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.

Rodrigo Carrasco-Davis, Guillermo Cabrera-Vives, Francisco Förster, Pablo A. Estévez, Pablo Huijse, Pavlos Protopapas, Ignacio Reyes, Jorge Martínez-Palomera, Cristóbal Donoso, Deep Learning for Image ...

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 ...

Deep learning, used to classify images, recognize voices and analyze video images, is emerging as the next big wave of artificial intelligence. Written by Joe McKendrick, Contributing Writer July ...

Deci, a company aiming to optimize deep learning models, is releasing a new family of models for image classification. These models outperform well-known alternatives in both accuracy and runtime ...

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 ...

Many deep-learning image classification networks have already made heavy use of transfer learning, and it's likely that the design approach will continue to play an important role in pushing ...

The novel method was presented in “ SPF-Net: Solar panel fault detection using U-Net based deep learning image classification,” published in Energy Reports.

In traditional semiconductor packaging, manual defect review after automated optical inspection (AOI) is an arduous task for operators and engineers, involving review of both good and bad die. It is ...