Grass Quality Segmentation
A grass quality segmentation system that converts weak labels into patch-level supervision and produces quality heatmaps.
Data Pipeline
- Generated SEEM + SAMv2 labels for dirt and grass classes.
- Converted YOLO labels into a grass-quality format: dirt and winter grass become bad, summer grass becomes good.
- Used CLIP to filter mislabeled data and create
64x64patches. - Applied OpenCLIP scoring to keep patches as good unless confidence is
>= 85%for bad, and vice versa.
Model
- Trained a ResNet-18 patch classifier with
64x64input. - Ran stride-1 inference to produce grass quality heatmaps.
Demonstration
The embedded demonstration shows the resulting grass-quality predictions and spatial heatmap output.
Evidence & demonstration
Demos & project media
Project demonstration
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