Image Annotation Business Models [Reviewed]

Author profile picture

@taqadamKarina Grosheva

Democratizing AI for social good

In the rise of robotics, computer vision and image processing cameras, image annotation comes as the first step to get the right AI training data for Deep Learning models. Whether you build an app to allow users to snap fashion items at the store as a new omni-channel sales or use machine vision installed at edge device at the industrial facility to monitor anomalies: it starts with training massive image data sets.

The current markets estimates $1.6 B.        

Billion + dollar industry includes the essential four. What is the best Image Annotation Package for your needs? 
Image data sets for training, testing and  validation of ML model
Workforce teams, annotators or labeling service providers
Image Tagging Tool designed specifically for ML image training
AI Data Management fit for AI training and pipelines

Typical Journey in AI Data training:

The image annotation projects usually start (after getting imagery for AI training) with finding outsourced annotating team. Expecting that  annotators are well-trained for the task itself, with good foundation in understanding data and AI flows.
But defining specific project requirements, labels, and domain knowledge require training, instructions, monitoring. In the course of ML model training, these instructions, labels, class structure, and layers of attributes may change.
Finally,  the annotating team can start. Now, you need an image annotation tool. There are plenty of these, typically used in DIY (“Do it Yourself”) environments, and are open-source
However, imagine hiring turkers or freelancers as crowdsourced team? AI Data management comes as a next challenge: deploying web or desktop tools for teams, distributing images, setting up project management and roles , collecting results, executing quality control.
Finally, the training of ML starts.  What happens with testing, validation, active learning pipelines as the project scales? End to end Platform.

Image Annotation Packages

Outsourcing labor
How: Thanks to exponential growth, the former BPO centers turned AI-human labs. Employees use custom tools provided by companies.
Target: Companies  accustomed to BPO standards and shared services.
Advantage: Scale and adaptive managed dedicated team; large volume of practice in outsourcing
Example: Cloud Factory
Image annotation tools
How: Manual or AI-augmented tools. The first part of any image sample gets manually annotated, augmenting the rest with AI. Manual annotation tools are used for more unique training sets.
Target: Small companies and start-ups. DIY (Do it Yourself).
Advantage: Typically used in training AI with small image data sets, in-house.
Example: Supervisely
End to End platforms
How: Combining both: labeling service and tools. Enables ML process optimization with data management pipeline.
Target:  Larger companies, growth stage start ups.
Advantage: Platform approach allowing companies to set requirements, monitor results, upload instructions, provide real-time feedback, change labels, change volumes.
Example: TaQadam and Labelbox
API for data training
How: Customer is involved significantly less in image annotation process. The label structure, or industries are standard.
Target: Large autonomous driving companies with standard output results. 
Advantage: Turnkey project. 
Example: Scale
We, at TaQadam, have been experimenting with business models. And excited to join the competitive and growing community of startups making next AI revolution, diversifying human workforce in AI training for fair and inclusive AI models.


The Noonification banner

Subscribe to get your daily round-up of top tech stories!