The outputs are great, although not the same as yolov5, maybe some pre-processing/post-processing steps are different.
That's a great catch! I think it is caused by the different pre-processing operations. I've verified the the post-processing stages before, it can get the same results as ultralytics/yolov5 (when w/o TTA predict). And I've uploaded a [notebook](https://github.com/zhiqwang/yol
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.
That's a great catch! I think it is caused by the different pre-processing operations. I've verified the the post-processing stages before, it can get the same results as ultralytics/yolov5 (when w/o TTA predict). And I've uploaded a [notebook](https://github.com/zhiqwang/yol