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Image Annotation for Machine Learning Models: 5 Common Misconceptions

Donu2019t let common misconceptions hamper the quality of image annotation and hurt the lifecycle of your machine learning models. The presentation clarifies 5 common misconceptions to help you build quality image datasets and rightly drive your machine learning implementation.

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Image Annotation for Machine Learning Models: 5 Common Misconceptions

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  1. Annotating Images for Machine Learning Models 5 Common Misconceptions

  2. 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.

  3. 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

  4. 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

  5. Sacrificing pixel accuracy is acceptable Misconceptions Facts  Pixel is just a dot  A single pixel accuracy matters • Single pixel manipulations don’t affect quality • E.g. medical imaging, autonomous vehicles  Doesn’t affect model performance  Affects model training

  6. 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

  7. Crowdsourcing is a viable option to scale Misconceptions Facts  Numerous annotators are available  Anonymous labelers affect scalability  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

  8. Data once annotated holds valid forever Misconceptions Facts  In future, annotated datasets hold  Data properties don’t change  Annotated datasets are valid forever • Invalid or • Partially valid  Data properties are subjective

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

  10. Real-world insights: Swiss food waste analysis specialist trains its ML model with accurately annotated images by Hitech BPO

  11. Our Image Annotation Solution Company  Documented workflow  Swiss food waste assessment solution provider  Iterative labeling and Segmentation  Audit and Review  Raises food waste awareness  Real time image annotation intelligence Business Need Business Impact  Identify, categorize & label thousands of  100% accuracy across categories • Customer waste and kitchen waste food  Low TATs, faster model training images  Seamless CV modeling efficiency • Help data scientists train ML models Click here to read more…

  12. Avail unmatched image annotation services by collaborating with Hitech BPO www.hitechbpo.com info@hitechbpo.com Connect with our image annotation experts

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