Trained Tags¶

Trained Tags let you teach Fast Video Cataloger keywords of your own from example images. Pick a handful of thumbnails that show the thing you mean — a person's dog, a particular set, a type of shot — and from then on the AI can find it across your catalog and keyword it for you. There is no dataset to annotate and no training run to wait for: teaching is done by right-clicking the examples, and about ten of them give good results.

Matched frames get purple keywords on the thumbnail and video, searchable like any other keyword (they show up in the Keyword Manager with the type Trained).

Trained tags complement the other AI options rather than replace them: image tagging and scene classification work from fixed vocabularies, and object detection finds 80 common object types. Trained tags are for the subjects those lists do not cover — and unlike a custom object detection model they need no training pipeline, just examples.

Download the model (first use)¶

Trained tags use their own AI model, downloaded on demand:

1. Open AI Models (from the start page's Maintenance section).
2. Under Trained tags, click Download.

The model is stored per user and works without admin rights. A GPU is recommended, as with the other AI features — matching works on CPU but is much slower.

Teach a tag¶

The fastest way is from the thumbnails you already have:

1. Select one or more thumbnails that show your subject (multi-select works — every selected thumbnail becomes an example).
2. Right-click and choose Teach trained tag, then pick an existing tag or New trained tag....

That is the whole loop: find good examples, teach them. Repeat with a few more thumbnails from different videos and angles — about 10 varied examples give good results, and 5 is a workable minimum.

You can also manage everything from the Trained Tags window (open it from the start page's Maintenance section): create tags, add example images from files or the clipboard (useful when your best examples are photos rather than catalog thumbnails), remove bad examples, and disable a tag without deleting it. For a tag that applies to a detected object, a supplied image goes through object detection and the example is cropped to the object; when nothing is detected the whole picture is taught instead, since an image you picked yourself is usually a portrait of the subject anyway. The status line says how many were taught that way.

Whole frame or inside a detection¶

When you create a tag you can optionally set what it applies to:

• Leave it empty and the tag matches against the whole frame. Use this for scenes, sets, locations, styles — my studio, drone shot. Whole-frame tags are independent: a frame can match several of them at once.
• Pick a detector label (like dog or car) and the tag matches inside detections of that object instead of the whole frame. Use this to tell apart kinds of the same thing — teach labrador and beagle both applying to dog, and each detected dog is compared against both, with only the best match winning. This needs object detection's model, since it supplies the detections to look inside.

What a tag applies to is fixed when it is created; for a different subject, create a new tag.

When you teach a refining tag from a thumbnail, the example is automatically cropped to the detected object. A thumbnail where the object was not detected, or is too small to use, teaches nothing — the notification tells you how many were skipped and why, so you know the teaching landed. The teach menu is honest about this up front: refining tags whose object has not been detected in the selected thumbnails are greyed out, and the new-tag dialog only suggests objects that were actually detected there. If everything is greyed out, run object detection on those videos first.

Test before running¶

In the Trained Tags window, select some videos or thumbnails in the main window and click Test. You see exactly what a run would tag, with a confidence value for each match — and nothing is written to the catalog. This is the quick loop for checking whether a tag is ready: test, add a few more examples where it misses, test again.

Run it on your catalog¶

• As a batch: the AI Scenes tool has a Trained Tags checkbox, alongside scene classification, object detection and image tagging. Run it over selected, filtered or all videos.
• While indexing: tick Trained Tags under Preferences > AI Classification and new videos are matched as they are added. The setting carries over to catalogs shared through the server, so a server indexes with your trained tags too.

Tags that apply to a detector label need object detection enabled in the same run — the detections are what they look inside, so without object detection only your whole-frame tags can match. This holds both locally and on the server.

Correct a wrong match¶

When a trained tag lands on the wrong frame, the correction is itself teaching: right-click the purple keyword on the scene and choose Remove and teach as, then pick the tag it should have been (or a new one). The wrong keyword is removed and the frame becomes an example of the right tag — so the mistake makes the tags better.

Tips¶

• Vary the examples: different videos, angles and lighting beat ten near-identical frames.
• Matching reads your captured thumbnails, so what was never captured cannot match. The default capture width is 320 — picking the 640 preset (Preferences > Video Indexer, Resize video-frame to be) gives the AI features noticeably more detail to work with. It matters most when the subject is small in the frame, because a tag that applies to a detected object sees only that detection's crop of the thumbnail.
• Each tag has its own threshold in the Trained Tags window. The default is deliberately conservative; if a tag misses real occurrences, first add examples, then try lowering its match threshold a little and re-test.
• Deleting a trained tag cleans up after it: its keyword, its matches and its taught examples are removed from the catalog.

Troubleshooting¶

The Trained Tags checkbox is greyed out -- either the model is not downloaded yet (get it from the AI Models dialog) or no tag has examples yet. Teach a few examples first.

A tag never matches -- add more varied examples and use Test on current selection to watch the confidence values. If they sit just under the threshold, lower that tag's match threshold slightly. For a tag that applies to a detector label, also check that object detection finds the object at all — no detection, nothing to match inside — and that the subject is not tiny in the thumbnail.

A tag works in Test but not when indexing -- the Test button runs its own object detection, but the indexing and batch passes only detect objects when Object detection is ticked alongside Trained Tags. If a tag that applies to a detector label matches in Test and never during a run, enable object detection for that run — on the server too.

Tags are listed as disabled because of a different model -- those examples were taught with an earlier trained-tags model and cannot be matched by the current one. Delete the affected tags and teach them again; this only happens when the underlying model changes between versions.