labeling
Datasets
All datasets matching “labeling”INRIA-Aerial-Image-Labeling
Inria Aerial Image Labeling Dataset
Description
The Inria Aerial Image Labeling Dataset is a building semantic segmentation dataset proposed in "Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark," Maggiori et al.. It consists of 360 high-resolution (0.3m) RGB images, each with a size of 5000x5000 pixels. These images are extracted from various international GIS services, such as the USGS National Map.
Project page:… See the full description on the dataset page: https://huggingface.co/datasets/blanchon/INRIA-Aerial-Image-Labeling.pick_and_place_failure_labelingThis dataset was created using LeRobot.
Pick and Place Failure Labeling
This dataset is a failure-only, frame-labeled subset derived from KGB0/pick_and_place_annotation_image.
It keeps complete episodes that contain at least one failure label and adds frame-level failure annotation columns for failure detection and recovery experiments.
Generated at: 2026-06-16T02:22:18+00:00Target repo: KGB0/pick_and_place_failure_labeling
Dataset Structure
meta/info.json:
{… See the full description on the dataset page: https://huggingface.co/datasets/KGB0/pick_and_place_failure_labeling.metagaming-labeling-v2
camgeodesic/metagaming-labeling-v2
Auto-generated by dataset-builder.
Each config below is a separate dataset produced from a versioned YAML build
config. Load with:
from datasets import load_dataset
ds = load_dataset("camgeodesic/metagaming-labeling-v2", "<config_name>", revision="<commit-sha>")
Pin revision= to the specific commit SHA you want; without it, you get the
current HEAD of the dataset repo, which may change when the builder re-pushes.
Requires datasets v4+. These… See the full description on the dataset page: https://huggingface.co/datasets/camgeodesic/metagaming-labeling-v2.INRIA-Aerial-Image-Labeling
Inria Aerial Image Labeling Dataset
Description
The Inria Aerial Image Labeling Dataset is a building semantic segmentation dataset proposed in "Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark," Maggiori et al.. It consists of 360 high-resolution (0.3m) RGB images, each with a size of 5000x5000 pixels. These images are extracted from various international GIS services, such as the USGS National Map.
Project page:… See the full description on the dataset page: https://huggingface.co/datasets/usmanhf/INRIA-Aerial-Image-Labeling.gta5-cityscapes-labelingScaleEdit-labeling-25k
ScaleEdit 25k Fine-Grained Editing Masks
This release contains 25,664 samples that passed both quality and fine-grained editability filtering and reached final mask labeling. It joins source and edited images, instructions, filtering decisions and audits, grounding, and masks. The original ScaleEdit-12M dataset declares CC BY-NC-SA 4.0; this derivative is shared under the same license. A filtered candidate subset was sourced from QingyuShi/scaleedit-filtered-6m.… See the full description on the dataset page: https://huggingface.co/datasets/TTangenty/ScaleEdit-labeling-25k.