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changes.mady.by.user Julius Höfler

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Within the attribute selection you can decide which information of from your products should be visible during the annotation process. For example, if you select the attribute Color, the color information is displayed for each product. These This information should help you to decide which attribute value annotation is the best fitting fit for a product.

You can use the search bar to easily find an attribute easier. All selected attributes are displayed below the checkbox list as tags (max. selection is 10). They can be removed via the "x" icon in the tags or by deselecting the attribute in the checkbox list.

The order of the selected attributes represents the order within the annotation. Hence, the first selected attribute is the first information you see during the annotation.

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You can select when you want to see the products. You can choose between these three options:

  • OrignalOriginal:
    See all products in the order of your data feed. That means that the first product of your data file will be the first product to annotate and so on.

  • Random:
    The product sequence you will see during the annotation process is completely random. That means that it is unpredictable which product will be presented next.

  • Active Learning:
    The product sequence is will be adapted depending on the annotated products. The annotation process will present products where the prediction model is uncertain about the fitting attribute value.

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AdvantagesDisadvantages
Original
  • the annotation order is known
  • less product variety which can result in annotating dozens of similar products (e.g. product variants)
  • this does not help the model in its value prediction for completely different products
  • can take quite  longer to get enough products for a good prediction
Random
  • more likely to get a bigger product variety
    → saves time until the model can generate a good prediction
  • annotation order is not known
  • it is not secured to get the needed product variety 
  • at worst, it can take a similar the same amount of time as for the Original presentation
Active Learning
  • product presentation is based on the uncertainty of the prediction model
  • it saves a lot of time until you get a very good prediction for all products
  • the user has to annotate at least 50 products
  • a prediction model have to  must exist already

Annotation Mode

Lastly, you have to decide which products you want to annotate. This step is only relevant if you already have product annotations. The two decisions are:

  • Remaining:
    You only see products which have not been annotated at all.

  • All Again (Rerun):
    All products independent of its current annotation status are shown during the starting annotation. All existing annotation annotations are deleted.