Sample Dataset/Sample Dataset-README.md

metadata

license: cc-by-nc-sa-4.0
pretty_name: Urban-ImageNet — Sample Dataset
task_categories:
  - image-classification
  - image-to-text
  - text-to-image
  - zero-shot-image-classification
  - image-segmentation
modalities:
  - image
  - text
language:
  - zh
  - en
size_categories:
  - n<1K
tags:
  - urban-perception
  - social-media
  - weibo
  - image-text-retrieval
  - instance-segmentation
  - computational-urban-studies
  - urban-ai
  - chinese-cities
  - husic
  - cross-modal-retrieval
  - multi-modal
  - scene-classification
  - urban-space-perception
  - sample-dataset

🏙️ Urban-ImageNet — Sample Dataset

A balanced 100-image quality-inspection sample drawn from the Urban-ImageNet 100K benchmark, covering all three annotation tasks.

This repository is a compact, self-contained sample of Urban-ImageNet, designed to let anyone quickly inspect data quality, annotation completeness, and label fidelity without downloading the 100K large corpus (6+ GB). It faithfully mirrors the structure, format, and annotation conventions of the 100K benchmark across all three tasks.

---

Overview

Urban-ImageNet is a large-scale multi-modal dataset for urban space perception (see the full dataset page and paper for complete documentation). This sample repository provides:

• 100 images — 10 HUSIC classes × 10 images per class, drawn from the training split of the 100K dataset.
• Complete Task 1 labels — images stored in ImageFolder-compatible class subdirectories.
• Complete Task 2 annotations — the corresponding 100 rows from the 100K text-image pairs file, with all metadata columns preserved.
• Complete Task 3 annotations — a filtered COCO-format JSON containing all instance segmentation masks for the 100 images.
• Instance mask visualisations — side-by-side original / coloured-mask / legend images for every one of the 100 images.

This sample represents approximately 0.1% of the 100K benchmark by image count and is strictly class-balanced, making it representative of the 100K dataset's overall structure and label distribution.

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How the Sample Was Created

The sample was constructed through the following transparent, reproducible procedure:

1. Source pool: The training split of the Urban-ImageNet 100K balanced benchmark (80,000 images; 8,000 per HUSIC class).

2. Stratified random sampling: For each of the 10 HUSIC classes, 10 images were drawn uniformly at random without replacement, independently per class, guaranteeing strict class balance (10 images × 10 classes = 100 images total).

3. Privacy verification: All selected images had already undergone automated face blurring, licence-plate blurring, and QR-code blurring as part of the full dataset pipeline. The 100 selected images were additionally subject to manual human inspection to confirm that all privacy-sensitive regions are fully obscured before inclusion in this sample.

4. Annotation extraction:

   • T1: Images placed into their HUSIC class subdirectory — no additional annotation step required.
   • T2: The 100 Image Filename values were used to filter rows from the 100K train.xlsx file. All original metadata columns are preserved without modification.
   • T3: The 100 image filenames were used to filter entries from the 100K COCO annotation file, extracting all matching images entries and their associated annotations. Image IDs and annotation IDs are re-indexed sequentially within the sample file.

5. Mask visualisation generation: For each of the 100 images, a side-by-side visualisation was rendered using a standard pipeline. Each visualisation shows: left panel — original image; right panel — instance mask overlay with per-instance colour coding; right margin — colour-coded legend of detected object labels.

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File Structure

Sample Dataset/
│
├── 01 Images with labels/              ← Task 1: Scene Classification
│   ├── Exterior urban spaces with people/        (10 images)
│   ├── Exterior urban spaces without people/     (10 images)
│   ├── Food or drink items/                      (10 images)
│   ├── Hotel or commercial lodging spaces/       (10 images)
│   ├── Human-centered portrait/                  (10 images)
│   ├── Interior urban spaces with people/        (10 images)
│   ├── Interior urban spaces without people/     (10 images)
│   ├── Other non-spatial content/                (10 images)
│   ├── Private home interiors/                   (10 images)
│   └── Retail products and merchandise/          (10 images)
│                                        ────────────────────
│                                        Total: 100 images
│
├── 02 Text-Image Pairs/                ← Task 2: Cross-Modal Retrieval
│   └── Sample Dataset Paired Texts.xlsx
│
└── 03 Instance Segmentation/           ← Task 3: Instance Segmentation
    ├── Sample Dataset Annotations.json
    └── Visualization of instance labels/
        ├── Exterior urban spaces with people/    (10 visualisations)
        ├── Exterior urban spaces without people/ (10 visualisations)
        ├── Food or drink items/                  (10 visualisations)
        ├── Hotel or commercial lodging spaces/   (10 visualisations)
        ├── Human-centered portrait/              (10 visualisations)
        ├── Interior urban spaces with people/    (10 visualisations)
        ├── Interior urban spaces without people/ (10 visualisations)
        ├── Other non-spatial content/            (10 visualisations)
        ├── Private home interiors/               (10 visualisations)
        └── Retail products and merchandise/      (10 visualisations)

Image format: All images are JPEG, privacy-protected (faces, licence plates, and QR codes blurred), resized to a maximum long edge of 512 px.

---

Correspondence Between Files

All three annotation modalities share a common image filename key in the format {UserID}_{PostTime}_{Index} (e.g., 1197195715_2023年12月15日_5). The table below shows how each component references this key:

Component	Key field	Example value
01 Images with labels/{class}/	Filename stem (without .jpg)	1197195715_2023年12月15日_5.jpg
Sample Dataset Paired Texts.xlsx	Image Filename column	1197195715_2023年12月15日_5
Sample Dataset Annotations.json	images[].file_name	1197195715_2023年12月15日_5.jpg
Visualization of instance labels/{class}/	Filename stem (without .jpg)	1197195715_2023年12月15日_5.jpg

---

Task 1: Scene Classification Labels

Images are stored in an ImageFolder-compatible layout. The subdirectory name is the ground-truth HUSIC label, directly loadable with standard PyTorch or TensorFlow pipelines:

from torchvision.datasets import ImageFolder

dataset = ImageFolder(root="Sample Dataset/01 Images with labels")
# dataset.classes → ['Exterior urban spaces with people', ...]
# dataset.class_to_idx → {'Exterior urban spaces with people': 0, ...}
print(len(dataset))  # 100

The 10 HUSIC classes (integer IDs assigned by ImageFolder's lexicographic sort):

Class ID	Class Label
0	Exterior urban spaces with people
1	Exterior urban spaces without people
2	Food or drink items
3	Hotel or commercial lodging spaces
4	Human-centered portrait
5	Interior urban spaces with people
6	Interior urban spaces without people
7	Other non-spatial content
8	Private home interiors
9	Retail products and merchandise

Note: In the full Urban-ImageNet dataset, HUSIC class IDs 0–9 follow the theoretical ordering (exterior-before-interior, spatial-before-non-spatial). When loading with ImageFolder, always use class_to_idx to map label strings to IDs rather than assuming a fixed integer correspondence.

---

Task 2: Text-Image Pair Metadata

Sample Dataset Paired Texts.xlsx contains 100 rows — one per image — with all metadata columns from the original 100K dataset preserved without modification.

Column Schema

Column	Type	Description
Image Label	string	HUSIC class label
Image Filename	string	Join key linking to image file and T3 annotation
Post ID	integer	Anonymised numerical post identifier
User ID	integer	Anonymised numerical user identifier
Post Time	string	Original post timestamp
Post Text	string	Original Weibo post text (Chinese, unmodified)
City	string	City of the location tag
Place Tag	string	Location hashtag / commercial-site place tag
Posting Tool	string	Client or posting-source string
Mentioned Users	string	Anonymised or empty mentioned-user field
Extracted Topics	string	Topic / hashtag terms extracted from post text
Extracted Locations	string	Location mentions extracted from post text
Like Count	integer	Public engagement count at collection time
Repost Count	integer	Public repost count at collection time
Comment Count	integer	Public comment count at collection time

Post Text retains original Chinese to preserve linguistic authenticity for Task 2 evaluation. All columns retain the original data types and formatting from the 100K source file.

---

Task 3: Instance Segmentation Annotations

Sample Dataset Annotations.json uses the same extended COCO format as the full Urban-ImageNet dataset.

JSON Structure

{
  "info": {
    "description": "Urban-ImageNet Instance Segmentation Annotations",
    "split": "train",
    "version": "1.0",
    "annotation_tool": "Grounding DINO + SAM2"
  },
  "categories": [
    {"id": 0, "name": "Exterior urban spaces with people"},
    ...
  ],
  "images": [
    {
      "id": 0,
      "file_name": "1197195715_2023年12月15日_5.jpg",
      "width": 512,
      "height": 384,
      "classification_label": 0
    }
  ],
  "annotations": [
    {
      "id": 0,
      "image_id": 0,
      "category_id": 0,
      "detected_label": "person",
      "detection_score": 0.8732,
      "bbox": [x, y, width, height],
      "area": 4512,
      "segmentation": {"counts": "...", "size": [384, 512]},
      "iscrowd": 0
    }
  ]
}

Extended fields beyond standard COCO:

• classification_label (in images): HUSIC class ID — enables multi-task joint training.
• detected_label (in annotations): the specific object term detected by Grounding DINO (e.g., "escalator", "retail shelf", "hotel bed").
• detection_score (in annotations): Grounding DINO confidence score, enabling threshold-based downstream filtering.
• Segmentation masks are stored in COCO RLE format, directly compatible with pycocotools.

Instance Mask Visualisations

The Visualization of instance labels/ subfolder provides a visual verification of every annotation in this sample. Each visualisation image shows:

• Left half: the original image.
• Right half: the same image with coloured per-instance mask overlays (each unique instance receives a distinct colour).
• Right margin: a colour-coded legend listing the detected_label for each coloured mask.

Visualisation filenames match their source images exactly (e.g., 1197195715_2023年12月15日_5.jpg), within the same class subdirectory, enabling direct one-to-one correspondence without any filename transformation.

---

Relationship to the Full Dataset

Property	This Sample	100K Benchmark
Images	100	100,000
Classes	10	10
Images per class	10	10,000
Source split	train only	train / val / test
T1 labels	✅	✅
T2 text metadata	✅ 100 rows	✅ 80K / 10K / 10K rows
T3 instance annotations	✅ filtered JSON	✅ train / val / test JSON
T3 visualisations	✅ all 100 images	✅ selected examples
Privacy protection	✅ automated + manual review	✅ automated + manual review
File format & schema	Identical to full dataset	—

Code written against this sample runs unchanged on the full dataset by substituting the root path.

---

Quick-Start

import json
import pandas as pd
from pathlib import Path
from torchvision.datasets import ImageFolder

root = Path("Sample Dataset")

# ── Task 1 ──────────────────────────────────────────────────────────────
dataset = ImageFolder(root=root / "01 Images with labels")
print(dataset.classes)   # 10 HUSIC class names
print(len(dataset))      # 100

# ── Task 2 ──────────────────────────────────────────────────────────────
df = pd.read_excel(root / "02 Text-Image Pairs" / "Sample Dataset Paired Texts.xlsx")
print(df.shape)                 # (100, 15)
print(df["Post Text"].iloc[0])  # Chinese post text

# ── Task 3 ──────────────────────────────────────────────────────────────
with open(root / "03 Instance Segmentation" / "Sample Dataset Annotations.json") as f:
    coco = json.load(f)
print(f"Images:      {len(coco['images'])}")       # 100
print(f"Annotations: {len(coco['annotations'])}")  # typically 500–1500

# Join image filename → annotations
img_id_map = {img["file_name"]: img["id"] for img in coco["images"]}
ann_by_img = {}
for ann in coco["annotations"]:
    ann_by_img.setdefault(ann["image_id"], []).append(ann)

---

Privacy and Responsible Use

All images are derived from public Weibo posts and have undergone:

• Automated face detection and blurring (all detected faces).
• Automated licence-plate detection and blurring.
• Automated QR-code detection and blurring, supplemented by manual spot-checks.
• Additional manual human review of all 100 sample images, confirming complete protection of all privacy-sensitive regions.

Original usernames have been replaced with opaque numerical identifiers. Images are released at ≤ 512 px long edge. Use is restricted to non-commercial academic research. Re-identification, biometric profiling, facial recognition, and surveillance applications are strictly prohibited. See the full dataset page for the complete data-use agreement.

---

Citation

@article{ou2026urbanimagenet,
  title   = {Urban-ImageNet: A Large-Scale Multi-Modal Dataset and Evaluation Framework for Urban Space Perception},
  author  = {Ou, Yiwei and Cheung, Chung Ching and Ang, Jun Yang and Ren, Xiaobin and Sun, Ronggui and Gao, Guansong and Zhao, Kaiqi and Manfredini, Manfredo},
  journal = {arXiv preprint arXiv:2605.09936},
  year    = {2026},
  eprint  = {2605.09936},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url     = {https://arxiv.org/abs/2605.09936}
}

Paper: arXiv:2605.09936
Full Dataset: huggingface.co/datasets/Yiwei-Ou/Urban-ImageNet
Benchmark code: github.com/yiasun/dataset-2

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License

Released under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).
Non-commercial academic research use only. See LICENSE for full terms.