Unify segmentation classes (17) across GTA-UAV and UAV_VisLoc
Extract shared UNIFIED_PROMPTS (17 classes, ID 0-16) into scripts/seg_classes.py for transfer learning compatibility. Both run_gta_uav.py and run_uav_visloc.py now import from it. Key change: swimming pool moved from ID 13 → ID 16, so sports field (ID 13), muddy ground (14), embankment (15) have stable IDs across both datasets. Missing classes in a dataset = 0 pixels = 0 loss. Updated: README, CLAUDE.md, segmentation_class_analysis.md, palette. Deleted old UAV_VisLoc segmentations (need regeneration with 17 classes). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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@@ -27,6 +27,7 @@ from src.conf.pipeline_conf import PipelineConfig
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from src.conf.seg_conf import SegConfig
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from src.augmentor.io_utils import setup_logging
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from src.main import run_pipeline
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from scripts.seg_classes import UNIFIED_PROMPTS
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INPUT_ROOT = "/home/servml/Документы/datasets/UAV_VisLoc_processed"
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@@ -73,24 +74,7 @@ def main() -> None:
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)
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input_conf = InputConfig(image_size=256)
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seg_conf = SegConfig(threshold=0.1, prompts=[
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"background", # 0
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"building", # 1
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"road", # 2
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"vegetation", # 3
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"water", # 4
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"sand and gravel ground", # 5
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"rocky terrain", # 6
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"farmland", # 7
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"railway", # 8
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"parking lot", # 9
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"sidewalk", # 10
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"bare soil and plowed field", # 11
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"roof and rooftop", # 12
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"sports field and playground", # 13
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"muddy ground and wetland", # 14
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"embankment and levee", # 15
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])
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seg_conf = SegConfig(threshold=0.1, prompts=UNIFIED_PROMPTS)
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models_conf = ModelsConfig(weights_dir=str(_PROJECT_ROOT / "in" / "weights"))
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setup_logging(
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