Whether for medical imaging, autonomous driving, agriculture automation, or robotics, scaling a computer vision (CV) project is tough and takes tons of micromanaging, tracking, and analysis for the best results. The data is usually annotated in batches because of the volume and multiplicity of iterations needed along the way. The batches undergo several revisions for […]
Computer Vision software needs hardware, and combined innovations from Xailient and silicon manufacturers are accelerating the move to AI at the Edge. Intel Movidius™ is one leader in AI hardware innovation, transforming the future of computer vision and artificial intelligence (AI). They have enabled new levels of intelligence for AI assistants, drones, robots, cameras, virtual […]
In computer vision, there are number of general, pretrained models available for deployment to edge devices (such as OpenCV AI Kit). However, the real power in computer vision deployment today lies in custom training your own computer vision model on your own data to apply to your custom solution on your own device. To train […]
We have already discussed several ways to convert your DL model into OpenVINO in previous blogs (PyTorch and TensorFlow). Let’s try something more advanced now.
How TensorFlow trained model may be used and deployed to run with OpenVINO Inference Engine
Are you looking for a fast way to run neural network inferences on Intel platforms? Then OpenVINO toolkit is exactly what you need. It provides a large number of optimizations that allow blazingly fast inference on CPUs, VPUs, integrated graphics, and FPGAs. In the previous post, we’ve learned how to prepare and run DNN models […]
Nowadays, many ground-breaking solutions based on neural network are developed daily and more people are adopting this technique for solving problems such as voice recognitions in their life. Because of the recent advancement in computing and the growing trend of using neural networks in a production environment, there is a significant focus of having such […]
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