IBM released the annotation tool that taps ai to label images

IBM released the annotation tool that taps ai to label images

Data labeling is an arduous — if necessary — part of the AI model training process. Currently, it takes around 200-500 samples of annotated images for a model to learn to detect a single object. Fortunately, freely available tools help automate the most monotonous sub-tasks, and IBM has recently published a new one on GitHub. It’s part of the company’s Cloud Annotations project, which seeks to develop easy and collaborative open source image annotation tools for teams and individuals.

The new tool uses AI to help developers annotate data without having to manually draw labels on an entire data set of images. Simply selecting the “Auto label” button from the dashboard automatically labels uploaded image samples. And it’s backed by IBM Cloud Object Storage, which is optimized for data-hungry machine learning and cloud-native workloads.

Here’s how to access and use the new Cloud Annotations tool:

  • Upload and label a subset of photos via the Cloud Annotations GUI.
  • Train a model following these instructions. The tool will use that model to label more photos.
  • Select “Auto label” in the GUI.
  • Review new labels.

A number of companies offer tools that automatically label images for the purpose of machine learning model training. In March 2019, Intel open-sourced Computer Vision Annotation Tool (CVAT), a toolkit for data labeling that’s deployed via Docker and accessed through a browser-based interface (or optionally embedded into platforms like Onepanel). Roughly a year before that, Google released Fluid Annotation, which leverages AI to annotate class labels and outline every object and background region in a picture.

We at Dutytaker offers a bespoke model-training service to clients in customer service, automotive, retail, health care, and enterprise sectors.

We also support with the deployment of the tool at your cloud or use our source and computing power.