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---
license: agpl-3.0
language:
- en
metrics:
- mae
- mse
- accuracy
tags:
- biology
- plant
- vitis
- downey mildew
- Plasmopara viticola
- OIV 452-1
base_model: microsoft/swin-tiny-patch4-window7-224
---
# OIV Leaf Disc Phenotyping
Companion repository for the article
"Phenotyping grapevine resistance to downy mildew: deep learning as a promising tool to assess sporulation and necrosis" found [Here](https://link.springer.com/article/10.1186/s13007-024-01220-4?utm_source=rct_congratemailt&utm_medium=email&utm_campaign=oa_20240613&utm_content=10.1186/s13007-024-01220-4)
## Folder Structure
### checkpoints
Contains checkpoint files for leaf disc detector and OIV 452-1 scorer.
### data
Contains all datasets data in CSV format
#### Leaf Disc Detection Files
- ldd_train.csv, ldd_val.csv and ldd_test.csv contain bounding box annotations in Pascal VOC format
- train_ld_bounding_boxes.csv contains predictions for all available plates.
#### OIV 452-1 Predictions
- oiv_train.csv, oiv_val.csv and oiv_test.csv contain OIV 452-1 annotated scores.
#### Genotype Differenciation
- genotype_differenciation_dataset.csv contains annotated scores and predictions for leaf patches used in to validate model on genptype differenciation.
### images
Contains all images in three different folders:
- plates contains full plate images.
- leaf_discs contains full leaf discs. Output folder for predicted leaf discs.
- leaf_patches contains extracted patches. Output folder for predicted leaf patches.
### src
Contains source code under two formats:
- *.py files contain base functionality and classes
- *.ipynb files contain code to reproduce the article data
## Notebooks
### repo_manager.ipynb
Utility notebook to create this repository |