Computer vision can easily distinguish between well-defined shapes, for instance, a sphere and a cube. Things go awry with less distinct forms. It’s easy for the human eye to differentiate between a cat and a dog — you know what is what. But computers have no such innate capability, and even the most advanced computer vision algorithms often mistake a cat for a dog and vice versa.
Computers have to be trained rigorously to classify fuzzy objects. That training is imparted with special hardware and algorithms which constitute the platform termed ‘Deep Learning,’ a subset of Artificial Intelligence (AI). Conceptually, deep learning uses Artificial Neural Networks (ANNs) to simulate how the human brain learns, thinks and adapts.
Over the last decade, deep learning has seen explosive growth in computer vision. The platform has made many advances in a range of computer vision problems: object detection, motion tracking, action recognition, human pose estimation, and semantic segmentation.
A key component in training deep learning systems for computer vision is data annotation. Data annotation denotes labeling objects of interest in training media. This enables computer vision algorithms to recognize objects and interpret their surroundings correctly. The quality of the training data you use hinges on the quality of your annotations. Annotations thus underpin computer vision projects and their success or failure.
There are various data annotation modalities, depending on the data form. In the world of computer vision, common types of data that are annotated are:
Annotations are rendered with dedicated software tools, many of which are Open Source. Widely used annotation tools include Labellmg, Computer Vision Annotation Tool (CVAT), Visual Object Tagging Tool (VOTT), VGG Image Annotator (VIA), and LabelMe.
When choosing the right annotation tool for your computer vision project, there are a few factors to consider.
You know a tool is right for you when it works well for your project on all counts mentioned above.
Despite the use of annotation tools, data annotation remains largely a manual process demanding patient and accurate work. Much of the efficacy of the dataset used to train a computer vision model depends on how well the data annotations educate the model in perceiving objects of interest.
Poor annotations could result in a man being identified as a woman or your algorithm interpreting the text “Buck Up!” as a salary increment. In short, annotations decide whether your model is ready for production or not.
Below are some commonly used annotation techniques.
Over the last decade, Python has emerged as the programming language of choice for deep learning. Python’s PyTorch module enables moving datasets from disk to cloud storage and progressively streaming labels as you train your deep learning model. If you are starting a computer vision project, you should consider scaling computer vision with PyTorch.
Computer vision annotations need you to make a few balanced choices to train your deep learning model well enough. You have to select the right annotation tool for your project and then choose a sound annotation technique that quickly delivers high-quality annotations. Even with the correct choices, you still have to put in the time and hard work to ensure that annotations attain the quality needed for your model to be adequately trained for production environments.