Image and Video Annotation

Image and video annotation is the process of adding labels to images and videos in a dataset to train machine learning models. It’s essentially teaching machines how to “see” and understand the content of visual data.

Here’s a breakdown of the key points:

  • Purpose: Train AI models for tasks like object detection, image classification, and self-driving cars.
  • Process: Annotators describe the visual content using techniques like bounding boxes, polygons, and keypoint annotation.
  • Importance: High-quality annotations are crucial for creating accurate and reliable machine learning models.

There are different annotation types depending on the information you want the model to learn from the data. Here are some common examples:

  • Bounding Boxes: Draws a box around an object in the image.
  • Image Classification: Assigns a category label to the entire image (e.g., cat, dog, car).
  • Semantic Segmentation: Labels every individual pixel in the image according to its category (e.g., sky, road, person).