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README.md ADDED
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+ ---
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+ license: cc-by-4.0
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+ tags:
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+ - coco
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+ - computer-vision
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+ - object-detection
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+
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+ configs:
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+ - config_name: rects
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+ default: true
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+ data_files:
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+ - split: train
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+ path: "data/rects/train2017/*.parquet"
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+ - split: validation
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+ path: "data/rects/val2017/*.parquet"
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+
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+ - config_name: images
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+ data_files:
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+ - split: train
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+ path: "data/images.parquet"
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+ ---
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+
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+
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+
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+ # COCO-Spatial-Join-1B
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+
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+ ## Abstract
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+
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+ **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.
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+
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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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+
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+ Dataset construction details and the reference builder are available at:
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+ https://github.com/DANNHIROAKI/COCO-Spatial-Join-1B-Builder
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+
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+ ---
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+
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+ ## Contents and Scale
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+
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+ ### Upstream sources
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+
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+ - **Images and annotations:** MS COCO 2017 `train2017` and `val2017`
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+ - `train2017`: 118,287 images
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+ - `val2017`: 5,000 images
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+ - **Ground truth (GT):** all COCO detection annotations, including `iscrowd ∈ {0,1}` (no filtering)
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+ - **Proposals:** RPN proposals from Detectron2 Faster R-CNN (ResNet-50-FPN) for each image
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+
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+ ### Dataset cardinalities
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+
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+ Let $N$ be the number of images:
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+ - $N = 118{,}287 + 5{,}000 = 123{,}287$
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+ - **Proposals:** exactly 10,000 proposals per image
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+ Total proposals: $N \times 10{,}000 = 1{,}232{,}870{,}000$
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+ - **GT:** equals the number of COCO detection annotations (including crowd)
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+
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+ ---
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+
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+ ## Geometric Model
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+
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+ ### Half-open 3D box representation
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+
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+ Each object is represented as a 3D axis-aligned half-open box:
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+ $$
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+ b = [x_{\min}, x_{\max}) \times [y_{\min}, y_{\max}) \times [z_{\min}, z_{\max})
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+ $$
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+ with strict validity constraints:
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+ - $x_{\min} < x_{\max}$, $y_{\min} < y_{\max}$, $z_{\min} < z_{\max}$
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+ - $x,y$ are expressed in the **original COCO image pixel coordinate system**
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+ - Coordinate precision: `min_x/min_y/max_x/max_y` are stored as **float32**
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+
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+ ### z-dimension slicing
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+
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+ Each COCO image is assigned a global integer index $z\_{\text{idx}} \in \{0,\dots,N-1\}$.
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+ For every object in that image:
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+ - $z_{\min} = z\_{\text{idx}}$
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+ - $z_{\max} = z\_{\text{idx}} + 1$
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+
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+ Therefore, each image occupies a disjoint unit-thickness z-slab $[z\_{\text{idx}}, z\_{\text{idx}} + 1)$.
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+
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+ ### Intersection predicate (strict overlap, half-open semantics)
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+
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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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+ $$
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+ \max(L_i(b), L_i(b')) < \min(R_i(b), R_i(b')),\quad i \in \{x,y,z\}.
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+ $$
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+ This strict inequality implies that **boundary-touching boxes do not intersect**.
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+
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+ ---
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+
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+ ## Benchmark Tasks
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+
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+ All tasks use the same join predicate: **3D `intersects`** (strict overlap under half-open semantics).
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+
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+ ### Task A: GT × PROPOSAL
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+
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+ - 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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+
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+ ### Task B: PROPOSAL × PROPOSAL (rank split)
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+
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+ Within each image, the 10,000 proposals are partitioned by `rank`:
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+
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+ - Relation $R$: `type = PROPOSAL` and `rank ∈ [1, 5000]`
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+ - Relation $S$: `type = PROPOSAL` and `rank ∈ [5001, 10000]`
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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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+
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+ ---
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+
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+ ## Deterministic Construction Protocol
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+
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+ ### Image ordering and global $z\_{\text{idx}}$
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+
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+ All images from COCO 2017 are merged and globally ordered by:
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+ 1. `split` (all `train2017` images first, then all `val2017` images)
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+ 2. `coco_image_id` in ascending order
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+
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+ The $i$-th image in this ordering is assigned $z\_{\text{idx}} = i$ (0-based).
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+
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+ ### Model and proposal generation
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+
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+ - Framework: Detectron2 (PyTorch)
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+ - Model: Faster R-CNN + ResNet-50-FPN
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+ - Proposals are taken from the **RPN proposal generator** (not ROI head detections)
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+ - Test-time RPN configuration:
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+ - `PRE_NMS_TOPK_TEST = 20000`
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+ - `POST_NMS_TOPK_TEST = 20000`
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+ - `NMS_THRESH = 1.0` (configured to avoid suppressing candidates)
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+
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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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+
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+ ### Inference preprocessing and coordinate mapping
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+
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+ Input preprocessing follows the standard Detectron2 inference pipeline:
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+ - `ResizeShortestEdge`: shortest edge to 800, longest edge capped at 1333, aspect ratio preserved
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+ - Standard Detectron2 normalization and tensor layout
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+
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+ RPN proposals are produced in the resized coordinate system. Before writing, each proposal is:
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+ 1. Mapped back to the **original image coordinate system**
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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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+
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+ ### Proposal score and deterministic ranking
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+
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+ RPN outputs an `objectness_logit` for each candidate. The stored `score` is:
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+ $$
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+ \text{score} = \sigma(\text{objectness\_logit}) = \frac{1}{1+\exp(-\text{logit})}
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+ $$
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+ Scores are stored as **float32** in $[0,1]$.
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+
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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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+ 1. `score` (descending)
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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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+
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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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+
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+ ---
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+
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+ ## Coordinate Normalization Guarantees
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+
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+ Every GT or PROPOSAL box written to disk satisfies:
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+
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+ - $0 \le \text{min\_x} < \text{max\_x} \le \text{width}$
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+ - $0 \le \text{min\_y} < \text{max\_y} \le \text{height}$
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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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+
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+ Non-finite handling is applied before clipping:
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+ - `NaN → 0.0`
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+ - `+Inf → upper bound` (`width` for x, `height` for y)
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+ - `-Inf → 0.0`
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+
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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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+ If `max_x > width`, set `max_x = width` and `min_x = nextafter(width, -∞)`.
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+ - The same rule is applied to the y-dimension using `height`.
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+
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+ ---
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+
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+ ## File Organization
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+
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+ The dataset is distributed as **Parquet** shards with auxiliary metadata:
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+
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