So it's time to make a face detection function which will be named as cvDnnDetectFaces() Note: The more the confidence value of detection, the more accurate the results will be. We are done with all the image pre-processing tasks, and we have also created a function. Now it's time to display the visual results with the help of the function that we have created. So from the above output, it is visible how well our model has performed to even detect the multiple faces in the image with a reasonable confidence score and show the time taken for the face to be detected. Here we have come to the conclusion part of this article.

Modern artificial intelligence is often lauded for its growing sophistication, but mostly in doomer terms. If you're on the apocalyptic end of the spectrum, the AI revolution will automate millions of jobs, eliminate the barrier between reality and artifice, and, eventually, force humanity to the brink of extinction. Along the way, maybe we get robot butlers, maybe we're stuffed into embryonic pods and harvested for energy. But it's easy to forget that most AI right now is terribly stupid and only useful in narrow, niche domains for which its underlying software has been specifically trained, like playing an ancient Chinese board game or translating text in one language into another. Ask your standard recognition bot to do something novel, like analyze and label a photograph using only its acquired knowledge, and you'll get some comically nonsensical results.

The project will be updated continuously ...... Note: This is not one convertor for all frameworks, but a collection of different converters. Because github is an open source platform, I hope we can help each other here, gather everyone's strength. Because of these different frameworks, the awesome convertors of deep learning models for different frameworks occur. It should be noted that I did not test all the converters, so I could not guarantee that each was available. But I also hope this convertor collection may help you!