Dataset

This dataset was created by Suryansh Gupta

Contents

DRAM dataset is the first dataset to introduce a fully labeled test set for the task of semantic segmentation of art paintings. The dataset uses a subset of 12 classes used in the PascalVoc12 dataset: Bird, Boat, Bottle, Cat, Chair, Cow, Dog,Horse, Sheep, Person, Potted-Plant, and Background.

The dataset consists of 5677 unlabeled and 718 labeled paintings from 152 painters. The dataset is divided into 5 categories: Realism, Impressionism, Post-Impressionism, Expressionism and 'Unseen', each holding paintings from a specific art movement. the Unseen category appears only in the test set and it consists from 135 image of the following art movements: Art-Nouveau, Baroque, Cubism, Divisionism, Fauvism, Chinese Ink and Wash, Japonism and Rococo.

The dataset was constructed for the perceptual task of understanding how computers see images from various styles.

Potential use cases: 1) Evaluating semantic segmentation models on a diverse and complex domain. 2) Investigation on latent representations of abstraction-varying art styles 3) Domain adaptation solutions for semantic segmentation of art paintings.

By Technology:By technology, the India paint market is segmented into water-based, solvent-based, and powder coatings. Water-based paints dominate this segment, owing to their low environmental impact and growing consumer preference for eco-friendly products. These paints have become a preferred choice for homeowners and businesses due to their reduced volatile organic compound (VOC) emissions and superior performance in terms of durability and aesthetics. By Product Type:The India paint market is segmented by product type into decorative paints, industrial paints, powder coatings, and speciality coatings. Decorative paints hold the dominant market share in the product type segmentation, driven by the ongoing demand for interior and exterior wall finishes in residential and commercial buildings. Brands like Asian Paints and Berger Paints have a stronghold in this segment due to their wide product range, catering to varying consumer preferences for textures, colours, and finishes. The India Paint Market is segmented by product type, technology, resin type, application, and region. India Paint Market Segmentation The Indian government, through the Bureau of Indian Standards (BIS), has set strict standards for permissible VOC content in paints. As of 2023, architectural coatings are required to contain no more than 150 grams per liter of VOCs, while industrial coatings have a limit of 250 grams per liter. These regulations are part of a broader effort to reduce air pollution and promote eco-friendly products. Compliance with these standards is mandatory for all paint manufacturers operating in India, ensuring adherence to quality and safety measures.

Semantic Seg For Paint Spill

## Overview

Semantic Seg For Paint Spill is a dataset for semantic segmentation tasks - it contains Paint annotations for 487 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Explore the detailed segmentation analysis of the Conductive Paint market. Understand detailed breakdown for each segment and uncover market opportunities.

By Product Type:The India paint market is segmented by product type into decorative paints, industrial paints, powder coatings, and speciality coatings. Decorative paints hold the dominant market share in the product type segmentation, driven by the ongoing demand for interior and exterior wall finishes in residential and commercial buildings. Brands like Asian Paints and Berger Paints have a stronghold in this segment due to their wide product range, catering to varying consumer preferences for textures, colours, and finishes. The India Paint Market is segmented by product type, technology, resin type, application, and region. India Paint Market Segmentation The Indian government, through the Bureau of Indian Standards (BIS), has set strict standards for permissible VOC content in paints. As of 2023, architectural coatings are required to contain no more than 150 grams per liter of VOCs, while industrial coatings have a limit of 250 grams per liter. These regulations are part of a broader effort to reduce air pollution and promote eco-friendly products. Compliance with these standards is mandatory for all paint manufacturers operating in India, ensuring adherence to quality and safety measures.

Here are a few use cases for this project:

1. Aviation Maintenance: The model can be used to automate the inspection routine of aircraft cockpits and other parts of the aircraft, detecting those five common structural damages. Early detection and subsequent repair can contribute to safer and more efficient aviation operations.

2. Automobile Industry: The AI model can be applied to assess and inspect the condition of cars in production lines or used cars, identifying any imperfections such as dents, cracks, scratches or paint-offs before the car goes to market.

3. Building Inspection: In civil engineering, the model could be used to monitor the structural health of buildings or bridges, using the crack and dent detection capabilities to timely identify potential structural issues.

4. Insurance Claim Processing: Insurance companies could use this model to streamline their claim processing by automatically identifying damage in pictures of insured properties like cars, homes or commercial properties, that have been submitted for claims.

5. Artwork Preservation: Art galleries and museums could use this model to identify early signs of damage on art pieces (paint-off or cracks) and take preventative measures to help save valuable pieces of art.

Dataset

This dataset was created by Shashil

Contents

Explore the detailed segmentation analysis of the Paints and Coatings market. Understand detailed breakdown for each segment and uncover market opportunities.

The spray painting machine market is projected to grow from a value of USD 2.5 billion in 2025 to USD 4.2 billion by 2033, exhibiting a CAGR of 6.79% during the forecast period (2025-2033). Increasing demand from automotive, construction, and industrial sectors, growing popularity of high-volume low-pressure (HVLP) spray painting technology, and stringent regulations regarding volatile organic compounds (VOCs) emissions are the key factors driving the market's growth. Furthermore, advancements in automation and the integration of robotics are expected to create lucrative opportunities for manufacturers in the coming years. In terms of segments, electric spray painting machines held the largest market share in 2025, owing to their versatility, efficiency, and ease of use. Airless spray painting machines are anticipated to witness the fastest growth during the forecast period due to their high efficiency, ability to handle thick coatings, and reduced overspray. The automotive segment accounted for the largest application share in 2025, driven by the rising production of vehicles globally. However, the construction segment is expected to exhibit significant growth due to increasing construction activities in emerging economies. The Asia Pacific region is projected to dominate the market throughout the forecast period, driven by rapid industrialization, growing automotive production, and increasing construction spending in countries such as China, India, and Japan. Recent developments include: , The spray painting machine market is projected to reach USD 4.51 billion by 2032, exhibiting a CAGR of 6.79% during the forecast period (2024-2032). The rising demand for high-quality finishes, increasing use of automated painting systems, and growing construction activities are key factors driving market growth., Advancements in technology, such as the introduction of eco-friendly and electrostatic spray painting machines, are further propelling market expansion. Additionally, the growing popularity of spray painting in the automotive and aerospace industries is expected to contribute to market growth., Spray Painting Machine Market Segmentation Insights. Key drivers for this market are: 1 Increasing demand for automotive refinishing 2 Growing popularity of DIY projects 3 Expansion of the construction industry . Potential restraints include: Increased automation, growing demand from automotive industry; technological advancements; rising environmental concerns and expanding construction sector .

Considered are only tools with a graphical user interface since end users should not need programming expertise. Non deep learning segmentation methods may require expert knowledge for parametrization and are not state-of-the-art anymore. Data format support does not necessarily mean that each image can be processed: if no data management system (DMS) with metadata support is used, e.g., the channel dimension can be the first or the last dimension for.tif files, and the method may have requirements on the channel dimension position. ━: feature not fulfilled/supported, : feature only fulfilled/supported with restrictions, ✔: feature fulfilled/supported.

The "Nails Contour Segmentation Dataset" is crafted for the beauty industry, featuring a collection of offline human fingernail images, all at a uniform resolution of 1920 x 1080 pixels. This dataset specializes in semantic segmentation, with a focus on the detailed contour of fingernails, supporting applications in nail art design and virtual nail try-on technologies.

The acrylic paints market is anticipated to grow at a 4.4% CAGR, reaching $165Million by 2031 from an estimated $122.4Million in 2024

The global canvas painting software market is experiencing robust growth, driven by the increasing popularity of digital art and the accessibility of powerful software solutions. While precise market size figures for 2025 are unavailable, considering the presence of established players like Adobe, Corel, and emerging competitors, and a projected Compound Annual Growth Rate (CAGR) – let's assume a conservative CAGR of 15% – we can estimate a market size of approximately $800 million USD in 2025. This substantial value reflects the growing demand for sophisticated digital painting tools among professional artists, hobbyists, and students. The market's expansion is fueled by several key factors: the rise of digital art platforms and communities fostering creativity and collaboration, the increasing affordability and accessibility of high-performance computers and tablets, and the continuous development of innovative features within the software itself, including AI-powered tools and enhanced brush engines. The market segmentation, encompassing on-premise and cloud-based solutions along with user segments (artists, painting enthusiasts, and others), presents opportunities for specialized software tailored to unique user needs. The diverse geographical distribution highlights significant regional variations in market penetration and growth potential. North America and Europe currently hold substantial market share, but the Asia-Pacific region is poised for rapid expansion, driven by growing digital literacy and the burgeoning creative industries in countries like China and India. Restraints to market growth include the ongoing learning curve associated with mastering sophisticated software and the competition from free or low-cost alternatives. However, the overall outlook remains positive, with continued technological advancements and widening adoption expected to propel significant growth over the forecast period (2025-2033).

Organ segmentation has become a preliminary task for computer-aided intervention, diagnosis, radiation therapy, and critical robotic surgery. Automatic organ segmentation from medical images is a challenging task due to the inconsistent shape and size of different organs. Besides this, low contrast at the edges of organs due to similar types of tissue confuses the network’s ability to segment the contour of organs properly. In this paper, we propose a novel convolution neural network based uncertainty-driven boundary-refined segmentation network (UDBRNet) that segments the organs from CT images. The CT images are segmented first and produce multiple segmentation masks from multi-line segmentation decoder. Uncertain regions are identified from multiple masks and the boundaries of the organs are refined based on uncertainty data. Our method achieves remarkable performance, boasting dice accuracies of 0.80, 0.95, 0.92, and 0.94 for Esophagus, Heart, Trachea, and Aorta respectively on the SegThor dataset, and 0.71, 0.89, 0.85, 0.97, and 0.97 for Esophagus, Spinal Cord, Heart, Left-Lung, and Right-Lung respectively on the LCTSC dataset. These results demonstrate the superiority of our uncertainty-driven boundary refinement technique over state-of-the-art segmentation networks such as UNet, Attention UNet, FC-denseNet, BASNet, UNet++, R2UNet, TransUNet, and DS-TransUNet. UDBRNet presents a promising network for more precise organ segmentation, particularly in challenging, uncertain conditions. The source code of our proposed method will be available at https://github.com/riadhassan/UDBRNet.

In 2023, the architectural segment contributed around 46.2 billion U.S. dollars to the global paints and coatings industry. In comparison, the automotive segment contributed around 12.2 billion U.S. dollars to the industry. The contribution from both these segments is forecast to increase in the coming years.
Additional information on the global paints and coatings market can be found here.

Dice accuracy and HD (± variance) of our proposed method and existing methods for LCTSC dataset.

Explore the detailed segmentation analysis of the Thermoplastic Polyurethane Paint Protection Film market. Understand detailed breakdown for each segment and uncover market opportunities.

Synthetic Faces High Quality (SFHQ) part 1

This dataset consists of 89,785 high quality 1024x1024 curated face images, and was created by "bringing to life" various art works (paintings, drawings, 3D models) using a process similar to what is described in this short twitter thread which involve encoding the images into StyleGAN2 latent space and performing a small manipulation that turns each image into a photo-realistic image.

The dataset also contains facial landmarks (extended set) and face parsing semantic segmentation maps. An example script is provided and demonstrates how to access landmarks, segmentation maps, and textually search withing the dataset (with CLIP image/text feature vectors), and also performs some exploratory analysis of the dataset. link to github repo of the dataset.

The process that "brings to life" paintings and creates several candidate photo-realistic images is illustrated below: https://i.ibb.co/6sykFrj/Figure-1.jpg" alt="">

More Details

1. The original inspiration images are taken from Artstation-Artistic-face-HQ Dataset (AAHQ) which contains mainly painting, drawing and 3D models of faces, Close-Up Humans Dataset that contains 3D models of faces and UIBVFED Dataset that also contain 3D models of faces.
2. Each inspiration image was encoded by encoder4editing (e4e) into StyleGAN2 latent space (StyleGAN2 is a generative face model tained on FFHQ dataset) and multiple candidate images were generated from each inspiration image
3. These candidate images were then further curated and verified as being photo-realistic and high quality by a single human (me) and a machine learning assistant model that was trained to approximate my own human judgments and helped me scale myself to asses the quality of all images in the dataset
4. Near duplicates and images that were too similar were removed using CLIP features (no two images in the dataset have CLIP similarity score of greater than ~0.92)
5. From each image various pre-trained features were extracted and provided here for convenience, in particular CLIP features for fast textual query of the dataset
6. From each image, semantic segmentation maps were extracted using Face Parsing BiSeNet and are provided in the dataset under "segmentations"
7. From each image, an extended landmark set was extracted that also contain inner and outer hairlines (these are unique landmarks that are usually not extracted by other algorithms). These landmarks were extracted using Dlib, Face Alignment and some post processing of Face Parsing BiSeNet and are provided in the dataset under "landmarks"
8. NOTE: semantic segmentation and landmarks were first calculated on scaled down version of 256x256 images, and then upscaled to 1024x1024

Parts 1,2,3,4

• Part 1 of the dataset consists of 89,785 HQ 1024x1024 curated face images. It uses "inspiration" images from Artstation-Artistic-face-HQ dataset (AAHQ), Close-Up Humans dataset and UIBVFED dataset.
• Part 2 of the dataset consists of 91,361 HQ 1024x1024 curated face images. It uses "inspiration" images from Face Synthetics dataset and by sampling from the Stable Diffusion v1.4 text to image generator using varied face portrait prompts.
• Part 3 of the dataset consists of 118,358 HQ 1024x1024 curated face images. It uses "inspiration" images by sampling from StyleGAN2 mapping network with very high truncation psi coefficients to increase diversity of the generation. Here, the e4e encoder is basically used a new kind of truncation trick.
• Part 4 of the dataset consists of 125,754 HQ 1024x1024 curated face images. It uses "inspiration" images by sampling from the Stable Diffusion v2.1 text to image generator using varied face portrait prompts.
• See also dataset github repo with full details and links

Summary

Overall, the SFHQ dataset contains ~425,000 high quality and curated synthetic face images that have no privacy issues or license issues surrounding them.

This dataset contains a high degree of variability on the axes of identity, ethnicity, age, pose, expression, lighting conditions, hair-style, hair-color, facial hair. It lacks variability in accessories axes such as hats or earphones as well as various jewelry. It also doesn't contain any occlusions except the self-occlusion of hair occluding the forehead, the ears and rarely the eyes. This dataset naturally inherits all the biases of it's original datasets (FFHQ, AAHQ, Close-Up Humans, Face Synthetics, LAION-5B) and the StyleGAN2 and Stable Diffusion models.

The purpose of this dataset is to be of sufficiently high quality that new machine learning models can be trained using this data, including even generative face models such as StyleGAN. The dataset may be extended from time to time with additional supervision labels (e.g. text descriptions), but no promises.

Hope this is helpful to some of you, feel free to use as you see fit...