In the dynamic world of machine learning and computer vision, high-quality annotations are the foundation of successful models. However, the annotation process can be time-consuming and resource-intensive. Let's explore tips to significantly boost your annotation speed, without compromising accuracy, inside the Kognic platform.
Why efficient annotations matter
Before diving into specific tips, let's consider why efficient annotations are crucial. In the AI development pipeline, annotations often become a bottleneck, potentially slowing down model training and iteration. By streamlining this process, we can accelerate development cycles, reduce costs, and bring solutions to market faster - all key factors in staying ahead of your competitors.
4 tips to speed up your annotations
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Make sure your team uses Keyboard Shortcuts
Proficiency with keyboard shortcuts can dramatically reduce time spent on repetitive actions. Most annotation tools offer shortcuts, whereas Kognic allows further customization of the shortcuts to personalize your workflow. This initial investment of shortcut customization will pay off in long-term efficiency gains as most annotators prefer different shortcuts for different actions. For instance, use 'T' to access your 'Tape measure' and 'Shift+S' to use the 'scissors'.
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Implement guided workflows
Guided workflows streamline the annotation process by leveraging Kognic's automations in the most effective sequence. This structured approach organizes tasks in a logical order, maximizing efficiency and minimizing errors. By guiding annotators through a predetermined series of steps, these workflows ensure consistency across team members and projects. This not only accelerates the overall annotation process but also allows teams to handle larger datasets more effectively, ultimately contributing to faster model development and deployment cycles.
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Machine Assisted drawing tools
Machine-assisted drawing tools can significantly speed up the annotation process. In the annotation view, you want to maximize output by automating anything that can be easily handled by a computer. Our machine-assisted box drawing tool is an excellent example of this. With this tool, annotators simply select a class, place a point at the front or back (center) of the object, and then place another point at the opposite side to set the length. A cuboid automatically appears, streamlining the annotation process and reducing manual effort.
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Leverage your auto-labels as guidance
We often discuss auto-labels because of their significant impact on costs - in some cases, we've observed time savings of up to 68%. At Kognic, we've named our main auto-labeling tool "co-pilot”. Rather than automatically generating annotations, we use the input as a reference for our guided workflow. This means human annotators only need to review the labels instead of creating objects from scratch. Even a less-than-perfect auto-label model can yield substantial cost savings. For more details about our co-pilot and how it works, you can access our latest Kognic KSheet PDF.
Balancing speed and accuracy
While these tips can significantly boost annotation speed, it's crucial to maintain high accuracy standards. Implement a robust quality assurance process.
Kognic's Continuous Quality Control (CQC) is a useful tool for keeping annotations accurate. It works by checking a sample of the annotations to estimate the overall quality. CQC also allows managers to break large annotation tasks into smaller batches, where each batch goes through its own quality check.
By implementing these tips, you can create a more efficient annotation pipeline that not only speeds up the process but also maintains the high-quality standards required for developing accurate and reliable AI models. Remember, the goal is not just to annotate faster, but to optimize the entire annotation workflow for both speed and precision - because with annotations, every second counts.
