The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.
13,883 PAPERS • 100 BENCHMARKS
The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection. The publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld. ILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”. The ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.
13,218 PAPERS • 40 BENCHMARKS
ImageNet-Sketch data set consists of 50,889 images, approximately 50 images for each of the 1000 ImageNet classes. The data set is constructed with Google Image queries "sketch of ", where is the standard class name. Only within the "black and white" color scheme is searched. 100 images are initially queried for every class, and the pulled images are cleaned by deleting the irrelevant images and images that are for similar but different classes. For some classes, there are less than 50 images after manually cleaning, and then the data set is augmented by flipping and rotating the images.
192 PAPERS • 3 BENCHMARKS
The IAM database contains 13,353 images of handwritten lines of text created by 657 writers. The texts those writers transcribed are from the Lancaster-Oslo/Bergen Corpus of British English. It includes contributions from 657 writers making a total of 1,539 handwritten pages comprising of 115,320 words and is categorized as part of modern collection. The database is labeled at the sentence, line, and word levels.
164 PAPERS • 1 BENCHMARK
The PROMISE12 dataset was made available for the MICCAI 2012 prostate segmentation challenge. Magnetic Resonance (MR) images (T2-weighted) of 50 patients with various diseases were acquired at different locations with several MRI vendors and scanning protocols.
69 PAPERS • 1 BENCHMARK
The xBD dataset contains over 45,000KM2 of polygon labeled pre and post disaster imagery. The dataset provides the post-disaster imagery with transposed polygons from pre over the buildings, with damage classification labels.
39 PAPERS • 2 BENCHMARKS
Contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of COVID-19.
31 PAPERS • NO BENCHMARKS YET
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.
21 PAPERS • NO BENCHMARKS YET
The George Washington dataset contains 20 pages of letters written by George Washington and his associates in 1755 and thereby categorized into historical collection. The images are annotated at word level and contain approximately 5,000 words.
19 PAPERS • NO BENCHMARKS YET
VehicleX is a large-scale synthetic dataset. Created in Unity, it contains 1,362 vehicles of various 3D models with fully editable attributes.
16 PAPERS • NO BENCHMARKS YET
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.
14 PAPERS • NO BENCHMARKS YET
BRATS 2016 is a brain tumor segmentation dataset. It shares the same training set as BRATS 2015, which consists of 220 HHG and 54 LGG. Its testing dataset consists of 191 cases with unknown grades. Image Source: https://sites.google.com/site/braintumorsegmentation/home/brats_2016
13 PAPERS • NO BENCHMARKS YET
Logo-2K+:A Large-Scale Logo Dataset for Scalable Logo Classification The Logo-2K+ dataset contains a diverse range of logo classes from real-world logo images. It contains 167,140 images with 10 root categories and 2,341 leaf categories. The 10 different root categories are: Food, Clothes, Institution, Accessories, Transportation, Electronic, Necessities, Cosmetic, Leisure and Medical.
11 PAPERS • NO BENCHMARKS YET
The Hotels-50K dataset consists of over 1 million images from 50,000 different hotels around the world. These images come from both travel websites, as well as the TraffickCam mobile application, which allows every day travelers to submit images of their hotel room in order to help combat trafficking. The TraffickCam images are more visually similar to images from trafficking investigations than the images from travel websites.
6 PAPERS • NO BENCHMARKS YET
Contains 446,684 images annotated by humans that cover 43 incidents across a variety of scenes.
Bentham manuscripts refers to a large set of documents that were written by the renowned English philosopher and reformer Jeremy Bentham (1748-1832). Volunteers of the Transcribe Bentham initiative transcribed this collection. Currently, >6 000 documents or > 25 000 pages have been transcribed using this public web platform. For our experiments, we used the BenthamR0 dataset a part of the Bentham manuscripts.
4 PAPERS • 1 BENCHMARK
Konzil dataset was created by specialists of the University of Greifswald. It contains manuscripts written in modern German. Train sample consists of 353 lines, validation - 29 lines and test - 87 lines.
3 PAPERS • NO BENCHMARKS YET
Patzig contains handwritten texts written in modern German. Train sample consists of 485 lines, validation - 38 lines and test -118 lines.
Ricordi contains handwritten texts written in Italian. Train sample consists of 295 lines, validation - 19 lines and test - 69 lines.
Schiller contains handwritten texts written in modern German. Train sample consists of 244 lines, validation - 21 lines and test - 63 lines.
Schwerin contains handwritten texts written in medieval German. Train sample consists of 793 lines, validation - 68 lines and test - 196 lines.
The Vocal Folds dataset is a dataset for automatic segmentation of laryngeal endoscopic images. The dataset consists of 8 sequences from 2 patients containing 536 hand segmented in vivo colour images of the larynx during two different resection interventions with a resolution of 512x512 pixels.
Extended Labeled Faces in-the-Wild (ELFW) is a dataset supplementing with additional face-related categories —and also additional faces— the originally released semantic labels in the vastly used Labeled Faces in-the-Wild (LFW) dataset. Additionally, two object-based data augmentation techniques are deployed to synthetically enrich under-represented categories which, in benchmarking experiments, reveal that not only segmenting the augmented categories improves, but also the remaining ones benefit.
2 PAPERS • NO BENCHMARKS YET
EgoHOS is a labeled dataset consisting of 11243 egocentric images with per-pixel segmentation labels of hands and objects being interacted with during a diverse array of daily activities. The data are collected form multiple sources: 7,458 frames from Ego4D, 2,212 frames from EPIC-KITCHEN, 806 frames from THU-READ, and 350 frames of our own collected egocentric videos with people playing Escape Room. This dataset is designed for tasks including hand state classification, video activity recognition, 3D mesh reconstruction of hand-object interactions, and video inpainting of hand-object foregrounds in egocentric videos.
MVTec D2S is a benchmark for instance-aware semantic segmentation in an industrial domain. It contains 21,000 high-resolution images with pixel-wise labels of all object instances. The objects comprise groceries and everyday products from 60 categories. The benchmark is designed such that it resembles the real-world setting of an automatic checkout, inventory, or warehouse system. The training images only contain objects of a single class on a homogeneous background, while the validation and test sets are much more complex and diverse.
Saint Gall dataset contains handwritten historical manuscripts written in Latin that date back to the 9th century. It consists of 60 pages, 1 410 text lines and 11 597 words.
2 PAPERS • 1 BENCHMARK
This dataset contains 2,000 images taken from inside a warehouse of the Energy Company of Paraná (Copel), which directly serves more than 4 million consuming units in the Brazilian state of Paraná.
COCO Earthquake is a dataset similar to Common Objects in Context (COCO) used for cracking segmentation. The images selected in the dataset are at various scales, and the tool referred to as the COCO Annotator is used to label cracks for training. In these labeled images, cracks are in yellow and background is in purple. Size of the training and labeling images is varied from 168×300 to 4600×3070. By excluding steel structures, 2,021 images are labeled when surface cracks appeared on structural or nonstructural materials at various scales.
1 PAPER • NO BENCHMARKS YET
ImagiFilter focusses on photographic and/or natural images, a very common use-case in computer vision research. Annotations for coarse prediction are provided, i.e. photographic vs. non-photographic, and smaller fine-grained prediction tasks where the non-photographic class is broken down into five classes: maps, drawings, graphs, icons, and sketches.
A public open dataset of synthetic chest X-ray images of COVID-19.