2D Classification

TinyPerson is a benchmark for tiny object detection in a long distance and with massive backgrounds. The images in TinyPerson are collected from the Internet. First, videos with a high resolution are collected from different websites. Second, images from the video are sampled every 50 frames. Then images with a certain repetition (homogeneity) are deleted, and the resulting images are annotated with 72,651 objects with bounding boxes by hand. Source: https://arxiv.org/abs/1912.10664 Image Source: https://arxiv.org/pdf/1912.10664.pdf

The Chinese City Parking Dataset (CCPD) is a dataset for license plate detection and recognition. It contains over 250k unique car images, with license plate location annotations. Source: Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline Image Source: Xu et al

The INRIA Person dataset is a dataset of images of persons used for pedestrian detection. It consists of 614 person detections for training and 288 for testing. Source: http://pascal.inrialpes.fr/data/human/ Image Source: https://www.researchgate.net/figure/Some-human-examples-of-the-INRIA-person-dataset-4-Though-the-examples-are-aligned_fig2_224135181

RADIATE (RAdar Dataset In Adverse weaThEr) is new automotive dataset created by Heriot-Watt University which includes Radar, Lidar, Stereo Camera and GPS/IMU. The data is collected in different weather scenarios (sunny, overcast, night, fog, rain and snow) to help the research community to develop new methods of vehicle perception. The radar images are annotated in 7 different scenarios: Sunny (Parked), Sunny/Overcast (Urban), Overcast (Motorway), Night (Motorway), Rain (Suburban), Fog (Suburban) and Snow (Suburban). The dataset contains 8 different types of objects (car, van, truck, bus, motorbike, bicycle, pedestrian and group of pedestrians). Source: https://github.com/marcelsheeny/radiate_sdk Image Source: https://github.com/marcelsheeny/radiate_sdk

RecipeQA is a dataset for multimodal comprehension of cooking recipes. It consists of over 36K question-answer pairs automatically generated from approximately 20K unique recipes with step-by-step instructions and images. Each question in RecipeQA involves multiple modalities such as titles, descriptions or images, and working towards an answer requires (i) joint understanding of images and text, (ii) capturing the temporal flow of events, and (iii) making sense of procedural knowledge. Source: RecipeQA

Collected with the Autonomoose autonomous vehicle platform, based on a modified Lincoln MKZ. Source: Canadian Adverse Driving Conditions Dataset

DSEC is a stereo camera dataset in driving scenarios that contains data from two monochrome event cameras and two global shutter color cameras in favorable and challenging illumination conditions. In addition, we collect Lidar data and RTK GPS measurements, both hardware synchronized with all camera data. One of the distinctive features of this dataset is the inclusion of VGA-resolution event cameras. Event cameras have received increasing attention for their high temporal resolution and high dynamic range performance. However, due to their novelty, event camera datasets in driving scenarios are rare. This work presents the first high-resolution, large-scale stereo dataset with event cameras.

There exist several datasets for saliency detection, but none of them is specifically designed for high-resolution salient object detection. High-Resolution Salient Object Detection (HRSOD) dataset, containing 1610 training images and 400 test images. The total 2010 images are collected from the website of Flickr with the license of all creative commons. Pixel-level ground truths are manually annotated by 40 subjects. The shortest edge of each image in HRSOD is more than 1200 pixels.

KAIST-Radar (K-Radar) is a novel large-scale object detection dataset and benchmark that contains 35K frames of 4D Radar tensor (4DRT) data with power measurements along the Doppler, range, azimuth, and elevation dimensions, together with carefully annotated 3D bounding box labels of objects on the roads. K-Radar includes challenging driving conditions such as adverse weathers (fog, rain, and snow) on various road structures (urban, suburban roads, alleyways, and highways). In addition to the 4DRT, we provide auxiliary measurements from carefully calibrated high-resolution Lidars, surround stereo cameras, and RTK-GPS.

OpenImages V6 is a large-scale dataset , consists of 9 million training images, 41,620 validation samples, and 125,456 test samples. It is a partially annotated dataset, with 9,600 trainable classes

The Cars Overhead With Context (COWC) data set is a large set of annotated cars from overhead. It is useful for training a device such as a deep neural network to learn to detect and/or count cars. Source: A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning Image Source: https://gdo152.llnl.gov/cowc/

IP102 contains more than 75,000 images belonging to 102 categories, which exhibit a natural long-tailed distribution. Source: IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition

MINOS is a simulator designed to support the development of multisensory models for goal-directed navigation in complex indoor environments. MINOS leverages large datasets of complex 3D environments and supports flexible configuration of multimodal sensor suites.

Satlas is a remote sensing dataset and benchmark that is large in both breadth, featuring all of the aforementioned applications and more, as well as scale, comprising 290M labels under 137 categories and 7 label modalities. Source: Satlas: A Large-Scale, Multi-Task Dataset for Remote Sensing Image Understanding Image Source: https://arxiv.org/pdf/2211.15660v1.pdf

The Sku110k dataset provides 11,762 images with more than 1.7 million annotated bounding boxes captured in densely packed scenarios, including 8,233 images for training, 588 images for validation, and 2,941 images for testing. There are around 1,733,678 instances in total. The images are collected from thousands of supermarket stores and are of various scales, viewing angles, lighting conditions, and noise levels. All the images are resized into a resolution of one megapixel. Most of the instances in the dataset are tightly packed and typically of a certain orientation in the rage of [−15∘, 15∘]. Source: Rethinking Object Detection in Retail Stores Image Source: https://github.com/eg4000/SKU110K_CVPR19

MIR-1K (Multimedia Information Retrieval lab, 1000 song clips) is a dataset designed for singing voice separation. It contains: * 1000 song clips with the music accompaniment and the singing voice recorded as left and right channels, respectively, * Manual annotations of pitch contours in semitone, indices and types for unvoiced frames, lyrics, and vocal/non-vocal segments, * The speech recordings of the lyrics by the same person who sang the songs. The duration of each clip ranges from 4 to 13 seconds, and the total length of the dataset is 133 minutes. These clips are extracted from 110 karaoke songs which contain a mixture track and a music accompaniment track. These songs are freely selected from 5000 Chinese pop songs and sung by researchers from MIR lab (8 females and 11 males). Most of the singers are amateur and do not have professional music training. Source: https://sites.google.com/site/unvoicedsoundseparation/mir-1k Audio Source: https://sites.google.com/site/unvoicedsoundseparation/sounddemosforjournal

A composite dataset that unifies semantic segmentation datasets from different domains. Source: MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation

We introduce an object detection dataset in challenging adverse weather conditions covering 12000 samples in real-world driving scenes and 1500 samples in controlled weather conditions within a fog chamber. The dataset includes different weather conditions like fog, snow, and rain and was acquired by over 10,000 km of driving in northern Europe. The driven route with cities along the road is shown on the right. In total, 100k Objekts were labeled with accurate 2D and 3D bounding boxes. The main contributions of this dataset are: - We provide a proving ground for a broad range of algorithms covering signal enhancement, domain adaptation, object detection, or multi-modal sensor fusion, focusing on the learning of robust redundancies between sensors, especially if they fail asymmetrically in different weather conditions. - The dataset was created with the initial intention to showcase methods, which learn of robust redundancies between the sensor and enable a raw data sensor fusion in case of asymmetric sensor failure induced through adverse weather effects. - In our case we departed from proposal level fusion and applied an adaptive fusion driven by measurement entropy enabling the detection also in case of unknown adverse weather effects. This method outperforms other reference fusion methods, which even drop in below single image methods. - Please check out our paper for more information.

The Extended Optical Remote Sensing Saliency Detection (EORSSD) dataset is an extension of the ORSSD dataset. This new dataset is larger and more varied than the original. It contains 2,000 images and corresponding pixel-wise ground truth, which includes many semantically meaningful but challenging images. Source: https://github.com/rmcong/EORSSD-dataset Image Source: Zhang et al

The iKala dataset is a singing voice separation dataset that comprises of 252 30-second excerpts sampled from 206 iKala songs (plus 100 hidden excerpts reserved for MIREX data mining contest). The music accompaniment and the singing voice are recorded at the left and right channels respectively. Additionally, the human-labeled pitch contours and timestamped lyrics are provided. This dataset is not available anymore. Source: http://mac.citi.sinica.edu.tw/ikala/ 1. "T" as a sofa: The "T" horizontal strip can mimic the back of a sofa with a delicate cushion or details of the uphols or appliances with the color button. The "T" vertical strip can show a feet or arm of the sofa, shiny, yet firm. 2. Merge "P": Put "P" next to "T", your curve to delicately with the top "T." It is intertwined. The circular part of "P" can show a cushion or a curved chair and synchronize the subject of furniture. Make sure "P" is visually relying on "T", which reflects the relationship of cohesion and balance. 5. Coherence of "B" and "I": "B" can be aligned as a pair of cushions or a modern chair, with mild curves with glossy and modern aesthetics. "I" can be a symbol of a shiny furniture or a vertical light bar and completes the shapes without overburdess them. Color palette 4: Includes soft soil colors such as beige, top and gray shades, along with silent or silver gold tips to touch elegance. Consider a slope effect to enhance modernity, to keep colors elegant and complex. 5. Connect the letters: Use the overlap or intertwined edges that the letters meet for the symbol of unity. The plan should allow viewers to distinguish each letter while feeling part of the same "structure". 6. Background patterns: Use delicate geometric patterns or textures that mimic fabrics or furniture materials such as wood seeds or woven fibers. These patterns must remain minimalist and focus on highlighting the logo, while maintaining communication. While it deals with the subject of furniture and design, this concept conveys modernity, creativity and professional. If you like, I can create a draft design for better visualization.

PlantDoc is a dataset for visual plant disease detection. The dataset contains 2,598 data points in total across 13 plant species and up to 17 classes of diseases, involving approximately 300 human hours of effort in annotating internet scraped images. Source: PlantDoc: A Dataset for Visual Plant Disease Detection

Argoverse-HD is a dataset built for streaming object detection, which encompasses real-time object detection, video object detection, tracking, and short-term forecasting. It contains the video data from Argoverse 1.1 with our own MS COCO-style bounding box annotations with track IDs. The annotations are backward-compatible with COCO as one can directly evaluate COCO pre-trained models on this dataset to estimate the efficiency or the cross-dataset generalization capability of the models. The dataset contains high-quality and temporally-dense annotations for high-resolution videos (1920 x 1200 @ 30 FPS). Overall, there are 70,000 image frames and 1.3 million bounding boxes. Argoverse-HD is the dataset used in the Streaming Perception Challenge, which includes two tracks: - Detection-only (real-time object detection). In this track, the participants will develop single-frame object detectors as they would for COCO and LVIS challenges. The crucial distinction is that the evaluation will score latency through streaming accuracy. - Full-stack. In this track, the method is unrestricted. However, most likely tracking and forecasting will be used to compensate for the latency of the detectors. By default, all submissions measure their latency on a V100 GPU with the official toolkit.

DeepFish as a benchmark suite with a large-scale dataset to train and test methods for several computer vision tasks. The dataset consists of approximately 40 thousand images collected underwater from 20 habitats in the marine environments of tropical Australia. It contains classification labels as well as point-level and segmentation labels to have a more comprehensive fish analysis benchmark. These labels enable models to learn to automatically monitor fish count, identify their locations, and estimate their sizes. Source: https://github.com/alzayats/DeepFish Image Source: https://github.com/alzayats/DeepFish

The National Institutes of Health’s Clinical Center has made a large-scale dataset of CT images publicly available to help the scientific community improve detection accuracy of lesions. While most publicly available medical image datasets have less than a thousand lesions, this dataset, named DeepLesion, has over 32,000 annotated lesions (220GB) identified on CT images. DeepLesion, a dataset with 32,735 lesions in 32,120 CT slices from 10,594 studies of 4,427 unique patients. There are a variety of lesion types in this dataset, such as lung nodules, liver tumors, enlarged lymph nodes, and so on. It has the potential to be used in various medical image applications

The Freiburg-Berkeley Motion Segmentation Dataset (FBMS-59) is a dataset for motion segmentation, which extends the BMS-26 dataset with 33 additional video sequences. A total of 720 frames is annotated. FBMS-59 comes with a split into a training set and a test set. Typical challenges appear in both sets. Source: https://lmb.informatik.uni-freiburg.de/resources/datasets/moseg.en.html Image Source: https://lmb.informatik.uni-freiburg.de/resources/datasets/moseg.en.html

Consists of annotated frames containing GI procedure tools such as snares, balloons and biopsy forceps, etc. Beside of the images, the dataset includes ground truth masks and bounding boxes and has been verified by two expert GI endoscopists. Source: Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy

ModaNet is a street fashion images dataset consisting of annotations related to RGB images. ModaNet provides multiple polygon annotations for each image. Each polygon is associated with a label from 13 meta fashion categories. The annotations are based on images in the PaperDoll image set, which has only a few hundred images annotated by the superpixel-based tool. Source: ModaNet Image Source: ModaNet

Touchdown is a corpus for executing navigation instructions and resolving spatial descriptions in visual real-world environments. The task is to follow instruction to a goal position and there find a hidden object, Touchdown the bear. Source: https://github.com/lil-lab/touchdown Image Source: https://github.com/lil-lab/touchdown

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.

An annotated image memorability dataset to date (with 60,000 labeled images from a diverse array of sources). Source: Understanding and Predicting Image Memorability at a Large Scale

The RSBlur dataset provides pairs of real and synthetic blurred images with ground truth sharp images. The dataset enables the evaluation of deblurring methods and blur synthesis methods on real-world blurred images. Training, validation, and test sets consist of 8,878, 1,120, and 3,360 blurred images, respectively.

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.

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

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