Automated Image Labeling and Iterative Learning


Automated image labeling helps to reduce the cost and time that it takes to label your dataset. MATLAB significantly reduces the time required to preprocess and label datasets with domain-specific apps for audio, video, images and text data. Whether you train your models in MATLAB or Python, we support your entire image processing and AI workflow, from acquisition to deployment.

In this session we show you how to use apps for labeling image and video data to build AI models. We explore preprocessing to facilitate feature extraction and present approaches to building models in an iterative fashion, validating predicted labels and incorporating on-the-fly models to label large datasets. We also discuss an approach to automating pixel-level labeling for semantic segmentation workflows.


  • Preprocess and label datasets faster with domain-specific apps for audio, video, images and text data
  • Use interactive apps to label, crop and identify important features and automate the process of labeling
  • Create, visualize and edit deep learning networks with our easy-to-use Deep Network Designer app
  • Incorporate deep learning models without having to create complex network architectures from scratch
  • Accelerate development and training of deep learning networks with GPUs, clusters and cloud resources

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