Ground Truth Data Applications

Applications for labeled and trained image and video ground truth data

Labeled training data is a cornerstone in many applications, enabling algorithms to learn and make accurate predictions or classifications. Applications span across supervised learning, object detection, anomaly detection, text and image classification, semantic segmentation, recommendation systems, speech recognition, and medical diagnosis, among many others.

Apps

Image Labeler	Label images for computer vision applications
Video Labeler	Label video for computer vision applications

Topics

Training Data for Object Detection and Semantic Segmentation
Create training data for object detection or semantic segmentation using the Image Labeler or Video Labeler.
Datastores for Deep Learning (Deep Learning Toolbox)
Learn how to use datastores in deep learning applications.
Get Started with SOLOv2 for Instance Segmentation
Perform multiclass instance segmentation using SOLOv2 and deep learning.
Get Started with Image Preprocessing and Augmentation for Deep Learning
Preprocess data for deep learning applications with deterministic operations such as resizing, or augment training data with randomized operations such as random cropping.