Annotating Images for Machine Learning Models 5 Common Misconceptions
Annotated Images for ML algorithms
Image annotation is pivotal to the success of Machine Learning model. Machine learning and AI are ushering in: • Fully autonomous vehicles • Unmanned drones • Improved facial recognition Image annotation has lot of misconceptions around it.
Let’s clear the myths to attain accurate image annotation and high-performing AI and ML models.
Debunking 5 Common Myths for Image Annotation 1. AI can annotate as efficiently as humans 2. Sacrificing pixel accuracy is acceptable 3. In-house annotation is easily manageable 4. Crowdsourcing is a viable option to scale 5. Data once annotated holds valid forever
AI can annotate as efficiently as humans Misconceptions
Facts
Cost saving
High-implementation cost
Faster execution
Progressive evolution
Great accuracy
Human-in-the-Loop (HITL) is must
Sacrificing pixel accuracy is acceptable Misconceptions
Facts
Pixel is just a dot
A single pixel accuracy matters
• Single pixel manipulations don’t affect quality Doesn’t affect model performance
• E.g. medical imaging, autonomous vehicles Affects model training
In-house annotation is easily manageable Misconceptions
Facts
Just a repetitive work
A task that grows and requires
No AI expertise required
• Knowledge
Can scale easily
• Technical expertise • Experience Outsourcing essential to scale
Crowdsourcing is a viable option to scale Misconceptions
Facts
Numerous annotators are
Anonymous labelers affect scalability
available
Annotators need not
Annotators remain till project-end
• Be domain experts
Guarantees fast and quality work
• Familiar with your use case Quality is not an accountability
Data once annotated holds valid forever Misconceptions
Facts
Data properties don’t change
In future, annotated datasets hold
Annotated datasets are valid forever
• Invalid or • Partially valid Data properties are subjective
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Company
Our Image Annotation Solution
Swiss food waste assessment
Documented workflow
solution provider Raises food waste awareness
Iterative labeling and Segmentation Audit and Review Real time image annotation intelligence
Business Need Identify, categorize & label thousands of • Customer waste and kitchen waste food images
Business Impact 100% accuracy across categories Low TATs, faster model training Seamless CV modeling efficiency
• Help data scientists train ML models Click here to read more…
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