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You need to be familiar with the different algorithm training types to be able to choose the most suitable one for your training. To help you achieve this we prepared this section for you.

What to expect from this page?

📑 Semantic segmentation

If you need to know which label each pixel of the image belongs to, or need to find areas or boundaries of objects, semantic segmentation is the best training type for you.

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Semantic segmentation is one of the main tasks performed in autonomous driving, e.g. the car needs to be aware of where the road is and where its exact boundaries to other objects are. In medical applications, semantic segmentation is used to, e.g. find the boundaries of organs in CT scans and reconstruct the 3D structure of the observed organ.

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📑 Image classification

If you need to know what the picture shows, image classification might be the best training algorithm type for you.

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The output of a typical image classification model is a list of probabilities for categories that the image might contain. The category with the highest probability corresponds to what the model “thinks“ the image represents.

📑 Object detection

If you need to know what is on your image and where it is located in your image, object detection might be the most suitable training type for your use case.

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This algorithm type is thus useful for analyzing large whole slide images, where you are e.g., interested in the counting objects. Object detection algorithms are widely used for identifying objects in images and separating them for further analysis. For instance, this is a standard technique for recognizing faces in digital camera applications — or even smiling faces in views.

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📑 Instance segmentation

Combining object detection and semantic segmentation capabilities, instance segmentation is one of the more versatile training types. Just like semantic segmentation, an instance segmentation algorithm training yields a pixel-wise map assigning each pixel in the image to one of the detected labels. At the same time it provides a bounding box location for all individual instances of this object class shown in the image.

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If you still have questions regarding your algorithm training, feel free to send us an email at support@ikosa.ai. Copy-paste your training ID in the subject line of your email.

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