There are a lot of applications and industries where it is needed to apply Neural Networks to videos: agriculture, self-driving cars, robotics, manufacturing, consumer electronics and so on.

But 80% of existing tools are closed and are not available for the Deep Learning community, the other 20% — are not designed for annotation at scale.

We at Supervisely are proud to announce the web-based Video Labeling Tool. Community Edition (CE) is free for research purposes and we hope the entire Machine Learning community will benefit from it. Contact us to learn more about Enterprise Edition (EE).

Video Labeling Tool — user-friendly and intuitive

  • inspired by professional video…

Creating big and diverse computer vision datasets requires working with a huge labeling workforce. That, in turn, requires professional tools to educate and examine them.

Why do we need Labeling Guides and Labeling Exams? Let’s consider several important observations:

  • usually, an annotator has concrete domain specialization: self-driving cars, agriculture, satellite imagery, medicine, and so on
  • not every annotator properly understands labeling requirements, thus sometimes they make systematic errors during labeling
  • it is a good idea to choose for your custom task the most efficient and accurate labelers based on Quality Score
  • from labelers perspective, comprehensive annotation policy should be presented…

We are happy to announce the new collaboration & tracking tool — Labeling Issues. Every annotation team needs to organize its work, but no one wants to stop working in order to track work. Supervisely Issues allow keeping the work of a huge labeling workforce all in one place.

What are Supervisely Issues?

Data Labeling is about collaboration at scale: managers, domain experts, data scientists, inhouse labelers and dedicated external labeling teams. Hundreds of people are involved in. Also, this complicated process requires multi-stage reviewing and correction to guarantee quality. And it is hard to organize the entire process without specially designed tools.


Since the launch of Supervisely, image annotation and management was the core of our platform. Indeed, this is what would require most of your team’s time to build a computer vision model — and it’s gonna be as good, as your data.

We already have best-in-the class tools for image and video annotation, user collaboration, and pipelines to train custom neural networks.

But one thing we’ve noticed: when it comes to data manipulation, like merging two projects together, filtering good and bad images and so, our users would like to use an instrument they already got used too: a regular…

It’s not a secret that the most time-consuming part of any computer vision project is data preparation, especially labeling. Moreover, it’s the most important part — without high quality training data even the most recent neural network architecture will fail to learn.

But as AI becomes widely accepted in many different areas and industries, annotation becomes more and more complex. Ten years ago labeling with bounding boxes for object detection was among the most popular annotation tasks. Today, there are tons of models that solve the task beautifully.

Training data trends

Semantic segmentation is the next challenging problem. Even a small picture requires…

Why now?

Here at Supervisely we spend a lot of time developing annotation tools for machine learning. While 2D labeling (i.e. images or videos) is still the most convenient and well-known source of data for machine learning, recent advances in tasks like robotics, self-driving vehicles, augmented reality and urban planning require other type of training data — point cloud labeling.

The problem is, despite the fact, that LIDAR and radar sensors have become more available than ever, it’s not like there are tons of tools for 3D labeling on the internet. …

Hi there!

Supervisely already has a variety of tools to deal with labeling: rectangles, polygons, polylines, brush and even Smart Tool. But what if you need to annotate a skeleton of a person? Or label a keypoints of a face?

We are happy to introduce a new powerful tool that allows to define custom shapes for your figures — keypoints tool.

How does it work?

First, open your Project, go to a Classes page and define a new class. Choose “Keypoints” shape. You will see a graph editor:

Step 1: Define a graph template…

Use “Add Node” and “Add Edge” button to define the shape of your future objects. In…

Hi there!

We want to thank you guys for all the feedback over the last few months. A lot of new cool features were developed because of your help, but we haven’t managed to share those updates with you. It’s time to fix it!

So here are some top features that you probably haven’t heard of yet:

1. Key-points aka Landmarks tool

You can read more about keypoints tool here.

A long requested feature is finally here!

Supervisely already has a variety of tools to deal with labeling: rectangles, polygons, polylines, brush and even Smart Tool. But what if you need to annotate a skeleton…


Building an AI powered product in computer vision is a long, complex and expensive process. The source of complexity is a very large number of tasks to perform during the development process. Data collection, annotation, thousand of experiments with Deep Learning models, continuous model improvement, sharing and collaboration. From high level perspective it’s just a huge pile of tasks to be solved by people with various expertise by means of dozens of software packages.

Figure 1. Pile of tasks to solve during AI product development in computer vision

A general solution is needed to put all people, data, algorithms inside a single ecosystem and to provide tools for efficient interaction and development.

Enterprise World…

Manual data annotation is a bottleneck that greatly slows down AI products development. In this post we show how to leverage pre-trained detection model to speed up labeling process.

What we are fighting for is to minimize human labor spent on:

  1. Searching for images where objects of interest are present
  2. Putting a bounding box around each object

To be more concrete, let’s consider a self-driving related task. Suppose we need to label with bounding boxes the following objects:

  • Cars
  • Persons
  • Traffic lights

As an input, we have this video:

As an output, we expect to get a list of…

First available IDE for computer vision: (made in

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