针对您在mmdet中使用自定义数据集继承CustomDataset类时,类别信息没有加载进去的问题,可以按照以下步骤进行排查和解决: 1. 确认自定义数据集类已正确继承CustomDataset类 首先,确保您的自定义数据集类正确继承了CustomDataset。例如: python from mmdet.datasets import CustomDataset @DATASETS.register_module() class My...
@DATASETS.register_module() class TileDataset(CustomDataset): CLASSES = ('_bkg', 'edge', 'corner', 'whitespot', 'lightblock', 'darkblock', 'aperture') def __init__(self, min_size=None, **kwargs): super(TileDataset, self).__init__(**kwargs) # self.cat2label = {cat: i for...
# 3dssd_meg-19classes.pycustom_imports=dict(imports=['projects.Meg_Dataset.meg_dataset.meg_datase...
# 3dssd_meg-19classes.pycustom_imports=dict(imports=['projects.Meg_Dataset.meg_dataset.meg_datase...
/datasets/custom.py: @DATASETS.register_module()classCustomDataset(Dataset): CLASSES =None#种类名称,可以直接定义(常用直接类内定义),也可以外部传入# 读取全部标签,格式如下:‘’‘ {'filename':'a.jpg','width':1280,'height':720,'ann':
/datasets/dataset_wrappers.py: 重写concatDataset、RepeatDataset上面已经详细说明,增加数据类别平衡类(具体没看) /datasets/custom.py: @DATASETS.register_module() class CustomDataset(Dataset): CLASSES = None #种类名称,可以直接定义(常用直接类内定义),也可以外部传入 # 读取全部标签,格式如下: ‘’‘ { '...
results (dict): Result dict from :obj:`mmdet.CustomDataset`. Returns: dict: The dict contains loaded bounding box, label, mask and semantic segmentation annotations. """ if self.with_pts: results = self._load_pts(results) if self.with_label: ...
自定义数据测试mmdet可以通过以下步骤实现：1.准备自定义数据集。自定义数据集应该包含图像和标注文件，图像应该是JPEG或PNG格式，标注文件可以使用VOC格式或COCO格式。2.准备数据加载器。用户可以使用mmdetection提供的基本加载器，也可以使用自定义加载器，以满足特定数据集的加载需求。3.更新配置文件。用户...
I was trying to train a CascadeMaskRCNN Hrnet model on a custom dataset in my local system with COCO style annotations. This is the message that I get while training my dataset. On further looking I found that when running the single_gpu_test for validation data, the results are all emp...
dataset (:obj:`CustomDataset`): The dataset repeat_thr (float): The threshold of frequency. If an image contains the categories whose frequency below the threshold, it would be repeated. Returns: list[float]: The repeat factors for each images in the dataset. """ # 1. For each...