When testing the model on a set of images collected from trusted online sources - i. e. taken under conditions different from the images used for training - the model still achieves an accuracy of 31. 4%.
The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
What will happen if we increase the dataset size by 10x or 100x?
#20 best model for Semantic Segmentation on PASCAL VOC 2012 test
Ristretto simulates the hardware arithmetic of a custom hardware accelerator.
Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data.
Therefore, designing lightweight networks with low memory requirement and computational cost is one of the most practical solutions for face verification on mobile platform.
We propose a new supervized learning framework for oversegmenting 3D point clouds into superpoints.
While current methods extract descriptors for the single task of localization, SegMap leverages a data-driven descriptor in order to extract meaningful features that can also be used for reconstructing a dense 3D map of the environment and for extracting semantic information.