This study provides a UAV target dataset for small target UAV detection. The dataset includes 10,800 images taken by high-definition cameras and their annotation files. Each image includes a target drone model DJI Mini 4 Pro. The data set includes a variety of shooting angles and is classified, including 0-30°, 30-60°, and 60-90°. It also includes good daytime lighting conditions and poor evening lighting conditions. According to our experiments, this dataset is sufficiently complex and of high quality.

The dataset is a self-constructed wafer surface defect dataset, with each image captured in real-time. The extraction and segmentation of wafer image have been performed, and each image represents a single individual die. The dataset primarily includes images of defect-free dies, as well as four types of defective images: particle, scratch, stain, and liquid residual. A total of 500 images are included, and the various types of defects within the images have been annotated using the Make Sense online annotation tool.

Human pose estimation has applications in numerous fields, including action recognition, human-robot interaction, motion capture, augmented reality, sports analytics, and healthcare. Many datasets and deep learning models are available for human pose estimation within the visible domain. However, challenges such as poor lighting and privacy issues persist. These challenges can be addressed using thermal cameras; nonetheless, only a few annotated thermal human pose datasets are available for training deep learning-based human pose estimation models.

CAPG Grocery Product (CAPG-GP) is a grocery product dataset with 102 fine-grained classes. We organize these 102 classes into five categories based on brands to create hierarchical labels. The original CAPG-GP dataset was first published in 'Fine-Grained Grocery Product Recognition by One-Shot Learning' by Geng et al. in 2018. The original dataset is publicly available at http://zju-capg.org/capg-gp.html.