During the training process, the output of the student model is compared with the output of the updated teacher model to calculate the distillation loss. The loss function of the RBSKD method is shown in Equation (3) below: (3)𝓛YOLO(S) is the YOLOv8 student model in the current ...
Nonetheless, the initial appearance model and re-recognition, motion model, and data association elements of DeepSORT are no longer able to match the current real-time and efficient target tracking requirements due to the growing complexity of the tracking scenarios. For this reason, the feature ...
ChatGPT(全名:Chat Generative Pre-trained Transformer),是OpenAI研发的聊天机器人程序,于2022年11月30日发布。ChatGPT是人工智能技术驱动的自然语言处理工具,它能够通过理解和学习人类的语言来进行对话,还能根据聊天的上下文进行互动,真正像人类一样来聊天交流,甚至能完成撰写邮件、视频脚本、文案、翻译、代码,写论文等...
Knowledge Distillation: Train the smaller model using a larger, pre-trained model as a teacher to transfer knowledge. Hyperparameter Tuning: After making architectural changes, you'll likely need to fine-tune hyperparameters such as the learning rate, weight decay, and others for optimal performance...
Knowledge distillation [21] is an efficient model simplification technique that extracts and transfers deep knowledge from large, complex models into more streamlined ones, enabling the smaller models to inherit and replicate the core cognitive abilities of the teacher models. (3) Input Data-based ...
Finally, a knowledge distillation strategy is employed using the YOLOv8x as the teacher model to further improve the model’s accuracy. Figure 1. The network structure of GCP-YOLO, an improved YOLOv8 algorithm incorporating GSCDown, CSPBlock, and PAM. Figure 2. The structure of GSCDown. ...
(Efficient Channel and Spatial Attention), which is integrated into the network to forge LP-YOLO(l). Moreover, assessing the trade-offs between parameter reduction and computational efficiency, considering both the backbone and head components of the network, we use structured pruning methods for ...
Their proposed strategy consists of two stages: the teacher guiding the student in the first stage and the student fine-tuning independently in the second stage. Additionally, they incorporate two enhancements in the distillation approach: the Align Module, which adapts student features to the same ...
This iteration continues to extend the frontiers of object detection technology, positioning it as an optimal solution for tasks requiring real-time, efficient computing. YOLOv8 is disseminated in five distinct models, n, s, m, l, and x, with each model representing a progressive increase in ...