Supervisely VS VGG Image Annotator (VIA)

Compare Supervisely VS VGG Image Annotator (VIA) and see what are their differences

TimeLake.io

Improve team performance continuously with time tracking automation. featured

Contents:

» Base Details
» Videos
» Reviews
» Alternatives

Supervisely

Supervisely helps people with and without machine learning expertise to create state-of-the-art...

VGG Image Annotator (VIA)

VGG Image Annotator is a simple and standalone manual annotation software for image, audio and video. VIA runs in a web browser and does not require any installation or setup.

Landing page //
2023-08-06

Landing page //
2022-09-08

Supervisely

Website: supervisely.com
Pricing URL: Official Supervisely Pricing

Edit details

VGG Image Annotator (VIA)

Website: robots.ox.ac.uk
Pricing URL: -

Edit details

Supervisely features and specs

Comprehensive Toolset
Supervisely offers a wide range of tools for image annotation, data management, and deep learning model training, providing a one-stop solution for computer vision projects.
Collaborative Platform
It supports team collaboration with features for sharing projects, annotating data, and reviewing work, making it easier for teams to work together.
High Customizability
Supervisely allows users to create custom plugins and automation scripts, offering flexibility to tailor the platform according to specific project needs.
Extensive Dataset Support
The platform supports a wide variety of data formats and types, including images, videos, and 3D data, making it versatile for different applications.
Integrated Machine Learning
Supervisely integrates machine learning capabilities, enabling users to train models directly on the platform and test them using their own annotated data.

Possible disadvantages of Supervisely

Cost
Supervisely can be expensive, particularly for small teams or individual users, as it primarily targets enterprise customers.
Complexity
Due to the breadth of features and tools, there may be a steep learning curve for new users, making it more challenging to get started quickly without adequate training.
Performance Issues
Some users may experience performance issues, particularly when handling very large datasets or running multiple simultaneous tasks.
Cloud Dependency
While a cloud-based platform offers accessibility advantages, it also means that users are dependent on internet connectivity and may face latency or downtime problems.
Limited Offline Features
Supervisely's offline functionality is limited, which can be a drawback for users who need to work in environments with restricted or unreliable internet access.

VGG Image Annotator (VIA) features and specs

Ease of Use
VIA is user-friendly and simple to set up, making it accessible to users without extensive technical knowledge.
No Installation Required
As a web-based tool, VGG Image Annotator runs directly in a browser and doesn't require any installation or special software.
Lightweight
The tool has a small footprint and can be run effectively on systems with limited resources, making it efficient for quick tasks and analysis.
Versatility
VIA supports various annotation types like points, rectangles, polygons, and allows for both manual and automatic annotation, catering to diverse project needs.
Customizable
VIA's source code is available for modification, offering customization possibilities to fit specific project requirements.
Collaboration Features
It allows users to save annotations in JSON format, making it easy to share and integrate into larger workflows or collaborate within teams.

Possible disadvantages of VGG Image Annotator (VIA)

Limited Performance for Large Datasets
When dealing with large datasets, VIA can become slow or unresponsive due to its reliance on browser-based operation which hampers performance scalability.
Basic Interface
The interface is quite simplistic and may lack the advanced features or aesthetics found in more sophisticated, dedicated annotation software.
Lack of Automation for Advanced Needs
While it supports basic automatic annotation, it is not as advanced or robust for complex tasks, which might require more manual input or additional tools.
Limited Support
Being an open-source project, it may not offer the same level of professional customer support or regular updates as commercial tools.

Analysis of Supervisely

Overall verdict

Overall, Supervisely is a good platform for computer vision projects due to its versatility and ease of use. It offers a complete ecosystem that caters to various stages of the machine learning pipeline, making it an efficient choice for both beginners and experienced practitioners.

Why this product is good

Supervisely is considered a robust platform for its comprehensive suite of tools designed for computer vision tasks. It provides capabilities for data labeling, neural network training, and deployment. Its user-friendly interface, collaborative features, and support for a wide range of formats and integrations make it appealing to both individual developers and enterprise teams.

Recommended for

Data scientists looking for a comprehensive tool for computer vision.
Companies needing a collaborative environment for AI projects.
Researchers who require a platform with extensive format support and integrations.
Developers wanting an easy-to-use interface for data annotation and model training.

Supervisely videos

+ Add

🛠️Basic annotation overview - Supervisely

VGG Image Annotator (VIA) videos

No VGG Image Annotator (VIA) videos yet. You could help us improve this page by suggesting one.

Add video

Category Popularity

0-100% (relative to Supervisely and VGG Image Annotator (VIA))

VGG Image Annotator (VIA)

Image Annotation

82 82%

Image Annotation

18% 18

Data Labeling

89 89%

Data Labeling

11% 11

85 85%

15% 15

Data Science And Machine Learning

86 86%

Data Science And Machine Learning

14% 14

User comments

Share your experience with using Supervisely and VGG Image Annotator (VIA). For example, how are they different and which one is better?

Social recommendations and mentions

Based on our record, Supervisely seems to be more popular. It has been mentiond 6 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Supervisely mentions (6)

Way to label yolov7 images fast
Another annotation tool that integrates prediction and training within the application is supervisely supervisely.com., unfortunately it's pretty expensive unless you are satisfied with the community version. I saw that they have an integration for owl-vit, which might be helpful for annotation of animals. https://ecosystem.supervisely.com/apps/serve-owl-vit. Source: about 2 years ago
65 Blog Posts to Learn Data Science
Hello world. This tutorial is a gentle introduction to building modern text recognition system using deep learning in 15 minutes. It will teach you the main ideas of how to use Keras and Supervisely for this problem. This guide is for anyone who is interested in using Deep Learning for text recognition in images but has no idea where to start. - Source: dev.to / over 2 years ago
Bounding Box for Text Annotation
If they were videos, I would have suggested trying supervise.ly as it has a very good tracking functionality. Source: almost 3 years ago
CVAT alternatives for video frame annotation
Hi, I'm exactly in the same boat like you are. I looked around for a while and the better solutions I found was supervise.ly and CVAT for video annotation. The pricetag on supervisely is pretty high, so I analyzed CVAT for a couple days and was positively surprised. Source: almost 3 years ago
Accessing 2022 Machine Learning Imagery from WPI's Photo Album
Under the WPI Photo Ambum section of the page for FRC field photos (https://www.firstinspires.org/robotics/frc/playing-field#WPIPhotos), they have a section of machine learning imagery. However, this link goes to supervise.ly, the website they use for machine learning. I created an account to attempt to download the images, however, whenever I try to 'clone' the project, it stalls at 0% and gives me an error... Source: almost 3 years ago