. It allows users to enhance their existing dataset through positional augmentations or color space augmentation. These augmentation techniques enable the model to not lean on specific features while training.‍

AutoML leads to automating the tasks to optimize the training models for application to the real world by themselves. It contains the whole process from loading a raw dataset to deploying the ML model.

Backpropagation is a two-stage training process of how neural networks improve themselves. It is an ML algorithm that adjusts the parameters from the error calculation of each neuron.

A bounding box is a rectangular region of an image that concludes an object and is portrayed by its (x,y) coordinates‍.

COCO is an image dataset stored in the JSON format, gathering to compare different models’ performance and solve common object detection problems.‍

Classification is a task for categorizing variables to understand if a specific of the class is included based on preset attributes‍.

Clustering is an unsupervised technique that groups similar instances according to similarity, and the data points will not include labels.

Computer Vision is the science of enabling computers to see and understand images and video. This is accomplished by developing algorithms that can make sense of visual content, for example detecting people or objects in an image or video, or being able to read road signs.‍

CNN is a neural network that at least has one convolutional layer. It is typically used for image recognition and identification.‍