tlpss/gitw-256x256
GITW (Glasses-in-the-Wild), re-split for 2D-keypoints-benchmark A repackaging of the Glasses-in-the-Wild dataset (DOI 10.5281/zenodo.17288503) for use in 2D-keypoints-benchmark. The images and keypoint annotations are unchanged; only the splits and some metadata differ. 1000 crowdsourced 256x256 images of transparent and partially filled drinking glasses, from 11 participants over 60 scenes and 93 unique glass types. Each image shows one annotated glass, with 5 keypoints:… See the full description on the dataset page: https://huggingface.co/datasets/tlpss/gitw-256x256.
GITW (Glasses-in-the-Wild), re-split for 2D-keypoints-benchmark
A repackaging of the Glasses-in-the-Wild dataset (DOI 10.5281/zenodo.17288503) for use in 2D-keypoints-benchmark. The images and keypoint annotations are unchanged; only the splits and some metadata differ.
1000 crowdsourced 256x256 images of transparent and partially filled drinking glasses, from 11 participants over 60 scenes and 93 unique glass types. Each image shows one annotated glass, with 5 keypoints: bottom_front, top_left, top_right, top_front, fluid_level.
What was changed
- Splits. The published validation split is used here as the test split, and a new validation split (10%, seed 2024) is carved out of the published train split, so that model selection never touches the reported test set.
- Bounding boxes added. The published annotations contain keypoints only. An axis-aligned box around the labeled keypoints (padded by 10%, clipped to the image) was added to each annotation, because the YOLO conversion requires one. These boxes span the keypoints, not the full glass silhouette.
The upstream annotations.xml (a CVAT export) is not used: it refers to the original pre-anonymisation file names and shares no file name with the shipped images. The annotations.json inside each images/ folder is the authoritative source and was used instead.
Notes
Images are crops around a single glass, taken from photos that often contain several. Other glasses are frequently visible inside a crop and are not annotated, so this data should not be used to train or evaluate multi-instance detection.
License and attribution
CC-BY-4.0, inherited from the original dataset. Please cite the original authors: Louis Adriaens, Thomas Lips, Mathieu De Coster, Andreas Verleysen and Francis wyffels (Ghent University), Glasses-in-the-Wild Dataset, 2025, DOI 10.5281/zenodo.17288503.