Sketch

PACS is an image dataset for domain generalization. It consists of four domains, namely Photo (1,670 images), Art Painting (2,048 images), Cartoon (2,344 images) and Sketch (3,929 images). Each domain contains seven categories. Source: Deep Domain-Adversarial Image Generation for Domain Generalisation Image Source: https://www.researchgate.net/figure/Sample-images-from-PACS-dataset-Each-row-represents-a-domain-and-each-column-represents_fig1_334695033

The CUHK03 consists of 14,097 images of 1,467 different identities, where 6 campus cameras were deployed for image collection and each identity is captured by 2 campus cameras. This dataset provides two types of annotations, one by manually labelled bounding boxes and the other by bounding boxes produced by an automatic detector. The dataset also provides 20 random train/test splits in which 100 identities are selected for testing and the rest for training Source: Attention Driven Person Re-identification Image Source: Person Re-Identification Techniques for Intelligent Video Surveillance Systems

The DukeMTMC-reID (Duke Multi-Tracking Multi-Camera ReIDentification) dataset is a subset of the DukeMTMC for image-based person re-ID. The dataset is created from high-resolution videos from 8 different cameras. It is one of the largest pedestrian image datasets wherein images are cropped by hand-drawn bounding boxes. The dataset consists 16,522 training images of 702 identities, 2,228 query images of the other 702 identities and 17,661 gallery images. NOTE: This dataset has been retracted. Source: Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification

MPI (Max Planck Institute) Sintel is a dataset for optical flow evaluation that has 1064 synthesized stereo images and ground truth data for disparity. Sintel is derived from open-source 3D animated short film Sintel. The dataset has 23 different scenes. The stereo images are RGB while the disparity is grayscale. Both have resolution of 1024×436 pixels and 8-bit per channel. Source: Fast Disparity Estimation using Dense Networks*

Grammarly’s Yahoo Answers Formality Corpus (GYAFC) is the largest dataset for any style containing a total of 110K informal / formal sentence pairs. Yahoo Answers is a question answering forum, contains a large number of informal sentences and allows redistribution of data. The authors used the Yahoo Answers L6 corpus to create the GYAFC dataset of informal and formal sentence pairs. In order to ensure a uniform distribution of data, they removed sentences that are questions, contain URLs, and are shorter than 5 words or longer than 25. After these preprocessing steps, 40 million sentences remain. The Yahoo Answers corpus consists of several different domains like Business, Entertainment & Music, Travel, Food, etc. Pavlick and Tetreault formality classifier (PT16) shows that the formality level varies significantly across different genres. In order to control for this variation, the authors work with two specific domains that contain the most informal sentences and show results on training and testing within those categories. The authors use the formality classifier from PT16 to identify informal sentences and train this classifier on the Answers genre of the PT16 corpus which consists of nearly 5,000 randomly selected sentences from Yahoo Answers manually annotated on a scale of -3 (very informal) to 3 (very formal). They find that the domains of Entertainment & Music and Family & Relationships contain the most informal sentences and create the GYAFC dataset using these domains. Source: Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer

WikiArt contains painting from 195 different artists. The dataset has 42129 images for training and 10628 images for testing. Source: Adding New Tasks to a Single Network with Weight Transformations using Binary Masks Image Source: https://towardsdatascience.com/the-non-treachery-of-dataset-df1f6cbe577e

MPIIGaze is a dataset for appearance-based gaze estimation in the wild. It contains 213,659 images collected from 15 participants during natural everyday laptop use over more than three months. It has a large variability in appearance and illumination. Source: https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/research/gaze-based-human-computer-interaction/appearance-based-gaze-estimation-in-the-wild Image Source: https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/research/gaze-based-human-computer-interaction/appearance-based-gaze-estimation-in-the-wild

The Quick Draw Dataset is a collection of 50 million drawings across 345 categories, contributed by players of the game Quick, Draw!. The drawings were captured as timestamped vectors, tagged with metadata including what the player was asked to draw and in which country the player was located. Source: https://github.com/googlecreativelab/quickdraw-dataset Image Source: https://github.com/googlecreativelab/quickdraw-dataset

POP909 is a dataset which contains multiple versions of the piano arrangements of 909 popular songs created by professional musicians. The main body of the dataset contains the vocal melody, the lead instrument melody, and the piano accompaniment for each song in MIDI format, which are aligned to the original audio files. Furthermore, annotations are provided of tempo, beat, key, and chords, where the tempo curves are hand-labelled and others are done by MIR algorithms. Source: https://arxiv.org/pdf/2008.07142.pdf Image Source: https://arxiv.org/pdf/2008.07142.pdf

ShoeV2 is a dataset of 2,000 photos and 6648 sketches of shoes. The dataset is designed for fine-grained sketch-based image retrieval. Source: Sketch Me That Shoe

Multi-Modal-CelebA-HQ is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA dataset by following CelebA-HQ. Each image has high-quality segmentation mask, sketch, descriptive text, and image with transparent background. Multi-Modal-CelebA-HQ can be used to train and evaluate algorithms of text-to-image-generation, text-guided image manipulation, sketch-to-image generation, and GANs for face generation and editing. Source: Multi-Modal CelebA-HQ Dataset Image Source: Xia et al

The CUHK Face Sketch FERET (CUFSF) is a dataset for research on face sketch synthesis and face sketch recognition. It contains two types of face images: photo and sketch. Total 1,194 images (one image per subject) were collected with lighting variations from the FERET dataset. For each subject, a sketch is drawn with shape exaggeration. Source: Deeply Coupled Auto-encoder Networks forCross-view Classification Image Source: http://mmlab.ie.cuhk.edu.hk/archive/cufsf/

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.

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

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

TextSeg is a large-scale fine-annotated and multi-purpose text detection and segmentation dataset, collecting scene and design text with six types of annotations: word- and character-wise bounding polygons, masks and transcriptions. Source: https://github.com/SHI-Labs/Rethinking-Text-Segmentation Image Source: https://github.com/SHI-Labs/Rethinking-Text-Segmentation

A new dataset of handwritten text with fine-grained annotations at the character level and report results from an initial user evaluation. Source: DeepWriting: Making Digital Ink Editable via Deep Generative Modeling

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.

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.

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

FS2K is a high-quality Facial Sketch Synthesis (FSS). It consists of 2,104 image-sketch pairs spanning three types of sketch styles, image backgrounds, lighting conditions, skin colors, and facial attributes. FS2K differs from previous FSS datasets in difficulty, diversity, and scalability, and should thus facilitate the progress of FSS research.

PASTEL is a parallelly annotated stylistic language dataset. The dataset consists of ~41K parallel sentences and 8.3K parallel stories annotated across different personas. Source: PASTEL

CUHK Face Sketch database (CUFS) is for research on face sketch synthesis and face sketch recognition. It includes 188 faces from the Chinese University of Hong Kong (CUHK) student database, 123 faces from the AR database [1], and 295 faces from the XM2VTS database [2]. There are 606 faces in total. For each face, there is a sketch drawn by an artist based on a photo taken in a frontal pose, under normal lighting condition, and with a neutral expression. [1] A. M. Martinez, and R. Benavente, “The AR Face Database,” CVC Technical Report #24, June 1998. [2] K. Messer, J. Matas, J. Kittler, J. Luettin, and G. Maitre, “XM2VTSDB: the Extended of M2VTS Database,” in Proceedings of International Conference on Audio- and Video-Based Person Authentication, pp. 72-77, 1999. Source Paper: X. Wang and X. Tang, “Face Photo-Sketch Synthesis and Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 31, 2009 Image Source: CUHK Face Sketch Database (CUFS) Source: CUHK Face Sketch Database (CUFS)

Im4Sketch is a large-scale dataset with shape-oriented set of classes for image-to-sketch generalization . It consists of a collection of natural images from 874 categories for training and validation, and sketches from 393 categories (a subset of natural image categories) for testing. The images and sketches are collected from existing popular computer vision datasets. The categories are selected having shape similarity in mind, so that object with same shape belong to the same category. The natural-image part of the dataset is based on the ILSVRC2012 version of ImageNet. The original ImageNet categories are first merged according to the shape criteria. Object categories for objects whose shape, e.g. how a human would draw the object, is the same are merged. For this step, semantic similarity of categories, obtained through WordNet and category names, is used to obtain candidate categories for merging. Based on visual inspection of these candidates, the decision to merge the original ImageNet classes is made by a human. For instance, ”Indian Elephant” and ”African Elephant”, or ”Laptop” and ”Notebook” are merged. An extreme case of merging is the new class “dog” that is a union of 121 original ImageNet classes of dog breeds. In the second step, classes from datasets containing sketches are used. In particular, DomainNet, Sketchy, PACS, and TU-Berlin. Note that merging is not necessary for classes in these datasets, because the shape criteria are guaranteed since they are designed for sketches. In this step, a correspondence between the merged ImageNet categories and categories of the other datasets is found. As in the merging step, semantic similarity is used to guide the correspondence search. Sketch categories that are not present in the merged ImageNet are added to the overall category set, while training natural images of those categories are collected from either DomainNet or Sketchy. In the end, ImageNet is used for 690 classes, DomainNet for 183 classes, and Sketchy for 1 class, respectively.

SKSF-A consists of seven distinct styles drawn by professional artists. SKSF-A contains 134 identities and corresponding sketches, making a total of 938 face-sketch pairs. SKSF-A is introduced in StyleSketch, Eurographics 2024. https://kwanyun.github.io/stylesketch_project/

A new text effects dataset with 141,081 text effect/glyph pairs in total. The dataset consists of 152 professionally designed text effects rendered on glyphs, including English letters, Chinese characters, and Arabic numerals. Source: TE141K: Artistic Text Benchmark for Text Effect Transfer

AtyPict is a dataset of atypical sketch content designed for atypical sketch content detection tasks. Source: DrawMon: A Distributed System for Detection of Atypical Sketch Content in Concurrent Pictionary Games Image Source: https://arxiv.org/pdf/2211.05429v1.pdf

The Chinese Traditional Painting dataset for style transfer contains 1000 content images and 100 style images. The content images are mostly the photorealistic scenes of mountain, lake, river, bridge, and buildings in regions south of the Yangtze River. It includes not only the scenes of China, but also beautiful pictures of Rhine, Alps, Yellow Stone, Grand Canyon, etc. The content images include diverse types of Chinese traditional paintings. Source: https://github.com/lbsswu/Chinese_style_transfer Image Source: https://github.com/lbsswu/Chinese_style_transfer

Gutenberg Poem Dataset is used for the next verse prediction component. Source: Gutenberg Poem Dataset

Image dataset with about 600K Flickr photos. Source: Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks

A high-resolution version of VGGFace2 for academic face editing purposes. This project uses GFPGAN for image restoration and insightface for data preprocessing (crop and align).

CodeSCAN is the first large-scale and diverse dataset of coding screenshots with pixel-perfect annotations. It features: - 24 popular programming languages (according to Github) - 100 random repositories per language (with MIT, BSD-3 or WTFPL License), i.e. 2.400 repositories in total - Per repository we use 5 files, i.e. 12.000 files in total - ~100 different themes and 25 different fonts - Diverse layouts changes, such as menu bar visibility, sidebar position, output window content, etc. - Numerous realistic interactions such as searching, typing and selecting within a file, etc. Check our project page (https://a-nau.github.io/codescan/) for details.

This is a dataset of 18624 fonts labeled as 100% Free and Public domain / GPL / OFL on https://www.dafont.com/ with .ttf and .otf extensions. Code used to create it can be found at: https://github.com/duskvirkus/dafonts-free