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Computer Vision API

Computer vision API-1

Computer vision APIs let you run computer vision tasks programmatically at scale in real time. Once set up, the computer vision API can run computer vision tasks simultaneously on millions of data.

This makes it easy to integrate these APIs into your apps or websites and deliver cutting edge computer vision backed experiences to your customers easily. For example, you might have a reverse image search engine which takes in a photo as an input and returns a set of similar images from the web. You can implement this in no time using computer vision APIs even though you do not have any expertise in machine learning or computer vision. This is the advantage of using vision APIs.

Table of Contents

What is a computer vision API?

For people who are new to APIs, API stands for Application programming interface. An API takes in certain inputs and gives out desired results after interfacing with an external software. For example,  a computer vision API looking to find celebrities in a list of images will take in an image URL, API key, as inputs, interface with Skyl's image recognition algorithms and give out the names of celebrities in the images as an output.

computer vision API-1

Imagine a scenario where you have a million images and you are looking to run computer vision algorithms on them, or you have a image search engine application that lets users upload images. In both these scenarios you can use the computer vision API to programmatically to extract information from these images much faster and accurately without manual intervention.

If your images are on a cloud storage such as AWS, you can append the image URL to the computer vision API and extract information from them programmatically.

Related: Download the checklist that lays out everything you should consider  before implementing a machine learning project or workflow →

What to look for before choosing a Computer vision API?

  1. Accuracy- You should choose a computer vision API that leverages models built on large data sets, which makes the accuracy of these algorithms are pretty high. These pre-trained models built on large data sets make better predictions than other models that are built on smaller sized data. Ask your computer vision API provider for more details.
  2. Lower response times- As an enterprise, you would want your computer vision application to bee real time and give out the desired results within short time periods. Make sure that the computer vision API you choose has a lower latency.
  3. Built for scale- As an enterprise, having millions of customers is the norm. When your traffic spikes, you wouldn't want your application to go down. Do a stress testing on your API before taking to live production.

What are some applications of computer vision APIs?

Skyl computer vision APIApplications of computer vision APIs

  1. Detecting objects within images- Computer vision API can detect the different objects, their number within images.
  2. Image search- Search for images that are similar to a given image. It can also be used to find duplicate images.
  3. Search for text within images- Using OCR (optical character recognition, the computer vision API, can find text within a given set of images.
  4. Detect faces and facial parameters- Detect the number of faces in a given picture and whether they are happy, sad etc.
  5. Find explicit content- Find adult or violent.
  6. Detecting Logos- Vision API can help in identifying misuse of your company or brand logo in a database of images.
  7. Classifying images- Classify images into desired categories at scale easily.

Visit Skyl.ai to learn more about different natural language processing and computer vision projects!

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