Truck Logo Recognition using MobileNet and Yolo

MobileNet V2

MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. MobileNet neural networks occupy less space. The size of MobileNets is 17 Mb. And the number of parameters is 4.2 million. Because of the small size, MobileNets are used in mobile devices.

Yolo V4

Yolo stands for Yolo Only Look Once and is a popular model. While training the model, we give an image and the coordinates of the class present in that image. The coordinates will look like this (bx, by, bh, bw). Here bx and by are the center of the class, bh and bw are the height and width of the class. The Yolo model has conv.net, which is a convolution layer, a deep neural network, and an output layer that has a softmax activation layer. The output variable is a vector and looks like the following.

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Lotus Labs

Lotus Labs

Transform your business into an AI-driven enterprise. We specialize in Machine learning for Retail, Insurance, and Healthcare industries. www.lotuslabs.ai