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Mathematica implementation of YOLO, a computer vision object detection mode

Posted 7 years ago

Hi Folks,

I have a small zoo of computer vision models implemented using the new neural network functionality in Mathematica v11:

AlexNet: similar to ImageIdentify, maybe of interest to those interested in understanding the internals of how a neural network does classification.

FaceDetection: Implements a face detector, similar to FindFaces, of interest to those who are interested in the internals.

Yolo: Implements Joseph Redmon's tiny YOLO model for object detection and localisation. Tiny YOLO recognises twenty object categories {"aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"}:

enter image description here

Project is in a github repository: https://github.com/jfrancis71/CognitoZoo

The codebase and weights are all under an MIT licence which is fairly liberal, with the exception of the tiny YOLO weights file, which comes from Joseph Redmon (and that has a very different but also liberal license).

Please feel free to download and experiment with, and I hope it is of interest to the Mathematica community,

Kind regards,

Julian Francis.

POSTED BY: Julian Francis
Posted 7 years ago

Since posting my message, I have seen another post:

xslittlegrass YOLO implementation

Above is a link to another user's Mathematica YOLO implementation.

I haven't had a chance to look at it in detail. It looks as if it uses an earlier version of tiny YOLO (in which the last 3 layers are fully connected). The latest tiny YOLO (in my implementation) has convolutional layers for the final stage. In principle that might make it easier to scale to larger images or use with different aspect ratios, although I haven't taken advantage of this.

The previous link is included here as it does look like a very nice implementation and is surely of interest to anyone doing object detection in Mathematica.

Kind regards, Julian.

POSTED BY: Julian Francis
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