Data labeling

Data labeling is a way of giving an algorithm (model) some information, facts, details about particular data unit – in our case, image. Labeled images that you use to train the model are commonly called training data.
There are many things that describe an image – apparent objects, colors, style, level of light. The contours of different parts of an image may be given, or a count of certain elements, etc. This labeling information is commonly divided into three groups: image classification, object detection and semantic/instance segmentation.

How to use the SentiSight platform

  1. Labeling for image classification

If you plan to do image classification, you can label the images already during the image upload – just write one or more comma separated labels into a label field. Alternatively, you can add or adjust image labels after the upload using the panel on the left screen side. Select some images and press '+' to add label or press '-' to remove the label. You can also adjust the label name, by clicking on it with a mouse, entering the new name and pressing 'Enter'.

In case an image has more than one label, one of those labels is called the "default" label and it is encircled in white. This is the label that will be used if you train a single-label classification model on images with multiple labels.

You can change which label is the "default" one, by clicking on the label of interest with a mouse. Alternatively, you can select some images that already have the label of interest, and label them again using the '+' button with the same label. The default label will change in all of those images.

  1. Labeling for object detection or image segmentation
  • Select a group of pictures that you want to label
  • Choose label group: select Object detection or Segmentation on the left menu
  • You will now see the first image and the necessary tools to draw a bounding box or contours.
  • Try it! Add a new object and draw your chosen figure (label). You will also find some hints, look for blue question marks!
  • Write a new name for it or choose from existing labels.
  • You get to the next picture from your selection by pressing Next.

Learn more about the process

  • How do I choose the labeling type?

    When using the trained model for your predictions on new “unseen” images, you must label the training data giving the same sort of information that you wish to “get back”. For example, if you want to find a certain object showing its location on the image – you label training images for object detection, giving the information about the target objects class and its location (bounding box).