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README.md
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| 1 |
+
---
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| 2 |
+
license: cc-by-4.0
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| 3 |
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tags:
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| 4 |
+
- coco
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| 5 |
+
- computer-vision
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| 6 |
+
- object-detection
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| 7 |
+
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| 8 |
+
configs:
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| 9 |
+
- config_name: rects
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| 10 |
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default: true
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+
data_files:
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| 12 |
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- split: train
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| 13 |
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path: "data/rects/train2017/*.parquet"
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| 14 |
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- split: validation
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path: "data/rects/val2017/*.parquet"
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| 16 |
+
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- config_name: images
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| 18 |
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data_files:
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| 19 |
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- split: train
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path: "data/images.parquet"
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| 21 |
+
---
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| 22 |
+
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| 23 |
+
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| 24 |
+
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| 25 |
+
# COCO-Spatial-Join-1B
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| 26 |
+
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| 27 |
+
## Abstract
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| 28 |
+
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| 29 |
+
**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.
|
| 30 |
+
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| 31 |
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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.
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| 32 |
+
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| 33 |
+
Dataset construction details and the reference builder are available at:
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| 34 |
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https://github.com/DANNHIROAKI/COCO-Spatial-Join-1B-Builder
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| 35 |
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| 36 |
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---
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| 37 |
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| 38 |
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## Contents and Scale
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| 39 |
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| 40 |
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### Upstream sources
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| 41 |
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- **Images and annotations:** MS COCO 2017 `train2017` and `val2017`
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| 43 |
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- `train2017`: 118,287 images
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| 44 |
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- `val2017`: 5,000 images
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| 45 |
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- **Ground truth (GT):** all COCO detection annotations, including `iscrowd ∈ {0,1}` (no filtering)
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| 46 |
+
- **Proposals:** RPN proposals from Detectron2 Faster R-CNN (ResNet-50-FPN) for each image
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| 47 |
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|
| 48 |
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### Dataset cardinalities
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| 49 |
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| 50 |
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Let $N$ be the number of images:
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| 51 |
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- $N = 118{,}287 + 5{,}000 = 123{,}287$
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| 52 |
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- **Proposals:** exactly 10,000 proposals per image
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| 53 |
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Total proposals: $N \times 10{,}000 = 1{,}232{,}870{,}000$
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| 54 |
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- **GT:** equals the number of COCO detection annotations (including crowd)
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| 55 |
+
|
| 56 |
+
---
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| 57 |
+
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| 58 |
+
## Geometric Model
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| 59 |
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| 60 |
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### Half-open 3D box representation
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| 61 |
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| 62 |
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Each object is represented as a 3D axis-aligned half-open box:
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| 63 |
+
$$
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| 64 |
+
b = [x_{\min}, x_{\max}) \times [y_{\min}, y_{\max}) \times [z_{\min}, z_{\max})
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| 65 |
+
$$
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| 66 |
+
with strict validity constraints:
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| 67 |
+
- $x_{\min} < x_{\max}$, $y_{\min} < y_{\max}$, $z_{\min} < z_{\max}$
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| 68 |
+
- $x,y$ are expressed in the **original COCO image pixel coordinate system**
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| 69 |
+
- Coordinate precision: `min_x/min_y/max_x/max_y` are stored as **float32**
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| 70 |
+
|
| 71 |
+
### z-dimension slicing
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| 72 |
+
|
| 73 |
+
Each COCO image is assigned a global integer index $z\_{\text{idx}} \in \{0,\dots,N-1\}$.
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| 74 |
+
For every object in that image:
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| 75 |
+
- $z_{\min} = z\_{\text{idx}}$
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| 76 |
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- $z_{\max} = z\_{\text{idx}} + 1$
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| 77 |
+
|
| 78 |
+
Therefore, each image occupies a disjoint unit-thickness z-slab $[z\_{\text{idx}}, z\_{\text{idx}} + 1)$.
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| 79 |
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|
| 80 |
+
### Intersection predicate (strict overlap, half-open semantics)
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| 81 |
+
|
| 82 |
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Two 3D boxes $b$ and $b'$ **intersect** if and only if they strictly overlap on all dimensions:
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| 83 |
+
$$
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| 84 |
+
\max(L_i(b), L_i(b')) < \min(R_i(b), R_i(b')),\quad i \in \{x,y,z\}.
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| 85 |
+
$$
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| 86 |
+
This strict inequality implies that **boundary-touching boxes do not intersect**.
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| 87 |
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|
| 88 |
+
---
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| 89 |
+
|
| 90 |
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## Benchmark Tasks
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| 91 |
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|
| 92 |
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All tasks use the same join predicate: **3D `intersects`** (strict overlap under half-open semantics).
|
| 93 |
+
|
| 94 |
+
### Task A: GT × PROPOSAL
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| 95 |
+
|
| 96 |
+
- Relation $R$: all objects with `type = GT`
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- Relation $S$: all objects with `type = PROPOSAL`
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- Output: ordered pairs $(r,s)$ where $r \in R$, $s \in S$, and `intersects(r, s)` holds
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| 99 |
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| 100 |
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### Task B: PROPOSAL × PROPOSAL (rank split)
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| 102 |
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Within each image, the 10,000 proposals are partitioned by `rank`:
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- Relation $R$: `type = PROPOSAL` and `rank ∈ [1, 5000]`
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| 105 |
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- Relation $S$: `type = PROPOSAL` and `rank ∈ [5001, 10000]`
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| 106 |
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- Output: ordered pairs $(r,s)$ where $r \in R$, $s \in S$, and `intersects(r, s)` holds
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| 107 |
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| 108 |
+
---
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| 109 |
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## Deterministic Construction Protocol
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| 111 |
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| 112 |
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### Image ordering and global $z\_{\text{idx}}$
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| 113 |
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| 114 |
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All images from COCO 2017 are merged and globally ordered by:
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| 115 |
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1. `split` (all `train2017` images first, then all `val2017` images)
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| 116 |
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2. `coco_image_id` in ascending order
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| 117 |
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| 118 |
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The $i$-th image in this ordering is assigned $z\_{\text{idx}} = i$ (0-based).
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| 119 |
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| 120 |
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### Model and proposal generation
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| 122 |
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- Framework: Detectron2 (PyTorch)
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| 123 |
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- Model: Faster R-CNN + ResNet-50-FPN
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| 124 |
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- Proposals are taken from the **RPN proposal generator** (not ROI head detections)
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| 125 |
+
- Test-time RPN configuration:
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| 126 |
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- `PRE_NMS_TOPK_TEST = 20000`
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| 127 |
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- `POST_NMS_TOPK_TEST = 20000`
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| 128 |
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- `NMS_THRESH = 1.0` (configured to avoid suppressing candidates)
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| 129 |
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| 130 |
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Each image must produce a candidate pool of at least 10,000 proposals. The final set is obtained by sorting and truncation as described below.
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| 132 |
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### Inference preprocessing and coordinate mapping
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| 133 |
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| 134 |
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Input preprocessing follows the standard Detectron2 inference pipeline:
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| 135 |
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- `ResizeShortestEdge`: shortest edge to 800, longest edge capped at 1333, aspect ratio preserved
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| 136 |
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- Standard Detectron2 normalization and tensor layout
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| 137 |
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| 138 |
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RPN proposals are produced in the resized coordinate system. Before writing, each proposal is:
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| 139 |
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1. Mapped back to the **original image coordinate system**
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| 140 |
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2. Clipped to $[0, \text{width}] \times [0, \text{height}]$
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3. Normalized by the coordinate validity rules below
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| 142 |
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| 143 |
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### Proposal score and deterministic ranking
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| 144 |
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| 145 |
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RPN outputs an `objectness_logit` for each candidate. The stored `score` is:
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| 146 |
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$$
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| 147 |
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\text{score} = \sigma(\text{objectness\_logit}) = \frac{1}{1+\exp(-\text{logit})}
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| 148 |
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$$
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| 149 |
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Scores are stored as **float32** in $[0,1]$.
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| 150 |
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| 151 |
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Proposals are first normalized (including float32 conversion and degeneracy handling), then sorted by the following lexicographic key:
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| 152 |
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1. `score` (descending)
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| 153 |
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2. `(min_x, min_y, max_x, max_y)` (ascending; float32 tuple)
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3. `cand_idx` (ascending; 0-based index in the candidate list, used solely to enforce a strict total order)
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The first 10,000 proposals after sorting are retained and assigned:
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- `rank = 1..10000` (in sorted order)
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- `type = PROPOSAL`
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| 159 |
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| 160 |
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---
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| 161 |
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## Coordinate Normalization Guarantees
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| 163 |
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| 164 |
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Every GT or PROPOSAL box written to disk satisfies:
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| 165 |
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| 166 |
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- $0 \le \text{min\_x} < \text{max\_x} \le \text{width}$
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| 167 |
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- $0 \le \text{min\_y} < \text{max\_y} \le \text{height}$
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| 168 |
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- `z_max = z_min + 1`
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- All of `min_x/min_y/max_x/max_y/score` are **finite float32** (no NaN or Inf)
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| 170 |
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| 171 |
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Non-finite handling is applied before clipping:
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- `NaN → 0.0`
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| 173 |
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- `+Inf → upper bound` (`width` for x, `height` for y)
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- `-Inf → 0.0`
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Degenerate boxes (`max ≤ min`) are deterministically repaired in float32:
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- If `max_x ≤ min_x`, set `max_x = nextafter(min_x, +∞)`.
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| 178 |
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If `max_x > width`, set `max_x = width` and `min_x = nextafter(width, -∞)`.
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| 179 |
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- The same rule is applied to the y-dimension using `height`.
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| 181 |
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---
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## File Organization
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The dataset is distributed as **Parquet** shards with auxiliary metadata:
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data/images.parquet
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version https://git-lfs.github.com/spec/v1
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data/rects/train2017/shard-000000.parquet
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version https://git-lfs.github.com/spec/v1
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data/rects/train2017/shard-002048.parquet
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version https://git-lfs.github.com/spec/v1
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data/rects/train2017/shard-003072.parquet
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version https://git-lfs.github.com/spec/v1
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data/rects/train2017/shard-006144.parquet
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|
| 1 |
+
{
|
| 2 |
+
"coco_train_json": "instances_train2017.json",
|
| 3 |
+
"coco_val_json": "instances_val2017.json",
|
| 4 |
+
"coco_train_json_sha256": "610fce4944abdeb15354cc765333805529359d12d88f2f711393ca586901d01d",
|
| 5 |
+
"coco_val_json_sha256": "e8c7f7908f1d7278341fae127d0da654f102f11bd7b21d8aeefa635b8c810b6f",
|
| 6 |
+
"builder_version": "coco-spatial-join-1b-builder-1.0.0",
|
| 7 |
+
"python_version": "3.10.19",
|
| 8 |
+
"numpy_version": "2.2.6",
|
| 9 |
+
"pyarrow_version": "16.1.0",
|
| 10 |
+
"detectron2_version": {
|
| 11 |
+
"version": "0.6",
|
| 12 |
+
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