Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## 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).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
https://i.imgur.com/eEWi4PT.png" alt="EgoHands 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.
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.
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}
}
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
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.
Regions primarily containing graphic representations or illustrations, often found centrally on a page.
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.
Decorative elements often used as borders or fillers around main text or graphics.
Focus on annotating smaller decorative elements that do not convey primary content, such as ornamental borders. Ensure to exclude surrounding main text.
Illustrative or decorative elements located at the heads of sections, often introducing the content.
Identify and annotate header illustrations or decorations that introduce sections. Do not include text unless part of the illustration or decoration.
The primary body of text in the document that serves as a main entry.
Outline the main textual content without including any decorative elements or illustrations. Ensure the text is cleanly captured within boundaries.
Continuation of the main body text from a prior page or section.
Annotate where the text resumes, maintaining continuity from the previous page. Ensure exclusion of any introductory headers or decorations transitioning into the continued text.
Headings or titles that introduce the main text sections.
Encircle clearly identified headings that stand at the beginning of main sections. Do not include adjacent body text.
Zones containing enumerated lists or series of items.
Mark areas that contain ordered lists or bullet points. Ensure that complete list items are captured, avoiding adjacent explanatory paragraphs.
Paragraphs of text excluding lists or numerical data.
Enclose full paragraphs, differentiating them from lists or other formatted text, ensuring paragraph ends and beginnings are clearly defined.
Paragraphs specifically describing catalogue items.
Highlight paragraphs focused on descriptive catalog entries, distinct from regular narrative text or headings. Capture explicit item descriptions without images.
Textual additions or comments typically found in the margins.
Focus on texts residing outside the main body and note added commentary or references in margins. Exclude main body and footnotes.
Small notes or annotations found typically in the margins of pages.
Isolate smaller margin notes or brief annotations not part of the primary text. Exclude any marginal header or visual borders.
Sections of a page that display page or item numbers.
Circle areas specifically containing numbers, whether for pagination or enumeration, irrespective of whether it's at the top or bottom of the page.
Titles or headers appearing at the top or bottom of the pages, serving as running titles.
Encapsulate titles that repeat across pages as headers or footers. Exclude any body text unconventionally placed nearby.
Sections containing stamps or seals, often used for identification or authenticity.
Recognize and encircle all stamped areas. Ensure to separate from adjacent text or graphics.
Regions where sticker labels or adhesive notes are placed.
Anno
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## 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).