Introduction

Computers are unable to analyze visual information in the same manner that human brains do: in order to make judgments, a computer must be informed of what it is interpreting and given context. These relationships are made by data annotation. It’s the process of labeling material like text, audio, pictures, and video so that machine learning models can identify it and make predictions.

  • When you consider the current rate of data production, data annotation is both an essential and amazing achievement.

According to The Visual Capitalist, 463 exabytes of data would be generated globally on a daily basis by 2025 — and this study was conducted before the COVID-19 epidemic boosted the importance of data in daily interactions. According to GM Insights, the worldwide data annotation tools market is expected to expand about 30% annually over the next six years, particularly in the automotive, retail, and healthcare industries.

Annotation Formatting

Annotations can be saved in a variety of formats  – Pascal VOC XMLs, COCO JSONs,, YOLO, text files, and picture masks. Despite the fact that we can constantly convert annotations from one to another, having a tool that can make annotations in your desired format directly is a wonderful approach to saving crucial time.

  • COCO JSON – The most common detection dataset at the present is COCO = Microsoft’s Common Objects in Context dataset. It is commonly used to assess the performance of computer vision algorithms.

Because of the dataset’s popularity, the COCO format for storing annotations is frequently used when developing a new custom object detection dataset. Annotations for other tasks, such as segmentation, are also supported by the COCO dataset.

The COCO format specifies how your annotations (object classes and bounding boxes,) and picture metadata (image sources, height, width) are saved on disk at a high level.

If you’re new to object detection and need to create a fresh dataset, the COCO format is a suitable choice because of its relative simplicity and broad use.

  • Pascal VOC XMLs – For object detection, Pascal VOC provides standardized picture data sets.

Understanding the differences between COCO and Pascal VOC data types will save you time.

  1. Unlike COCO, which contains a JSON file, Pascal VOC has an XML file.
  2. You make a file for each image in the collection in Pascal VOC. You have one file for each dataset in COCO for testing and validation.
  3. In the COCO and Pascal VOC data formats, the Bounding Box is different.
  • YOLO – YOLO proposes the usage of an end-to-end neural network that produces predictions of bounding boxes and class probabilities all at once, as opposed to the method used by object detection algorithms before YOLO, which repurposed classifiers to conduct detection.

YOLO delivers state-of-the-art results by outperforming existing real-time object identification algorithms by a significant margin by taking a fundamentally new approach to object recognition.

Importance of Data Annotation

The consumer experience is built on data. The quality of your clients’ experiences is strongly influenced by how well you know them. As companies amass more information about their consumers, AI can assist in turning that information into actionable information.

According to a survey conducted by data science platform Anaconda, data scientists presently spend a substantial amount of their time preparing data. Part of that time is spent correcting or deleting anomalous/non-standard data and double-checking measurements. These are critical jobs because algorithms rely largely on pattern recognition to make judgments, and incorrect data can lead to biases and bad predictions.