How to Train a Computer Vision Model

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Computer vision is a result of scientists having succeeded in enabling machines to mimic the process of human visual cognition. The foundation for computer vision and image recognition dates back to the 1970s. However, it is only recently that computer vision found application outside of labs and research centers.

The global computer vision market size is expected to expand at a compound annual growth rate (CAGR) of 7.3% between now and 2028.

However, in order to create a successful and powerful computer vision application, it is necessary to first create and train models. Training a computer vision model is a time-consuming process that requires specific skills and knowledge. 

In this article, you will find answers that address the process of training a computer vision model to create a powerful computer vision application.

What are the Existing Datasets for Computer Vision Models

Computer vision algorithms work using the data you feed them. And when you feed them with data that has been cleaned and properly prepared, they will work perfectly. 

Let’s take a look at what some sources are for collecting data:

  1. One of the biggest and the most well-known dataset is ImageNet. This dataset contains 14 million images that are manually annotated with WordNet concepts. One million of the images have bounding box annotations. 
  2. Labelme is a large dataset created by the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). It contains 187,240 images, 62,197 annotated images, and 658,992 labeled objects.
  3. Another popular dataset is Microsoft Common Objects in Context (COCO). It consists of 328,000 images including 91 object types that can be recognized even by a 4-year old child. It has 2.5 million labeled instances in total.
  4. There are also different datasets for various requests. A few examples include: the CelebFaces Attributes Dataset with 200K celebrity images, the Plant Image Analysis dataset with 1M images of plants from 11 different species, and the Indoor Scene Recognition dataset with over 15K images of indoor scenes.


How does a Computer Vision Model Work?

In a nutshell, computer vision operates via three main steps:

  1. Image acquisition. Images, even heavy ones, can be acquired in real-time using video, photos, or 3D technologies for analysis.
  2. Image processing. Deep learning models automate much of this process, but models are often trained by first receiving thousands of labeled or pre-identified images.
  3. Image understanding. The last stage is the interpretation stage when the object is identified or classified.

Today’s AI systems go further by taking actions based on image understanding. There are many types of computer vision that are used in different ways:

  • Image segmentation breaks the image into multiple areas or fragments for a separate study.
  • Object detection identifies a specific object in an image. Advanced Object Detection recognizes multiple objects in a single image: football field, attacker, defender, ball, and so on. These models use X and Y coordinates to create a bounding box and identify everything inside it.
  • Facial recognition is an advanced type of object detection that not only recognizes a human face in an image but also identifies a specific person.
  • Edge detection is a technique used to detect the outer edge of an object or landscape in order to better determine what is in the image.
  • Pattern recognition is the process of recognizing repeating shapes, colors, and other visual indicators in images.
  • Image classification groups images into different categories.
  • Feature matching is a type of pattern detection that matches similarities in images to help classify them.

Simple computer vision applications only use one of these methods. However, more complex ones, such as computer vision for self-driving cars, rely on different methods to achieve their goal.

You can read more about how computer vision works HERE.

What is a General Computer Vision Model Training Strategy

Over the past several years, deep learning techniques transformed computer vision, making it more and more customizable and powerful. Unicsoft, a technology consulting company, delivering AI and Blockchain solutions to businesses, takes a four-step approach to building a computer vision model:

  1. Creating a dataset. A dataset should be composed of annotated images, or you can use a pre-existing dataset. The image category, pairs of bounding boxes, classes, and pixel-wise segmentation of an object can be used as annotations.
  2. Extract features. Choosing and extracting features from each image that are pertinent to the task at hand. These can be features based on facial criteria, tourist attractions, street objects, and so on.
  3. Train a deep learning model. The training will be performed on the extracted features. During the training, you “feed” the machine learning model images so that it learns the isolated features and solves the necessary task.
  4. Evaluate the model. Check whether the model has been trained using the best means possible. It can be done by using the images that weren’t used during the training phase. 

This approach is known as supervised machine learning and requires a dataset that encompasses the phenomenon the model has to learn.

Summing Up

Computer vision is a new and booming field that is at the forefront of the software development frontier. This tech breakthrough will continue to affect every industry, from car manufacturing to agriculture. When facing challenges with regards to choosing the right set of emerging technologies and building a computer vision model, turn to Unicsoft. Unicsoft helps startups and enterprises develop intelligent computer vision systems with unique requirements.