The latest release of openvino has changed the inference engine API, providing a new API that takes advantage of the full potential of Intel's latest version of IR (IR 11). More information can be found at this link. We should adapt nebullvm's OpenVinoInferenceLearner to their latest API.
I used following commad to export the ONNX models, and I use the 5.0 tag of ultralytics/yolov5 to train the model.pt. It raises an AttributeError: conv object has no attribute weight. How can I do for this error?
Large input size REAL-TIME Face Detector on Cpp. It can also support face verification using MobileFaceNet+Arcface with real-time inference. 480P Over 30FPS on CPU
This project contains a code generator that produces static C NN inference deployment code targeting tiny micro-controllers (TinyML) as replacement for other µTVM runtimes. This tools generates a runtime, which statically executes the compiled model. This reduces the overhead in terms of code size and execution time compared to having a dynamic on-device runtime.
Canopy is a machine learning learning compiler stack with the capability of adopting high-end FPGAs. As a part of OpenAIOS project, Canopy is an evolved version of Apache TVM. Canopy is able to support a variety of hardware backends such as PCIE-based cloud FPGAs, CPUs and GPUs.
The latest release of openvino has changed the inference engine API, providing a new API that takes advantage of the full potential of Intel's latest version of IR (IR 11). More information can be found at this link. We should adapt
nebullvm'sOpenVinoInferenceLearnerto their latest API.Usef