“YOLO uses sum-squared error between the predictions and the ground truth to calculate loss. The loss function composes of: the classification loss. the localization loss (errors between the predicted boundary box and the ground truth). the confidence loss (the objectness of the box).” 可以...
❔Question Hi @glenn-jocher, May I know the meaning of each of these parameters of the backbone and head inside the .yaml file? Also there are three integers in front of "Concat", what is the meaning of these values? Many thanks, JiaLim98
Support Tiny-Yolo, Mobilenet and TensorFlow Lite for deep learning Checkout the RTG4 Development Kit board from Microsemi RTG4 Development Kit Hardware Specs Two 1GB DDR3 synchronous dynamic random access memory (SDRAM) 2GB SPI flash memory PCI Express Gen 1 x1 interface PCIe x4 edge conne...
However, the analysis of surveillance video still relies on manual work at present. This way of working has the following disadvantages: first, the detection of abnormal behaviour depends on the skill level of the viewer, and there are few experienced experts; second, long-term watching is easy...
International Journal of Molecular Sciences Review Understanding the Intricate Web of Phytohormone Signalling in Modulating Root System Architecture Manvi Sharma †, Dhriti Singh †, Harshita B. Saksena †, Mohan Sharma † , Archna Tiwari ‡, Prakhar Awasthi ‡, Halidev Krishna Bot...
In addition, to obtain richer context information, We design an adaptive fusion feature pyramid network (AF-FPN) to combine the adaptive context information fusion module (ACIFM) with the feature fusion module (FFM) to improve the neck structure of YOLOV7. The proposed method is validated on ...
This repo contains the official implementation of"DynamicDet: A Unified Dynamic Architecture for Object Detection". Performance MS COCO ModelEasy / HardSizeFLOPsFPSAPvalAPtest Dy-YOLOv790% / 10%640112.4G11051.4%52.1% 50% / 50%640143.2G9652.7%53.3% ...
This network leverages the pre-trained parameters of YOLOv10-vit to more quickly learn the features of different corrosion levels. To avoid subjective factors in the corrosion level annotation process and reduce annotation difficulty, a cascaded corrosion detection architecture combining YOLOv10-vit and...
YOLOv5s-Fog: An Improved Model Based on YOLOv5s for Object Detection in Foggy Weather Scenarios. Sensors 2023, 23, 5321. [Google Scholar] [CrossRef] [PubMed] Bijelic, M.; Gruber, T.; Ritter, W. A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down? In Proceedings of ...
electronics Article FLIA: Architecture of Collaborated Mobile GPU and FPGA Heterogeneous Computing Nan Hu * , Chao Wang and Xuehai Zhou School of Computer Science, University of Science and Technology of China, Hefei 230052, China * Correspondence: hunan@mail.ustc.edu.cn Abstract: Accelerators, ...