How to Build Training Data for Computer Vision?
The groundbreaking applications of Artificial intelligence are attracting tech multinationals like Apple, Microsoft, Amazon and Facebook to work on their future projects with more AI focused strategies. The AI effect is influencing the product road map of all such companies having the renowned AI-based applications that are launched at regular intervals in a year to automate their business operations with more promising results.
Computer Vision is an important development under AI that has been extensively explored and applied into various industries from outdated to innovative self-driving cars moving on roads without human intervention. Such AI-backed innovative technologies work on such principles that encompass a huge amount of training data for computer vision.
How to Start or Implement Computer Vision?
To start Computer Vision or CV you need to follow certain steps that are listed below:
- Collection of a huge amount of data.
- Labeling of Data.
- GPUs required for – Training ML models that also needs huge computational resources.
- Choosing a right algorithm -> Train your model -> Test it -> Teach the model what it doesn’t know yet.
- Repeating of above points till acceptable quality results not come.
All these steps have their own challenges in terms of technical know-how and operational activities, so here we will discuss and help you how to deal with the labeling of training data and other related aspects required to complete this process.
The Popular Usages of Computer Vision
Before we start labeling of training data, you need aware where the technology of Computer Vision is effectively used to produce an AI-backed system or machine that can perform without too much human instructions and do their job independently as per the changing situations.
Self-driving Cars, Drones, Robotics, Mapping & Satellites, OCR / BFSI, Agriculture Technology, Medicine and many more other fields where computer vision is playing a vital role in allowing machines to view and perceive like humans and perform with favorable actions.
How to Collect Data for Computer Vision?
The first steps towards AI-based computer vision technology are the collection of data that you need to gather from reliable sources. Though, there are many free online tools and paid standard datasets like Google’s Open Images and Image Net etc. are available that you can use to collect the data for developing computer vision applications.
Anyone looking to get started with the learning of machine learning can use these datasets that can be useful as a starting point for them. And these datasets can be also useful for the people looking to build a simple model for side projects. However, if you want to develop a real or effective computer vision model you need to collect proprietary training data similar to the data you expect for your final model to work flawlessly.
These data sets are quite different and nuanced in terms of quality and accuracy. Actually, there are many ways to collect such data and you can use the internet or various other online sources like Facebook, Google Photos or cameras of cars used for self-driving or automation driving. But if you are looking for high-quality training data you need to pay for it, and Cogito is one them providing a quality training data sets for computer vision and for AI-based other projects.
How to Label the Data?
Once you collected the data, you have to label it in the right manner. And there are two aspects you need to consider while labeling the data:
- How do you label the data?
- Who labels the data?
How to Choose Right Image Annotation Tools?
You can find lots of free image annotation tools available online, however, choosing the right one for your needs would be a challenging task. You can find here the considerable points while choosing the image annotation tool.
Things to Consider while Choosing the Right Image Annotation Tool:
- Tool setup time and effort
- Labeling Accuracy
- Labeling Speed
How to Find Best Image Annotation Tool?
Though, there are many free online image annotation tools you can find. Comma Coloring, Annotorious and LabelME are few of them very popular helps to annotate images and label them from machine learning perspective.
However, quality and accuracy are one the important factors to annotate and label images in a precise manner, and you have to pay few bucks for availing high-quality annotation services for computer vision. If these free tools not fit your needs, you have to put some money in acquiring the quality annotated images labeled with right descriptions to get the best results.
Outsource your Image Annotation Needs
Companies like Cogito have dedicated resources and tools to annotate images and other types of pictures with the help of the world-class UX designers who have learned from annotating thousands of images every day across a variety of scenarios & use-cases improving their annotating skills and experience to make the annotation process more effectual.
Cogito offers image annotation service for computer vision using the best annotation tool and API to annotate images with quality and high accuracy. Developing a real and effective business model, especially an AI-backed application you need a quality data. Cogito works with the international standards to deliver a quality service to its clients across the globe.
What, How and When to Choose?
Choosing the right image annotator from the right source is an important thing you need to consider while working with such things. Either you can get the annotated images from in-house sources like hiring an intern or ask your colleagues to help you out or you can set up operations team but these options could be profitable when you scale up and your business is growing.
Outsourcing to Professionals
The best way to get such data is outsourcing to professional companies involves in providing image annotation service as per the customized needs. And outsourcing annotation, tagging images to experts like Cogito would be more favorable from every point of view.
You just need to share the data, few gold standard examples and labeling guidelines and Cogito will label training data as per your requirements. It is offering image annotation and label training data to mid-size business enterprises to large companies across the world with enterprise-grade service level agreement to deliver quality results with scalable turnaround times.
However, there are certain situations when you should do it in-house, especially for a small set but outsource when the data is a huge level. Actually, these outsourcing firms are also not scalable enough to handle even 100,000 image annotations in a small amount of time. Though few industry leaders provide a scalable service but customary crowd sourcing platforms like Amazon Mechanical Turk is merely a microtasks freelancing marketplace where all the efforts of task creation, worker incentivization, QA is the task creator.
Here, computer vision training data with Cogito means the use of a right mix of technical know-how and experience to annotate images for training data needs. Use of humans with AI-enabled resources Cogito works with fully automated annotation process by using the latest technology and most suitable algorithms capable to detect objects for computer vision learning.