Practical and Efficient Crowdsourcing in Machine Learning

by | Aug 6, 2021

2 mins read

First, let’s recap — what is crowdsourcing in machine learning?

Crowdsourcing in machine learning entails facilitating recruiting data annotators at a large scale. Data labeling is useful for machines to perform NLP and NLU tasks such as sentiment analysis, entity extraction and text categorization. Crowdsourcing data service companies could also provide text translation for multilingual datasets.

Using crowdsourced data for machine learning or data labeling services is a practical approach that saves time, and ensures the diversity and scalability of your labels. There are numerous image annotation free and paid tools that have currently been used.

Here are five common free image annotation tools: 



Source: Hard Drones



A free image data annotation tool designed for computer vision datasets.  It is very useful for object detection like aerial imagery for drones and autonomous drones.

Argo AI autonomous vehicles

Source: Argo AI – Autonomous Vehicles



An online image annotation tool for computer vision research developed by MIT CSAIL (Computer Science and Artificial Intelligence), very useful for bounding box annotation.



Source: Blippar – Computer Vision



A graphic image annotation tool by Python with Qt for a graphical interface. It can support PASVACL VOC, YOLO, CreateML formats. 



Source: scylla.ai – Face Recognition




Israel Microsoft developed this Visual Object Tagging Tool (VoTT) back in 2018. The image, video annotation and labeling tool is used fo object detection models. Besides Microsoft Azure Custom Vision Service and CNTK formats, it also supports CSV, Pascal VOC, TFR Records, generic JSON schema formats.


waste management facility with Greyparrot

Source: Grey Parrot – Object Recognition



This free web-based image annotation tool is available in dlib XML, dlib pts, Pascal VOC, COCO. It enables easy resizing for any annotation shape from landmarks to feature points.

The Reason Why Crowdsourcing

in Machine Learning is Useful

 At MarsCrowd—we won’t claim we are the best crowdsourcing company—or even the best crowdsourcing platform out there. Not even on our own article.

Yet, we would encourage you to try the MarsCrowd crowdsourcing platform        because of these ➕ positive reasons:

💰 Cost-effective. For businesses—assessing between best crowdsourcing companies— even using free annotation tools requires hiring a team of experienced developers to run and maintain the system. This would require you to engage or hire specialize data annotators, which is not cost-effective.

🏓 Flexible. Free image annotation tools provide no technical support. For example, a system could go down, make upgrades, or even could suddenly stop maintaining its library. But — by using crowdsourced data for machine learning — you can ensure flexibility, speed, and quality in the type of data (image, video, audio, text). Also, it provides security of assurance if there are changes in the requirements of the machine learning project.

🎯 Precise. Every data annotation task has access to a specific team dedicated to ensure the project’s completion, quantity, and quality. Compared to a fraction of the price of in-house annotators.

Crowdsourcing in machine learning leverages cost, time, and efficiency with long-lasting data labeling partners.  


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