Your model isn’t lying. It’s just silently learning from data you haven’t fully understood.

In machine learning, there’s a dangerous comfort in watching metrics.
The loss curve drops — good sign.
Precision looks acceptable — close enough.
No errors during training — must be working.

But the longer I work in this field, the more clearly I see:
Most model failures don’t come from architecture.
They come from data that only looks correct.

Not because data is missing. But because it's misaligned — in quiet, insidious ways.

A data.yaml file lists 5 classes. The annotations contain 6.
One class appears 4,800 times. Another only 12 — no one notices.
A few bounding boxes quietly drift beyond image boundaries.
An image or two have the wrong resolution, leftover from a test run.
Two labels differ by a single character or space — and now the model treats them as separate classes.

These aren't exceptions.
They’re patterns — and they rarely trigger visible errors.
Instead, they inject noise into the signal and weaken the foundation of your entire pipeline.

This is why I built Yololint.

Not as a linter in the trivial sense.
But as a tool that helps you think critically about your dataset before you ever hit "train".

Yololint inspects YOLO-style datasets and answers essential questions:

• Is your directory structure valid?
• Does data.yaml match the actual data?
• Are annotation files internally consistent?
• Are image dimensions correct and uniform?
• Are bounding boxes reasonable, complete, and within bounds?

But we're not stopping there.

I'm currently building features that go deeper:

• Class frequency statistics — to uncover dataset imbalance
• Real vs. declared class count comparisons
• Bounding box size distributions — to detect annotation scale issues
• Dataset summaries to give you a snapshot of data reality, not just metadata

This isn’t about automatic fixing.
It’s about making invisible problems visible — before they become expensive.

Because in deep learning, your model doesn’t hallucinate.
It reflects.

And what it reflects most faithfully is not your architecture.
But your assumptions about your data.

Yololint doesn’t solve problems for you.
It helps you notice the ones that silently degrade your model’s intelligence.

Repository: https://github.com/Gabrli/YoloLint
Website: https://yololint.vercel.app/