Image Recognition

The Caltech-UCSD Birds-200-2011 (CUB-200-2011) dataset is the most widely-used dataset for fine-grained visual categorization task. It contains 11,788 images of 200 subcategories belonging to birds, 5,994 for training and 5,794 for testing. Each image has detailed annotations: 1 subcategory label, 15 part locations, 312 binary attributes and 1 bounding box. The textual information comes from Reed et al.. They expand the CUB-200-2011 dataset by collecting fine-grained natural language descriptions. Ten single-sentence descriptions are collected for each image. The natural language descriptions are collected through the Amazon Mechanical Turk (AMT) platform, and are required at least 10 words, without any information of subcategories and actions. Source: Fine-grained Visual-textual Representation Learning Image Source: http://www.vision.caltech.edu/visipedia/CUB-200-2011.html

MINC is a large-scale, open dataset of materials in the wild. Source: Material Recognition in the Wild with the Materials in Context Database Image Source: Bell et al

Omni-Realm Benchmark (OmniBenchmark) is a diverse (21 semantic realm-wise datasets) and concise (realm-wise datasets have no concepts overlapping) benchmark for evaluating pre-trained model generalization across semantic super-concepts/realms, e.g. across mammals to aircraft. [ECCV2022]

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

Multimodal material segmentation (MCubeS) dataset contains 500 sets of images from 42 street scenes. Each scene has images for four modalities: RGB, angle of linear polarization (AoLP), degree of linear polarization (DoLP), and near-infrared (NIR). The dataset provides annotated ground truth labels for both material and semantic segmentation for every pixel. The dataset is divided training set with 302 image sets, validation set with 96 image sets, and test set with 102 image sets. Each image has 1224 x 1024 pixels and a total of 20 class labels per pixel.

In this project, we formally present the task of Open-domain Visual Entity recognitioN (OVEN), where a model need to link an image onto a Wikipedia entity with respect to a text query. We construct OVEN-Wiki by re-purposing 14 existing datasets with all labels grounded onto one single label space: Wikipedia entities. OVEN challenges models to select among six million possible Wikipedia entities, making it a general visual recognition benchmark with the largest number of labels.

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

The application-oriented license plate (AOLP) benchmark database has 2049 images of Taiwan license plates. This database is categorized into three subsets: access control (AC) with 681 samples, traffic law enforcement (LE) with 757 samples, and road patrol (RP) with 611 samples. AC refers to the cases that a vehicle passes a fixed passage with a lower speed or full stop. This is the easiest situation. The images are captured under different illuminations and different weather conditions. LE refers to the cases that a vehicle violates traffic laws and is captured by roadside camera. The background are really cluttered, with road sign and multiple plates in one image. RP refers to the cases that the camera is held on a patrolling vehicle, and the images are taken with arbitrary viewpoints and distances. Source: Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs Image Source: http://aolpr.ntust.edu.tw/lab/index.html

This dataset, called RodoSol-ALPR dataset, contains 20,000 images captured by static cameras located at pay tolls owned by the Rodovia do Sol (RodoSol) concessionaire, which operates 67.5 kilometers of a highway (ES-060) in the Brazilian state of Espírito Santo. There are images of different types of vehicles (e.g., cars, motorcycles, buses and trucks), captured during the day and night, from distinct lanes, on clear and rainy days, and the distance from the vehicle to the camera varies slightly. All images have a resolution of 1,280 × 720 pixels. An important feature of the proposed dataset is that it has images of two different license plate (LP) layouts: Brazilian and Mercosur (to maintain consistency with existing works, we refer to “Brazilian” as the standard used in Brazil before the adoption of the Mercosur standard). Every image has the following information available in a text file: the vehicle’s type (car or motorcycle), the LP’s layout (Brazilian or Mercosul), its text (e.g., ABC-1234), and the position (x, y) of each of its four corners. We labeled the corners instead of just the LP bounding box to enable the training of methods that explore LP rectification, as well as the application of a wider range of data augmentation techniques. Regarding privacy concerns related to our dataset, we remark that in Brazil the LPs are related to the respective vehicles, i.e., no public information is available about the vehicle drivers/owners. Moreover, all human faces (e.g., drivers or RodoSol’s employees) were manually redacted (i.e., blurred) in each image.

This dataset aims at evaluating the License Plate Character Segmentation (LPCS) problem. The experimental results of the paper Benchmark for License Plate Character Segmentation were obtained using a dataset providing 101 on-track vehicles captured during the day. The video was recorded using a static camera in early 2015. The images of the dataset were acquired with a digital camera in Full-HD and are available in the Portable Network Graphics (PNG) format with 1920×1080 pixels each. The average size of each file is 4.08 Megabytes (a total of 8.60 Gigabytes for the entire dataset). In addition, since there are some approaches that track the car to utilize redundant information to improve the recognition results, we decided to make a dataset with multiples frames per car. In this dataset, there are, on average, 19.80 image frames per vehicle (with a standard deviation of 4.14). Source: Benchmark for License Plate Character Segmentation

WebFG-496 is a dataset for fine-grained recognition that contains 200 subcategories of the "Bird" (Web-bird), 100 subcategories of the Aircraft" (Web-aircraft), and 196 subcategories of the "Car" (Web-car). It has a total number of 53339 web training images.

LabPics Chemistry Dataset Dataset for computer vision for materials segmentation and classification in chemistry labs, medical labs, and any setting where materials are handled inside containers. The Vector-LabPics dataset comprises 7900 images of materials in various phases and processes within mostly transparent vessels in chemistry labs, medical labs and hospitals, and other environments. The images are annotated for both the vessels and the individual material phases inside them, and each instance is assigned one or more classes (liquid, solid, foam, suspension, powder, gel, granular, vapor) . The fill level, labels, corks, and other parts of the vessel are also annotated. The material classes cover the main states of matter, including liquids, solids, vapors, foams, gels, and subcategories like powder, granular, and suspension. Relationships between materials, such as which material is immersed inside other materials, are annotated. The vessel class cover glassware, labware plates, bottles, and any other type of vessel that is used to contain or carry materials. The type of vessel (e.g., syringe, tube, cup, infusion bottle/bag), and the properties of the vessel (transparent, opaque) are annotated. In addition, vessel parts such as corks, labels, spikes, and valves are annotated. Relations and hierarchies between vessels and materials are also annotated, such as which vessel contains which material or which vessels are linked or contain each other. The images were collected from various contributors and covered most aspects of chemistry lab works as well as a variety of other fields where materials are handled in container vessels. Documents specifying annotation formats are available inside the dataset file. Version 1 contain 2200 images with simple instance and semantic annotations, and is relatively simple to use, it is described in the paper "Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Data Set" Format The dataset contains annotated images for both material and vessels in chemistry labs, medical labs, and any area where liquids and solids are handled within vessels. There are two levels of annotation for each image. One annotation set for vessels and the second for the material phases inside these vessels. Vessels are defined as any container that can carry materials such as Jars, Erlenmayers, Tubes, Funnels, syringes, IV bags, and any other labware or glassware that can contain or carry materials. Material phases are any material contained within or on the vessel. For example, for two-phase separating liquids, each liquid phase is annotated as one instance. If there is foam above the liquid or a chunk of solid inside the liquid, the foam, liquid, and solid will be annotated as different phases. In addition, vessel parts like cork, labels, and valves are annotated as instances. For each instance, there is a list of all the classes it belongs to, and a list of its property. For vessels, the instance classes are the vessel type (Cup, jar, Separatory-funnel…) and the vessel properties (Transparent, Opaque…). For materials, the classes are the material types ( Liquid, solid, suspension, foam, powder…) and their properties (Scattered, On vessel surface…), and for parts, the part type (cork/label). In addition, the relations between instances are annotated. This includes which material instances are inside which vessels, which vessels are linked to each other or are inside each other (for vessels inside other vessels), and which material phase is immersed inside another material phase. In addition to instance segmentation maps, the dataset also includes semantic segmentation maps that give each pixel in the image all the classes to which it belongs. In other words, for each class (Liquid, Solid, Vessel, Foam), there is a map of all the regions in the image belonging to this class. Note that every pixel and every instance can have several classes. In addition, instances often overlap, like in the case of material inside the vessel, vessel inside the vessel, and material phase immersed inside other material (like solid inside liquid).

The ChineseLP dataset contains 411 vehicle images (mostly of passenger cars) with Chinese license plates (LPs). It consists of 252 images captured by the authors and 159 images downloaded from the internet. The images present great variations in resolution (from 143 × 107 to 2048 × 1536 pixels), illumination and background.

CUB-GHA is a dataset for fine-grained classification with human attention annotations. The dataset collects human gaze data for the fine-grained classification dataset CUB and builds a dataset named CUB-GHA (Gaze-based Human Attention).

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Goldfinch is a dataset for fine-grained recognition challenges. It contains a list of bird, butterfly, aircraft, and dog categories with relevant Google image search and Flickr search URLs. In addition, it also includes a set of active learning annotations on dog categories. Source: The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition

Vehicle-Rear is a novel dataset for vehicle identification that contains more than three hours of high-resolution videos, with accurate information about the make, model, color and year of nearly 3,000 vehicles, in addition to the position and identification of their license plates.

The Caltech Cars dataset consists of 126 rear-view photographs captured within parking lots. These images possess a resolution of 896 × 592 pixels, featuring a solitary vehicle as the primary subject. The acquisitions were made during daylight hours employing a handheld camera at roughly equivalent distances for all instances. "The dataset was presented in a PhD thesis titled 'Unsupervised learning of models for object recognition.'"

The DAPlankton dataset consists of over 110k expert-labeled plankton images. The data is divided into two subsets: DAPlankton_LAB and DAPlankton_SEA. DAPlankton_LAB consists of images captured from multiple mono-specific phytoplankton cultures, which were analysed using three different imaging instruments: Imaging FlowCytoBot (IFCB), CytoSense (CS) flow cytometer, and FlowCam (FC) imaging microscope each producing cropped images with one plankton particle in each. An expert further verified the class of each image, ensuring that there was no cross contamination between different cultures. This process resulted in a balanced dataset with negligible label uncertainty. DAPlankton_SEA consists of images captured from water samples collected from the Baltic Sea using two different imaging instruments: IFCB and CS. Each image was manually labeled by an expert. DAPlankton_SEA provides a realistic and more challenging dataset with a large class imbalance and natural intra-class variance.

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

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Introduced originally by Xiaohan Yu, Yang Zhao, Yongsheng Gao, Xiaohui Yuan, Shengwu Xiong (2021). Benchmark Platform for Ultra-Fine-Grained Visual Categorization Beyond Human Performance. In ICCV 2021. For visual categorization of leaves at the ultra-fine-grained level, in this case at the cultivar level. Includes 10 datasets: Cotton, SoyAgeing, SoyGene, SoyGlobal, SoyLocal, SoyAgeingR1, SoyAgeingR3, SoyAgeingR4, SoyAgeingR5, SoyAgeingR6.

CD-HARD comprises 102 images featuring vehicles with oblique license plates sourced from the Cars dataset. Each image within this dataset exclusively depicts a single vehicle and was captured during daylight hours. While the dataset encompasses images from diverse geographic regions, it predominantly consists of images seemingly taken in European locales. The dataset was presented in the 'License Plate Detection and Recognition in Unconstrained Scenarios' research paper.

The CLPD dataset comprises 1200 images that encompass various regions within mainland China. These images were sourced from diverse origins, including the internet, mobile devices, and in-car recording devices. While the majority of the images were recorded during daylight hours, a portion of them were captured at nighttime. The dataset predominantly features passenger cars, with a limited number of images depicting trucks and buses. The dataset was presented in the research paper titled "A Robust Attentional Framework for License Plate Recognition in the Wild."

CNFOOD-241 Contains a dataset of 241 Chinese dishes with 191,811 images. There are 170843 images in the training set and 20943 images in the validation set. All images are resized to 600x600. As some of the images in the dataset are from ChineseFoodNet, they are not supported for commercial use. CNFOOD-241-Chen is the CNFOOD-241 dataset spilt with the list introduced in the paper "Res-VMamba: Fine-Grained Food Category Visual Classification Using Selective State Space Models with Deep Residual Learning," which has random split as train, val, test three parts.

This dataset concentrates on the activities of the crowd for a fine-grained image classification task, named as Crowd Activity dataset, as automatically understanding crowd activity is meaningful for social security. This dataset is newly collected, where the images are mainly searched on the Internet and collected from streets by mobile phones. All images in this dataset contain at least one text instance. The categories come from activities of daily living and demonstrations stimulated by hot events in recent years. Specifically, this dataset consists of 21 categories and 8785 images in total. The 21 categories broadly fall into two types: activities of daily living(i.e., celebrating Christmas, holding sport meeting, holding concert, celebrating birthday party, celebrity speech, teaching, graduation ceremony, picnic, press briefing, shopping, celebrating Thanks giving day) and demonstrations (i.e., protecting animals, protecting environment, appealing for peace, Brexit, COVID-19, election, immigrant, respecting female, racial equality, mouvement des gilets jaunes).

fruit-SALAD is a synthetic image dataset with 10,000 generated images of fruit depictions. This combined semantic category and style benchmark comprises 100 instances each of 10 easily recognizable fruit categories and 10 easy distinguishable styles. See the paper on ArXiv. The carefully designed Style Aligned Artwork Dataset (SALAD) provides a controlled and balanced platform for the comparative analysis of similarity perception of different computational models. The SALAD framework allows the comparison of how these models perform semantic category and style recognition tasks, going beyond the level of anecdotal knowledge, making them robustly quantifiable and qualitatively interpretable. We used Stable Diffusion XL and StyleAligned to create the fruit-SALAD by carefully crafting text prompts and overseing the image generation process. The code to reproduce the fruit-SALAD_10k is available at GitHub. Please note that this dataset is available for academic research purposes only.

Large Shape and Texture dataset (LAS&T) is a giant dataset of shapes and textures for tasks of visual shapes and textures identification and retrieval from single image. LAS&T is the largest and most diverse dataset for shape, texture and material recognition and retrieval in 2D and 3D with 650,000 images, based on real world shapes and textures ## Overview The LAS&T Dataset aims to test the most basic aspect of vision in the most general way. Mainly the ability to identify any shape, texture, and material in any setting and environment, without being limited to specific types or classes of objects, materials, and environments. For shapes, this means identifying and retrieving any shape in 2D or 3D with every element of the shape changed between images, including the shape material and texture, orientation, size, and environment. For textures and materials, the goal is to recognize the same texture or material when appearing on different objects, environments, and light conditions. The dataset relies on shapes, textures, and materials extracted from real-world images, leading to an almost unlimited quantity and diversity of real-world natural patterns. Each section of the dataset (shapes, and textures), contains 3D parts that rely on physics-based scenes with realistic light materials and object simulation and abstract 2D parts. In addition, the real-world benchmark for 3D shapes. ## The dataset divided to several parts 3D shape recognition and retrieval. 2D shape recognition and retrieval. 3D Materials recognition and retrieval. 2D Texture recognition and retrieval. Each can be used independently for training and testing. Additional assets are a set of 350,000 natural 2D shapes extracted from real-world images 3D shape recognition real-world images benchmark

The Matador dataset is a material image dataset with hierarchical labels. The hierarchical labels are derived from a new taxonomy. For each sample of a material, we collect a local appearance image, local surface structure LiDAR scan, global context image, and record any camera motion that takes place during the capture sequence. The dataset is intended to grow over time. To date, Matador contains 57 different material categories and a total of ~7,200 images, averaging 126 samples of intraclass variance.

MatSeg Dataset for Zero-Shot Material States Segmentation: The dataset contains large-scale synthetic images for training data and highly diverse real-world image benchmarks for testing. Focusing on zero-shot class-agnostic segmentation of materials and their states. This means finding the region of materials states without pre-training on the specific material classes or states. The benchmark contains a wide range of real-world materials and states. For example: wet regions of the surface, scattered dust, minerals of rocks, the sediment of soils, rotten parts of fruits, degraded and corrosive surface regions, food and liquid states, and many others. The focus is on scattered and fragmented materials, as well as soft boundaries partial transition, and partial similarity between regions. It contains both hard segmentation maps and soft and partial similarity annotations for similar but not identical materials.

MatSim is a synthetic dataset, and natural image benchmark for computer vision-based recognition of similarities and transitions between materials and textures, focusing on identifying any material under any conditions using one or a few examples (one-shot learning), including materials states and subclasses. Source: One-shot recognition of any material anywhere using contrastive learning with physics-based rendering Image Source: https://arxiv.org/pdf/2212.00648v1.pdf

SpectroVision is a dataset of 14,400 high resolution texture images and spectral measurements collected from a PR2 mobile manipulator that interacted with 144 household objects from eight material categories. Source: https://github.com/Healthcare-Robotics/spectrovision Image Source: https://github.com/Healthcare-Robotics/spectrovision

The dataset contains procedurally generated images of transparent vessels containing liquid and objects . The data for each image includes segmentation maps, 3d depth maps, and normal maps of of the liquid or object inside the transparent vessel, and the vessel. In addition, the properties of the materials inside the containers are given(color/transparency/roughness/metalness). In addition, a natural image benchmark for the 3d/depth estimation of objects inside transparent containers is supplied. 3d models of the objects (GTLF) are also supplied.

The UFPR-VCR dataset contains 10,039 images of 9,502 distinct vehicles across various categories, including cars, vans, buses, and trucks. The images capture a broad spectrum of real-world conditions, such as frontal and rear views, partial occlusions, diverse lighting situations, and nighttime scenes. The dataset was designed to address Vehicle Color Recognition (VCR) in more challenging scenarios than those explored in previous studies.

WikiChurches is a dataset for architectural style classification, consisting of 9,485 images of church buildings. Both images and style labels were sourced from Wikipedia. The dataset can serve as a benchmark for various research fields, as it combines numerous real-world challenges: fine-grained distinctions between classes based on subtle visual features, a comparatively small sample size, a highly imbalanced class distribution, a high variance of viewpoints, and a hierarchical organization of labels, where only some images are labeled at the most precise level. In addition, we provide 631 bounding box annotations of characteristic visual features for 139 churches from four major categories. These annotations can, for example, be useful for research on fine-grained classification, where additional expert knowledge about distinctive object parts is often available.

A high-quality, balanced dataset of 330,000 images featuring various types of Chinese license plates. The dataset is generated using Generative Adversarial Networks (GANs), ensuring excellent image quality and a balanced distribution of different license plate types. This dataset is perfect for training and evaluating license plate recognition models. Note: In order to ensure the balance of the characters, there may be some license plate numbers that do not conform to common sense (for example, all letters), but this phenomenon does not affect the training.