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.
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.
Besides, our experiments converting CIFAR-10 into a point cloud showed that networks built on PointConv can match the performance of convolutional networks in 2D images of a similar structure.