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In this video we will explain about dataset annotation because YOLO is a supervised learning object
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detection model, you must use annotated data to train the model.
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The annotation provides a bonding box for each object in the image along with the object class name.
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This is the annotation format on YOLO.
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The first of these values is object class, its value ranging from zero to total class minus one.
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The second and third values represent the bounding boxes midpoint.
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The midpoint value is relative to the width and height of the image.
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X relative to inmates with.
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Y relative to image height.
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The width of the bounding box is the fourth value.
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This width is relative to the image.
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With the fifth value is the bonding box is high.
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This height is relative to the image height.
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Here's an example.
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Consider the following example.
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There is an image that is 512 pixels wide and 366 pixels high.
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In this case, zero represents a mask.
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This is the burning boxes midpoint.
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X divided by image with.
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Equal to this, and it is written as the second value in the annotation.
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Why?
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Divided by the image.
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Equal to this, and it is written as the third value in the annotation.
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The width of the box divided by image with.
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Equal to this.
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And it is is the fourth value in the annotation.
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The height of the box divided by image height.
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Equal to this, and it is written as the fifth value in the annotation.
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There are also several tools available for any city.
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YOLO, Mark.
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Labor emits.
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And let me.
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In this course, we will use the label image tools.
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The next video will demonstrate how to use label image.
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See you then.
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