Machine learning engineers need to assess their models' performance, but judging accuracy alone doesn't help identify underlying issues due to the accuracy paradox. Imbalanced datasets can lead to biased models that perform poorly on underrepresented classes. Class imbalance in object detection occurs when properties like foreground-foreground and foreground-background imbalances are not uniformly distributed. Tools like Encord Index address these challenges through data curation, ensuring balanced representation and data quality validation. To solve class imbalance, three major methods exist: hard sampling, soft sampling, and generative methods. Hard sampling involves selecting a subset of labeled bounding boxes to correct the imbalance, while soft sampling adjusts sample contributions and generative methods produce artificial samples to inject into the training dataset. Spatial imbalances also occur in object detection, particularly with regression loss, IoU distribution, and object location, which require specific solutions like stable loss functions, convolutional neural networks, and hierarchical shot detectors. Analyzing data distribution before building an object detection model is crucial to identify and solve these imbalances. Tools like Encord Active visualize outliers and provide metrics for data quality and label-quality analysis, allowing users to build balanced datasets and compare algorithm performance.