The task of image-based virtual try-on aims to transfer a target clothing item onto the corresponding region of a person, which is commonly tackled by fitting the item to the desired body part and fusing the warped item with the person. While an increasing number of studies have been conducted, the resolution of synthesized images is still limited to low (e.g., 256x192), which acts as the critical limitation against satisfying online consumers. We argue that the limitation stems from several challenges: as the resolution increases, the artifacts in the misaligned areas between the warped clothes and the desired clothing regions become noticeable in the final results; the architectures used in existing methods have low performance in generating high-quality body parts and maintaining the texture sharpness of the clothes. To address the challenges, we propose a novel virtual try-on method called VITON-HD that successfully synthesizes 1024x768 virtual try-on images. Specifically, we first prepare the segmentation map to guide our virtual try-on synthesis, and then roughly fit the target clothing item to a given person's body. Next, we propose ALIgnment-Aware Segment (ALIAS) normalization and ALIAS generator to handle the misaligned areas and preserve the details of 1024x768 inputs. Through rigorous comparison with existing methods, we demonstrate that VITON-HD highly surpasses the baselines in terms of synthesized image quality both qualitatively and quantitatively.

Collection methodology: using software Processing with Python Mode. To generate a plane shapes like a hand-drawn, the Perlin Noise method is used (More info: https://github.com/reevald/FlatShapeNet). The dataset is used for the educational game Ariga.Currently the dataset (Version 4) is constructed by choosing 8 largest classes : "Circle", "Kite", "Parallelogram", "Square", "Rectangle", "Rhombus", "Trapezoid", and "Triangle". Each class contains 1,500 training samples, 500 validation samples, and 500 test samples. The total number of training samples is 12,000, validation samples 4,000, and testing 4,000. Each sample is an image measuring (224 x 224 x 3) (RGB).

A comprehensive X-ray image dataset for bone fracture detection has been created to support computer vision projects. The dataset includes images categorized by fracture types such as Elbow Positive, Fingers Positive, Forearm Fracture, Humerus Fracture, Shoulder Fracture, and Wrist Positive. Each image is annotated with bounding boxes or pixel-level segmentation masks to indicate fracture locations. This dataset is ideal for training and evaluating machine learning models, particularly for object detection algorithms aimed at automated fracture detection. It accelerates the development of computer vision solutions for medical diagnostics, enhancing patient care.

The Age Detection dataset is built upon a collection of selfies and ID card images, featuring high-quality facial photographs of individuals between the ages of 18 and 60. The dataset is divided into five distinct age groups: 18–20, 21–30, 31–40, 41–50, and 51–60, with separate folders for training and testing purposes. Each image is accompanied by a CSV file containing rich metadata, including the individual’s exact age, true gender, country, ethnicity, as well as the file extension and resolution for each photo. The demographic diversity within the dataset—covering various ethnicities, genders, and nationalities—makes it highly suitable for developing and evaluating deep learning models for age estimation, facial recognition, and biometric analysis. The full commercial version contains over 95,000 photos and is available for purchase via the TrainingData platform. In addition, several supplementary datasets are offered, including selfie-video datasets, bald-person datasets, and anti-spoofing datasets, making this a comprehensive resource for advanced biometric system development.