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SearchAD

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Terms and conditions:

The SearchAD dataset is provided to you under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (CC BY-NC-SA 4.0), with the additional terms included herein. When you download or use the dataset, you are agreeing to comply with the terms of CC BY-NC-SA 4.0 as applicable, and also agreeing to the dataset terms (listed below).

Dataset terms:

In case you use the dataset within your research papers, you refer to at least one of our publications listed below. If the dataset is used in media, a link to our websites (https://huggingface.co/datasets/iis-esslingen/SearchAD) is included.
We reserve all rights that are not explicitly granted to you. The dataset is provided as is, and you take full responsibility for any risk of using it.

Publications:

Embacher et al.: SearchAD: Large-Scale Rare Image Retrieval Dataset for Autonomous Driving. In arXiv, 2026

SearchAD Dataset

Main Project Page

You can find more information about the SearchAD Dataset on its official project page: https://iis-esslingen.github.io/searchad/

SearchAD Benchmark

The official SearchAD rare image retrieval competition including the leaderboard can be found here

Dataset Overview

The SearchAD dataset is a large-scale autonomous driving datasets, specifically targeting rare and safety-critical objects and scenes. It's designed to provide a comprehensive and challenging environment for semantic image retrieval research. Due to the dataset licenses, the dataset images have to be downloaded at the official dataset hosts (see Table below).

Name: SearchAD
Dataset Size: 423,798 frames (images).

Origin: Uniquely compiled by integrating data from 11 established AD datasets, ensuring diversity and real-world variability.

Dataset [Download Link] and Instructions	Val. Set	#Frames	# Original Classes	# SearchAD Classes	# Objects
Lost and Found [1] - Then download leftImg8bit/	X	2,239	42	18	2,098
WildDash2 [2] - Then download wd_public_v2p0.zip and wd_both_02.zip	✓	5,068	26	80	5,032
ACDC [3] - Then download rgb_anon_trainvaltest.zip	✓	8,012	19*	60	7,471
IDD Segmentation [4] - Then download IDD Segmentation (IDD 20k Part I) (18.5 GB)	✓	10,003	30*	52	12,192
KITTI [5] - Then download left color images of object data set (12 GB)	X	14,999	8	47	9,840
Cityscapes [6] - Then download leftImg8bit_trainvaltest.zip (11GB) [md5] and leftImg8bit_trainextra.zip (44GB) [md5]	✓	24,998	30*	75	31,037
Mapillary Vistas [7] - Then download mapillary-vistas-dataset_public_v2.0.zip	✓	25,000	66*	86	35,093
ECP [8] - Then download ECP day and night, train, val, test (6 download .zip files)	✓	47,335	8	76	33,081
nuScenes [9] - Then download Trainval and Test	✓	80,314	32*	56	166,152
BDD100K [10] - Then download 100K Images	✓	100,000	12*	80	83,102
Mapillary Sign [11] - Then click on Download dataset	✓	105,830	313**	90	128,167
SearchAD [12]	Combined	423,798	N/A	90	513,265

SearchAD Class Overview

Annotations: Features more than 513,265 high-quality manual bounding box annotations across 90 rare classes.

Categories: The 90 rare classes are grouped into broader categories:

Category	SearchAD Classes
Animal	Real: Cat, Cow, Deer, Dog, Donkey, Horse, Sheep, Wildlife
	Statue: Cow, Deer, Elephant, Horse, Lion
Human	Construction Worker, Firefighter, Medical, On Loading Area, Police, Refuse Collector, With Sticks or Crutches
Marking	Bicycle Symbol, Bus Text, Stop Text, Temporarily Invalidated, Yellow Lane Arrow
Object	Ball, Beacon, Euro Pallet, Hand Dolly, Hydrant, Office Chair, Pallet Truck, Platform Truck, Rollator,
	Shopping Cart, Shopping Trolley, Suitcase Trolley, Traffic Cone, Trash Bin, Wheelbarrow
Rideable	Cityscooter, Police Motorcycle, Quad, Segway, Skateboard, Skates, Ski, Stroller, Three Wheeler, Toy Car, Wheelchair
Scene	Active Amber Lights, Active Emergency Lights, Fog, Open Door, Open Hood, Open Trunk, Snow, Tunnel
Sign	Animal Sign, Road Bumper Sign, Temporarily Invalidated Sign, Train Sign, Warning Triangle
Trailer	Agricultural Trailer, Bicycle Trailer, Boat Trailer, Car Trailer, Caravan Trailer, Carriage, Warning Trailer
Vehicle	Construction: Concrete Mixer, Excavator, Forklift, Harvester, Loader, Steamroller, Tractor, Truck Crane
	Duty: Fire, Garbage, Medical, Military, Police, Winter
	Special: Bicycle On Back, Bicycle On Roof, Car Truck, Recreational, Train

Dataset Structure

Please note the following regarding the dataset structure:

This is the default structure assumed for the dataset.
It is specifically used by the annotation JSON files and the default queries vision support set image paths.
If you use a different dataset structure or different dataset names (e.g., bdd100k instead of bdd100k_images_100k), the datasets must be either symlinked or the corresponding image paths must be modified within the annotation files and default queries vision support sets.
This structure is crucial for correct submission on the benchmark server.

searchad/
├── ECP/
│   ├── ...
├── IDD_Segmentation/
│   ├── ...
├── acdc/
│   ├── ...
├── bdd100k_images_100k/
│   ├── ...
├── cityscapes/
│   ├── ...
├── kitti/
│   ├── ...
├── lostandfound/
│   ├── ...
├── mapillary_sign/
│   ├── ...
├── mapillary_vistas/
│   ├── ...
├── nuscenes/
│   ├── ...
├── wd_both02/
│   ├── ...
├── wd_publicv2p0/
│   ├── ...
├── searchad_annotations_train.json
├── searchad_annotations_val.json
├── searchad_test_mapping_id_to_imagepath.json
└── default_queries/
    ├── ...

Setup for Evaluation

Data Splits: SearchAD provides distinct training, validation, and a held-out test set.
- The test set is constructed from the union of test splits of the underlying datasets and is hosted on a private benchmark server to prevent any form of test leakage and ensure unbiased evaluation.
- Training and validation splits are derived from the respective partitions of the original datasets, with all 90 SearchAD classes represented in each split.
Query Modalities: The benchmark supports two primary query types:
- Text-based Queries: Consist of precise keywords defining the class of interest, complemented by comprehensive, extended descriptions that offer detailed characterization.
- Image-based Queries: Utilize a Vision Support Set of 5 carefully selected reference images per class. These images are chosen from the training set based on size, variance, and low occlusion to represent diverse variations.

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