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…

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…

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…

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…

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…

Why now?

Here at Supervisely we spend a lot of time developing annotation tools for machine learning. While 2D labeling (i.e. …

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…

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…

Introduction

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. …

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

Supervise.ly

First available IDE for computer vision: supervise.ly (made in deepsystems.ai)

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