Accurate data labeling significantly reduces the time and effort required to create datasets for training. Here are more reasons why visual data labeling is important for computer vision projects.

1. Why it is Important for Computer Vision
Visual Data Labeling

2. Computer Vision models are
incomplete without accurate
visual data labeling.
Growing at a CAGR of 7.8%, the Computer Vision market has been
rapidly extending and taking businesses by storm.
Source:

3. Why visual data labeling is
important for computer vision
 Streamlined data preprocessing
 Robust datasets
 Precision enhancement
 Easy-to-scale AI & ML models
 Continuous improvement

4. Streamlined Data Preprocessing
 Accurate tagging / labelling
 Perfect feature extraction
 Accurate data re-scaling

5. Robust Dataset
 Noise elimination
 QC-based data
 Quantity data

6. Precision Enhancement
 Continuous data feeding
 Perfectly fitted model
 Zero bias

7. Easy-to-scale Models
 Voluminous Data Labeling
 Context adjustability
 Execution consistency

8. Continuous Improvement
 Constant data addition
 Model retraining
 Statistical refinement

9. Hitech BPO accurately annotated thousands of images to be
used as training dataset for Computer Vision models of a
Swiss food waste assessment solution provider.
Company
• Swiss food waste assessment company
Business need
• Label thousands of food waste images
• Build a high-accuracy computer vision model
Hitech BPO’s visual data labeling solution
• Iterative labeling and segmentation
• Real time visual labeling intelligence
Business impact
• 100% accuracy across categories
• Seamless computer vision modeling efficiency
Click here to read the full case study

10. Build scalable computer vision models using
Hitech BPO’s high-grade visual data labeling.
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