Image Recognition Systems
One of the most powerful applications of machine learning and computer vision is image recognition. While it is trivial for a human being to recognize, categorize, or differentiate between images, the sheer volume of data encoded in a given image—especially one at a very high resolution—has until quite recently made computer-powered image recognition very challenging.
Today, image recognition systems powered by Neural Magic’s Inference Engine can be used to develop sophisticated computer vision applications for industries and use cases ranging from industrial manufacturing to retail to media—helping companies achieve previously impossible feats.
Bringing Computer Vision into its Own
Today, when machine learning engineers run image recognition models on a CPU, they often make sacrifices that affect the quality of outcomes, because they are forced to make untenable sacrifices between:
- Performance
- Batch Size
- Accuracy
- Cost
Neural Magic addresses these limitations by generating GPU-class performance on a CPU.
Software Flexibility
Seamless Integration
Better Price Performance
Currently supports image recognition models such as ResNet, MobileNet, VGGNet, EfficientNet
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The Neural Magic Inference Engine fits seamlessly into existing CI/CD pipelines, can be deployed in containers or virtual machines, and can be managed with Kubernetes like any modern software application.