Object Detection

AI-TOD comes with 700,621 object instances for eight categories across 28,036 aerial images. Compared to existing object detection datasets in aerial images, the mean size of objects in AI-TOD is about 12.8 pixels, which is much smaller than others.

CoIR (Code Information Retrieval) benchmark, is designed to evaluate code retrieval capabilities. CoIR includes 10 curated code datasets, covering 8 retrieval tasks across 7 domains. In total, it encompasses two million documents. It also provides a common and easy Python framework, installable via pip, and shares the same data schema as benchmarks like MTEB and BEIR for easy cross-benchmark evaluations.

SeaDronesSee is a large-scale data set aimed at helping develop systems for Search and Rescue (SAR) using Unmanned Aerial Vehicles (UAVs) in maritime scenarios. Building highly complex autonomous UAV systems that aid in SAR missions requires robust computer vision algorithms to detect and track objects or persons of interest. This data set provides three sets of tracks: object detection, single-object tracking and multi-object tracking. Each track consists of its own data set and leaderboard. Object Detection: 5,630 train images, 859 validation images, 1,796 testing images Single-Object Tracking: 58 training video clips, 70 validation video clips and 80 testing video clips Multi-Object Tracking: 22 video clips with 54,105 frames Additionally, we provide multi-spektral footage: Multi-Spektral Object Detection: 246 train images, 61 validation images, 125 testing images We will continue to update this data set to make it more versatile and reflect real-world requirements in dynamic situations.

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The original Moving Camouflaged Animals (MoCA) Dataset includes 37K frames from 141 YouTube Video sequences with resolution and sampling rate of 720 × 1280 and 24fps in the majority of cases. The dataset covers 67 types of animals moving in natural scenes, but some are not camouflaged animals. Also, the ground truth of the original dataset is bounding boxes rather than dense segmentation masks, which makes it hard to evaluate the VCOD segmentation performance. To this end, we reorganize the dataset as MoCA-Mask and build a comprehensive benchmark with more comprehensive evaluation criteria.

RPC is a large-scale retail product checkout dataset and collects 200 retail SKUs. The collected SKUs can be divided into 17 meta categories, i.e., puffed food, dried fruit, dried food, instant drink, instant noodles, dessert, drink, alcohol, milk, canned food, chocolate, gum, candy, seasoner, personal hygiene, tissue, stationery. Source: RPC: A Large-Scale Retail Product Checkout Dataset Image Source: Wei et al

SceneNet is a dataset of labelled synthetic indoor scenes. There are several labeled indoor scenes, including: - 11 Bedroom scenes with 428 objects - 15 Office scenes with 1,203 objects - 11 Kitchen scenes with 797 objects - 10 Living Room scenes with 715 objects - 10 Bathrooms with 556 objects Source: https://robotvault.bitbucket.io/ Image Source: https://robotvault.bitbucket.io/big_scene.html

MinneApple is a benchmark dataset for apple detection and segmentation. The fruits are labelled using polygonal masks for each object instance to aid in precise object detection, localization, and segmentation. Additionally, the dataset also contains data for patch-based counting of clustered fruits. The dataset contains over 41, 000 annotated object instances in 1000 images. Source: https://github.com/nicolaihaeni/MinneApple Image Source: https://github.com/nicolaihaeni/MinneApple

The SKM-TEA dataset pairs raw quantitative knee MRI (qMRI) data, image data, and dense labels of tissues and pathology for end-to-end exploration and evaluation of the MR imaging pipeline. This 1.6TB dataset consists of raw-data measurements of ~25,000 slices (155 patients) of anonymized patient knee MRI scans, the corresponding scanner-generated DICOM images, manual segmentations of four tissues, and bounding box annotations for sixteen clinically relevant pathologies. ## Challenge Tracks DICOM Track: The DICOM benchmarking track uses scanner-generated DICOM images as the input for image segmentation and detection tasks. Raw Data Track: The Raw Data benchmarking track uses raw MRI data (i.e. k-space) as the input for image reconstruction, segmentation and detection tasks.

The View-of-Delft (VoD) dataset is a novel automotive dataset containing 8600 frames of synchronized and calibrated 64-layer LiDAR-, (stereo) camera-, and 3+1D radar-data acquired in complex, urban traffic. It consists of more than 123000 3D bounding box annotations, including more than 26000 pedestrian, 10000 cyclist and 26000 car labels.

The MALF dataset is a large dataset with 5,250 images annotated with multiple facial attributes and it is specifically constructed for fine grained evaluation. Source: Pushing the Limits of Unconstrained Face Detection:a Challenge Dataset and Baseline Results Image Source: http://www.cbsr.ia.ac.cn/faceevaluation/

This dataset includes 4,500 fully annotated images (over 30,000 license plate characters) from 150 vehicles in real-world scenarios where both the vehicle and the camera (inside another vehicle) are moving. The images were acquired with three different cameras and are available in the Portable Network Graphics (PNG) format with a size of 1,920 × 1,080 pixels. The cameras used were: GoPro Hero4 Silver, Huawei P9 Lite, and iPhone 7 Plus. We collected 1,500 images with each camera, divided as follows: - 900 of cars with gray license plates; - 300 of cars with red license plates; - 300 of motorcycles with gray license plates. The dataset is split as follows: 40% for training, 40% for testing and 20% for validation. Every image has the following annotations available in a text file: the camera in which the image was taken, the vehicle’s position and information such as type (car or motorcycle), manufacturer, model and year; the identification and position of the license plate, as well as the position of its characters. Source: A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

Washington RGB-D is a widely used testbed in the robotic community, consisting of 41,877 RGB-D images organized into 300 instances divided in 51 classes of common indoor objects (e.g. scissors, cereal box, keyboard etc). Each object instance was positioned on a turntable and captured from three different viewpoints while rotating. Source: Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work Image Source: https://rgbd-dataset.cs.washington.edu/

A novel dataset for traffic accidents analysis. Source: CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis

Vision-language modeling has enabled open-vocabulary tasks where predictions can be queried using any text prompt in a zero-shot manner. Existing open-vocabulary tasks focus on object classes, whereas research on object attributes is limited due to the lack of a reliable attribute-focused evaluation benchmark. This paper introduces the Open-Vocabulary Attribute Detection (OVAD) task and the corresponding OVAD benchmark. The objective of the novel task and benchmark is to probe object-level attribute information learned by vision-language models. To this end, we created a clean and densely annotated test set covering 117 attribute classes on the 80 object classes of MS COCO. It includes positive and negative annotations, which enables open-vocabulary evaluation. Overall, the benchmark consists of 1.4 million annotations. For reference, we provide a first baseline method for open-vocabulary attribute detection. Moreover, we demonstrate the benchmark's value by studying the attribute detection performance of several foundation models. Source: Open-vocabulary Attribute Detection Image Source: https://arxiv.org/pdf/2211.12914v1.pdf

Detecting vehicles and representing their position and orientation in the three dimensional space is a key technology for autonomous driving. Recently, methods for 3D vehicle detection solely based on monocular RGB images gained popularity. In order to facilitate this task as well as to compare and drive state-of-the-art methods, several new datasets and benchmarks have been published. Ground truth annotations of vehicles are usually obtained using lidar point clouds, which often induces errors due to imperfect calibration or synchronization between both sensors. To this end, we propose Cityscapes 3D, extending the original Cityscapes dataset with 3D bounding box annotations for all types of vehicles. In contrast to existing datasets, our 3D annotations were labeled using stereo RGB images only and capture all nine degrees of freedom. This leads to a pixel-accurate reprojection in the RGB image and a higher range of annotations compared to lidar-based approaches. In order to ease multitask learning, we provide a pairing of 2D instance segments with 3D bounding boxes. In addition, we complement the Cityscapes benchmark suite with 3D vehicle detection based on the new annotations as well as metrics presented in this work. Dataset and benchmark are available online.

Prophesee’s GEN1 Automotive Detection Dataset is the largest Event-Based Dataset to date. The dataset was recorded using a PROPHESEE GEN1 sensor with a resolution of 304×240 pixels, mounted on a car dashboard. The labels were obtained using the gray level estimation feature of the ATIS camera by labelling manually. It contains 39 hours of open road and various driving scenarios ranging from urban, highway, suburbs and countryside scenes. Manual bounding box annotations are available for two classes are present: pedestrians and cars. (Truck and buses are not labelled).

Consists of over 50,000 frames and includes high-definition images with full resolution depth information, semantic segmentation (images), point-wise segmentation (point clouds), and detailed annotations for all vehicles and people. Source: Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception

TTPLA is a public dataset which is a collection of aerial images on Transmission Towers (TTs) and Power Lines (PLs). It can be used for detection and segmentation of transmission towers and power lines. It consists of 1,100 images with the resolution of 3,840×2,160 pixels, as well as manually labelled 8,987 instances of TTs and PLs. Source: https://github.com/r3ab/ttpla_dataset Image Source: https://github.com/r3ab/ttpla_dataset

Unconstrained Face Detection Dataset (UFDD) aims to fuel further research in unconstrained face detection. Source: Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results Image Source: https://ufdd.info/

A Multi-Task 4D Radar-Camera Fusion Dataset for Autonomous Driving on Water Surfaces description of the dataset * WaterScenes, the first multi-task 4D radar-camera fusion dataset on water surfaces, which offers data from multiple sensors, including a 4D radar, monocular camera, GPS, and IMU. It can be applied in multiple tasks, such as object detection, instance segmentation, semantic segmentation, free-space segmentation, and waterline segmentation. * Our dataset covers diverse time conditions (daytime, nightfall, night), lighting conditions (normal, dim, strong), weather conditions (sunny, overcast, rainy, snowy) and waterway conditions (river, lake, canal, moat). An information list is also offered for retrieving specific data for experiments under different conditions. * We provide 2D box-level and pixel-level annotations for camera images, and 3D point-level annotations for radar point clouds. We also offer precise timestamps for the synchronization of different sensors, as well as intrinsic and extrinsic parameters. * We provide a toolkit for radar point clouds that includes: pre-processing, labeling, projection and visualization, assisting researchers in processing and analyzing our dataset.

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Datasets for multi-view crowd counting in wide-area scenes. Includes our CityStreet dataset, as well as the counting and metadata for multi-view counting on PETS2009 and DukeMTMC. CityStreet is a real-world city scene dataset collected around the intersection of a crowded street. The scene size of the dataset is around 58m×72m. The ground plane map resolution is 320×384.

Urban-scene detection dataset that consists of five different weather conditions: daytime-sunny, night-sunny, dusk-rainy, daytime-foggy, and night-rainy. The images are collected from diverse weather datasets: Cityscapes, BDD-100k, FoggyCityscapes, and Adverse-Weather. The dataset is used to evaulate the model performance on the single-domain generalized object detection (Single-DGOD). - Source paper: Single-Domain Generalized Object Detection in Urban Scene via Cyclic-Disentangled Self-Distillation - Source page: AmingWu/Single-DGOD - Source dataset: DWD (Diverse Weather Dataset)

GMOT-40 is the first public dense dataset for Generic Multiple Object Tracking (GMOT). It contains 40 carefully annotated sequences evenly distributed among 10 object categories. Beyond the data, a challenging protocal, one-shot GMOT, is adopted and a series of baseline algorithms is introduced. GMOT-40 is featured in Dense objects. Over 80 objects of the same class could appear in one frame. High-quality label. Manual annotation with careful inspection in each frame. Diversity in target. Large variability between classes and within the sequences of the same class. Real world challenges. Occlusion, target enter/exiting, motion blur, deformation and so on. Challenging protocol. One-shot GMOT protocol is adopted for evaluation. New baselines. A series of baseline methods for one-shot GMOT task is introduced.

High-resolution ship collections 2016 (HRSC2016) is a data set used for scientific research. Currently, all of the images in HRSC2016 were collected from Google Earth.

HyperKvasir dataset contains 110,079 images and 374 videos where it captures anatomical landmarks and pathological and normal findings. A total of around 1 million images and video frames altogether.

People-Art is an object detection dataset which consists of people in 43 different styles. People contained in this dataset are quite different from those in common photographs. There are 42 categories of art styles and movements including Naturalism, Cubism, Socialist Realism, Impressionism, and Suprematism Source: Point Linking Network for Object Detection Image Source: https://www.researchgate.net/figure/Generalization-results-on-Picasso-and-People-Art-datasets-Joseph-Redmon-2016_fig12_328175597

RICE is a remote sensing image dataset for cloud removal. The proposed dataset consists of two parts: RICE1 contains 500 pairs of images, each pair has images with cloud and cloudless size of 512512; RICE2 contains 450 sets of images, each set contains three 512512 size images, respectively, the reference picture without clouds, the picture of the cloud and the mask of its cloud. Source: https://github.com/BUPTLdy/RICE_DATASET

An important factor in advancing autonomous driving systems is simulation. Yet, there is rather small progress for transferability between the virtual and real world. We revisit this problem for 3D object detection on LiDAR point clouds and propose a dataset generation pipeline based on the CARLA simulator. Utilizing domain randomization strategies and careful modeling, we are able to train an object detector on the synthetic data and demonstrate strong generalization capabilities to the KITTI dataset. The dataset contains point clouds and bounding box labels for training 3D object detection.

SODA10M is a large-scale object detection benchmark for standardizing the evaluation of different self-supervised and semi-supervised approaches by learning from raw data. SODA10M contains 10 million unlabeled images and 20K images labeled with 6 representative object categories. To improve diversity, the images are collected every ten seconds per frame within 32 different cities under different weather conditions, periods and location scenes.

TJU-DHD is a high-resolution dataset for object detection and pedestrian detection. The dataset contains 115,354 high-resolution images (52% images have a resolution of 1624×1200 pixels and 48% images have a resolution of at least 2,560×1,440 pixels) and 709,330 labelled objects in total with a large variance in scale and appearance. Source: https://github.com/tjubiit/TJU-DHD Image Source: https://github.com/tjubiit/TJU-DHD

The TrashCan dataset is an instance-segmentation dataset of underwater trash. It is comprised of annotated images (7,212 images) which contain observations of trash, ROVs, and a wide variety of undersea flora and fauna. The annotations in this dataset take the format of instance segmentation annotations: bitmaps containing a mask marking which pixels in the image contain each object. The imagery in TrashCan is sourced from the J-EDI (JAMSTEC E-Library of Deep-sea Images) dataset, curated by the Japan Agency of Marine Earth Science and Technology (JAMSTEC). Source: TrashCan 1.0 An Instance-Segmentation Labeled Dataset of Trash Observations Image Source: https://conservancy.umn.edu/handle/11299/214865

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4D-OR includes a total of 6734 scenes, recorded by six calibrated RGB-D Kinect sensors 1 mounted to the ceiling of the OR, with one frame-per-second, providing synchronized RGB and depth images. We provide fused point cloud sequences of entire scenes, automatically annotated human 6D poses and 3D bounding boxes for OR objects. Furthermore, we provide SSG annotations for each step of the surgery together with the clinical roles of all the humans in the scenes, e.g., nurse, head surgeon, anesthesiologist.

The LandCover.ai (Land Cover from Aerial Imagery) dataset is a dataset for automatic mapping of buildings, woodlands, water and roads from aerial images. ### Dataset features * land cover from Poland, Central Europe * three spectral bands - RGB * 33 orthophotos with 25 cm per pixel resolution (~9000x9500 px) * 8 orthophotos with 50 cm per pixel resolution (~4200x4700 px) * total area of 216.27 sq. km ### Dataset format * rasters are three-channel GeoTiffs with EPSG:2180 spatial reference system * masks are single-channel GeoTiffs with EPSG:2180 spatial reference system