The Challenge

  • High volume of raw image data to train AI model
  • Manual identification and labeling of diverse complex objects and backgrounds
  • Inconsistency in bounding box placement and semantic segmentation 
  • Incapability to understand specific visual attributes due to different categories 
  • Inaccurate labeling in object recognition

The Solution

  • Custom annotation protocols to establish strict labeling guidelines 
  • Specialized team of data annotation to meet unique client requirements 
  • Multi technique labeling to capture detailed object boundaries 
  • Refined annotation standards as the AI evolved 
  • Utilized maker checker system for individual verification of annotated image

The Results

  • Clent acquired accurate labeled datasets to train computer vision model 
  • Accuracy in object detection algorithms and reduction in labeling errors 
  • Uniform labeling across large dataset for reliable AI predictions
  • Scalable annotation framework for continuous use  
  • Reduced labor intensive annotation process