YOLO Leaf Object Detection Pipeline

Project overview

Dataset preparation and validation for leaf detection; prepared data does not establish trained-detector performance.

A reproducible leaf-detection workflow combining PlantVillage, PlantDoc and generated hard negatives. It prepares and validates data, configures YOLO training and provides export and browser-inference hooks. No committed trained weights or held-out results establish detector accuracy, latency or production readiness.

Inside this build

• Prepared 57,164 images and 63,225 boxes in deterministic training, validation and test splits.
• Converted Pascal VOC annotations to normalised YOLO coordinates.
• Generated collision-resistant filenames across source datasets.
• Validated image-label pairs, class IDs, coordinates and split leakage before training.
• Configured three YOLO candidates, a bounded smoke-training path and ONNX export hooks.
• Kept dataset-tool tests separate from external-data preparation and GPU training.

Source code & links