LabelMe to YOLO: format conversion is the easy part
Converting annotations is step one. The full path in Easy Dataset: format conversion, quality checks, label consistency and splits that do not leak.
You receive a folder of LabelMe annotations. The instinct is to convert to YOLO and start training. Training will run — whether the results mean anything depends on what you checked before and after the conversion.
1. Conversion across four formats
LabelMe (JSON), VOC (XML), YOLO (TXT) and COCO (JSON), in any direction. Two failure modes dominate:
- Normalization: YOLO uses relative coordinates. Read the image size wrong and every box shifts.
- Class order: reorder the class list and labels silently change meaning.
So open the output and look at it. Do not feed a conversion straight into training.
2. Quality checks: find what is impossible
Visual browsing with boxes is for this. Sample thirty images and look for:
- boxes shifted or out of bounds;
- duplicate annotations on the same object;
- small objects labelled as a big region.
Errors get learned. A five percent annotation error rate usually costs more than any hyperparameter tweak.
3. Splits that do not leak
Adjacent frames from one video must not straddle train and validation. Random image-level splits inflate your metrics. Split by video or by scene instead.
Same when merging datasets: unify the class list first, merge second, split last.
4. Frame extraction
Surveillance footage is full of near-duplicates. Extract at a sensible interval and drop blurry frames — annotation effort drops sharply.
All of this runs locally. For projects with site footage or customer material, that is usually a hard requirement rather than a preference.
Full feature list on the Easy Dataset product page, with Windows, macOS and Linux builds.