datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
SpatialForge
SpatialForge-10M
SpatialForge: Bootstrapping 3D-Aware Spatial Reasoning from Open-World 2D Images
📑 Paper
Zishan Liu, Ruoxi Zang, Yanglin Zhang, Wei Liu, Yin Zhang, Jian Yao, Jiayin Zheng, Zhengzhe Liu
Lingnan University · XPENG Robotics
📦 SpatialForge-10M
A large-scale vision-language dataset designed for 3D-aware spatial perception and reasoning from open-world 2D images.
SpatialForge-10M contains over 10 million QA pairs generated from 2.8 million curated… See the full description on the dataset page: https://huggingface.co/datasets/shana643/SpatialForge.SpatialLadder-26k
SpatialLadder-26k
This repository contains the SpatialLadder-26k, introduced in SpatialLadder: Progressive Training for Spatial Reasoning in Vision-Language Models.
Dataset Description
SpatialLadder-26k is a large-scale training dataset designed to develop spatial perception and reasoning capabilities in Vision-Language Models (VLMs). It contains 26,610 multimodal samples spanning four complementary task categories, forming a… See the full description on the dataset page: https://huggingface.co/datasets/hongxingli/SpatialLadder-26k.Spatial-SSRL-81k
Spatial-SSRL-81k
📖Paper| 🏠Github |🤗Spatial-SSRL-7B Model |
🤗Spatial-SSRL-3B Model | 🤗Spatial-SSRL-Qwen3VL-4B Model |
🤗Spatial-SSRL-81k Dataset | 📰Daily Paper
Spatial-SSRL-81k is a training dataset for enhancing spatial understanding in large vision-language models. It contains 81,053 samples of five pretext tasks for self-supervised learning, offering simple, intrinsic supervision that scales RLVR efficiently.
📢 News
🚀 [2026/04/05] We have released… See the full description on the dataset page: https://huggingface.co/datasets/cvis-tmu/Spatial-SSRL-81k.this-that-spatial-bench
spatial-decisions
7,305 multiple-choice decision questions over 6,525 distinct simulated states, in
15 families and two environments. Every answer is computed from the simulator, not
annotated by a person and not taken from a model. That is the point of the set: on a question whose
answer is derived from the rules of the environment, a disagreement is a mistake, and there is
nothing to argue about.
The set was built to replace a much narrower public artefact: a recording of 68… See the full description on the dataset page: https://huggingface.co/datasets/limberc/this-that-spatial-bench.