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Inception is a deep convolutional neural network architecture designed for image recognition tasks. Introduced by Google researchers in 2014, it uses a novel ‘Inception module’ to improve computational efficiency and accuracy in visual recognition systems.
AlexNet is a pioneering convolutional neural network architecture that significantly advanced image recognition technology. Developed in 2012, it demonstrated the effectiveness of deep learning in large-scale visual tasks.
SSD (Single Shot MultiBox Detector) is a popular deep learning model for object detection that performs object localization and classification in a single forward pass, enabling real-time applications. It was introduced to balance accuracy and speed in detecting multiple objects within images.
VGGNet is a convolutional neural network architecture known for its simplicity and depth, developed by the Visual Geometry Group at the University of Oxford. It gained prominence for its performance in image recognition tasks, especially in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014.
MobileNet is a family of efficient convolutional neural network architectures designed for mobile and embedded vision applications. It balances accuracy and computational cost, enabling deployment on devices with limited resources.
PointCNN is a convolutional neural network architecture designed specifically for processing point cloud data by learning an X-transformation to reorder and weight points before applying convolution.