RGB scenes paired with class or instance masks and documented class IDs and bounding boxes — for scoring semantic and instance segmentation against a known ground truth.
An apple on a busy, colour-matched background — the hard input for a background remover or matting model. Score a prediction against the paired ground-truth cut-out and alpha mask.
The ground-truth transparent cut-out of the apple used in the cluttered-background hard case — the ideal output of a perfect background remover.
The binary ground-truth alpha mask (white = subject, black = background) for the cluttered-background apple — compute IoU or boundary F-score of a predicted mask against this file.
A robot mascot on a cluttered background — the input for a salient-object-detection or image-matting model. Ground-truth binary mask and trimap ship alongside it.
The binary ground-truth segmentation mask for the salient robot (white = foreground). Compute IoU / F-score of a predicted mask against this file.
A three-level matting trimap (black = background, grey = unknown edge band, white = foreground) for the salient robot — the standard auxiliary input for alpha-matting methods.
A four-object fruit still life (apple, orange, banana, grapes) on white — the easy input for instance segmentation and object detection, paired with an indexed instance mask.
The same four-object fruit still life on a cluttered background — the hard input for instance segmentation and object detection. The indexed instance mask gives per-object ground truth.
An indexed (palette) instance mask for the fruit still life: background = 0 and each fruit painted with its own instance id. Per-object class and normalised bounding box are listed in the specs.
A cat 'portrait' on a busy background — a portrait-style subject-detection and matting input, paired with its ground-truth mask.
The binary ground-truth mask for the portrait subject (white = foreground) — the reference for scoring a portrait cut-out or background blur.
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