Pixel Perfect Labels
Precise annotations remove up to 15% of human errors and bias associated with manual human labeling.
Reduce iteration time from months to weeks.
Determine the predominant objects and confidence levels in your images.
Identify the location and class of objects in the dataset.
Detect the position and orientation of joints in images.
Group pixels by instance and class in each frame.
Supplement your existing real world data or start experimenting with low cost labeled images.
Precise annotations remove up to 15% of human errors and bias associated with manual human labeling.
Identify the position and types of objects in your images.
Find the location of individual instances of objects in the frame.
Locate all the types of an object in your dataset.
Increase the sample size, address tricky corner cases, and add distractors to your dataset.
Generate COCO keypoint locations of 17 common joints for your human pose models.
Detection rates can be increased by as much as 60% with domain randomized synthetic data.
SEE THE PAPERGenerated data avoids legal hassles with personally identifiable information and regulatory concerns.
SEE THE PAPERA solution for any use case or application.