| Summary: | 碩士 === 中國科技大學 === 資訊工程系資訊科技應用碩士在職專班 === 107 === In recent years, due to the rapid development of deep learning algorithms, the continuous development of high-efficiency hardware platforms, and the diversity of image data on the Internet, the accuracy of image recognition based on deep learning algorithms has been greatly improved, and computer vision-related applications have also been improved. It will flourish. However, the inference results of deep learning are closely related to domain knowledge and training test data. Only by continuously using the latest data to continuously train the deep learning model can we keep pace with the times and maintain the high accuracy of model identification.
This study uses Nvidia GPUs based on Docker Containers, with Nvidia Docker and Docker Compose to build visual object detection and training platforms, combined with continuous integration and continuous delivery (Continuous Integration / Continuous Delivery) continuous use of false alarms (False Alarm) information automatically retrains the model by re-marking to maintain a certain degree of accuracy.
The high flexibility and light weight of the soft container technology enables the rapid replacement of the visual object detection framework (Framework), combined with the components of the visual object detection in this study, in addition to quickly replacing different frames, it can also be based on different visual objects. The measured input, output, and algorithm are quickly replaced to realize the division of labor of the system, so as to facilitate the application of deep learning.
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