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rect_id
int64
z_min
int32
z_max
int32
min_x
float32
min_y
float32
max_x
float32
max_y
float32
type
int8
rank
int16
score
float32
category_id
int16
iscrowd
int8
coco_ann_id
int64
coco_image_id
int32
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Inroduction

COCO-Spatial-Join-1B is a large-scale, deterministic spatial join benchmark constructed from the MS COCO 2017 detection annotations and RPN proposals produced by Detectron2 Faster R-CNN (ResNet-50-FPN). The benchmark is designed to stress-test spatial join systems under high-overlap workloads while providing an unambiguous geometric semantics.

All objects (ground-truth and proposals) are represented as axis-aligned half-open 3D boxes in a shared coordinate system. A global spatial join can be evaluated over the complete corpus, while the z-dimension construction yields a clean per-image decomposition when desired.

Dataset construction details and the reference builder are available at: https://github.com/DANNHIROAKI/COCO-Spatial-Join-1B-Builder

Example

Installation

pip install -U huggingface_hub

Download the Entire Dataset

hf download DannHiroaki/COCO-Spatial-Join-1.23B \
  --repo-type dataset \
  --local-dir ./COCO-Spatial-Join-1.23B

Download specific shards from train2017

hf download DannHiroaki/COCO-Spatial-Join-1.23B \
  --repo-type dataset \
  data/rects/train2017/shard-000000.parquet \
  data/rects/train2017/shard-001024.parquet \
  --local-dir ./COCO-Spatial-Join-rects-sample
 

Dry Run (Check size before downloading)

hf download DannHiroaki/COCO-Spatial-Join-1.23B --repo-type dataset --include "data/rects/val2017/*.parquet" --dry-run
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