ZAsolar Exp003-C Targeted Hard Negatives
Mask R-CNN checkpoint for rooftop solar installation footprint segmentation in South Africa.
This model is the Exp003-C variant from the ZAsolar project. It was trained with targeted hard negatives extracted from reviewed false-positive detections, following the installation-level footprint segmentation task definition used in this repository.
Files
best_model.pth: trained PyTorch checkpoint
Training Summary
- Task: installation-level rooftop solar footprint segmentation
- Base architecture: Mask R-CNN
- Training variant: targeted hard negatives (
exp003_C_targeted_hn)
- Training data:
- 4,740 positive chips
- 883 targeted hard-negative chips
- 7,720 annotations
- Validation set:
- 2,640 images
- 2,948 annotations
Experiment Notes
According to the project experiment log, this targeted hard-negative variant substantially reduced false positives in independent inference evaluation compared with the earlier baseline, improving fixed-threshold precision and F1 while keeping recall reasonably close.
Repository reference:
- Experiment doc:
docs/experiments/exp_003_hard_negatives.md
- Training entrypoint:
train.py
Intended Use
This checkpoint is intended for research and internal geospatial analysis workflows around rooftop solar detection. It is not packaged as a drop-in Transformers model and should be loaded by the project's PyTorch inference/training code.
Limitations
- Trained for the ZAsolar data and labeling conventions
- Evaluation conclusions depend on the repository's explicit
installation profile
- Generalization outside the source geography, imagery characteristics, and annotation policy is not guaranteed
