--- annotations_creators: [] language: en size_categories: - 1K This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 6736 samples. ## Installation If you haven't already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo from fiftyone.utils.huggingface import load_from_hub # Load the dataset # Note: other available arguments include 'max_samples', etc dataset = load_from_hub("Voxel51/KubriCount") # Launch the App session = fo.launch_app(dataset) ``` license: apache-2.0 --- # Dataset Card for KubriCount (subset) ![image/png](kubricount.gif) KubriCount is a large-scale synthetic benchmark for **multi-grained visual counting**, introduced in the paper [*Count Anything at Any Granularity*](https://arxiv.org/abs/2605.10887) (Liu, Wu & Xie, SJTU 2026). It reframes open-world counting as a prompt-following problem across five explicit semantic granularity levels, supported by the most comprehensively annotated counting dataset published to date. This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 6736 samples. ## Installation If you haven't already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo from fiftyone.utils.huggingface import load_from_hub # Load the dataset # Note: other available arguments include 'max_samples', etc dataset = load_from_hub("Voxel51/KubriCount") # Launch the App session = fo.launch_app(dataset) ``` ### Dataset Description Most counting datasets treat "what to count" as a single category-level matching problem. KubriCount exposes this limitation by requiring models to follow fine-grained prompts that specify *which* semantic level the user intends — from counting a specific object identity all the way up to an abstract concept — while excluding controlled distractors that differ by exactly one semantic factor. Each scene is a 1024×1024 synthetic image produced by a four-stage automatic pipeline: controllable 3D rendering via [Kubric](https://github.com/google-research/kubric) + Blender, mask-conditioned image editing (Nano-Banana-Pro) to reduce the sim-to-real gap, and VLM-based quality filtering (Gemini-3-Pro) to guarantee annotation fidelity. - **Curated by:** Chang Liu, Haoning Wu, Weidi Xie — School of Artificial Intelligence, Shanghai Jiao Tong University - **License:** Apache-2.0 - **Paper:** [arXiv:2605.10887](https://arxiv.org/abs/2605.10887) ### Dataset Sources - **Repository:** [Verg-Avesta/KubriCount](https://github.com/Verg-Avesta/KubriCount) - **HuggingFace Dataset:** [liuchang666/KubriCount](https://huggingface.co/datasets/liuchang666/KubriCount) - **Project Page:** [verg-avesta.github.io/KubriCount](https://verg-avesta.github.io/KubriCount/) --- ## Counting Granularity Levels KubriCount defines five levels of counting granularity. Each level specifies a **target** set and, for levels 2–5, a controlled **distractor** set that differs by exactly one semantic factor: | Level | Granularity | Prompt example | Distractor | |-------|-------------|----------------|------------| | L1 | Identity | "Count all the dogs." | None | | L2 (size) | Attribute | "Count large cherries." | Small cherries | | L2 (color) | Attribute | "Count mustard sofas." | Dark gray sofas | | L3 | Category | "Count the cans." | Bags | | L4 | Instance type | "Count backpack A." | Backpack B | | L5 | Concept | "Count the lobsters." | Octopuses | Levels 2–5 generate **two annotation queries per scene** by swapping the target and distractor roles, which is why the total query count (198,702) exceeds the scene count (110,507). --- ## Dataset Statistics | Split | Scenes | Queries | Purpose | |-------|--------|---------|---------| | train | 99,639 | 179,140 | Seen categories (normal + dense configurations, ~4:1 ratio) | | testA | 5,462 | 9,837 | Unseen assets from training categories | | testB | 5,406 | 9,725 | Entirely unseen categories | | **Total** | **110,507** | **198,702** | | - **Categories:** 157 across 16 super-categories - **Total annotated objects:** ~7.3 million - **Objects per image:** 1–250 (capped at 250 by Kubric's 256-instance limit) - **Image resolution:** 1024 × 1024 px --- ## FiftyOne Dataset Structure The dataset is loaded into FiftyOne as a **flat image dataset** — one sample per counting query. Scenes with two queries (L2–L5) produce two samples pointing to the same `filepath`. ### Sample Fields | Field | FiftyOne Type | Description | |-------|--------------|-------------| | `filepath` | `StringField` | Path to `edited_00000.png` — the final benchmark image | | `image_id` | `StringField` | Relative path key matching the HuggingFace annotation files | | `split` | `StringField` | `"train"`, `"testA"`, or `"testB"` | | `level` | `IntField` | Counting granularity level: 1–5 | | `category` | `StringField` | Text label for the target objects to count | | `count` | `IntField` | Ground truth object count | | `target_points` | `fo.Keypoints` | One `fo.Keypoint` per target object, each with a single normalized center point `(x/W, y/H)` | | `example_boxes` | `fo.Detections` | 2–8 few-shot exemplar bounding boxes in `[x, y, w, h]` relative coords | | `segmentation` | `fo.Segmentation` | `mask_path` pointing to `segmentation_00000.png` on disk — the instance segmentation map | | `negative_category` | `StringField` | Distractor label (empty string for L1) | | `negative_count` | `IntField` | Ground truth distractor count (0 for L1) | | `negative_points` | `fo.Keypoints` | One `fo.Keypoint` per distractor object (None for L1) | | `negative_example_boxes` | `fo.Detections` | Few-shot exemplar boxes for the distractor class (None for L1) | | `tags` | `ListField` | e.g. `["testA", "level5"]` | ### Design Notes - **`target_points` as a counting sanity check:** for any sample, `len(sample.target_points.keypoints) == sample.count`. This invariant holds by construction and can be used to verify import correctness. - **`example_boxes` are not exhaustive:** these are 2–8 manually selected exemplar crops used as few-shot visual prompts, not full ground-truth box coverage of all objects. - **`segmentation` is an instance map:** pixel values encode per-instance IDs as rendered by Kubric. It is not a semantic segmentation map. - **Dual queries per scene (L2–L5):** two FiftyOne samples share the same `filepath` but have swapped `category` / `negative_category` fields, representing the two valid counting queries for that scene. ## Dataset Creation ### Generation Pipeline KubriCount is constructed in four automatic stages: 1. **3D asset curation** — ~58K assets across 157 categories sourced from ShapeNetCore-v2 and controllable 3D generation (TRELLIS family). ~5K HDRI environment maps sourced from Poly Haven and Text2Light. 2. **Prototype synthesis** — Kubric + PyBullet + Blender renders scenes with exact instance metadata (RGB, instance masks, 2D/3D boxes, center points). Level-specific composition rules control target/distractor selection. 3. **Consistent image editing** — Nano-Banana-Pro refines textures and harmonizes lighting while preserving topology (no instances added, removed, merged, or split). Level-aware constraints prevent edits that would corrupt the counting criterion. 4. **Automatic data filtering** — Gemini-3-Pro inspects each edited image against the prototype and masks, issuing PASS/FAIL. ~20% are rejected on the first pass; iterative re-editing reduces the final rejection rate to ~5%. ### Splits Dataset splits are enforced at the 3D asset level before synthesis: - **Train:** seen categories, full asset pool - **TestA:** unseen assets within training categories (~10% holdout per category) - **TestB:** unseen categories (~10% of total assets) Both test splits use only unseen HDRI backgrounds and evaluate on the normal (non-dense) scene configuration. ### Annotations All annotations are derived automatically from the Kubric rendering engine — there are no human annotators. The engine produces pixel-perfect instance masks, 2D/3D bounding boxes, and center points as part of the rendering process. VLM-based filtering (not annotation) is applied post-hoc to ensure label fidelity. ## Citation ```bibtex @article{liu2026count, title={Count Anything at Any Granularity}, author={Liu, Chang and Wu, Haoning and Xie, Weidi}, journal={arXiv preprint arXiv:2605.10887}, year={2026} } ``` **APA:** Liu, C., Wu, H., & Xie, W. (2026). Count Anything at Any Granularity. *arXiv preprint arXiv:2605.10887*.