Identifying and non-identifying relationships

本文解释了数据库中标识性关系与非标识性关系的概念,通过实例展示了如何区分这两种关系,并介绍了如何在设计数据库时定义它们。

An identifying relationship means that the child table cannot be uniquely identified without the parent. For example, you have this situation in the intersection table used to resolve a many-to-many relationship where the intersecting table’s Primary Key is a composite of the left and right (parents) table’s Primary Keys.

Example…
Account (AccountID, AccountNum, AccountTypeID)
PersonAccount (AccountID, PersonID, Balance)
Person(PersonID, Name)

The Account to PersonAccount relationship and the Person to PersonAccount relationship are identifying because the child row (PersonAccount) cannot exist without having been defined in the parent (Account or Person). In other words: there is no personaccount when there is no Person or when there is no Account.

A non-identifying relationship is one where the child can be identified independently of the parent ( Account – AccountType)

Example…
Account( AccountID, AccountNum, AccountTypeID )
AccountType( AccountTypeID, Code, Name, Description )

The relationship between Account and AccountType is non-identifying because each AccountType can be identified without having to exist in the parent table.

You can define the relationship type (identifying/non identifying) in the DeZign for Databases in the relationship dialog. Double click on the relationship line in the diagram window to display the relationship dialog.


转自:http://yangsiwei.com/difference-between-identifying-and-non-identifying-relationship

### RT-DETR Model for Small Object Detection Implementation and Optimization The RT-DETR (Real-Time Deformable Transformer Detector) model is designed to address challenges specific to detecting smaller objects within images or video frames. This model leverages advanced techniques like deformable transformers which enhance feature extraction from complex scenes, particularly beneficial when dealing with small-scale targets[^1]. For implementing an RT-DETR model focused on improving detection rates of tiny objects: #### Data Augmentation Techniques To improve the robustness of training data specifically targeting small objects, various augmentation methods are employed including scaling down larger instances into miniature versions during preprocessing stages. Additionally, cutout operations that randomly mask out regions containing large foreground elements help focus more attention towards less prominent features. ```python import albumentations as A transform = A.Compose([ A.RandomResizedCrop(height=640, width=640, scale=(0.8, 1.0)), A.Cutout(num_holes=8, max_h_size=32, max_w_size=32), ]) ``` #### Optimizing Network Architecture Adjustments made at architectural level include increasing receptive field sizes through dilated convolutions while maintaining computational efficiency by reducing channel dimensions where appropriate. Such modifications allow deeper penetration across spatial hierarchies thus capturing finer details necessary for identifying minute structures accurately. #### Loss Function Customization Custom loss functions tailored toward penalizing false negatives associated with hard-to-detect items ensure better recall metrics even under challenging conditions. Focal losses adjusted according to size distribution patterns observed within dataset annotations contribute significantly here. ```python class SizeAwareFocalLoss(nn.Module): def __init__(self, alpha=None, gamma=2., reduction='mean'): super(SizeAwareFocalLoss, self).__init__() def forward(self, inputs, targets, area_weights): BCE_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduce=False) pt = torch.exp(-BCE_loss) focal_loss = ((1 - pt)**gamma * BCE_loss).mean() weighted_focal_loss = focal_loss * area_weights return weighted_focal_loss.mean() if self.reduction == 'mean' else weighted_focal_loss.sum() ``` #### Post-processing Refinements Post-process steps involve applying non-maximum suppression thresholds tuned lower than usual settings so as not to discard valid detections prematurely due solely based upon overlap criteria alone but rather consider confidence scores alongside geometric relationships between bounding boxes.
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