Object Detection

Freiburg Groceries is a groceries classification dataset consisting of 5000 images of size 256x256, divided into 25 categories. It has imbalanced class sizes ranging from 97 to 370 images per class. Images were taken in various aspect ratios and padded to squares. Source: XNAS: Neural Architecture Search with Expert Advice Image Source: http://aisdatasets.informatik.uni-freiburg.de/freiburg_groceries_dataset/

This dataset contains 5955 painting images (from WikiCommons) : a train set of 2978 images and a test set of 2977 images (for classification task). 1480 of the 2977 images are annotated with bounding boxes for 7 iconographic classes : ‘angel’,‘Child_Jesus’,‘crucifixion_of_Jesus’,‘Mary’,‘nudity’, ‘ruins’,‘Saint_Sebastien’. The dataset IconArt dataset was introduced in the following paper : "Weakly Supervised Object Detection in Artworks" Gonthier et al. ECCV 2018 Workshop Computer Vision for Art Analysis - VISART 2018. https://wsoda.telecom-paristech.fr/ https://zenodo.org/record/4737435

MobilityAids is a dataset for perception of people and their mobility aids. The annotated dataset contains five classes: pedestrian, person in wheelchair, pedestrian pushing a person in a wheelchair, person using crutches and person using a walking frame. In total the hospital dataset has over 17, 000 annotated RGB-D images, containing people categorized according to the mobility aids they use. The images were collected in the facilities of the Faculty of Engineering of the University of Freiburg and in a hospital in Frankfurt. Source: http://mobility-aids.informatik.uni-freiburg.de/ Image Source: http://mobility-aids.informatik.uni-freiburg.de/

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This dataset is a collection of spoken language instructions for a robotic system to pick and place common objects. Text instructions and corresponding object images are provided. The dataset consists of situations where the robot is instructed by the operator to pick up a specific object and move it to another location: for example, Move the blue and white tissue box to the top right bin. This dataset consists of RGBD images, bounding box annotations, destination box annotations, and text instructions. Source: https://github.com/pfnet-research/picking-instruction Image Source: https://github.com/pfnet-research/picking-instruction

PIDray is a large-scale dataset which covers various cases in real-world scenarios for prohibited item detection, especially for deliberately hidden items. The dataset contains 12 categories of prohibited items in 47, 677 X-ray images with high-quality annotated segmentation masks and bounding boxes.

The PS-Battles dataset is gathered from a large community of image manipulation enthusiasts and provides a basis for media derivation and manipulation detection in the visual domain. The dataset consists of 102'028 images grouped into 11'142 subsets, each containing the original image as well as a varying number of manipulated derivatives. Source: The PS-Battles Dataset - an Image Collection for Image Manipulation Detection Image Source: Heller et al

The first real-world, large-scale Roadside Cooperative Perception Dataset, RCooper, is released to bloom research on roadside cooperative perception for practical applications. More than 50k images and 30k point clouds manually annotated with 3D bounding boxes and trajectories for ten semantic classes are provided.

SODA-A is a large-scale benchmark specialized for small object detection task under aerial scenes, which has 800203 instances with oriented rectangle box annotation across 9 classes. It contains 2510 high-resolution images extracted from Google Earth.

Recent salient object detection (SOD) methods based on deep neural network have achieved remarkable performance. However, most of existing SOD models designed for low-resolution input perform poorly on high-resolution images due to the contradiction between the sampling depth and the receptive field size. Aiming at resolving this contradiction, we propose a novel one-stage framework called Pyramid Grafting Network (PGNet), using transformer and CNN backbone to extract features from different resolution images independently and then graft the features from transformer branch to CNN branch. An attention-based Cross-Model Grafting Module (CMGM) is proposed to enable CNN branch to combine broken detailed information more holistically, guided by different source feature during decoding process. Moreover, we design an Attention Guided Loss (AGL) to explicitly supervise the attention matrix generated by CMGM to help the network better interact with the attention from different models. We contribute a new Ultra-High-Resolution Saliency Detection dataset UHRSD, containing 5,920 images at 4K-8K resolutions. To our knowledge, it is the largest dataset in both quantity and resolution for high-resolution SOD task, which can be used for training and testing in future research. Sufficient experiments on UHRSD and widely-used SOD datasets demonstrate that our method achieves superior performance compared to the state-of-the-art methods.

YouTube-BoundingBoxes (YT-BB) is a large-scale data set of video URLs with densely-sampled object bounding box annotations. The data set consists of approximately 380,000 video segments about 19s long, automatically selected to feature objects in natural settings without editing or post-processing, with a recording quality often akin to that of a hand-held cell phone camera. The objects represent a subset of the MS COCO label set. All video segments were human-annotated with high-precision classification labels and bounding boxes at 1 frame per second. Source: YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video

BAAI-VANJEE is a dataset for benchmarking and training various computer vision tasks such as 2D/3D object detection and multi-sensor fusion. The BAAI-VANJEE roadside dataset consists of LiDAR data and RGB images collected by VANJEE smart base station placed on the roadside about 4.5m high. This dataset contains 2500 frames of LiDAR data, 5000 frames of RGB images, including 20% collected at the same time. It also contains 12 classes of objects, 74K 3D object annotations and 105K 2D object annotations.

Comprises about 40,000 images where the most suitable objects for 14 tasks have been annotated. Source: What Object Should I Use? - Task Driven Object Detection

The EuroCity Persons dataset provides a large number of highly diverse, accurate and detailed annotations of pedestrians, cyclists and other riders in urban traffic scenes. The images for this dataset were collected on-board a moving vehicle in 31 cities of 12 European countries. With over 238,200 person instances manually labeled in over 47,300 images, EuroCity Persons is nearly one order of magnitude larger than person datasets used previously for benchmarking. The dataset furthermore contains a large number of person orientation annotations (over 211,200).

Falling Things (FAT) is a dataset for advancing the state-of-the-art in object detection and 3D pose estimation in the context of robotics. It consists of generated photorealistic images with accurate 3D pose annotations for all objects in 60k images. The 60k annotated photos of 21 household objects are taken from the YCB objects set. For each image, the dataset contains the 3D poses, per-pixel class segmentation, and 2D/3D bounding box coordinates for all objects. Source: Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation

IIIT-AR-13K is created by manually annotating the bounding boxes of graphical or page objects in publicly available annual reports. This dataset contains a total of 13k annotated page images with objects in five different popular categories - table, figure, natural image, logo, and signature. It is the largest manually annotated dataset for graphical object detection. Source: IIIT-AR-13K: A New Dataset for Graphical Object Detection in Documents Image Source: http://cvit.iiit.ac.in/usodi/iiitar13k.php

IndustReal is an ego-centric, multi-modal dataset where 27 participants are challenged to perform assembly and maintenance procedures on a construction-toy car. The dataset is annotated for action recognition, assembly state detection, and procedure step recognition. IndustReal includes 38 execution errors in a total of 84 videos, with 14 exclusive to validation and test sets and therefore suitable for testing the robustness of algorithms against unseen errors in procedural tasks. IndustReal offers open-source 3D models for all parts to promote the use of synthetic data for scalable approaches on this dataset, as well as reproducibility. All assembly parts used in the dataset are 3D printed. This ensures reproducibility and future availability of the model and allows for growth via community effort. IndustReal can be used to test methods towards (video) procedure understanding, industrial automation, and to quantify performance of augmented reality applications.

Lytro Illum is a new light field dataset using a Lytro Illum camera. 640 light fields are collected with significant variations in terms of size, textureness, background clutter and illumination, etc. Micro-lens image arrays and central viewing images are generated, and corresponding ground-truth maps are produced. Source: Lytro Illum

A large-scale video dataset for MOR in aerial videos. Source: MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos

Open Images is a computer vision dataset covering ~9 million images with labels spanning thousands of object categories. A subset of 1.9M includes diverse annotations types. - 15,851,536 boxes on 600 classes - 2,785,498 instance segmentations on 350 classes - 3,284,280 relationship annotations on 1,466 relationships - 675,155 localized narratives (synchronized voice, mouse trace, and text caption) - 66,391,027 point-level annotations on 5,827 classes - 61,404,966 image-level labels on 20,638 classes Images are under a CC BY 2.0 license, annotations under CC BY 4.0 license.

The dataset is split between train, test and val folders. Files consist of 60 seconds recordings that were cut from longer recording sessions. Cuts from a single recording session are all in the same training split. Each dat file is a binary file in which events are encoded using 4 bytes (unsigned int32) for the timestamps and 4 bytes (unsigned int32) for the data, encoding is little-endian ordering. The data is composed of 14 bits for the x position, 14 bits for the y position and 1 bit for the polarity (encoded as -1/1). Annotations use the numpy format and can simply be loaded form python using numpy boxes = np.load(path) Boxes have the following fields: * x abscissa of the top left corner in pixels * y ordinate of the top left corner in pixels * w width of the boxes in pixel * h height of the boxes in pixel * ts timestamp of the box in the sequence in microseconds * class_id 0 for pedestrians, 1 for two wheelers, 2 for cars, 3 for trucks, 4 for buses, 5 for traffic signs, 6 for traffic lights

Includes 4405 images with 111251 heads annotated. Source: Detecting Heads using Feature Refine Net and Cascaded Multi-Scale Architecture

VEDAI is a dataset for Vehicle Detection in Aerial Imagery, provided as a tool to benchmark automatic target recognition algorithms in unconstrained environments. The vehicles contained in the database, in addition of being small, exhibit different variabilities such as multiple orientations, lighting/shadowing changes, specularities or occlusions. Furthermore, each image is available in several spectral bands and resolutions. A precise experimental protocol is also given, ensuring that the experimental results obtained by different people can be properly reproduced and compared. We also give the performance of some baseline algorithms on this dataset, for different settings of these algorithms, to illustrate the difficulties of the task and provide baseline comparisons.

aiMotive dataset is a multimodal dataset for robust autonomous driving with long-range perception. The dataset consists of 176 scenes with synchronized and calibrated LiDAR, camera, and radar sensors covering a 360-degree field of view. The collected data was captured in highway, urban, and suburban areas during daytime, night, and rain and is annotated with 3D bounding boxes with consistent identifiers across frames.

[1]: https://www.projectaria.com/datasets/ase/ "" [2]: https://facebookresearch.github.io/projectaria_tools/docs/open_datasets/aria_synthetic_environments_dataset "" [3]: https://www.projectaria.com/research/ "" Aria Synthetic Environments is a large-scale, fully simulated dataset created by Project Aria¹[1]. It consists of procedurally-generated interior layouts filled with 3D objects, simulated with the sensor characteristics of Aria glasses¹[1]. Here are some key features of this dataset: - 100,000 unique multi-room interior scenes: These scenes are procedurally generated to produce a diverse set of interior scenes. Each scene has a unique room graph connecting multiple rooms, and unique placement of architectural features, such as windows, doors, and pillars¹[1]. - Populated with high-quality 3D objects: Each of the 100,000 unique scenes is filled with objects from a digital library, each with high-quality materials and geometry¹[1]. - Simulated sensor data per sequence: This includes 1 x outward-facing RGB camera stream simulated with Aria camera & lens characteristics¹[1]. - Ground Truth Annotations: These include 6DoF camera trajectory, 3D floor plan, 2D instance segmentation, and 2D depth map¹[1]. - Realistic simulated trajectory within each environment: Device trajectories are simulated within each environment according to a set of rules that mirror how users walk while wearing Project Aria glasses¹[1]. This dataset sets a new precedent for the scale of indoor environment datasets and surfaces exciting new research opportunities for tasks related to 3D scene reconstruction, and object detection and tracking¹[1]. It's designed to provide the wider research community with a dataset large enough to surface new challenges and research opportunities²[2]. (1) Aria Synthetic Environments Dataset | Project Aria. https://www.projectaria.com/datasets/ase/. (2) Aria Synthetic Environments Dataset | Project Aria Tools. https://facebookresearch.github.io/projectaria_tools/docs/open_datasets/aria_synthetic_environments_dataset. (3) Project Aria Research | Project Aria. https://www.projectaria.com/research/.

The complete blood count (CBC) dataset contains 360 blood smear images along with their annotation files splitting into Training, Testing, and Validation sets. The training folder contains 300 images with annotations. The testing and validation folder both contain 60 images with annotations. We have done some modifications over the original dataset to prepare this CBC dataset where some of the image annotation files contain very low red blood cells (RBCs) than actual and one annotation file does not include any RBC at all although the cell smear image contains RBCs. So, we clear up all the fallacious files and split the dataset into three parts. Among the 360 smear images, 300 blood cell images with annotations are used as the training set first, and then the rest of the 60 images with annotations are used as the testing set. Due to the shortage of data, a subset of the training set is used to prepare the validation set which contains 60 images with annotations.

The GMVD dataset consists of synthetic scenes captured using the GTA-V and Unity graphics engines. The dataset covers a variety of scenes, along with different conditions including day time variations (morning, afternoon, evening, night) and weather variations (sunny, cloudy, rainy, snowy). The purpose of the dataset is twofold. The first is to benchmark the generalization capabilities of Multi-View Detection algorithms. The second purpose is to serve as a synthetic training source from which the trained models can be directly applied on real-world data.

HJDataset is a large dataset of Historical Japanese Documents with Complex Layouts. It contains over 250,000 layout element annotations of seven types. In addition to bounding boxes and masks of the content regions, it also includes the hierarchical structures and reading orders for layout elements. The dataset is constructed using a combination of human and machine efforts. Source: A Large Dataset of Historical Japanese Documents with Complex Layouts Image Source: https://dell-research-harvard.github.io/HJDataset/

InsPLAD is a Dataset for Power Line Asset Inspection containing 10,607 high-resolution Unmanned Aerial Vehicles colour images. It contains 17 unique power line assets captured from real-world operating power lines. Some of those assets (five, to be precise) are also annotated regarding their conditions. They present the following defects: corrosion (4 of them), broken/missing cap (1 of them), and bird's nest presence (1 of them). Three image-level computer vision tasks covered by InsPLAD: * Object detection, evaluated through the AP metric * Defect classification, evaluated through Balanced Accuracy * Anomaly detection, evaluated through the AUROC metric

OAK is a dataset for online continual object detection benchmark with an egocentric video dataset. OAK adopts the KrishnaCam videos, an ego-centric video stream collected over nine months by a graduate student. OAK provides exhaustive bounding box annotations of 80 video snippets (~17.5 hours) for 105 object categories in outdoor scenes.

OoDIS is a benchmark dataset for anomaly instance segmentation, crucial for autonomous vehicle safety. It extends existing anomaly segmentation benchmarks to focus on the segmentation of individual out-of-distribution (OOD) objects. The dataset addresses the need for identifying and segmenting unknown objects, which are critical to avoid accidents. It includes diverse scenes with various anomalies, pushing the boundaries of current segmentation capabilities. The benchmark is focused on evaluation of detection and instance segmentation of unexpected obstacles on roads. For more details, refer to the OoDIS paper

The evaluation of object detection models is usually performed by optimizing a single metric, e.g. mAP, on a fixed set of datasets, e.g. Microsoft COCO and Pascal VOC. Due to image retrieval and annotation costs, these datasets consist largely of images found on the web and do not represent many real-life domains that are being modelled in practice, e.g. satellite, microscopic and gaming, making it difficult to assert the degree of generalization learned by the model. We introduce the Roboflow-100 (RF100) consisting of 100 datasets, 7 imagery domains, 224,714 images, and 805 class labels with over 11,170 labelling hours. We derived RF100 from over 90,000 public datasets, 60 million public images that are actively being assembled and labelled by computer vision practitioners in the open on the web application Roboflow Universe. By releasing RF100, we aim to provide a semantically diverse, multi-domain benchmark of datasets to help researchers test their model's generalizability with real-life data. RF100 download and benchmark replication are available on GitHub.

Comprises of 171,191 video segments from 346 high-quality soccer games. The database contains 702,096 bounding boxes, 37,709 essential event labels with time boundary and 17,115 highlight annotations for object detection, action recognition, temporal action localization, and highlight detection tasks. Source: SoccerDB: A Large-Scale Database for Comprehensive Video Understanding

SODA-D is a large-scale dataset tailored for small object detection in driving scenario, which is built on top of MVD dataset and owned data, where the former is a dataset dedicated to pixel-level understanding of street scenes, and the latter is mainly captured by onboard cameras and mobile phones. With 24704 well-chosen and high-quality images of driving scenarios, SODA-D comprises 277596 instances of 9 categories with horizontal bounding boxes.

SpaceNet 1: Building Detection v1 is a dataset for building footprint detection. The data is comprised of 382,534 building footprints, covering an area of 2,544 sq. km of 3/8 band WorldView-2 imagery (0.5 m pixel res.) across the city of Rio de Janeiro, Brazil. The images are processed as 200m×200m tiles with associated building footprint vectors for training. The main use case for the detection of building footprints from satellite imagery is to aid foundational mapping.

Specially designed to evaluate active learning for video object detection in road scenes. Source: Temporal Coherence for Active Learning in Videos