3b5778e303
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>
31 lines
1.1 KiB
Python
31 lines
1.1 KiB
Python
"""Unified segmentation classes shared across all datasets.
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All datasets MUST use the same prompt list and class IDs to enable
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transfer learning (e.g., pretrain on GTA-UAV → fine-tune on UAV_VisLoc).
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Not every dataset will have pixels for every class — that's fine.
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A class with 0 pixels simply won't contribute to training loss.
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"""
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UNIFIED_PROMPTS: list[str] = [
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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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"swimming pool", # 16
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]
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NUM_CLASSES = len(UNIFIED_PROMPTS) # 17
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