Best Image Annotation Tools

 

Machine learning is an application of artificial intelligence that has had a major impact on our daily lives by dramatically improving speech recognition, traffic prediction, and online fraud detection, just to name a few, on a large scale. Essentially, computer vision, an application of machine learning, allows machines to "see" and interpret the world around them, just like humans.

The performance of your computer vision model is highly dependent on the quality and accuracy of your training data, which essentially consists of annotations on images, videos, etc.

Image annotation can be understood as the process of tagging images to delineate the target characteristics of your data on a human level. The result is then used to train a model and, depending on the quality of the data, achieve the desired level of accuracy in computer vision tasks.

What is an Image annotation?

 

Image annotation is the process of labeling images to train AI and machine learning models that you can do with Image annotation tools. Human annotators typically use a diagramming tool to represent an image or represent relevant information, such as by assigning salient components to different entities in an image. The resulting data, known as structured data, is fed into a machine learning algorithm, often thought of as model training.

For example, you can ask annotators to provide annotations on a specific set of vehicle images. The resulting data can help you train the vehicle model to distinguish between pedestrians, traffic lights or possible road obstacles and for safe navigation.

Autonomous driving is an example of how image description powers computer vision. There are many use cases and we'll get to them in a minute.

Best Image Annotation Tools

 

The image annotation tools you choose in your computer vision projects has a significant influence. The more efficient you are in your image annotation tool – and the more productive the tool makes you – the sooner you will get at your end goal: a functional, solid computer vision model.

we are always working to reduce the amount of time you need to spend labeling data in order to begin utilizing a model in production. With this in mind, we studied numerous labeling solutions currently on the market and developed a review of the five finest products we discovered.

In each evaluation, we'll go through the functionality of each image annotation tool, as well as the price based on publicly accessible pricing data.

 

VoTT

 

VoTT is an image annotation tool from Microsoft that has several interesting features that differ from the other tools. The installation of this tool is very easy because you could just simply download the installer according to your OS from their Github page.

VoTT offers active learning which is interesting but I never had a chance to use it. You could choose between Predict Tag and Auto Detect in the active learning feature. Besides image annotation, the tool could also be used for video annotation.

VoTT also supports many formats for exporting such as Azure Custom Vision Service, CSV, CNTK, Pascal VOC, Tensorflow Records, and VoTT Json.

There are many features that can be explored in VoTT for your convenience in annotating images. The downside for me personally is that the types of annotation in VoTT is limited to rectangle and polygon only.

 

CVAT

 

CVAT is an open-source image annotation tool. Originally built by Intel, this tool is now maintained by OpenCV, the creators of many widely-used image and vision utilities. CVAT has a publicly-hosted interface available at cvat.ai, or you can download the software and run it on your computer. CVAT is used by over 60,000 developers.

·       CVAT has support for the following tasks:

·       Image classification

·       Object detection

·       Semantic and instance segmentation

·       Point clouds

·       3D cuboids

·       Video annotation

·       Skeleton (used for keyframe models)

CVAT has automated labeling integrations, including one with the Roboflow platform. You can use any of your private models or models hosted on Roboflow Universe to help you label images in CVAT. This reduces the number of manual annotations you have to make, thereby allowing you to prepare your dataset faster than ever.

 

Labelme

 

This is the best tool I currently use for image annotation projects. labelme has the most in common with Labelimg in terms of ease of installation and interface. The difference between the two is that labelme has a few features that make me want to use it as a daily note-taking tool.

I think one of its best features is the file list at the bottom right. When you have lots of photos to comment on, you may miss commenting on some photos. That's why File List is useful because it not only lists your files, but also provides a checkmark for each file you've already commented on.

You can freely select 6 types in Labelme, starting from polygon, rectangle, circle, line, point and line bar. labelme gives you the flexibility to annotate images while remaining easy to use.

The only downside to tagging for me is that it can only save your file as JSON. But this is not a problem if engineer ml is compatible with the format.

Image annotation tool in saiwa

Saiwa is a company that provides a range of AI services, including image annotation. Saiwa’s image annotation service offers a variety of annotation types, including:

·       Object detection involves drawing a bounding box or a boundary around the object of interest in an image and labeling it.

·       Image classification: This involves labeling an image with one or more categories or tags that describe the contents of the image.

·       Semantic segmentation involves labeling each pixel in an image with a corresponding class label, which can be used for image segmentation and object recognition tasks.

Saiwa’s image annotation service can be used for various applications, such as training machine learning models, improving search algorithms, and enhancing image recognition systems.

Saiwa’s image annotation service helps companies and organizations better understand and utilize their images’ content by adding metadata and labels.