Object detection is a task that performs position identification and label
classification of objects in images or videos. The information obtained through
this process plays an essential role in various tasks in the field of computer
vision. In object detection, the data utilized for training and validation
typically originate from public datasets that are well-balanced in terms of the
number of objects ascribed to each class in an image. However, in real-world
scenarios, handling datasets with much greater class imbalance, i.e., very
different numbers of objects for each class , is much more common, and this
imbalance may reduce the performance of object detection when predicting unseen
test images. In our study, thus, we propose a method that evenly distributes
the classes in an image for training and validation, solving the class
imbalance problem in object detection. Our proposed method aims to maintain a
uniform class distribution through multi-label stratification. We tested our
proposed method not only on public datasets that typically exhibit balanced
class distribution but also on custom datasets that may have imbalanced class
distribution. We found that our proposed method was more effective on datasets
containing severe imbalance and less data. Our findings indicate that the
proposed method can be effectively used on datasets with substantially
imbalanced class distribution.

我们提出了一种通过多标签分层来均匀分布图像中的类别从而解决目标检测中的类别不平衡问题的方法，并发现该方法在严重不平衡且数据较少的数据集上效果更好。

通过保持标签分布提高物体检测性能

Improving the performance of object detection by preserving label  distribution

In recent years, deep learning (DL) has shown great potential in the field of
dermatological image analysis. However, existing datasets in this domain have
significant limitations, including a small number of image samples, limited
disease conditions, insufficient annotations, and non-standardized image
acquisitions. To address these shortcomings, we propose a novel framework
called DermSynth3D. DermSynth3D blends skin disease patterns onto 3D textured
meshes of human subjects using a differentiable renderer and generates 2D
images from various camera viewpoints under chosen lighting conditions in
diverse background scenes. Our method adheres to top-down rules that constrain
the blending and rendering process to create 2D images with skin conditions
that mimic in-the-wild acquisitions, ensuring more meaningful results. The
framework generates photo-realistic 2D dermoscopy images and the corresponding
dense annotations for semantic segmentation of the skin, skin conditions, body
parts, bounding boxes around lesions, depth maps, and other 3D scene
parameters, such as camera position and lighting conditions. DermSynth3D allows
for the creation of custom datasets for various dermatology tasks. We
demonstrate the effectiveness of data generated using DermSynth3D by training
DL models on synthetic data and evaluating them on various dermatology tasks
using real 2D dermatological images. We make our code publicly available at
this https URL

本研究提出了一种名为 DermSynth3D 的新型框架，它将皮肤病模式与人类主体的 3D 网格相融合，并使用可微分渲染器从不同的摄像机视点在各种背景场景下生成 2D 图像。DermSynth3D 可以为各种皮肤病学任务创建自定义数据集，该数据集在训练深度学习模型时表现良好。