Annotating Images for Machine Learning Models: 5 Common Misconceptions

Annotating Images for Machine Learning Models 5 Common Misconceptions

Annotated Images for ML algorithms  

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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

Outsource to deploy Successful and Effective AI and ML models with Image Annotation

Real-world insights: Swiss food waste analysis specialist trains its ML model with accurately annotated ima ges

by Hitech BPO

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…

Avail unmatched image annotation services by collaborating with Hitech BPO Connect with our image annotation experts

www.hitechbpo.com info@hitechbpo.com