Data set is the basis of machine learning, a good data set can promote the development of various applications. Machine learning has been deeply involved in the protection and inheritance of cultural resources. However, there are few data sets about Thangka, and the types and quantity of Thangka images are relatively few. Therefore, we first establish a Thangka data set called RPTK1 (Religious Portrait Thangka Version 1), which contains 3,338 Thangka images, more than any other Thangka data set. The objects in the data set basically cover the common Thangka religious portraits, tools and headdresses, and are marked in the professional language of Buddhism. Then, on the basis of the RPTK1 data set, in order to achieve better detection of small Thangka objects (Thangka religious tools), we propose an improved Single Shot MultiBox Detector (SSD) method, called Single Shot MultiBox Detector with Improvement Feature Fusion And Loss Function (FALSSD). Finally, in order to verify the effectiveness of the FALSSD method, we conduct experiments on the RPTK1 data set. The experimental results show that the mAP of our method in the RPTK1 data set reaches 83.85%. Compared with the other ten state-of-the-art methods, the performance of our model is better.INDEX TERMS Thangka data set, object detection, SSD method, feature fusion.