Discover the Remote Sensing Object Segmentation Dataset Perfect for GIS, AI driven environmental studies, and satellite image analysis.

Penn-Fudan dataset for semantic segmentation. The dataset has been split into 146 training samples and 24 validation samples.

Corresponding blog post => Training UNet from Scratch using PyTorch

Original data set => https://www.cis.upenn.edu/~jshi/ped_html/

The Road Scene Semantic Segmentation Dataset is specifically designed for autonomous driving applications, featuring a collection of internet-collected images with a standard resolution of 1920 x 1080 pixels. This dataset is focused on semantic segmentation, aiming to accurately segment various elements of road scenes such as the sky, buildings, lane lines, pedestrians, and more, to support the development of advanced driver-assistance systems (ADAS) and autonomous vehicle technologies.

We established a large-scale plant disease segmentation dataset named PlantSeg. PlantSeg comprises more than 11,400 images of 115 different plant diseases from various environments, each annotated with its corresponding segmentation label for diseased parts. To the best of our knowledge, PlantSeg is the largest plant disease segmentation dataset containing in-the-wild images. Our dataset enables researchers to evaluate their models and provides a valid foundation for the development and benchmarking of plant disease segmentation algorithms.

Please note that due to the image limitations of Roboflow, the dataset provided here is not complete.

Project page: https://github.com/tqwei05/PlantSeg

Paper: https://arxiv.org/abs/2409.04038

Complete dataset download: https://zenodo.org/records/13958858

Reference: @article{wei2024plantseg, title={PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation}, author={Wei, Tianqi and Chen, Zhi and Yu, Xin and Chapman, Scott and Melloy, Paul and Huang, Zi}, journal={arXiv preprint arXiv:2409.04038}, year={2024} }

The Outdoor Objects Semantic Segmentation Dataset is developed for applications in media & entertainment and robotics, consisting of a variety of internet-collected images with resolutions ranging from 1024 x 726 to 2358 x 1801 pixels. This dataset employs bounding box and key points annotations to segment various outdoor elements, including human body parts, natural scenery, architectural structures, pavements, transportation means, and more.

The Drivable Area Segmentation Dataset is meticulously crafted to enhance the capabilities of AI in navigating autonomous vehicles through diverse driving environments. It features a wide array of high-resolution images, with resolutions ranging from 1600 x 1200 to 2592 x 1944 pixels, capturing various pavement types such as bitumen, concrete, gravel, earth, snow, and ice. This dataset is vital for training AI models to differentiate between drivable and non-drivable areas, a fundamental aspect of autonomous driving. By providing detailed semantic and binary segmentation, it aims to improve the safety and efficiency of autonomous vehicles, ensuring they can adapt to different road conditions and environments encountered in real-world scenarios.

The visuAAL Skin Segmentation Dataset contains 46,775 high quality images divided into a training set with 45,623 images, and a validation set with 1,152 images. Skin areas have been obtained automatically from the FashionPedia garment dataset. The process to extract the skin areas is explained in detail in the paper 'From Garment to Skin: The visuAAL Skin Segmentation Dataset'.

If you use the visuAAL Skin Segmentation Dataset, please, cite:

• https://doi.org/10.5281/zenodo.6973396
• https://doi.org/10.1007/978-3-031-13321-3_6

How to use:

1. Download the FashionPedia dataset from https://fashionpedia.github.io/home/Fashionpedia_download.html
2. Download the visuAAL Skin Segmentation Dataset. The dataset consists of two folders, namely train_masks and val_masks. Each folder corresponds to the training and validation sets in the original FashionPedia dataset.
3. After extracting the images from FashionPedia, for each image existing in the visuAAL skin segmentation dataset, the original image can be found with the same name (file_name in the annotations file).

A sample of image data in the FashionPedia dataset is:

{'id': 12305,

'width': 680,

'height': 1024,

'file_name': '064c8022b32931e787260d81ed5aafe8.jpg',

'license': 4,

'time_captured': 'March-August, 2018',

'original_url': 'https://farm2.staticflickr.com/1936/8607950470_9d9d76ced7_o.jpg',

'isstatic': 1,

'kaggle_id': '064c8022b32931e787260d81ed5aafe8'}

NOTE: Not all the images in the FashionPedia dataset have the correponding skin mask in the visuAAL Skin Segmentation Dataset, as there are images in which only garment parts and not people are present in them. These images were removed when creating the visuAAL Skin Segmentation Dataset. However, all the instances in the visuAAL skin segmentation dataset have their corresponding match in the FashionPedia dataset.

Context

The goal of this work is to provide an empirical basis for research on image segmentation and boundary detection.

Content

The dataset consists of 500 natural images, ground-truth human annotations and benchmarking code. The data is explicitly separated into disjoint train, validation and test subsets. The dataset is an extension of the BSDS300, where the original 300 images are used for training / validation and 200 fresh images, together with human annotations, are added for testing. Each image was segmented by five different subjects on average.

Acknowledgements

This dataset was obtained and modified from The Berkeley Segmentation Dataset and Benchmark from Computer Vision Group (University of California Berkeley). For more details on the dataset refer dataset's home page and related publication. Work based on the dataset should cite:

@InProceedings{MartinFTM01,
 author = {D. Martin and C. Fowlkes and D. Tal and J. Malik},
 title = {A Database of Human Segmented Natural Images and its
      Application to Evaluating Segmentation Algorithms and
      Measuring Ecological Statistics},
 booktitle = {Proc. 8th Int'l Conf. Computer Vision},
 year = {2001},
 month = {July},
 volume = {2},
 pages = {416--423}
}

Lub Blur Area Segmentation Dataset yog tsim los siv rau hauv cov neeg hlau thiab kev lom zem pom kev, muaj cov duab sau hauv internet nrog cov kev daws teeb meem ntawm 960 x 720 txog 1024 x 768 pixels. Cov ntaub ntawv no tsom mus rau kev faib semantic, tshwj xeeb yog tsom rau thaj chaw xiav hauv cov duab. Txhua cheeb tsam xiav yog sau tseg ntawm qib pixel, muab cov ntaub ntawv tseem ceeb rau cov ntawv thov uas xav tau xim raws li kev faib tawm lossis kev tsom xam.

Content

This repository contains pre-trained computer vision models, data labels, and images used in the pre-print publication "A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains":

ADPdevkit: a folder containing the 50 validation ("tuning") set and 50 evaluation ("segtest") set of images from the Atlas of Digital Pathology database formatted in the VOC2012 style--the full database of 17,668 images is available for download from the original website

VOCdevkit: a folder containing the relevant files for the PASCAL VOC2012 Segmentation dataset, with both the trainaug and test sets

DGdevkit: a folder containing the 803 test images of the DeepGlobe Land Cover challenge dataset formatted in the VOC2012 style

cues: a folder containing the pre-generated weak cues for ADP, VOC2012, and DeepGlobe datasets, as required for the SEC and DSRG methods

models_cnn: a folder containing the pre-trained CNN models

models_wsss: a folder containing the pre-trained SEC, DSRG, and IRNet models, along with dense CRF settings

More information

For more information, please refer to the following article. Please cite this article when using the data set.

@misc{chan2019comprehensive, title={A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains}, author={Lyndon Chan and Mahdi S. Hosseini and Konstantinos N. Plataniotis}, year={2019}, eprint={1912.11186}, archivePrefix={arXiv}, primaryClass={cs.CV} }

For the full code released on GitHub, please visit the repository at: https://github.com/lyndonchan/wsss-analysis

Contact

For questions, please contact: Lyndon Chan lyndon.chan@mail.utoronto.ca http://orcid.org/0000-0002-1185-7961

21,299 Images of Human Body and Face Segmentation Data. The data includes indoor scenes and outdoor scenes. The data covers female people and male people. The race distribution includes Asian, black race and Caucasian. The age distribution ranges from teenager to the elderly, the middle-aged and young people are the majorities. The dataset diversity includes multiple scenes, ages, races, postures, and appendages. In terms of annotation, we adpoted pixel-wise segmentation annotations on human face, the five sense organs, body and appendages. The data can be used for tasks such as human body segmentation.

Two large publicly-available datasets for building segmentation: Inria and DeepGlobe (termed DG)

Dataset

This dataset was created by AmitH2022

Released under Apache 2.0

Contents

Dentist Dataset Segmentation

## Overview

Dentist Dataset Segmentation is a dataset for instance segmentation tasks - it contains Teeth annotations for 649 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).

Hair Loss Segmentation Dataset - 1 080 images

The dataset comprises 1,080 images of 540 women with alopecia, featuring top-view scalp images paired with segmentation masks. Each image is annotated with precise segmentation masks, enabling analysis of hair follicles, hair density, and baldness patterns. — Get the data

  Dataset characteristics:

Characteristic Data

Description Photos of women with varying degrees of hair loss for segmentation tasks

Data… See the full description on the dataset page: https://huggingface.co/datasets/ud-medical/hair-loss-segmentation-dataset.

The Hair Semantic Segmentation Dataset serves the apparel and media & entertainment industries, featuring a curated collection of internet-collected images with resolutions varying from 343 x 358 to 2316 x 3088 pixels. This dataset specializes in high-precision contour and semantic segmentation of hair, offering detailed annotations for a wide range of hairstyles and textures.

Receipt Segmentation

## Overview

Receipt Segmentation is a dataset for instance segmentation tasks - it contains Receipts annotations for 1,434 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 [MIT license](https://creativecommons.org/licenses/MIT).

This dataset was acquired using various EPI protovols on multiple subjects, multiple sites and multiple MRI vendors and models to develop a method to automate the time-consuming segmentation of the spinal cord for fMRI. The list of subjects is available in participants.tsv.

This dataset follows the BIDS convention. The contributors have the necessary ethics & permissions to share the data publicly.

The dataset does not include any identifiable personal health information, including names,zip codes, dates of birth, facial features.

Each participant's data is in one subdirectory, which contains the mean of motion-corrected volumes (the mean image that was used to draw the spinal cord mask) as well as the associated metadata. Spinal cord masks that were generated based on mean of motion-corrected volumes are found under derivatives/label/sub-subjectID/sub-subjectID_task-rest_desc-spinalcordmask.nii.gz.

If you reference this dataset in your publications, please cite the following publication: Link to be added. Should you have any questions about this data set, please contact mkaptan@stanford.edu and banerjee.rohan98@gmail.com

This dataset contains synthetic and real images, with their labels, for Computer Vision in robotic surgery. It is part of ongoing research on sim-to-real applications in surgical robotics. The dataset will be updated with further details and references once the related work is published. For further information see the repository on GitHub: https://github.com/PietroLeoncini/Surgical-Synthetic-Data-Generation-and-Segmentation

Lub Ib Tes Tes Contour Segmentation Dataset yog tsom rau kev lag luam kev lom zem pom kev, nthuav tawm cov duab sau hauv internet nrog kev daws teeb meem ntawm 1080 x 1920 pixels. Cov ntaub ntawv no tsom mus rau contour segmentation, tshwj xeeb yog tsom rau cov lus piav qhia ntawm ib txhais tes. Yog tias cov khoom siv me me muaj nyob ntawm tes, lawv kuj tseem suav nrog segmentation, paub qhov txawv ntawm tes thiab nws cov khoom siv los ntawm keeb kwm yav dhau.