Pixel Perfect Labels
Precise annotations remove up to 15% of human errors and bias associated with manual human labeling.
Build and iterate
faster
Reduce iteration time from months to weeks.
Object Detection
Determine the predominant objects and confidence levels in your images.
Image Classification
Identify the location and class of objects in the dataset.
Human Pose Estimation
Detect the position and orientation of joints in images.
Image Segmentation
Group pixels by instance and class in each frame.
Save time and money with synthetic data
Supplement your existing real world data or start experimenting with low cost labeled images.
2D Bounding Boxes
Identify the position and types of objects in your images.
Instance Segmentation Masks
Find the location of individual instances of objects in the frame.
Semantic Segmentation Masks
Locate all the types of an object in your dataset.
Augmentations
Increase the sample size, address tricky corner cases, and add distractors to your dataset.
COCO Keypoint Labels
Generate COCO keypoint locations of 17 common joints for your human pose models.
BOOST ACCURACY
Improve model performance with domain randomization.
Detection rates can be increased by as much as 60% with domain randomized synthetic data.
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PRIVACY PRESERVED
No more anonymizing your datasets.
Generated data avoids legal hassles with personally identifiable information and regulatory concerns.
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Industries
A solution for any use case or application.
RESULTS
Synthetic data.
The future of computer vision.
By 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated.
July 2021
70% of computer vision implementation costs are in the upfront data acquistion and labeling costs.
Nov 2021
Per image costs of acquistion and labeling with traditional techniques, such as real world data and human labelers, range from $3 to $6
June 2021
Cut average costs from $150,000 to under $10,000 and development cycles from 6 months to under a week. That's almost a 95% savings in time and money.
