Instructions to use colour-science/colour-checker-detection-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use colour-science/colour-checker-detection-models with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("colour-science/colour-checker-detection-models") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Colour - Checker Detection - Models
Various YOLOv8 colour rendition charts detection models.
The models obtained by training YOLOv8 segmentation with the colour-science/colour-checker-detection-model dataset are supporting colour rendition charts detection in the Colour Checker Detection Python package.
Classes
- ColorCheckerClassic24: Calibrite / X-Rite ColorCheckerClassic 24
Models
| Name | Size | mAP50 | mAP50-95 |
|---|---|---|---|
| colour-checker-detection-l-seg.pt | 92.6 MB | 0.995 | 0.992 |
Contact & Social
The Colour Developers can be reached via different means:
About
Colour - Checker Detection - Models by Colour Developers
Copyright 2024 Colour Developers – mailto:colour-developers@colour-science.org
This software is released under terms of CC-BY-4.0: https://creativecommons.org/licenses/by/4.0/
https://huggingface.co/colour-science/colour-checker-detection-models
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Dataset used to train colour-science/colour-checker-detection-models
Evaluation results
- mAP@0.5 on colour-science/colour-checker-detection-datasetself-reported0.995