The annotation, evaluation, and data issues that most teams overlook—until it’s too late
Maybe your team has spent months perfecting a model—only to watch it fall apart in deployment. Not because the model was bad. But because it learned all the wrong things.
Across sectors—healthcare, agriculture, remote sensing, diagnostics—the same problems keep showing up. Not in code, but in how teams handle data, labeling, and evaluation. These issues won’t show up in your metrics—until it’s too late. But they quietly degrade performance, stall progress, and erode trust.
Whether you’re leading a startup, managing a research team, or scaling a product, these mistakes surface again and again—from labeling tumor cells to mapping crop boundaries to monitoring infrastructure from satellite imagery.
These issues appear whether you’re working with cells, crops, buildings, or satellites—because they stem from how teams manage data, not the domain itself.
Below are three of the most common mistakes that quietly undermine computer vision projects.
Mistake #1: When Your Labels Undermine Your Model
Labeled data is the foundation of any supervised learning system—but inconsistent labels quietly undermine everything built on top.
Inconsistent annotation happens more often than teams realize. Annotators disagree on subtle categories. They draw different boundaries or miss hard-to-spot features entirely. In one histopathology project, multiple expert pathologists labeled the same cells—and disagreed on nearly a third of them. If experts can’t agree, what chance does your model have?
This inconsistency doesn’t just introduce noise—it introduces bias. One annotator might consistently over-label a certain pattern. Another might miss it entirely. These patterns of error—when left unchecked—get baked into your model.
And the model can’t tell the difference. It just learns the pattern.
When your training data teaches one pattern but your real-world data shows something else, the model starts making unpredictable decisions. And it’s not just a performance issue—it slows you down. Many teams end up spending months re-annotating or cleaning datasets after unexplained performance dips.
Bottom line: If your annotations aren’t consistent, your model learns noise—not signal.
Mistake #2: Skipping Baselines? You May Be Flying Blind
In the rush to innovate, it’s tempting to go straight to the most powerful architecture you can find. Why start with a linear model when there’s a shiny new vision transformer available?
But skipping baselines removes one of your most powerful diagnostic tools: the ability to check for signal in your data.
If a simple model performs nearly as well as your complex one, that tells you something. If it performs terribly, that also tells you something. Either way, you get clarity on what’s driving your results—model architecture, training setup, or something deeper in the data.
Without this benchmark, it’s harder to:
- Spot data issues like label noise or class imbalance
- Track real improvements across experiments
- Justify architectural decisions to stakeholders
Skipping this step disconnects your technical work from product or research reality. And you risk optimizing complexity—not outcomes.
Bottom line: Without a baseline, you’re guessing whether complexity is paying off.
Mistake #3: The Invisible Error That Destroys Generalization
Data leakage is the silent killer. Your model trains smoothly. Your metrics look great. Everything seems on track—until the model hits real-world data and collapses.
Leakage happens when your model sees information during training that it shouldn’t. It could be obvious—like overlapping images in train and test splits—or deeply subtle:
- Images from the same patient or region split across folds
- Embedded cues like timestamps or pen markings that correlate with the label
- Using data that wouldn’t be available when the model is deployed
- Tuning the model too closely to a test set, instead of new data
In one pathology project, a model was trained on slides marked up by clinicians. It performed exceptionally well—until the team discovered it had learned to detect the pen circles, not the tumors.
The result? Inflated metrics, false confidence, and poor generalization. It doesn’t just hurt performance. It erodes trust in your model—and in your team.
Bottom line: Even great performance metrics can lie—if your data is leaking.
Why These Mistakes Keep Slipping Through
Even experienced teams miss these issues—not because they’re careless, but because these problems hide in plain sight.
These mistakes stick around because:
- They’re subtle. Your code runs. Your metrics look good. But your model isn’t learning what you think it is.
- They’re systemic. These issues aren’t about algorithms—they’re about process. Data strategy. Collaboration. Quality control.
- They’re easy to postpone. You’re under pressure to ship. So you move forward. You’ll fix it later. But later rarely comes—until it’s too late.
Learn to Spot—and Fix—These Pitfalls
These three mistakes quietly derail more projects than any algorithmic choice ever will. They hide behind clean curves and shiny validation scores. And they strike hardest in the real world.
This is why I created a short webinar, where I unpack how to spot these mistakes early—before they cost you months of work. You don’t need a deep ML background—this framework is built to guide strategic decisions, even if you’re not writing the code:
- Real-world examples of each mistake in action
- The early warning signs to watch for
- A practical framework you can use to audit your own projects—whether you’re still in R&D or already deploying
(This isn’t theory—it’s a roadmap to catch and correct these pitfalls in your own work.)
Ready to Dive Deeper?
These pitfalls aren’t just technical bugs. They’re signs of deeper gaps in how teams build, evaluate, and deploy vision systems.
If your team is working on high-stakes CV projects and needs expert guidance to avoid these traps, I offer targeted advisory support and workshops.
Like a cracked lens, these foundational problems distort everything the model sees—no matter how powerful the architecture behind it.
Because in the real world, flashy models don’t fail you. Foundational cracks do.