datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
rampnet-crop-model-dataset-round2RampNet is a two-stage pipeline that addresses the scarcity of curb ramp detection datasets by using government location data to automatically generate over 210,000 annotated Google Street View panoramas. This new dataset is then used to train a state-of-the-art curb ramp detection model that significantly outperforms previous efforts. In this repo, we provide "the tiny set of manually labeled crops" that we refer to in both RampNet's GitHub repository and the paper.
Renamed 2026-08-05: this… See the full description on the dataset page: https://huggingface.co/datasets/projectsidewalk/rampnet-crop-model-dataset-round2.rampnet-crop-model-dataset-round1
RampNet Crop-Model Dataset — Round 1 (Project Sidewalk crops)
The training data behind round 1 of the RampNet Stage 1 crop model — 27,704 crops,
13.37 GB — from RampNet: A Two-Stage Pipeline for Bootstrapping Curb Ramp Detection in
Streetscape Images from Open Government Metadata (O'Meara et al., ICCV'25 CV4A11y workshop,
arXiv:2508.09415).
The crop model is what turns a government curb ramp GPS coordinate into a pixel keypoint on a
panorama; every label in
rampnet-dataset was… See the full description on the dataset page: https://huggingface.co/datasets/projectsidewalk/rampnet-crop-model-dataset-round1.bl-crop-tighten-v1
bl-crop-tighten-v1
Training data for crop tightening on the British Library Book Images collection: 7,565 ABBYY picture-block crops (train 6,050 / validation 757 / test 758) with instance boxes and segmentation masks. The splits are book-safe — no book appears in more than one split (4,484 books total).
The labels are weak labels, not human annotations: tiiuae/Falcon-Perception-0.6B ran open-vocabulary segmentation over 8,400 stratified crops (embellishments, plates, medium… See the full description on the dataset page: https://huggingface.co/datasets/small-models-for-glam/bl-crop-tighten-v1.