d1ddb40036
All datasets now use the same 17-class prompt list for transfer learning compatibility. World-UAV segmentation needs regeneration. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
24 lines
1.4 KiB
Plaintext
24 lines
1.4 KiB
Plaintext
# Unified 17-class open-vocabulary segmentation (shared across all datasets)
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# See scripts/seg_classes.py for canonical source, docs/segmentation_class_analysis.md for rationale
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SegConfig.prompts = [
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'background', # 0 — unclassified
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'building', # 1 — buildings, rooftops
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'road', # 2 — roads, asphalt
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'vegetation', # 3 — trees, bushes, forest canopy
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'water', # 4 — rivers, canals, sea, lakes
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'sand and gravel ground', # 5 — soil, gravel, sand, dust, bare earth
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'rocky terrain', # 6 — rock, stone, lava, canyon walls
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'farmland', # 7 — agricultural terraces, fields
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'railway', # 8 — railway tracks, rails
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'parking lot', # 9 — parking areas
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'sidewalk', # 10 — sidewalks, pedestrian zones
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'bare soil and plowed field', # 11 — plowed fields, construction sites
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'roof and rooftop', # 12 — rooftops, solar panels
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'sports field and playground', # 13 — courts, pitches
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'muddy ground and wetland', # 14 — wet soil, marshes, levee banks
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'embankment and levee', # 15 — earthen dams, canal walls
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'swimming pool', # 16 — pools (GTA-UAV suburbs)
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]
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SegConfig.threshold = 0.15
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SegConfig.default_resolution = 1008
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