The AttendSeg deep learning model performs semantic segmentation at an accuracy that is almost on-par with RefineNet while cutting down the number of parameters to 1.19 million.

The proposed methodology utilizes the convolutional neural network (CNN) architecture U-Net for image segmentation and then applies the CNN architecture InceptionV3-Net for fault classification.

The Global Mapper Insight and Learning Engine™ (Beta) provides trained models for land cover classification, vehicle identification, and building extraction. This update to Global Mapper ...

OKI (TOKYO: 6703) has developed ‘ship classification AI system technology’ for the automatic classification of ship types through deep learning of und ...

Performance of DeciNets for image classification compared to other deep learning image classification models for Intel Cascade Lake CPUs. Image: Deci.

The rapid development of deep learning in recent years is largely due to the rapid increase in the scale of data. The availability of large amounts of data is revolutionary for model training by the ...