YOLOv8 - classes invertion in data.yaml
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Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 25/100
- Issue type
- Bug
- Clarity
- Needs clarification
- Activity status
- Stale
- Domain
- computer-vision, machine-learning
Research direction
Start by reproducing the Roboflow export using the listed preprocessing and augmentation settings, then inspect data.yaml alongside the generated annotations. Confirm whether the two class names are swapped in the export and whether correcting the ordering makes displayed labels match the dataset; the issue provides no repository file or test entry point.
Written by the indexing model from the issue text.
Description
Hello,
I'd like to report a minor bug. I've annotated a dataset with Roboflow. I exported my data in YOLOv8 format but 2 classes are inverted in data.yaml. The model have been trained sucessfully but when I display the labels on images, they are inverted...
train: ../train/images
val: ../valid/images
test: ../test/images
nc: 9
names: ['Bibendum', 'Casquette', 'Dunlop', 'Goodyear', 'Michelin bibendum', **'Michelin logo carre', 'Michelin logo',** 'Michelin sept', 'Pneu']
roboflow:
workspace: tmf
project: michelin-gt
version: 11
license: CC BY 4.0
url: https://universe.roboflow.com/tmf/michelin-gt/dataset/11
I expected :
names: ['Bibendum', 'Casquette', 'Dunlop', 'Goodyear', 'Michelin bibendum', 'Michelin logo', 'Michelin logo carre' 'Michelin sept', 'Pneu']
Here are the augmentations / preprocessing steps I choose in Roboflow before exporting :
PREPROCESSING
Resize: Stretch to 640x640
Modify Classes: 4 remapped, 3 dropped
AUGMENTATIONS
Outputs per training example: 3
Rotation: Between -15° and +15°
Saturation: Between -25% and +25%
Brightness: Between -25% and +25%
Exposure: Between -25% and +25%
- Dominant language
- Python
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- Merged PRs (30d)
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