Image Annotation for Machine Learning Models: 5 Common Misconceptions

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Annotating Images forMachine Learning Models
5 Common Misconceptions
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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.

Lets clear the myths to attain accurate image
annotation and high-performing AI and ML models.
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Debunking 5 Common Myths forImage 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

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AI can annotate as efficiently as humans
Misconceptions
Facts

• Cost saving
• Faster execution
• Great accuracy

• High-implementation cost
• Progressive evolution
• Human-in-the-Loop (HITL) is must

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Sacrificing pixel accuracy is acceptable
Misconceptions
Facts

• Pixel is just a dot
• Single pixel manipulations dont affect quality
• Doesnt affect model performance

• A single pixel accuracy matters
• E.g. medical imaging, autonomous vehicles
• Affects model training

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In-house annotation is easily manageable
Misconceptions
Facts

• Just a repetitive work
• No AI expertise required
• Can scale easily

• A task that grows and requires
• Knowledge
• Technical expertise
• Experience
• Outsourcing essential to scale

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Crowdsourcing is a viable option to scale
Misconceptions
Facts

• Numerous annotators are available
• Annotators remain till project-end
• Guarantees fast and quality work

• Anonymous labelers affect scalability
• Annotators need not
• Be domain experts
• Familiar with your use case
• Quality is not an accountability

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Data once annotated holds valid forever
Misconceptions
Facts

• Data properties dont change
• Annotated datasets are valid forever

• In future, annotated datasets hold
• Invalid or
• Partially valid
• Data properties are subjective

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Outsource to deploy Successful and Effective AI
and ML models with Image Annotation
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Real-world insights Swiss food waste analysis
specialist trains its ML model with accurately
annotated images by Hitech BPO
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• Our Image Annotation Solution
• Documented workflow
• Iterative labeling and Segmentation
• Audit and Review
• Real time image annotation intelligence

• Company
• Swiss food waste assessment solution provider
• Raises food waste awareness

• Business Need
• Identify, categorize label thousands of
• Customer waste and kitchen waste food images
• Help data scientists train ML models

• Business Impact
• 100 accuracy across categories
• Low TATs, faster model training
• Seamless CV modeling efficiency

Click here to read more
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• Avail unmatched image annotation services by
  collaborating with Hitech BPO

www.hitechbpo.com info_at_hitechbpo.com
Connect with our image annotation experts