Annotating your images

For the best results, follow these guidelines. You may also want to review the documentation on improving your model.

Label every object in the image: It is important that you label all objects in each image before verifying. If you don't label a car for example, the model will be taught that part of the image is not a car and it will start to get confused. You can exclude labels that you don't want detected on any of your cameras.

Make tight bounding boxes: Tighter bounding boxes improve the recognition and ensure that accurate bounding boxes are predicted at runtime.

Label the full object even when occluded: If you have a person standing behind a car, label the full person even though a portion of their body may be hidden behind the car. This helps predict accurate bounding boxes and improves zone accuracy and filters at runtime. If an object is partly out of frame, label it only when a person would reasonably be able to recognize the object from the visible parts.

Label objects hard to identify as difficult: When objects are truly difficult to make out, such as a car barely visible through a bush, or a dog that is hard to distinguish from the background at night, flag it as 'difficult'. This is not used in the model training as of now, but will in the future.

Delivery logos such as amazon, ups, and fedex should label the logo: For a Fedex truck, label the truck as a car and make a different bounding box just for the Fedex logo. If there are multiple logos, label each of them.

AI suggested labels

If you have an active Frigate+ subscription, new uploads will be scanned for the objects configured for your camera and you will see suggested labels as light blue boxes when annotating in Frigate+. These suggestions are processed via a queue and typically complete within a minute after uploading, but processing times can be longer.

Suggestions are converted to labels when saving, so you should remove any errant suggestions. There is already some logic designed to avoid duplicate labels, but you may still occasionally see some duplicate suggestions. You should keep the most accurate bounding box and delete any duplicates so that you have just one label per object remaining.

Reviewing annotations with Patches

The Patches tab on the Images page shows a cropped view of every annotation for a label in a grid. Scanning the crops makes it much easier to spot a mislabeled object than paging through full images one at a time. Pick a label to get started, and optionally narrow the results to a single camera. Clicking a patch opens that image in the editor so you can fix the annotation.

Patches only include annotations from images you have already verified.

False positive labels

False positives will be shown with a red box and the label will have a strike through. These can't be adjusted, but they can be deleted if you accidentally submit a true positive as a false positive from Frigate.

Misidentified objects should have a correct label added. For example, if a person was mistakenly detected as a cat, you should submit it as a false positive in Frigate and add a label for the person. The boxes will overlap.

Shortcuts for a faster workflow

Shortcut Key	Description
?	Show all keyboard shortcuts
w	Add box
d	Toggle difficult
s	Switch to the next label
Shift + s	Switch to the previous label
tab	Select next largest box
del	Delete current box
esc	Deselect/Cancel
← ↑ → ↓	Move box
Shift + ← ↑ → ↓	Resize box
scrollwheel	Zoom in/out
f	Hide/show all but current box
spacebar	Verify and save

Deleting images

In most cases, you should keep your existing images. Removing training data usually makes accuracy worse, not better, so you shouldn't delete images just because you think you made some annotation mistakes. Deleting images makes sense when you move to a new location, or significantly upgrade or reposition your cameras.

• To delete all images for a camera, use the Delete Images button for that camera on the Cameras page.
• To delete all images in your account, use Delete All Images on the Settings page.

Deletions run in the background, and their progress is shown in the bulk operations panel. Deleted images cannot be recovered and are no longer used to train your models.