First, let's lay out some generally accepted principles of AI/ML model development and image annotation:
- Image annotation is the foundational step in training computer vision AI models.
- The quality of the training data determines the quality of the AI — “garbage in, garbage out.”
- Clear image labeling guidelines ensure consistent and accurate image sets.
We covered the first two principles extensively in our earlier posts - the first and the second principle. Consistency in your source labeled images is crucial because AI/ML models learn patterns. If these patterns are inconsistent, or your dataset is so poor that no obvious patterns emerge, you end up with what we humorously call “garbage AI.” Hence the need for data labeling platforms with clear guidelines. In fact, the entire data annotation process must be carefully planned.
Everyone agrees we need high-quality, consistent, and accurate image sets to train and improve our computer vision models, but not many have concrete ideas how to do that efficiently. In fact, that’s exactly what we’re working on here at Unitlab AI, a collaborative and AI-powered data annotation platform.
We have already explained how machines can handle base-level annotation (auto labeling) through data auto labeling or image auto labeling tools, how human annotators step in to correct mistakes, and how reviewers conduct a final check to ensure that the annotated images meet quality and consistency requirements. We also discussed seven principles for accurate image labeling.
The only missing piece of the puzzle is defining and achieving the labeling requirements themselves. How do we set clear guidelines for image labeling so that our ML datasets remain consistent over time? What are common pitfalls to avoid? How does your team know if they’re doing the job right? It’s demoralizing to have your work judged by ambiguous or inconsistent standards, in any field.
This post completes the puzzle. We’ll explore the role of clear guidelines in data labeling within the AI/ML training pipeline. Whether you’re using an in-house data labeling tool or relying on a data labeling service, consistent guidelines are the foundation of successful computer vision AI.
Introduction
Accurate image labeling does not happen by chance; it requires annotators to follow precise, clear guidelines. Even if you have cutting-edge auto-annotation models or an expert data labeling team, you won’t maintain consistency for long without guidelines - a single source of truth. Inconsistent AI image sets can lead to weird, sometimes amusing results.
Therefore, although often overlooked, image labeling guidelines are essential for the AI/ML development pipeline. A clear, consistent guideline can be the differentiating factor between a well-annotated, high-quality dataset and a poorly annotated, inconsistent, nearly useless one.
That’s why we are highlighting this often-overlooked aspect: what makes a good guideline for image labeling, and which pitfalls should be avoided.
What Makes a Guideline Clear?
Although the exact details depend on your project and use case, these broad principles can help you formulate robust, adaptable guidelines:
1. Detailed Instructions
An ideal guideline should specify exactly what to label, how to label, and any exceptions to the rules. Imagine you’re building a street-level detection system for pedestrians and vehicles. Telling your team:
Annotate every pedestrian and vehicle.
…is too vague. For starters, it does not specify which image annotation type to use. How should occluded objects be labeled? What about shadows, reflections, or partially visible objects?
Such micro-decisions, repeated countless times, drain your team’s energy and morale. A better approach would be:
Annotate every pedestrian and vehicle using polygons. If an object is occluded, label it as if it were fully visible. Assign new classes called “Shadow” and “Reflection” for shadows and reflections.
2. Language Clarity
No matter how detailed your guidelines are, they must be straightforward, unambiguous, and free of complicated technical jargon. Your image labeling team may include people with various language backgrounds, so use terms everyone understands. Stay away from vague words like “stuff” or “things” that do not specify anything meaningful.
Even Elon Musk applies a similar principle at his companies. We can trust that this advice holds merit.
3. Visual Examples
Whenever possible, use visual examples (images or videos) to show what ideal annotations look like. This is especially helpful for human annotators and can also guide auto labeling systems in edge cases. Visualization is often the quickest, clearest way for everyone to align on the guidelines.
For example, you can show this image as an example that meets the requirements. It clearly annotates every vehicle with different class names and include occluded objects.
4. Edge Cases
Every image annotation project has edge cases you can anticipate—and others you can’t. If you can predict some, address them from the start. For instance, decide whether passengers visible inside a bus should be labeled, as in the case of the street detection system. If an unexpected situation arises, project managers or senior reviewers can make a decision. This ensures consistent data annotation and avoids confusion.
This ensures that in case of exceptions, edge cases are handled consistently with care, not haphazardly.
5. Regular Updates
Edge cases, requirements, project scopes, and techniques naturally evolve over time. So should your guidelines. Keep them updated to reflect the latest standards and ensure that data annotators (man, machine, or man-machine) remain aligned with your guidelines. This also ties in with dataset version control, making it easier to track guideline changes and compare different ML datasets over time.
Common Pitfalls in Labeling Guidelines (And How to Avoid Them)
Now, let’s talk about what not to do. Avoiding these pitfalls is just as crucial as following best practices:
1. Vague Instructions
We touched on this earlier, but it’s so important that it deserves repeating. Unclear guidelines often result in major deviations in labeling. A statement like “label all objects and stuff” is wide open to interpretation. Instead, specify precisely which objects and attributes should be labeled. Clarity is the unsung hero of consistent data labeling.
2. Overwhelming Detail
On the flip side, overloading annotators with a flood of detailed instructions is equally problematic. Human annotators are valuable precisely because they can step back and consider context. Too much micromanagement can kill morale. Stick to the most crucial aspects of the task and organize your guidelines into concise, logical sections. Too many details are good for training AI models, not for humans.
3. Lack of Context for Annotators
Human annotators benefit from context. You cannot just drop someone into a specialized image annotation task without explaining its purpose or industry context. This is especially critical in fields like medical imaging. Providing the necessary background helps annotators and data labeling tools understand what they’re looking for—and why.
Conclusion
We need high-quality, accurately labeled image sets to train our computer vision models effectively. Achieving this level of accuracy depends on consistent image labeling—and that, in turn, depends on clear labeling guidelines.
In this post, we discussed what to focus on when writing these guidelines and which pitfalls to avoid. By creating guidelines that are detailed, visually illustrated, regularly updated, and context-aware, you will not only maintain a high standard of image labeling but also keep your team motivated and aligned with project goals.
Invest in the clear, unambiguous image annotation guidelines to achieve and maintain consistent guidelines.
