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false-positives

a detection error where the algorithm falsely registers the presence of an object

How-to improve algorithm training results?

false-negatives

a detection error where the algorithm falsely registers the absence of an object

How-to improve algorithm training results?

ground truth

the annotations in the input image data done by the user. The deep learning model is trained to target morphological structures in novel image data, similar to the ground truth annotations.https://kmlvision.atlassian.net/wiki/spaces/KB/pages/3205070870

How-to understand quantitative results?

image classification

a computer vision task for categorizing groups of pixels or vectors within a given image

Algorithm Training Types

instance correctness

a metric that gives you information about the ability of the algorithm to correctly detect and label instances in the process of instance segmentation. Instance correctness relies on contour information to estimate the presence of an overlay of annotated and predicted instances.

How to understand instance segmentation qualitative results?

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Definition

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pixel correctness

a metric that gives you information about the ability of the algorithm to correctly detect and label instances in the process of instance segmentation. Pixel correctness relies on pixel-level information to estimate the presence of an overlay of annotated and predicted instances.

How to understand instance segmentation qualitative results?

predicted areas

areas within an image assigned to a particular label

How-to understand quantitative results?

predicted pixel label

the label the AI-algorithm assigns a pixel to. This means each pixel in an image is assigned a class label from a predefined set. In other words, the “predicted pixel label“ is the label the AI-algorithm assigned to a specific pixel.

How-to understand quantitative results?

random split

the process of letting the platform decide which images are used for training and which ones for validation.

Introduction into IKOSA AI

RGB images

images that consist of 3 channels: red, green and blue.

How to manage multichannel images?

semantic segmentation

a computer vision task for segmenting an image through assigning a label to each pixel within.

Algorithm Training Types

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