Page Comparison

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So far we've worked with two different approaches for image tagging.

**It's important to note that only the Image Type model training is managed by us. The color model however was created and managed by another company and it's not under our control.

ML Results caching

Most of the advertisers' inventories don't change too much from day to day, so in order to avoid classifying the same images multiple times, we use a cache system in the ML servers.

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1. Image Type received from ML system

   1. This value defaults to “Placeholder” if the ML system could not return a value

2. Color received from ML System

   1. This value defaults to null if the ML system could not return a value

   2. If the color value is configured in the scrape the ML classification for the model is not applied

3. Image Type Manual fix

   1. Image Type manual fix list selects the value returned from the ML system or defaults to Placeholder if None is received.

4. Color Manual fix

   1. Color manual fix list selects the value returned from the ML system or defaults to “Black” if None is received

5. Example of Placeholder tagged from ML system: {Image Type = Placeholder, Color = None}

   1. As you see, as color is not tagged, 5 is not showing anything

   2. Previously tagged Placeholders were {Image Type = Placeholder, Color = 'N/A'}. If you find some of these they of these they are cached values.

       Edit

When you change any value from the Manual fixes list and press the “update” button at the bottom of the list the following happens:

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Different criteria between advertisers that share image CDN

This case didn't happen so far but it is a possibility.

We currently have no images with conflicting classifications but there is potential when advertisers share inventory. When storing the classification of an image we store it by image_key|image_url without linking it to an advertiser. This can lead to the scenario where two advertisers that share CDN are manually reclassifying the images against each other.

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• A request is sent to the ML server to change the image type or color of that image (identified by its image_key).

• A historical entry of manual fix is saved in the DB with the data of the reclassification, the advertiser the image belongs to, and who did the fix.

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