Yolov3 Object Detection Tutorial #6 - Deploying Your Model | OpenCV Python | Computer Vision 2020

Yolo v3 Tutorial #6 - Deploying Your Neural Network model using OpenCV Python in this Computer Vision tutorial:

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In the last lecture, I showed you how to train YoloV3. Today you are going to be learning how to execute the trained Yolov3 weights for your own objection detector, using PyTorch. As you may know, we have already established the base for our PyTorch YoloV3 in the first lecture and the results were great.

I will also show how you take the new weights from Supervisely, and run the Xbox, PlayStation object detector with GPU acceleration. As you can see from this demo, the detector works quite well, considering that we have less than 200 images for each class.

If you implemented data augmentation, your results will be much better. I will show you a video in a following lecture to compare the results of the trained YoloV3 with and without data augmentation.

Okay so let’s start with the deploying our YoloV3 gaming console detector.

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