Zasolar Exp003 C Targeted Hn

--- license: mit library_name: pytorch pipeline_tag: image-segmentation tags: - solar - rooftop-solar - instance-segmentation - mask-rcnn - remote-sensing - geospatial --- # 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

Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Library pytorch · Repo botao0818/zasolar-exp003-c-targeted-hn