我们这里以ADE数据集源码为例,reduce_zero_label默认设置为True,然而,就算新手掌握了上一节的reduce_zero_label,也可能对ADE了解比较肤浅,会怀疑配置文件中开启的reduce_zero_label是不是把 150 个实例类中的第一个给忽略掉了,毕竟num_classes不就是150吗,然后想当然把reduce_zero_label关掉。 错误原因分析 # conf...
to_rgb=True) # 预训练里用于预训练主干网络的图像的通道顺序。 crop_size = (512, 512) # 训练时的裁剪大小 train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', reduce_zero_label=True), dict(type='Resize', # 变化图像和其注释大小的数据增广。 img_scale=(2048...
reduce_zero_label=True),dict(type='RandomResize',scale=(2048,512),ratio_range=(0.5,2.0),keep_ratio=True),dict(type='RandomCrop',crop_size=crop_size,cat_max_ratio=0.75),dict(type='RandomFlip',prob=0.5),dict
# configs/_base_/datasets/ade20k.pytrain_pipeline=[dict(type='LoadImageFromFile'),dict(type='LoadAnnotations', reduce_zero_label=True), # ADE中reduce_zero_label默认设置为Truedict(...),...] label 中实际参加训练的确实只有类,定义在中,但 label 文件中实际包含了类,而背景类(剩下仍没有标记的...
reduce_zero_label 导致的常见问题描述 我们这里以ADE数据集源码为例,reduce_zero_label默认设置为True,然而,就算新手掌握了上一节的reduce_zero_label,也可能对ADE了解比较肤浅,会怀疑配置文件中开启的reduce_zero_label是不是把 150 个实例类中的第一个给忽略掉了,毕竟num_classes不就是150吗,然后想当然把reduce...
train_pipeline = [dict(type='LoadImageFromFile'),dict(type='LoadAnnotations', reduce_zero_label=True),dict(type='Resize', img_scale=(2048,512), ratio_range=(0.5,2.0)),dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),dict(type='RandomFlip', prob=0.5),dict(type='Ph...
reduce_zero_label=True), dict(type='PackSegInputs') ] img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] tta_pipeline = [ dict(type='LoadImageFromFile', backend_args=None), dict( type='TestTimeAug', transforms=[ [ dict(type='Resize', scale_factor=r, keep_ratio=True) for r in ...
dict(type='LoadAnnotations', reduce_zero_label=True), dict(type='Resize',# 变化图像和其注释大小的数据增广。img_scale=(2048, 512),# 图像和标注的 resize 尺度ratio_range=(0.5, 2.0)),# 随机 resize 的比例范围。dict(type='RandomCrop',# 随机裁剪当前图像和其注释。crop_size=crop_size,# 随机...
/home/lyk/lyk/mmsegmentation-1.0.0rc6/mmseg/datasets/transforms/loading.py:78: UserWarning:reduce_zero_labelwill be deprecated, if you would like to ignore the zero label, please setreduce_zero_label=Truewhen dataset initialized warnings.warn('reduce_zero_labelwill be deprecated, ' ...
Therefore, in the dataset config potsdam.py reduce_zero_label=True。 If you are using labels without black edges, then there are only class 0-5 in the mask label. At this point, you should use reduce_zero_label=False. reduce_zero_label usage needs to be considered with your actual ...