In computer vision, accurate human pose labeling plays a crucial role in a wide range of applications, from action recognition to sports analytics. While there are already some pre-trained models available, like for example Ultralytics’ YOLOv8, they often require some finetuning on the task at hand. However, the process of manually annotating human pose skeletons is often time-consuming and tedious. Recognizing this challenge, we at trainYOLO have introduced innovative automation tools that significantly streamline the labeling and training of state-of-the-art pose estimation models. In this blog post, we will explore the automation of human pose labeling and unveil trainYOLO's game-changing features that enhance both accuracy and efficiency.
The Challenge of Manual Human Pose Labeling
Traditionally, labeling human pose skeletons has required manual intervention, where human annotators meticulously mark keypoints on each person's body. This process involves significant effort and expertise, and as the complexity of the images increases, so does the time and resources required. It is a laborious task that hampers the pace of model development and deployment.
To make the labeling process more efficient, we at trainYOLO have developed and integrated two main ways of automation. On the one hand, we introduce our Any-Pose automation tool, which automatically predicts all 17 keypoints given a bounding box. On the other hand, we introduce model-assisted labeling, which directly predicts skeletons for each person in the image. Both tools combined deliver an unprecedented labeling speed-up unseen in any other labeling tool.
Any-Pose Automation Tool
trainYOLO addresses the challenges associated with manual human pose labeling by introducing the revolutionary Any-Pose automation tool. This advanced feature automatically predicts human poses given a bounding box around a person. The Any-Pose automation tool is scale-agnostic, meaning it efficiently handles both large and small individuals within an image.
Any-Pose automation tool revolutionizes the labeling process by accelerating the skeleton annotation almost 10 times compared to manual labeling. By eliminating the need for annotators to individually mark each key point on a person's body, trainYOLO empowers users to label human poses with remarkable efficiency and accuracy.
trainYOLO takes automation a step further with its model-assisted labeling capabilities. By integrating state-of-the-art pose models like YOLOv8-Pose, the platform drastically accelerates the annotation process for images containing multiple persons. Instead of manually labeling every individual, users can rely on the powerful pose model to detect and label persons automatically.
With model-assisted labeling, users can swiftly review the results and focus on removing false positives and labeling any missing persons. This targeted approach optimizes the labeling workflow, reducing the overall time and effort required while maintaining high annotation quality.
Combining Any-Pose and Model-Assisted Labeling
trainYOLO offers a holistic solution for human pose labeling by allowing users to combine the benefits of Any-Pose automation and model-assisted labeling. Users can leverage the Any-Pose tool to rapidly annotate individual poses within a bounding box, and then employ YOLOv8-Pose to handle images with multiple persons.
By leveraging both automation tools, users can significantly reduce the manual effort in labeling human pose skeletons. This not only boosts productivity but also enhances the scalability of the labeling process, enabling faster model development and deployment.
The automation of human pose labeling is a game-changer for computer vision applications, and trainYOLO stands at the forefront of this technological advancement. With the introduction of the Any-Pose automation tool and model-assisted labeling, trainYOLO empowers users to label human pose skeletons with unprecedented speed and accuracy.
By automating the tedious and time-consuming aspects of the labeling process, trainYOLO enables researchers, developers, and businesses to focus on higher-level tasks, such as model refinement and application development.