Features#

The following tasks are supported:

Classification - binary classification of the presence of glasses and their types.
Detection - binary detection of worn/standalone glasses and eye area.
Segmentation - binary segmentation of glasses and their parts.

Each task has multiple kinds (task categories) and model sizes (architectures with pre-trained weights).

Classification#

Classification Kinds#
Kind	Description	Examples
`anyglasses`	Identifies any kind of glasses, googles, or spectacles.
`anyglasses`	Identifies any kind of glasses, googles, or spectacles.	Positive	Positive	Negative
`eyeglasses`	Identifies only transparent glasses (here referred as eyeglasses)
`eyeglasses`		Positive	Negative	Negative
`sunglasses`	Identifies only opaque and semi-transparent glasses (here referred as sunglasses)
`sunglasses`		Negative	Positive	Negative
`shadows`	Identifies cast shadows (only shadows of (any) glasses frames)
`shadows`		Positive	Negative	Negative

Check classifier performances

Detection Kinds#
Kind	Description	Examples
`eyes`	Detects only the eye region, no glasses.
`solo`	Detects any glasses in the wild, i.e., standalone glasses that are placed somewhere.
`worn`	Detects any glasses worn by people but can also detect non-worn glasses.

Check detector performances

Segmentation Kinds#
Kind	Description	Examples
`frames`	Segments frames (including legs) of any glasses
`full`	Segments full glasses, i.e., lenses and the whole frame
`legs`	Segments only frame legs of standalone glasses
`lenses`	Segments lenses of any glasses (both transparent and opaque).
`shadows`	Segments cast shadows on the skin by the glasses frames only (does not consider opaque lenses).
`smart`	Segments visible glasses parts: like `full` but does not segment lenses if they are transparent.

Check segmenter performances