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Datasets:
drksci
/
trade_vision_dataset

Tasks:
Zero-Shot Object Detection
Feature Extraction
Image Classification
Modalities:
Tabular
Text
Formats:
parquet
Size:
< 1K
Tags:
retail
physical-stores
spatial-intelligence
openstreetmap
multimodal
siglip
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
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Auto-converted to Parquet Duplicate
entity_id
string
photo_id
string
business_id
string
google_place_id
string
parent_entity_id
string
root_entity_id
string
hierarchy_depth
int32
relation_to_parent
string
entity_type
string
taxonomy_l1
string
taxonomy_l2
string
taxonomy_l3
string
taxonomy_version
string
detected_label
string
canonical_label
string
external_taxonomy_id
string
external_taxonomy_label
string
external_taxonomy_path
string
bbox_xyxy
list
bbox_normalized
list
bbox_within_parent
list
placement_estimate
string
confidence
float64
confidence_type
string
model_run_id
string
input_entity_id
string
validation_status
string
quality_flags
string
detected_at
string
ve_04e6ffe5_run_20260914_091146_0001
04e6ffe507864329d2bd9f329b86db173bfbae72b454a2781e04f887b6c7203a
0x6b12afdc30dc7f3b:0x7ac687c89d22839
0x6b12afdc30dc7f3b:0x7ac687c89d22839
null
ve_04e6ffe5_run_20260914_091146_0001
0
contains
fixture
fixtures_and_merchandising
refrigerated_fixture
open_refrigerated_display_case
commercevision-retail-v0.1
refrigerated display case
open_refrigerated_display_case
/m/01n5jq
Display case
/m/0jbk/Fixture/Display_case
[ 94, 61, 774.2, 900 ]
[ 0.0587, 0.0678, 0.4839, 1 ]
null
overhead_or_wall
0.552
detector_score
run_20260914_091146
null
accepted
[]
2026-09-14T09:12:33Z

TradeVision: Hierarchical Physical Business & Multimodal Retail Provenance Dataset

This dataset is continuously seeded from OpenStreetMap, matched to Google Place IDs, harvested for temporal store photos, and enriched with zero-shot computer vision using Hugging Face Hub native pipelines.

Dataset Structure

The dataset is partitioned into three relational subsets loadable via Hugging Face datasets:

from datasets import load_dataset

# 1. Load Canonical Businesses (Hierarchical Admin 0-3 with OSM & Google Place IDs)
registry = load_dataset("drksci/trade_vision_dataset", "registry")

# 2. Load Time-Stamped Photos & Dense SigLIP Embeddings
photos = load_dataset("drksci/trade_vision_dataset", "photos")

# 3. Load 3-Level Retail Taxonomy Detections
detections = load_dataset("drksci/trade_vision_dataset", "detections")

1. registry Subset

Contains every verified physical retail location with multi-authority cross-references:

  • business_id: Deterministic unique identifier
  • name, brand, category
  • admin_level_0 (Country) -> admin_level_1 (State) -> admin_level_2 (City/LGA) -> admin_level_3 (Suburb)
  • osm_id & osm_url: Canonical OpenStreetMap reference
  • google_place_id & google_maps_url: Google Maps live location
  • latitude, longitude

2. photos Subset

Contains full temporal provenance and visual semantic context:

  • photo_id: SHA-256 content hash
  • raw_source_uri & image_url_highres: Original extraction payload and uncompressed image
  • upload_date_raw & upload_timestamp_approx: Visible upload timestamp (e.g. Dec 2025, Mar 2026)
  • scene_coarse & scene_functional_zone: Zero-shot environmental classification
  • image_embedding: Normalized 768-dimensional dense vector (google/siglip-base-patch16-224)

3. detections Subset

Contains hierarchical retail merchandising objects detected via open-vocabulary vision:

  • taxonomy_l1 (Super-Category: signage, fixtures_and_merchandising, point_of_sale)
  • taxonomy_l2 (Category: digital_signage, static_signage, display_fixtures, etc.)
  • taxonomy_l3 (Leaf Artifact: wall_mounted_display, promotional_poster, gondola_shelving, etc.)
  • detected_label, confidence, bbox_xyxy, placement_estimate

Continuous Updates & Ingestion

This dataset updates automatically via background GitHub Actions runners and Hugging Face Serverless APIs.
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