Best Open Source Annotation Tools for Computer Vision

A Top 5 labeling tools to create Computer Vision datasets

Read the full article on Sicara’s blog here.

This article presents 5 awesome annotation tools which I hope will help you create Computer Vision datasets.

If you are a Data Scientist working in Computer Vision, you also probably realized that you need a fast and simple labeling tool for at least one of these two reasons:

• to create your datasets for PoC or R&D experiments
• to ensure the quality of your data so that it won’t affect the performance of your Deep Learning algorithms

I dug far into the world of computer vision labeling and realized that it is filled with quite an impressive number of tools (see these three awesome-lists here, here and there, or this blog post). I spent quite some time comparing the most promising (and active) projects to learn that most of these tools were designed to reach only one among three targets:

1. If you want to open a business in labeling and you need:
   - advanced project management features
   - tons of features so any task can be done
   - automation tools to increase efficiency
2. If you belong to a startup you probably require:
   - APIs or at least, simple ways to connect the labeling tool to private APIs
   - An intuitive user experience (UX) so each annotator you are temporarily hiring can start working instantly
3. If you are working on your own and you:
   - don’t care about APIs / project management
   - just want to start tagging as fast as you can!

Here is a quick list of my favorite tools which allow annotating bounding boxes (detection) and polygons (segmentation) for computer vision applications.

If you find out these tools do not work as expected, try to run them in Chrome!

[Optional] Quick basics on labeling in Computer Vision

In Computer Vision, there is mainly three types data for training your algorithms:

1. Picture + label for training classifiers (ResNets)
2. Bounding box + label for detectors (YOLOv3, Faster R-CNN…)
3. Polygon + label for segmentation applications (Mask R-CNN)

Difference between segmentation data (blue) and detection data (purple)

As you also probably realized, one of the most impacting factors for the success for an AI project is the quantity of “quality data” that you can use. What I mean by “quality data” for computer vision applications is:

• every picture/annotation has an appropriate label
• each bounding box or polygon accurately surrounds the entity to train on”

Even though the latter definition certainly lacks objectivity, we want our algorithms to achieve human-level performance. Thus, we require “human-level” annotations.

Want to see more Tools ? Read the full article here.