OpenVINO offers superior model comparison, testing, and deployment, supporting diverse models. It provides cost-effective inferencing and streaming from camera inputs, tailored for Movidius yet flexible for X86, Intel CPUs, and GPUs. Its ease of integration and custom model conversion is notable. The platform supports cross-platform use, runs on non-NVIDIA GPUs, and is valuable for CPU deployments. OpenVINO's benefits include GPU performance enhancement on Raspberry Pi and significant cost savings in hardware usage.
- "The solution's ability to stream data directly from camera inputs is the most valuable aspect for us."
- "Compared to Jetson Nano or Jetson TX2, or Jetson Xavier, OpenVINO is a much more cost-effective solution."
- "The features for model comparison, the feature for model testing, evaluation, and deployment are very nice, and it can work almost with all the models."
OpenVINO requires improvements in model conversion speed and integration with varied machine learning tools. It faces challenges with complex neural networks and lacks vehicle recognition capabilities. Cross-platform compatibility is limited, being heavily reliant on Intel hardware. There's a need for PyTorch model hub expansion and better support for Apple silicon. Sustaining availability of software packages for older devices like Raspberry Pi would be beneficial to users. Performance on non-Intel hardware is also a common concern.
- "At this point, the product could probably just use a greater integration with more machine learning model tools."
- "The model optimization is a little bit slow — it could be improved."
- "It has some disadvantages because when you're working with very complex models, neural networks, if OpenVINO cannot convert them automatically and you have to do a custom layer and later add it to the model, it is difficult."
