charlie880624
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- Add golf1/2/4/7/8 dataset classes for semantic segmentation - Add kneron-specific configs (meconfig series, kn_stdc1_golf4class) - Organize scripts into tools/check/ and tools/kneron/ - Add kneron_preprocessing module - Update README with quick-start guide - Update .gitignore to exclude data dirs, onnx, nef outputs Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
72 lines
2.0 KiB
Python
72 lines
2.0 KiB
Python
# dataset settings
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dataset_type = 'GolfDataset'
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data_root = 'data/cityscapes0/' # ✅ 你的資料根目錄
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img_norm_cfg = dict(
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mean=[128., 128., 128.], std=[256., 256., 256.], to_rgb=True)
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crop_size = (360, 720)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=(724, 362), ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg']),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(724, 362),
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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samples_per_gpu=2,
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workers_per_gpu=2,
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train=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='leftImg8bit/train',
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ann_dir='gtFine/train',
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pipeline=train_pipeline
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),
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val=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='leftImg8bit/val',
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ann_dir='gtFine/val',
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pipeline=test_pipeline
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),
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test=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='leftImg8bit/test',
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ann_dir='gtFine/test',
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pipeline=test_pipeline
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)
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)
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# ✅ 類別與對應的調色盤（不傳給 dataset，用於繪圖/推論可視化）
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classes = ('car', 'grass', 'people', 'road')
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palette = [
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[246, 14, 135], # car
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[233, 81, 78], # grass
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[220, 148, 21], # people
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[207, 215, 220], # road
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]
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