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 T-100 Domestic Market and Segment Data dataset was downloaded on April 08, 2025 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). It shows 2024 statistics for all domestic airports operated by US carriers, and all information are totals for the year across all four (4) service classes (F - Scheduled Passenger/ Cargo Service, G - Scheduled All Cargo Service, L - Non-Scheduled Civilian Passenger/ Cargo Service, and P - Non-Scheduled Civilian All Cargo Service). This dataset is a combination of both T-100 Market and T-100 Segments datasets. The T-100 Market includes enplanement data, and T-100 Segment data includes passengers, arrivals, departures, freight, and mail. Data is by origin airport. Along with yearly aggregate totals for these variables, this dataset also provides more granular information for the passenger and freight variable by service class and by scheduled vs non-scheduled statistics where applicable. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529081

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

The Segment Tool provides information on the causes of death and age groups that are driving inequalities in life expectancy at local area level. Targeting the causes of death and age groups which contribute most to the life expectancy gap should have the biggest impact on reducing inequalities.

The tool provides data tables and charts showing the breakdown of the life expectancy gap in 2020 to 2021 for 2 comparisons:

• England: the gap between each local area or region as a whole and England as a whole
• within area: the gap between the most deprived quintile of each area and the least deprived quintile of the area

The tool contains data for England, English regions and upper tier local authorities.

NIST, working with the FBI, has digitized 888 inked fingerprint arrest cards that were in various physical conditions, from pristine to badly damaged and faded, and were collected during law enforcement professionals' duties. This database contains images of the 10 rolled fingerprint impressions, the two four-finger slap impressions (finger positions 13 and 14), the two thumb slap impressions (finger positions 11 and 12) and the segmented impressions from the slap images (13,14). The database also includes the coordinates that were used to segment the impressions from the slap fingerprint images.The cards were scanned at three different resolutions: 500, 1,000, and 2,000 pixels per inch (PPI). All three resolutions were scanned in grayscale at a depth of 8 bits-per pixel.Data available as of July 2018 is Special Database 300a, in 500 ppi with PNG formatted impressions. Data at other resolutions, in other image formats, and in other record types may be forthcoming.

Manipulating cluttered cables, hoses or ropes is challenging for both robots and humans. Humans often simplify these perceptually challenging tasks by pulling or pushing tangled cables and observing the resulting motions. We would like to build a similar system -- in accordance with the interactive perception paradigm -- to aid robotic cable manipulation. A cable motion segmentation method that densely labels moving cable image pixels is a key building block of such a system. We present MovingCables, a moving cable dataset, which we hope will motivate the development and evaluation of cable motion (or semantic) segmentation algorithms. The dataset consists of real-world image sequences automatically annotated with ground truth segmentation masks and optical flow.

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}
}

The pathway representation consists of segments and intersection elements. A segment is a linear graphic element that represents a continuous physical travel path terminated by path end (dead end) or physical intersection with other travel paths. Segments have one street name, one address range and one set of segment characteristics. A segment may have none or multiple alias street names. Segment types included are Freeways, Highways, Streets, Alleys (named only), Railroads, Walkways, and Bike lanes. SNDSEG_PV is a linear feature class representing the SND Segment Feature, with attributes for Street name, Address Range, Alias Street name and segment Characteristics objects. Part of the Address Range and all of Street name objects are logically shared with the Discrete Address Point-Master Address File layer. Appropriate uses include: Cartography - Used to depict the City's transportation network location and connections, typically on smaller scaled maps or images where a single line representation is appropriate. Used to depict specific classifications of roadway use, also typically at smaller scales. Used to label transportation network feature names typically on larger scaled maps. Used to label address ranges with associated transportation network features typically on larger scaled maps. Geocode reference - Used as a source for derived reference data for address validation and theoretical address location Address Range data repository - This data store is the City's address range repository defining address ranges in association with transportation network features. Polygon boundary reference - Used to define various area boundaries is other feature classes where coincident with the transportation network. Does not contain polygon features. Address based extracts - Used to create flat-file extracts typically indexed by address with reference to business data typically associated with transportation network features. Thematic linear location reference - By providing unique, stable identifiers for each linear feature, thematic data is associated to specific transportation network features via these identifiers. Thematic intersection location reference - By providing unique, stable identifiers for each intersection feature, thematic data is associated to specific transportation network features via these identifiers. Network route tracing - Used as source for derived reference data used to determine point to point travel paths or determine optimal stop allocation along a travel path. Topological connections with segments - Used to provide a specific definition of location for each transportation network feature. Also provides a specific definition of connection between each transportation network feature. (defines where the streets are and the relationship between them ie. 4th Ave is west of 5th Ave and 4th Ave does intersect with Cherry St) Event location reference - Used as source for derived reference data used to locate event and linear referencing.Data source is TRANSPO.SNDSEG_PV. Updated weekly.

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.

General

For more details and the most up-to-date information please consult our project page: https://kainmueller-lab.github.io/fisbe.

Summary

• A new dataset for neuron instance segmentation in 3d multicolor light microscopy data of fruit fly brains
  • 30 completely labeled (segmented) images
  • 71 partly labeled images
  • altogether comprising ∼600 expert-labeled neuron instances (labeling a single neuron takes between 30-60 min on average, yet a difficult one can take up to 4 hours)
• To the best of our knowledge, the first real-world benchmark dataset for instance segmentation of long thin filamentous objects
• A set of metrics and a novel ranking score for respective meaningful method benchmarking
• An evaluation of three baseline methods in terms of the above metrics and score

Abstract

Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have long-ranging, thin filamentous and widely branching morphologies, multiple neurons are tightly inter-weaved, and partial volume effects, uneven illumination and noise inherent to light microscopy severely impede local disentangling as well as long-range tracing of individual neurons. These properties reflect a current key challenge in machine learning research, namely to effectively capture long-range dependencies in the data. While respective methodological research is buzzing, to date methods are typically benchmarked on synthetic datasets. To address this gap, we release the FlyLight Instance Segmentation Benchmark (FISBe) dataset, the first publicly available multi-neuron light microscopy dataset with pixel-wise annotations. In addition, we define a set of instance segmentation metrics for benchmarking that we designed to be meaningful with regard to downstream analyses. Lastly, we provide three baselines to kick off a competition that we envision to both advance the field of machine learning regarding methodology for capturing long-range data dependencies, and facilitate scientific discovery in basic neuroscience.

Dataset documentation:

We provide a detailed documentation of our dataset, following the Datasheet for Datasets questionnaire:

>> FISBe Datasheet

Our dataset originates from the FlyLight project, where the authors released a large image collection of nervous systems of ~74,000 flies, available for download under CC BY 4.0 license.

Files

• fisbe_v1.0_{completely,partly}.zip
  • contains the image and ground truth segmentation data; there is one zarr file per sample, see below for more information on how to access zarr files.
• fisbe_v1.0_mips.zip
  • maximum intensity projections of all samples, for convenience.
• sample_list_per_split.txt
  • a simple list of all samples and the subset they are in, for convenience.
• view_data.py
  • a simple python script to visualize samples, see below for more information on how to use it.
• dim_neurons_val_and_test_sets.json
  • a list of instance ids per sample that are considered to be of low intensity/dim; can be used for extended evaluation.
• Readme.md
  • general information

How to work with the image files

Each sample consists of a single 3d MCFO image of neurons of the fruit fly.
For each image, we provide a pixel-wise instance segmentation for all separable neurons.
Each sample is stored as a separate zarr file (zarr is a file storage format for chunked, compressed, N-dimensional arrays based on an open-source specification.").
The image data ("raw") and the segmentation ("gt_instances") are stored as two arrays within a single zarr file.
The segmentation mask for each neuron is stored in a separate channel.
The order of dimensions is CZYX.

We recommend to work in a virtual environment, e.g., by using conda:

conda create -y -n flylight-env -c conda-forge python=3.9
conda activate flylight-env

How to open zarr files

1. Install the python zarr package:

   pip install zarr

2. Opened a zarr file with:
   

   import zarr
raw = zarr.open(
seg = zarr.open(

   # optional:
import numpy as np
raw_np = np.array(raw)

Zarr arrays are read lazily on-demand.
Many functions that expect numpy arrays also work with zarr arrays.
Optionally, the arrays can also explicitly be converted to numpy arrays.

How to view zarr image files

We recommend to use napari to view the image data.

1. Install napari:

   pip install "napari[all]"

2. Save the following Python script:
   

   import zarr, sys, napari

   raw = zarr.load(sys.argv[1], mode='r', path="volumes/raw")
gts = zarr.load(sys.argv[1], mode='r', path="volumes/gt_instances")

   viewer = napari.Viewer(ndisplay=3)
for idx, gt in enumerate(gts):
viewer.add_labels(
gt, rendering='translucent', blending='additive', name=f'gt_{idx}')
viewer.add_image(raw[0], colormap="red", name='raw_r', blending='additive')
viewer.add_image(raw[1], colormap="green", name='raw_g', blending='additive')
viewer.add_image(raw[2], colormap="blue", name='raw_b', blending='additive')
napari.run()

3. Execute:

   python view_data.py 

Metrics

• S: Average of avF1 and C
• avF1: Average F1 Score
• C: Average ground truth coverage
• clDice_TP: Average true positives clDice
• FS: Number of false splits
• FM: Number of false merges
• tp: Relative number of true positives

For more information on our selected metrics and formal definitions please see our paper.

Baseline

To showcase the FISBe dataset together with our selection of metrics, we provide evaluation results for three baseline methods, namely PatchPerPix (ppp), Flood Filling Networks (FFN) and a non-learnt application-specific color clustering from Duan et al..
For detailed information on the methods and the quantitative results please see our paper.

License

The FlyLight Instance Segmentation Benchmark (FISBe) dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Citation

If you use FISBe in your research, please use the following BibTeX entry:

@misc{mais2024fisbe,
 title =    {FISBe: A real-world benchmark dataset for instance
         segmentation of long-range thin filamentous structures},
 author =    {Lisa Mais and Peter Hirsch and Claire Managan and Ramya
         Kandarpa and Josef Lorenz Rumberger and Annika Reinke and Lena
         Maier-Hein and Gudrun Ihrke and Dagmar Kainmueller},
 year =     2024,
 eprint =    {2404.00130},
 archivePrefix ={arXiv},
 primaryClass = {cs.CV}
}

Acknowledgments

We thank Aljoscha Nern for providing unpublished MCFO images as well as Geoffrey W. Meissner and the entire FlyLight Project Team for valuable
discussions.
P.H., L.M. and D.K. were supported by the HHMI Janelia Visiting Scientist Program.
This work was co-funded by Helmholtz Imaging.

Changelog

There have been no changes to the dataset so far.
All future change will be listed on the changelog page.

Contributing

If you would like to contribute, have encountered any issues or have any suggestions, please open an issue for the FISBe dataset in the accompanying github repository.

All contributions are welcome!

Dataset

This dataset was created by AmitH2022

Released under Apache 2.0

Contents

Description The data diversity includes multiple scenes, ages, races, poses, and appendages. In terms of annotation, we adpoted segmentation annotations on human body and appendages.18 landmarks were also annotated for each human body. The data can be used for tasks such as human body segmentation and human behavior recognition. For more details, please visit: https://www.nexdata.ai/datasets/computervision/958?source=Kaggle

Specifications

Data size 50,356 images Race distribution 18,458 images of Asians, 27,191 images of Caucasians, 4,707 images of black race Gender distribution 18,381 images of male, 31,975 images of female Age distribution young: 5,196 image; middle aged and young: 44,751 images; middle aged and elderly: 409 images Collecting environment including indoor scenes and outdoor scenes (such as natural scenery, street, square, architecture, etc.) Data diversity multiple scenes, ages, races, poses, and appendages Data format the image data format is .jpg and .png, the annotation file format is .json Annotation content segmentation annotation of human body and appendages, 18 landmarks annotation of human body Accuracy the mask edge location errors in x and y directions are less than 3 pixels, which is considered as a qualified annotation; Accuracy requirement of segmentation annotation: the annotation part (each part of mask) is regarded as the unit, the accuracy rate shall be more than 97%; Accuracy requirement of landmark annotation: the annotation part (each landmark) is regarded as the unit, the accuracy rate shall be more than 97%;

Get the Dataset This is just an example of the data. To access more sample data or request the price, contact us at info@nexdata.ai

This dataset consists of four sets of flower images, from three different species: apple, peach, and pear, and accompanying ground truth images. The images were acquired under a range of imaging conditions. These datasets support work in an accompanying paper that demonstrates a flower identification algorithm that is robust to uncontrolled environments and applicable to different flower species. While this data is primarily provided to support that paper, other researchers interested in flower detection may also use the dataset to develop new algorithms. Flower detection is a problem of interest in orchard crops because it is related to management of fruit load. Funding provided through ARS Integrated Orchard Management and Automation for Deciduous Tree Fruit Crops. Resources in this dataset:Resource Title: AppleA images. File Name: AppleA.zipResource Description: 147 images of an apple tree in bloom acquired with a Canon EOS 60D.Resource Title: Training image names from Apple A dataset. File Name: train.txtResource Description: This is a list of filenames used in training; see related paper for details.Resource Title: AppleA labels. File Name: AppleA_Labels.zipResource Description: Binary images for the Apple A set, where white represents flower pixels and black, non-flower pixels. June 25, 2018: 5 files added: 275.png, 316.png, 328.png, 336.png, 369.png.Resource Title: Validation image names from Apple A dataset. File Name: val.txtResource Description: This is a list of filenames used in testing; see related paper for details. June 25, 2018: 5 filenames added. IMG_0275.JPG IMG_0316.JPG IMG_0328.JPG IMG_0336.JPG IMG_0369.JPGResource Title: AppleB images. File Name: AppleB.zipResource Description: 15 images of an apple tree in bloom acquired with a GoPro HERO 5. June 25, 2018: 3 files added. 23.bmp 28.bmp 42.bmpResource Title: AppleB labels. File Name: AppleB_Labels.zipResource Description: Binary images for the Apple B set, where white represents flower pixels and black, non-flower pixels. June 25, 2018: 3 files added. 23.bmp 28.bmp 42.bmpResource Title: Peach. File Name: Peach.zipResource Description: 20 images of an peach tree in bloom acquired with a GoPro HERO 5. June 25, 2018: 4 files added. 14.bmp 34.bmp 40.bmp 41.bmpResource Title: Peach labels. File Name: PeachLabels.zipResource Description: Binary images for the Peach set, where white represents flower pixels and black, non-flower pixels. June 25, 2018: 4 files added. 14.bmp 34.bmp 40.bmp 41.bmpResource Title: Pear. File Name: Pear.zipResource Description: 15 images of a free-standing pear tree in bloom, acquired with a GoPro HERO5. June 25, 2018: 3 files added. 1_25.bmp 1_62.bmp 2_28.bmpResource Title: Pear labels. File Name: PearLabels.zipResource Description: Binary images for the pear set, where white represents flower pixels and black, non-flower pixels. June 25, 2018: 3 files added. 1_25.bmp 1_62.bmp 2_28.bmpResource Title: Apple A Labeled images from training set . File Name: AppleALabels_Train.zipResource Description: Binary images for the Apple A set, where white represents flower pixels and black, non-flower pixels. These images form the training set. Resource added August 20, 2018. User noted that this resource was missing.

Segmentation of the adult England population with interactive tool and raw data to help understand where different types of people are located and how to reach them. Postcode level data with segment counts available to download. Youth segmentation is being developed and will be added to this tool in autumn 2013

RISE Data Segmentation

## Overview

RISE Data Segmentation is a dataset for instance segmentation tasks - it contains Steam And Smoke R9f8 annotations for 1,872 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).

Segmentation models perform a pixel-wise classification by classifying the pixels into different classes. The classified pixels correspond to different objects or regions in the image. These models have a wide variety of use cases across multiple domains. When used with satellite and aerial imagery, these models can help to identify features such as building footprints, roads, water bodies, crop fields, etc.Generally, every segmentation model needs to be trained from scratch using a dataset labeled with the objects of interest. This can be an arduous and time-consuming task. Meta's Segment Anything Model (SAM) is aimed at creating a foundational model that can be used to segment (as the name suggests) anything using zero-shot learning and generalize across domains without additional training. SAM is trained on the Segment Anything 1-Billion mask dataset (SA-1B) which comprises a diverse set of 11 million images and over 1 billion masks. This makes the model highly robust in identifying object boundaries and differentiating between various objects across domains, even though it might have never seen them before. Use this model to extract masks of various objects in any image.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS. Fine-tuning the modelThis model can be fine-tuned using SamLoRA architecture in ArcGIS. Follow the guide and refer to this sample notebook to fine-tune this model.Input8-bit, 3-band imagery.OutputFeature class containing masks of various objects in the image.Applicable geographiesThe model is expected to work globally.Model architectureThis model is based on the open-source Segment Anything Model (SAM) by Meta.Training dataThis model has been trained on the Segment Anything 1-Billion mask dataset (SA-1B) which comprises a diverse set of 11 million images and over 1 billion masks.Sample resultsHere are a few results from the model.

The Segment Tool provides information on the causes of death and age groups that are driving inequalities in life expectancy at local area level. Targeting the causes of death and age groups which contribute most to the life expectancy gap should have the biggest impact on reducing inequalities.

The tool provides data tables and charts showing the breakdown of the life expectancy gap in 2015 to 2017 for 2 comparisons:

• England: the gap between each local authority or region as a whole and England as a whole
• within area: the gap between the most deprived quintile of each area and the least deprived quintile of the area

Target Image Segmentation Data

## Overview

Target  Image Segmentation Data is a dataset for instance segmentation tasks - it contains Targets annotations for 293 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).

We propose a new database for information extraction from historical handwritten documents. The corpus includes 5,393 finding aids from six different series, dating from the 18th-20th centuries. Finding aids are handwritten documents that contain metadata describing older archives. They are stored in the National Archives of France and are used by archivists to identify and find archival documents.

Each document is annotated at page-level, and contains seven fields to retrieve. The localization of each field is not available in such a way that this dataset encourages research on segmentation-free systems for information extraction.