3 datasets found
  1. R

    Yolo Segment Dataset

    • universe.roboflow.com
    zip
    Updated Mar 11, 2025
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    Toki (2025). Yolo Segment Dataset [Dataset]. https://universe.roboflow.com/toki-gvcck/yolo-segment-pxc0h
    Explore at:
    zipAvailable download formats
    Dataset updated
    Mar 11, 2025
    Dataset authored and provided by
    Toki
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Test Polygons
    Description

    Yolo Segment

    ## Overview
    
    Yolo Segment is a dataset for instance segmentation tasks - it contains Test annotations for 7,356 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).
    
  2. R

    EgoHands Object Detection Dataset - specific

    • public.roboflow.com
    zip
    Updated Apr 22, 2022
    + more versions
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    IU Computer Vision Lab (2022). EgoHands Object Detection Dataset - specific [Dataset]. https://public.roboflow.com/object-detection/hands/1
    Explore at:
    zipAvailable download formats
    Dataset updated
    Apr 22, 2022
    Dataset authored and provided by
    IU Computer Vision Lab
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Bounding Boxes of hands
    Description

    https://i.imgur.com/eEWi4PT.png" alt="EgoHands Dataset">

    About this dataset

    The EgoHands dataset is a collection of 4800 annotated images of human hands from a first-person view originally collected and labeled by Sven Bambach, Stefan Lee, David Crandall, and Chen Yu of Indiana University.

    The dataset was captured via frames extracted from video recorded through head-mounted cameras on a Google Glass headset while peforming four activities: building a puzzle, playing chess, playing Jenga, and playing cards. There are 100 labeled frames for each of 48 video clips.

    Our modifications

    The original EgoHands dataset was labeled with polygons for segmentation and released in a Matlab binary format. We converted it to an object detection dataset using a modified version of this script from @molyswu and have archived it in many popular formats for use with your computer vision models.

    After converting to bounding boxes for object detection, we noticed that there were several dozen unlabeled hands. We added these by hand and improved several hundred of the other labels that did not fully encompass the hands (usually to include omitted fingertips, knuckles, or thumbs). In total, 344 images' annotations were edited manually.

    We chose a new random train/test split of 80% training, 10% validation, and 10% testing. Notably, this is not the same split as in the original EgoHands paper.

    There are two versions of the converted dataset available: * specific is labeled with four classes: myleft, myright, yourleft, yourright representing which hand of which person (the viewer or the opponent across the table) is contained in the bounding box. * generic contains the same boxes but with a single hand class.

    Using this dataset

    The authors have graciously allowed Roboflow to re-host this derivative dataset. It is released under a Creative Commons by Attribution 4.0 license. You may use it for academic or commercial purposes but must cite the original paper.

    Please use the following Bibtext: @inproceedings{egohands2015iccv, title = {Lending A Hand: Detecting Hands and Recognizing Activities in Complex Egocentric Interactions}, author = {Sven Bambach and Stefan Lee and David Crandall and Chen Yu}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2015} }

  3. R

    Macro Segmentation Kaer8 Yajkb Fsod Blok Dataset

    • universe.roboflow.com
    zip
    Updated Mar 14, 2025
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    RF 100 VL FSOD (2025). Macro Segmentation Kaer8 Yajkb Fsod Blok Dataset [Dataset]. https://universe.roboflow.com/rf-100-vl-fsod/macro-segmentation-kaer8-yajkb-fsod-blok/model/4
    Explore at:
    zipAvailable download formats
    Dataset updated
    Mar 14, 2025
    Dataset authored and provided by
    RF 100 VL FSOD
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Variables measured
    Macro Segmentation Kaer8 Yajkb Fsod Blok Bounding Boxes
    Description

    Overview

    Introduction

    This dataset provides a comprehensive collection for segmenting various zones in historical documents. The task is to accurately annotate different zones that help in categorizing text and graphical elements. The dataset consists of distinct classes such as textual, graphical, and decorative zones.

    Object Classes

    Graphic

    Description

    Regions primarily containing graphic representations or illustrations, often found centrally on a page.

    Instructions

    Annotate the entire area that contains central illustrations. Ensure to include complete borders if present. Do not include any peripheral text associated with the graphics.

    Graphic Decoration

    Description

    Decorative elements often used as borders or fillers around main text or graphics.

    Instructions

    Focus on annotating smaller decorative elements that do not convey primary content, such as ornamental borders. Ensure to exclude surrounding main text.

    Graphic Head

    Description

    Illustrative or decorative elements located at the heads of sections, often introducing the content.

    Instructions

    Identify and annotate header illustrations or decorations that introduce sections. Do not include text unless part of the illustration or decoration.

    Main Entry

    Description

    The primary body of text in the document that serves as a main entry.

    Instructions

    Outline the main textual content without including any decorative elements or illustrations. Ensure the text is cleanly captured within boundaries.

    Main Entry Continued

    Description

    Continuation of the main body text from a prior page or section.

    Instructions

    Annotate where the text resumes, maintaining continuity from the previous page. Ensure exclusion of any introductory headers or decorations transitioning into the continued text.

    Main Head

    Description

    Headings or titles that introduce the main text sections.

    Instructions

    Encircle clearly identified headings that stand at the beginning of main sections. Do not include adjacent body text.

    Main List

    Description

    Zones containing enumerated lists or series of items.

    Instructions

    Mark areas that contain ordered lists or bullet points. Ensure that complete list items are captured, avoiding adjacent explanatory paragraphs.

    Main Paragarph

    Description

    Paragraphs of text excluding lists or numerical data.

    Instructions

    Enclose full paragraphs, differentiating them from lists or other formatted text, ensuring paragraph ends and beginnings are clearly defined.

    Main Paragraph Catalogue Description

    Description

    Paragraphs specifically describing catalogue items.

    Instructions

    Highlight paragraphs focused on descriptive catalog entries, distinct from regular narrative text or headings. Capture explicit item descriptions without images.

    Margin Text Manuscript Addendum

    Description

    Textual additions or comments typically found in the margins.

    Instructions

    Focus on texts residing outside the main body and note added commentary or references in margins. Exclude main body and footnotes.

    Margin Text Note

    Description

    Small notes or annotations found typically in the margins of pages.

    Instructions

    Isolate smaller margin notes or brief annotations not part of the primary text. Exclude any marginal header or visual borders.

    Numbering

    Description

    Sections of a page that display page or item numbers.

    Instructions

    Circle areas specifically containing numbers, whether for pagination or enumeration, irrespective of whether it's at the top or bottom of the page.

    Running Title

    Description

    Titles or headers appearing at the top or bottom of the pages, serving as running titles.

    Instructions

    Encapsulate titles that repeat across pages as headers or footers. Exclude any body text unconventionally placed nearby.

    Stamp

    Description

    Sections containing stamps or seals, often used for identification or authenticity.

    Instructions

    Recognize and encircle all stamped areas. Ensure to separate from adjacent text or graphics.

    Stamp Sticker

    Description

    Regions where sticker labels or adhesive notes are placed.

    Instructions

    Anno

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Share
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Click to copy link
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Close
Cite
Toki (2025). Yolo Segment Dataset [Dataset]. https://universe.roboflow.com/toki-gvcck/yolo-segment-pxc0h

Yolo Segment Dataset

yolo-segment-pxc0h

yolo-segment-dataset

Explore at:
235 scholarly articles cite this dataset (View in Google Scholar)
zipAvailable download formats
Dataset updated
Mar 11, 2025
Dataset authored and provided by
Toki
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Variables measured
Test Polygons
Description

Yolo Segment

## Overview

Yolo Segment is a dataset for instance segmentation tasks - it contains Test annotations for 7,356 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).
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