VoTT
Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.
Top Related Projects
Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.
Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source data labeling tool for images, text, hypertext, audio, video and time-series data.
Label Studio is a multi-type data labeling and annotation tool with standardized output format
Quick Overview
VoTT (Visual Object Tagging Tool) is an open-source annotation and labeling tool for image and video assets. It's designed to provide a simple and fast way to build end-to-end machine learning models from a catalog of assets and labels. VoTT is particularly useful for computer vision tasks and can export to various formats for use with different machine learning frameworks.
Pros
- User-friendly interface with support for both image and video annotation
- Cross-platform compatibility (Windows, macOS, Linux)
- Supports multiple export formats (CNTK, TensorFlow, YOLO, etc.)
- Extensible architecture allowing for custom plugins and integrations
Cons
- Limited advanced features compared to some commercial annotation tools
- Occasional performance issues with large datasets or complex projects
- Learning curve for setting up and configuring projects
- Limited built-in collaboration features for team-based annotation
Getting Started
To get started with VoTT:
- Download the latest release from the GitHub releases page.
- Install the application on your system.
- Launch VoTT and create a new project:
- Set your source connection (local folder or cloud storage)
- Configure your target connection for exports
- Define your tags/labels
- Start annotating your images or videos:
- Use rectangle, polygon, or point annotations
- Apply tags to your annotations
- Export your annotations in your chosen format.
For more detailed instructions, refer to the official documentation.
Competitor Comparisons
Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.
Pros of CVAT
- More comprehensive annotation tools, supporting a wider range of tasks
- Web-based interface allows for easier collaboration and remote access
- Integrates with popular ML frameworks and supports model-assisted annotation
Cons of CVAT
- Steeper learning curve due to more complex features
- Requires server setup, which can be challenging for some users
- May be overkill for simple labeling tasks
Code Comparison
VoTT (JavaScript):
export default class Rect extends React.Component<IRectProps> {
public render() {
const { width, height, left, top, featureStyleName } = this.props;
return (
<rect className={`${featureStyleName} rect`}
x={left} y={top}
width={width} height={height} />
);
}
}
CVAT (Python):
class RectangleShape(Shape):
def __init__(self, x, y, w, h):
self.xtl = x
self.ytl = y
self.xbr = x + w
self.ybr = y + h
def area(self):
return (self.xbr - self.xtl) * (self.ybr - self.ytl)
Both repositories provide tools for image and video annotation, but CVAT offers more advanced features and better scalability for larger projects. VoTT is simpler to set up and use, making it suitable for smaller-scale labeling tasks or individual users.
Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
Pros of labelme
- Supports a wider variety of annotation shapes, including polygons and lines
- Offers a simpler, more lightweight interface
- Provides better support for multi-label annotations
Cons of labelme
- Less intuitive user interface compared to VoTT
- Fewer built-in export options for annotated data
- Limited video annotation capabilities
Code Comparison
labelme:
from labelme import utils
img = utils.img_data_to_arr(img_data)
label_name_to_value = {'_background_': 0}
for shape in sorted(data['shapes'], key=lambda x: x['label']):
label_name = shape['label']
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
VoTT:
export interface ITag {
name: string;
color: string;
}
export interface IRegion {
id: string;
type: RegionType;
tags: string[];
boundingBox: IBoundingBox;
points?: IPoint[];
}
export interface IAssetMetadata {
asset: IAsset;
regions: IRegion[];
version: string;
}
Both repositories provide tools for image and video annotation, but they cater to different use cases and preferences. VoTT offers a more polished, feature-rich experience with better video support, while labelme provides a simpler, more flexible approach to image annotation with support for various shapes and multi-label annotations.
LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source data labeling tool for images, text, hypertext, audio, video and time-series data.
Pros of labelImg
- Lightweight and easy to install with minimal dependencies
- Supports multiple annotation formats (YOLO, PascalVOC, CreateML)
- Faster for simple bounding box annotations
Cons of labelImg
- Limited to bounding box annotations only
- Less advanced project management features
- Fewer export options and integrations
Code Comparison
labelImg:
def saveFile(self, _value=False):
if self.filePath:
try:
self.saveLabels(self.filePath)
self.setClean()
return True
except:
self.errorMessage(u'Error saving file', u'Error saving labels to file')
return False
return False
VoTT:
public async save(): Promise<void> {
if (this.project.isDirty || this.state.assets.length !== this.project.assets.length) {
await this.projectService.save(this.project);
this.setState({ project: this.project });
}
}
Both examples show saving functionality, but VoTT's implementation is more robust with async handling and state management.
Label Studio is a multi-type data labeling and annotation tool with standardized output format
Pros of Label Studio
- Supports a wider range of data types and labeling tasks
- More customizable and flexible annotation interface
- Active community and frequent updates
Cons of Label Studio
- Steeper learning curve due to increased complexity
- Requires more setup and configuration compared to VoTT
Code Comparison
Label Studio configuration example:
<View>
<Image name="image" value="$image"/>
<RectangleLabels name="label" toName="image">
<Label value="Car"/>
<Label value="Pedestrian"/>
</RectangleLabels>
</View>
VoTT configuration example:
{
"tags": [
{ "name": "Car", "color": "#FF0000" },
{ "name": "Pedestrian", "color": "#00FF00" }
]
}
Label Studio offers more flexibility in defining labeling tasks through its XML-based configuration, while VoTT uses a simpler JSON structure for tag definitions. Label Studio's approach allows for more complex annotation scenarios, but may require more effort to set up initially.
Convert 
designs to code with AI
Introducing Visual Copilot: A new AI model to turn Figma designs to high quality code using your components.
Try Visual CopilotREADME
VoTT is no longer being maintained!
An open source annotation and labeling tool for image and video assets.
VoTT is a React + Redux Web application, written in TypeScript. This project was bootstrapped with Create React App.
Features include:
- The ability to label images or video frames
- Extensible model for importing data from local or cloud storage providers
- Extensible model for exporting labeled data to local or cloud storage providers
VoTT helps facilitate an end-to-end machine learning pipeline:
Table of Contents
- VoTT (Visual Object Tagging Tool)
Getting Started
VoTT can be installed as a native application or run from source. VoTT is also available as a stand-alone Web application and can be used in any modern Web browser.
Download and install a release package for your platform (recommended)
VoTT is available for Windows, Linux and OSX. Download the appropriate platform package/installer from GitHub Releases. v2 releases will be prefixed by 2.x.
Build and run from source
VoTT requires NodeJS (>= 10.x, Dubnium) and NPM
git clone https://github.com/Microsoft/VoTT.git
cd VoTT
npm ci
npm start
IMPORTANT
When running locally with
npm, both the electron and the browser versions of the application will start. One major difference is that the electron version can access the local file system.
Run as Web Application
Using a modern Web browser, VoTT can be loaded from: https://vott.z22.web.core.windows.net
As noted above, the Web version of VoTT cannot access the local file system; all assets must be imported/exported through a Cloud project.
V1 & V2
VoTT V2 is a refactor and refresh of the original Electron-based application. As the usage and demand for VoTT grew, V2 was started as an initiative to improve and make VoTT more extensible and maintainable. In addition, V2 uses more modern development frameworks and patterns (React, Redux) and is authored in TypeScript.
A number of code quality practices have been adopted, including:
- Code Linting
- Unit tests & mocks (Jest, Enzyme)
- Code coverage (CodeCov.io)
- Complexity analysis (Plato)
All V2 efforts are on the master branch
Where is V1
V1 will be on the v1 branch. There will not be any fixes or updates.
V1 releases
1.x releases can still be found under GitHub Releases.
V1 projects in V2
There is support for converting a V1 project into V2 format. Upon opening the JSON file, a window will pop up to confirm that the app should convert the project before redirecting to the editor screen. In this process, a .vott file will be generated in the same project directory, which may be used as the main project file going forward. We recommend backing up the V1 project file before converting the project.
Using VoTT
Creating Connections
VoTT is a 'Bring Your Own Data' (BYOD) application. In VoTT, connections are used to configure and manage source (the assets to label) and target (the location to which labels should be exported).
Connections can be set up and shared across projects. They use an extensible provider model, so new source/target providers can easily be added.
Currently, VoTT supports:
- Azure Blob Storage
- Bing Image Search
- Local File System
To create a new connection, click the New Connections (plug) icon, in the left hand navigation bar:
Creating a New Project
Labeling workflows in VoTT revolve around projects - a collection of configurations and settings that persist.
Projects define source and target connections, and project metadata - including tags to be used when labeling source assets.
As mentioned above, all projects require a source and target connection:
- Source Connection - Where to pull assets from
- Target Connection - Where project files and exported data should be stored
Project Settings
Project settings can be modified after a project has been created, by clicking on the Project Setting (slider) icon in the left hand navigation bar. Project metrics, such as Visited Assets, Tagged Assets, and Average Tags Per Asset can also be viewed on this screen.
Security Tokens
Some project settings can include sensitive values, such as API keys or other shared secrets. Each project will generate a security token that can be used to encrypt/decrypt sensitive project settings.
Security tokens can be found in Application Settings by clicking the gear icon in the lower corner of the left hand navigation bar.
NOTE: Project files can be shared among multiple people. In order to share sensitive project settings, all parties must have/use the same security token.
The token name and key must match in order for sensitive values to be successfully decrypted.
Labeling an Image
When a project is created or opened, the main tag editor window opens. The tag editor consists of three main parts:
- A resizeable preview pane that contains a scrollable list of images and videos, from the source connection
- The main editor pane that allows tags to be applied to drawn regions
- The tags editor pane that allows users to modify, lock, reorder, and delete tags
Selecting an image or video on the left will load that image in the main tag editor. Regions can then be drawn on the loaded asset and a tag can be applied.
As desired, repeat this process for any additional assets.
Labeling a Video
Labeling a video is much like labeling a series of images. When a video is selected from the left, it will begin automatically playing, and there are several controls on the player, as seen here:
In addition to the normal video playback controls, there are two extra pairs of buttons.
On the left, there are the previous and next frame buttons. Clicking these will pause the video, and move to the next appropriate frame as determined by the project settings. For example, if the project settings have a frame extraction rate of 1, these buttons will cause the video to be moved back or forward 1 second, while if the frame extraction rate is 10, the video will be moved back or forward a tenth of a second.
On the right, there are the previous and next tagged frame buttons. Clicking these will pause the video and move to the next or previous frame that has a previously tagged region on it, if a tagged frame exists.
Colored lines will also be visible along the video's timeline. These indicate the video frames that have already been visited. A yellow line denotes a frame that has been visited only, while a green line denotes a frame that has been both visited and tagged. The colored lines can be clicked for quick navigation to the indicated frame.
The timeline can also be used to manually scrub through the video to an arbitrary point, though the project settings for frame extraction rate are always obeyed. Pausing the video will move to the closest frame according to this project setting. This way, a very low frame extraction rate, such as 1 frame per second, can be set for sections of the video known to contain few taggable items, and a much higher frame extraction rate, such as 30 frames per second, to allow fine-grained control.
Tagging and drawing regions is not possible while the video is playing.
Exporting Labels
Once assets have been labeled, they can be exported into a variety of formats:
- Azure Custom Vision Service
- Microsoft Cognitive Toolkit (CNTK)
- TensorFlow (Pascal VOC and TFRecords)
- VoTT (generic JSON schema)
- Comma Separated Values (CSV)
In addition, users may choose to export
- all assets
- only visited assets
- only tagged assets
Click on the Export (arrow) icon in the left hand navigation. Select the appropriate export provider and which assets to export. The percentage separated into testing and training sets can be adjusted here too.
Keyboard Shortcuts
VoTT allows a number of keyboard shortcuts to make it easier to keep one hand on the mouse while tagging. It allows most common shortcuts:
- Ctrl or Cmd + C - copy
- Ctrl or Cmd + X - cut
- Ctrl or Cmd + V - paste
- Ctrl or Cmd + A - select all
- Ctrl or Cmd + Z - undo
- Ctrl or Cmd + Shift + Z - redo
Tag Ordering
Hotkeys of 1 through 0 are assigned to the first ten tags. These can be reordered by using the up/down arrow icons in in the tag editor pane.
Tag Locking
A tag can be locked for repeated tagging using the lock icon at the top of the tag editor pane. Tags can also be locked by combining Ctrl or Cmd and the tag hotkey, i.e. Ctrl+2 would lock the second tag in the list.
Editor Shortcuts
In addition, the editor page has some special shortcuts to select tagging tools:
- V - Pointer/Select
- R - Draw Rectangle
- P - Draw Polygon
- Ctrl or Cmd + S - Save Project
- Ctrl or Cmd + E - Export Project
VOTT allows you to fine tune the bounding boxes using the arrow keys in a few different ways. While a region is selected:
- Ctrl + Arrowkey - Move Region
- Ctrl + Alt + Arrowkey - Shrink Region
- Ctrl + Shift + Arrowkey - Expand Region
The slide viewer can be navigated from the keyboard as follows:
- W or ArrowUp - Previous Asset
- S or ArrowDown - Next Asset
When the video playback bar is present, it allows the following shortcuts to select frames:
- A or ArrowLeft - Previous Frame
- D or ArrowRight - Next Frame
- Q - Previous Tagged Frame
- E - Next Tagged Frame
Mouse Controls
- Two-point mode - Hold down Ctrl while creating a region
- Square mode - Hold down Shift while creating a region
- Multi-select - Hold down Shift while selecting regions
- Exclusive Tracking mode - Ctrl + N to block frame UI allowing a user to create a region on top of existing regions
Release Process
For more details on github/web releases and versions -- please review our release process document
To build VoTT executable using command:
npm run release
For details on packaging executable for the release -- please review our PACKAGING.md
Collaborators
VoTT was originally developed by the Commercial Software Engineering (CSE) group at Microsoft in Israel.
V2 is developed by the CSE group at Microsoft in Redmond, Washington.
Contributing to VoTT
There are many ways to contribute to VoTT -- please review our contribution guidelines.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
Top Related Projects
Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.
Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source data labeling tool for images, text, hypertext, audio, video and time-series data.
Label Studio is a multi-type data labeling and annotation tool with standardized output format
Convert designs to code with AI
Introducing Visual Copilot: A new AI model to turn Figma designs to high quality code using your components.
Try Visual Copilot