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.

Delve into the Pupils Segmentation Dataset Essential for ophthalmology tech, AI driven vision studies, and advanced eye research.

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} }

Waste disposal is a global challenge, especially in densely populated areas. Efficient waste segregation is critical for separating recyclable from non-recyclable materials. While developed countries have established and refined effective waste segmentation and recycling systems, our country still uses manual segregation to identify and process recyclable items. This study presents a dataset intended to improve automatic waste segmentation systems. The dataset consists of 784 images that have been manually annotated for waste classification. These images were primarily taken in and around Jadavpur University, including streets, parks, and lawns. Annotations were created with the Labelme program and are available in color annotation formats. The dataset includes 14 waste categories: plastic containers, plastic bottles, thermocol, metal bottles, plastic cardboard, glass, thermocol plates, plastic, paper, plastic cups, paper cups, aluminum foil, cloth, and nylon. The dataset includes a total of 2350 object segments.Other Information:Published in: Mendely DataLicense: http://creativecommons.org/licenses/by/4.0/See dataset on publisher's website: https://data.mendeley.com/datasets/gr99ny6b8p/1

Fruits Segmentation

## Overview

Fruits Segmentation is a dataset for instance segmentation tasks - it contains Fruits annotations for 590 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).

ATCS is a dataset designed to train deep learning models to volumetrically segment clouds from multi-angle satellite imagery. The dataset consists of spatiotemporally aligned patches of multi-angle polarimetry from the POLDER sensor aboard the PARASOL mission and vertical cloud profiles from the 2B-CLDCLASS product using the cloud profiling radar (CPR) aboard CloudSat.

A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics.

Bed Segmentation

## Overview

Bed Segmentation is a dataset for instance segmentation tasks - it contains Bed annotations for 305 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).

Sparsely Annotated Region and Organ Segmentation (SAROS) contributes a large heterogeneous semantic segmentation annotation dataset for existing CT imaging cases on TCIA. The goal of this dataset is to provide high-quality annotations for building body composition analysis tools (References: Koitka 2020 and Haubold 2023). Existing in-house segmentation models were employed to generate annotation candidates on randomly selected cases. All generated annotations were manually reviewed and corrected by medical residents and students on every fifth axial slice while other slices were set to an ignore label (numeric value 255). 900 CT series from 882 patients were randomly selected from the following TCIA collections (number of CTs per collection in parenthesis): ACRIN-FLT-Breast (32), ACRIN-HNSCC-FDG-PET/CT (48), ACRIN-NSCLC-FDG-PET (129), Anti-PD-1_Lung (12), Anti-PD-1_MELANOMA (2), C4KC-KiTS (175), COVID-19-NY-SBU (1), CPTAC-CM (1), CPTAC-LSCC (3), CPTAC-LUAD (1), CPTAC-PDA (8), CPTAC-UCEC (26), HNSCC (17), Head-Neck Cetuximab (12), LIDC-IDRI (133), Lung-PET-CT-Dx (17), NSCLC Radiogenomics (7), NSCLC-Radiomics (56), NSCLC-Radiomics-Genomics (20), Pancreas-CT (58), QIN-HEADNECK (94), Soft-tissue-Sarcoma (6), TCGA-HNSC (1), TCGA-LIHC (33), TCGA-LUAD (2), TCGA-LUSC (3), TCGA-STAD (2), TCGA-UCEC (1). A script to download and resample the images is provided in our GitHub repository: https://github.com/UMEssen/saros-dataset The annotations are provided in NIfTI format and were performed on 5mm slice thickness. The annotation files define foreground labels on the same axial slices and match pixel-perfect. In total, 13 semantic body regions and 6 body part labels were annotated with an index that corresponds to a numeric value in the segmentation file.

Body Regions

1. Subcutaneous Tissue
2. Muscle
3. Abdominal Cavity
4. Thoracic Cavity
5. Bones
6. Parotid Glands
7. Pericardium
8. Breast Implant
9. Mediastinum
10. Brain
11. Spinal Cord
12. Thyroid Glands
13. Submandibular Glands

Body Parts

1. Torso
2. Head
3. Right Leg
4. Left Leg
5. Right Arm
6. Left Arm

The labels which were modified or require further commentary are listed and explained below:

• Subcutaneous Adipose Tissue: The cutis was included into this label due to its limited differentiation in 5mm-CT.
• Muscle: All muscular tissue was segmented contiguously and not separated into single muscles. Thus, fascias and intermuscular fat were included into the label. Inter- and intramuscular fat is subtracted automatically in the process.
• Abdominal Cavity: This label includes the pelvis. The label does not separate between the positional relationships of the peritoneum.
• Mediastinum: The International Thymic Malignancy Group (ITMIG) scheme was used for the segmentation guidelines.
• Head + Neck: The neck is confined by the base of the trapezius muscle.
• Right + Left Leg: The legs are separated from the torso by the line between the two lowest points of the Rami ossa pubis.
• Right + Left Arm: The arms are separated from the torso by the diagonal between the most lateral point of the acromion and the tuberculum infraglenoidale.

For reproducibility on downstream tasks, five cross-validation folds and a test set were pre-defined and are described in the provided spreadsheet. Segmentation was conducted strictly in accordance with anatomical guidelines and only modified if required for the gain of segmentation efficiency.

Chest X-ray(CXR) images are prominent among medical images and are commonly administered in emergency diagnosis and treatment corresponding to cardiac and respiratory diseases. Though there are robust solutions available for medical diagnosis, validation of artificial intelligence (AI) in radiology is still questionable. Segmentation is pivotal in chest radiographs that aid in improvising the existing AI-based medical diagnosis process. We provide the CXLSeg dataset: Chest X-ray with Lung Segmentation, a comparatively large dataset of segmented Chest X-ray radiographs based on the MIMIC-CXR dataset, a popular CXR image dataset. The dataset contains segmentation results of 243,324 frontal view images of the MIMIC-CXR dataset and corresponding masks. Additionally, this dataset can be utilized for computer vision-related deep learning tasks such as medical image classification, semantic segmentation and medical report generation. Models using segmented images yield better results since only the features related to the important areas of the image are focused. Thus images of this dataset can be manipulated to any visual feature extraction process associated with the original MIMIC-CXR dataset and enhance the results of the published or novel investigations. Furthermore, masks provided by this dataset can be used to train segmentation models when combined with the MIMIC-CXR-JPG dataset. The SA-UNet model achieved a 96.80% in dice similarity coefficient and 91.97% in IoU for lung segmentation using CXLSeg.

Alabama Buildings Segmentation dataset is the combination of BingMap satellite images and masks from Microsoft Maps. It is almost from Alabama, US (99%). Others from Columbia. Dataset contains 10200 satellite images and 10200 masks with weight ~ 17Gb. The satellite images from this dataset have resolution 0.5m/pixel, image size 1024x1024, ~1.5Mb/image. Dataset only contains pictures that have the total area of builbuilding in mask >= 1% area of that pictures. It means there are no images that do not have any building in this dataset.

Dive into the Windows Segmentation Dataset. Ideal for architectural AI, building analysis, and urban planning research insights.

Not my dataset. Check the original dataset: https://www.kaggle.com/datasets/mehradaria/leukemia/data

Credit: Paper: A Fast and Efficient CNN Model for B-ALL Diagnosis and its Subtypes Classification using Peripheral Blood Smear Images Source code: https://github.com/MehradAria/ALL-Subtype-Classification

Data Citation: Mehrad Aria, Mustafa Ghaderzadeh, Davood Bashash, Hassan Abolghasemi, Farkhondeh Asadi, and Azamossadat Hosseini, “Acute Lymphoblastic Leukemia (ALL) image dataset.” Kaggle, (2021). DOI: 10.34740/KAGGLE/DSV/2175623.

Publication Citation: Ghaderzadeh, M, Aria, M, Hosseini, A, Asadi, F, Bashash, D, Abolghasemi, H. A fast and efficient CNN model for B-ALL diagnosis and its subtypes classification using peripheral blood smear images. Int J Intell Syst. 2022; 37: 5113- 5133. doi:10.1002/int.22753

Cov Khoom Siv Feem Ntau Segmentation Dataset ua haujlwm rau kev lag luam e-lag luam thiab kev lom zem pom kev lag luam nrog ntau cov duab sau hauv internet, muaj cov kev daws teeb meem xws li 800 × 600 txog 4160 × 3120. Cov ntaub ntawv no suav nrog ntau qhov sib txawv ntawm cov xwm txheej niaj hnub thiab cov khoom, suav nrog ntau tus neeg, tsiaj txhu thiab cov rooj tog zaum. segmentation.

The Liver Tumor Segmentation Benchmark (LiTS) dataset contains 130 CT scans of patients with liver cancer. This dataset includes 2D slices from 3D CT scans with masks for liver, tumor, bone, arteries, and kidneys.

This dataset facilitates slice based segmentation, which produces more accurate results (in most cases) than 3D segmentation.

Reference: https://doi.org/10.1016/j.media.2022.102680

File Description

This dataset contains the slices from the LiTS dataset in the format: Volume-{VolumeNumber}-{SliceNumber.png}.

Both the image and the mask files have the same naming convention.

The authors of India Driving Dataset (IDD): A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments highlight a notable gap in existing datasets, which primarily focus on structured driving environments with well-defined infrastructure, limited traffic categories, and adherence to traffic rules. To fill this void, the authors present IDD, a novel dataset tailored for road scene understanding in unstructured environments, specifically on Indian roads. The updated version of the dataset (acquired in Oct, 2023) comprises 20k images, meticulously annotated with 41 classes, derived from 182 drive sequence.

The Road Lane Instance Segmentation Dataset contains high-resolution dashcam images with pixel-perfect annotations of lane markings such as solid lines, dotted lines, double lines, divider lines, and road sign lines. It is designed for autonomous driving, ADAS, and computer vision research.

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.

The official data set for the NeurIPS 2022 competition: cell segmentation in multi-modality microscopy images.

https://neurips22-cellseg.grand-challenge.org/

Please cite the following paper if this dataset is used in your research.

@article{NeurIPS-CellSeg,
   title = {The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions},
   author = {Jun Ma and Ronald Xie and Shamini Ayyadhury and Cheng Ge and Anubha Gupta and Ritu Gupta and Song Gu and Yao Zhang and Gihun Lee and Joonkee Kim and Wei Lou and Haofeng Li and Eric Upschulte and Timo Dickscheid and José Guilherme de Almeida and Yixin Wang and Lin Han and Xin Yang and Marco Labagnara and Vojislav Gligorovski and Maxime Scheder and Sahand Jamal Rahi and Carly Kempster and Alice Pollitt and Leon Espinosa and Tâm Mignot and Jan Moritz Middeke and Jan-Niklas Eckardt and Wangkai Li and Zhaoyang Li and Xiaochen Cai and Bizhe Bai and Noah F. Greenwald and David Van Valen and Erin Weisbart and Beth A. Cimini and Trevor Cheung and Oscar Brück and Gary D. Bader and Bo Wang},
   journal = {Nature Methods},
   volume={21},
   pages={1103–1113},
   year = {2024},
   doi = {https://doi.org/10.1038/s41592-024-02233-6}
 }

This is an instance segmentation task where each cell has an individual label under the same category (cells). The training set contains both labeled images and unlabeled images. You can only use the labeled images to develop your model but we encourage participants to try to explore the unlabeled images through weakly supervised learning, semi-supervised learning, and self-supervised learning.

The images are provided with original formats, including tiff, tif, png, jpg, bmp... The original formats contain the most amount of information for competitors and you have free choice over different normalization methods. For the ground truth, we standardize them as tiff formats.

We aim to maintain this challenge as a sustainable benchmark platform. If you find the top algorithms (https://neurips22-cellseg.grand-challenge.org/awards/) don't perform well on your images, welcome to send us the dataset (neurips.cellseg@gmail.com)! We will include them in the new testing set and credit your contributions on the challenge website!

Dataset License: CC-BY-NC-ND