A dataset made of 3D image data and their embeddings to test TomoSAM

UV6K is a high-resolution remote sensing urban vehicle segmentation dataset. - Images: 6,313 - Vehicle: 245,141 - Resolution: 0.1m - Image Size: 1024x1024

🌽 CornHub: Instance-Segmentation Dataset of Corn Cobs Version: 1.0.0 Date: 2025-05-18 License: CC BY 4.0 Author: Sebastian Borukało ## 📦 Description CornHub is an instance-segmentation dataset of corn cobs in real field conditions. It contains 304 high-resolution RGB images in three variants: - FULL (2432×2432 px) - LITE (1024×1024 px) - ULTRALITE (512×512 px) Captured at varying distances (close-up and wide-angle), lighting conditions (sunny, overcast), and viewpoints, each corn cob is annotated as its own grayscale mask (single channel): - 0 = background - 1, 2, 3… = individual corn cob instances Images and masks are provided in PNG format. Use CornHub to train and evaluate instance-segmentation models in agriculture, field robotics, and computer vision research. ### DataSet view: | Version | Filename | Resolution | |------------------|:-------------------------:|:----------------:| | Full | CornHub_FULL_2432.zip | 2432×2432 px | | Lite | CornHub_LITE_1024.zip | 1024×1024 px | | Ultra-Lite | CornHub_ULTRALITE_512.zip | 512×512 px | ## 📂 Download Options Available on: - Kaggle - Zenodo - Papers With Code ## 🤝 Credits This dataset is free to use and you can use it in any project or paper, however I would be glad if you would cite me and give credit - as it is stated in the license. CornHub: Instance-Segmentation Dataset of Corn Cobs Author: Sebastian Borukało DOI: https://doi.org/10.5281/zenodo.15453713 DOI: 10.5281/zenodo.15453713

Overview The IITKGP_Fence dataset is designed for tasks related to fence-like occlusion detection, defocus blur, depth mapping, and object segmentation. The captured data vaies in scene composition, background defocus, and object occlusions. The dataset comprises both labeled and unlabeled data, as well as additional video and RGB-D data. The contains ground truth occlusion masks (GT) for the corresponding images. We created the ground truth occlusion labels in a semi-automatic way with user interaction. ## Key Dataset Features: - Fence Detection: Designed for detecting fences or fence-like structures that might occlude objects. - Defocus Blur: Also contains images and videos with blurred objects, likely to challenge detection and segmentation algorithms. - RGBD Data: Offers depth information alongside RGB images, which can be used for tasks like 3D reconstruction or occlusion handling. - Unlabeled and Labeled Data: Facilitates both supervised and unsupervised learning tasks. The Labeled <a href="https://huggingface.co/datasets/NeuroVizv0yaZ3R/IITKGP_Fence_dataset/tree/main/Labeled" style="color: darkorange;">folder</a> data provides ground truth occlusion masks, while the Unlabeled <a href="https://huggingface.co/datasets/NeuroVizv0yaZ3R/IITKGP_Fence_dataset/tree/main/Unlabeled" style="color: teal;">folder</a> data allows for further experimentation or self-supervised methods. ### Dataset Repository - GitHub Repository: <a href="https://github.com/Moushumi9medhi/Occlusion-Removal" style="color: magenta;">Occlusion-Removal</a> - Paper: Deep Generative Adversarial Network for Occlusion Removal from a Single Image - Authors: Sankaraganesh Jonna, Moushumi Medhi, Rajiv Ranjan Sahay ## Contact medhi.moushumi@iitkgp.ac.in

Description: <a href="https://gts.ai/dataset-download/linear-equation-image-dataset/" target="_blank">👉 Download the dataset here</a> The Linear Equation Image Dataset is designed to help solve high school-level algebraic problems using machine learning (ML). It provides extensive visual data, ideal for training models in equation recognition and solving. Download Dataset What’s New Expanded Image Dataset: The dataset now contains over 30,000 images, covering a wide array of linear equations with varying complexities. The generation of equations follows multiple randomization techniques, ensuring diversity in the visual representation. Data Diversity: Equations include both simple and complex forms, with some involving fractional coefficients, inequalities, or multi-variable formats to increase the challenge. The images also come in different resolutions, fonts, and formats (handwritten and digitally rendered) to further test ML algorithms’ robustness. Possible Use Cases Symbolic Equation Recognition: Train models to visually recognize equations and convert them into symbolic form. Equation Solving: Create ML models capable of solving linear equations through image recognition. Handwritten Recognition: Use this dataset for handwriting recognition, helping machines interpret handwritten linear equations. Educational Tools: Develop AI tutors or mobile apps that assist students in solving linear equations by merely taking a photo of the problem. Algorithm Training: Useful for those researching symbolic computation, this dataset allows for testing and improving various image-to-text and equation-solving algorithms. Enhanced Research Opportunities This dataset can be particularly useful for educational institutions, research teams, and AI developers focusing on enhancing problem-solving capabilities via machine learning and symbolic computation models. This dataset is sourced from Kaggle.

MapAI: Precision in Building Segmentation Dataset The dataset comprises 7500 training images and 1500 validation images from Denmark. The test dataset is split into two tasks, where the first task (1368 images) is to segment the buildings only using aerial images. In contrast, the second task (978 images) allows using aerial images and lidar data. All data samples have a resolution of 500x500. The aerial images are RGB images, while the lidar data are rasterized. The ground truth masks have two classes, building, and background. Source: MapAI: Precision in Building Segmentaiton Image Source: Kartai

Dataset: RGB-D Images for Real-World and Synthetic Object Scenes This dataset consists of both real-world and synthetic RGB-D images, designed for object detection, classification, and segmentation tasks, particularly for primitive shape recognition. ## Real-World Data - Objects: 50 distinct objects captured using a Kinect camera. - Scenes: The dataset includes both single objects and piles of objects stacked over each other. - Images: Approximately 300 RGB-D images have been collected. - Data Format: RGB images paired with corresponding depth images. ## Synthetic Data - Simulator: The synthetic dataset was automatically generated using the CoppeliaSim simulator. - Objects: Only primitive shapes, including cuboid, semisphere, sphere, cylinder, stick, ring, and cone. - Scenes: Objects are dropped over each other to form complex scenes. - Images: The dataset contains 10,000 unique RGB-D images generated by the simulator. ## Labels Both the real-world and synthetic datasets are labeled with geometric primitive shape classes and boundaries, including: - Cuboid - Semisphere - Sphere - Cylinder - Stick - Ring - Cone This dataset is designed to support research in grasp detection, object recognition, and scene understanding using RGB-D data.

Standardized Multi-Channel Dataset for Glaucoma (SMDG-19) is a collection and standardization of 19 public datasets, comprised of full-fundus glaucoma images, associated image metadata like, optic disc segmentation, optic cup segmentation, blood vessel segmentation, and any provided per-instance text metadata like sex and age. This dataset is the largest public repository of fundus images with glaucoma.