BigDetection is a new large-scale benchmark to build more general and powerful object detection systems. It leverages the training data from existing datasets (LVIS, OpenImages and Object365) with carefully designed principles, and curate a larger dataset for improved detector pre-training. BigDetection dataset has 600 object categories and contains 3.4M training images with 36M object bounding boxes.

The CocoChorales Dataset CocoChorales is a dataset consisting of over 1400 hours of audio mixtures containing four-part chorales performed by 13 instruments, all synthesized with realistic-sounding generative models. CocoChorales contains mixes, sources, and MIDI data, as well as annotations for note expression (e.g., per-note volume and vibrato) and synthesis parameters (e.g., multi-f0). ## Dataset We created CocoChorales using two generative models produced by Magenta: Coconet and MIDI-DDSP. The dataset was created in two stages. First, we used a trained Coconet model to generate a large set of four-part chorales in the style of J.S. Bach. The output of this first stage is a set of note sequences, stored as MIDI, to which we assign a tempo and add random timing variations to each note (for added realism). In the second stage, we use MIDI-DDSP to synthesize these MIDI files into audio, resulting in audio clips that sound like the chorales were performed by live musicians. This MIDI-DDSP model was trained on URMP. We define a set of ensembles that consist of the following instruments, in Soprano, Alto, Tenor, Bass (SATB) order: <ul> <li><strong>String Ensemble</strong>: Violin 1, Violin 2, Viola, Cello.</li> <li><strong>Brass Ensemble</strong>: Trumpet, French Horn, Trombone, Tuba.</li> <li><strong>Woodwind Ensemble</strong>: Flute, Oboe, Clarinet, Bassoon.</li> <li><strong>Random Ensemble</strong>: Each SATB part is randomly assigned an instrument according to the following: <ul class="nested"> <li><em>Soprano</em>: Violin, Flute, Trumpet, Clarinet, Oboe.</li> <li><em>Alto</em>: Violin, Viola, Flute, Clarinet, Oboe, Saxophone, Trumpet, French Horn.</li> <li><em>Tenor</em>: Viola, Cello, Clarinet, Saxophone, Trombone, French Horn.</li> <li><em>Bass</em>: Cello, Double Bass, Bassoon, Tuba.</li> </ul> </li> </ul> Each instrument in the ensemble is synthesized separately, with annotations for the high-level expressions used for each note (e.g., vibrato, note volume, note brightness, etc; all expressions shown here, and more details in Sections 3.2 and B.3 of the MIDI-DDSP paper) as well as detailed low-level annotations for synthesis parameters (e.g., f₀’s, amplitudes of each harmonic, etc). Because the MIDI-DDSP model skews sharp, we randomly applied pitch augmentation to the f₀’s (see Figure 2, here) to . All four audio clips for each instrument in the ensemble are then mixed together to produce an example in the dataset. Because all of the data in CocoChorales originate from generative models, all of the annotations perfectly correspond to the audio data. All in all, the dataset contains 240,000 examples, 60,000 mixes from each one of the four ensemble types above. Each ensemble has its own train/validation/test split All of the audio is 16 kHz, 16-bit PCM data. Each example contains: <ul> <li>A mixture</li> <li>Source audio for all four instruments <ul class="nested"> <li>Gain applied to each source</li> </ul> </li> <li>MIDI with tempo and precise timing</li> <li>The name of the ensemble with instrument names</li> <li>Note expression annotations for every note: <ul class="nested"> <li>Volume, Volume Fluctuation, Volume Peak Position, Vibrato, Brightness, and Attack Noise used by MIDI-DDSP to synthesize every note (see Sections 3.2 and B.3 of the MIDI-DDSP paper for more details)</li> </ul> </li> <li>Synthesis parameters for every source (250 Hz): <ul class="nested"> <li>Fundamental frequency (f₀), amplitude, amplitude of all harmonics, filtered noise parameters</li> <li>Amount of pitch augmentation applied</li> </ul> </li> </ul> ## Further Details A detailed view of the contents of the CocoChorales dataset is provided at this link. # Download For download instructions, please see this github page. The compressed version of the full dataset is 2.9 Tb, and the uncompressed version is larger than 4 Tb. There is a "tiny" version for download as well. MD5 Hashes for all zipped files in the download are provided here. # License The CocoChorales dataset was made by Yusong Wu and is available under the Creative Commons Attribution 4.0 International (CC-BY 4.0). # How to Cite If you use CocoChorales in your work, we ask that you cite the following paper where it was introduced: Yusong Wu, Josh Gardner, Ethan Manilow, Ian Simon, Curtis Hawthorne, and Jesse Engel. “The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling.” arXiv preprint, arXiv:2209.14458, 2022. You can also use the following bibtex entry: @article{wu2022chamber, title = {The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling}, author = {Wu, Yusong and Gardner, Josh and Manilow, Ethan and Simon, Ian and Hawthorne, Curtis and Engel, Jesse}, journal={arXiv preprint arXiv:2209.14458}, year = {2022}, }

Vehicle-to-Everything (V2X) network has enabled collaborative perception in autonomous driving, which is a promising solution to the fundamental defect of stand-alone intelligence including blind zones and long-range perception. However, the lack of datasets has severely blocked the development of collaborative perception algorithms. In this work, we release DOLPHINS: Dataset for cOllaborative Perception enabled Harmonious and INterconnected Self-driving, as a new simulated large-scale various-scenario multi-view multi-modality autonomous driving dataset, which provides a ground-breaking benchmark platform for interconnected autonomous driving. DOLPHINS outperforms current datasets in six dimensions: temporally-aligned images and point clouds from both vehicles and Road Side Units (RSUs) enabling both Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) based collaborative perception; 6 typical scenarios with dynamic weather conditions make the most various interconnected autonomous driving dataset; meticulously selected viewpoints providing full coverage of the key areas and every object; 42376 frames and 292549 objects, as well as the corresponding 3D annotations, geo-positions, and calibrations, compose the largest dataset for collaborative perception; Full-HD images and 64-line LiDARs construct high-resolution data with sufficient details; well-organized APIs and open-source codes ensure the extensibility of DOLPHINS. We also construct a benchmark of 2D detection, 3D detection, and multi-view collaborative perception tasks on DOLPHINS. The experiment results show that the raw-level fusion scheme through V2X communication can help to improve the precision as well as to reduce the necessity of expensive LiDAR equipment on vehicles when RSUs exist, which may accelerate the popularity of interconnected self-driving vehicles.

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/

Qualitative dataset with real blurred videos, created by using beam-splitter setup in lab environment

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset

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.

A dataset of over 65,000 pairs of incorrectly white-balanced images and their corresponding correctly white-balanced images. Source: When Color Constancy Goes Wrong: Correcting Improperly White-Balanced Images

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.

To comprehensively evaluate the effectiveness and generalization ability of style transfer methods, we build StyleBench that covers 73 distinct styles, ranging from paintings, flat illustrations, 3D rendering to sculptures with varying materials. For each style, we collect 5-7 distinct images with variations. In total, our StyleBench contains 490 images across diverse styles.

We construct a style-balanced dataset, called StyleGallery, covering several open source datasets. Specifically, StyleGallery includes JourneyDB, a dataset comprising a broad spectrum of diverse styles derived from MidJourney, and WIKIART, with extensive fine-grained painting styles, such as pointillism and ink drawing, and a subset of stylized images from LAION-Aesthetics.

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.

VIVOS is a free Vietnamese speech corpus consisting of 15 hours of recording speech prepared for Automatic Speech Recognition task. The corpus was prepared by AILAB, a computer science lab of VNUHCM - University of Science, with Prof. Vu Hai Quan is the head of. We publish this corpus in hope to attract more scientists to solve Vietnamese speech recognition problems. The corpus should only be used for academic purposes.

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

The scarcity of high-quality and multi-task singing datasets significantly hinders the development of diverse controllable and personalized singing tasks, as existing singing datasets suffer from low quality, limited diversity of languages and singers, absence of multi-technique information and realistic music scores, and poor task suitability. To tackle these problems, we present GTSinger, a large Global, multi-Technique, free-to-use, high-quality singing corpus with realistic music scores, designed for all singing tasks, along with its benchmarks. Particularly, (1) we collect 80.59 hours of high-quality songs, forming the largest recorded singing dataset; (2) 20 professional singers across nine languages offer diverse timbres and styles; (3) we provide controlled comparison and phoneme-level annotations of six singing techniques, helping technique modeling and control; (4) GTSinger offers realistic music scores, assisting real-world musical composition; (5) singing voices are accompanied by manual phoneme-to-audio alignments, global style labels, and 16.16 hours of paired speech for various singing tasks. Moreover, to facilitate the use of GTSinger, we conduct four benchmark experiments: technique-controllable singing voice synthesis, technique recognition, style transfer, and speech-to-singing conversion.

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.

Aims to facilitate research in caricature recognition. All the caricatures and face images were collected from the Web. Compared with two existing datasets, this dataset is much more challenging, with a much greater number of available images, artistic styles and larger intra-personal variations. Source: WebCaricature: a benchmark for caricature recognition

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/.

Using the proposed beam-splitter acquisition system, we have collected a new real-world video deblurring dataset (BSD). We collected blurry/sharp video sequences for three different blur intensity settings (sharp exposure time – blurry exposure time), 1ms–8ms, 2ms–16ms, and 3ms–24ms(most blur), respectively. Github