caffe-SPPNet | detection-developing | Machine Learning library

caffe-SPPNet is a C++ library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. caffe-SPPNet has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Caffe aims to provide computer vision scientists with a clean, modifiable implementation of state-of-the-art deep learning algorithms. Network structure is easily specified in separate config files, with no mess of hard-coded parameters in the code. Python and Matlab wrappers are provided. At the same time, Caffe fits industry needs, with blazing fast C++/Cuda code for GPU computation. Caffe is currently the fastest GPU CNN implementation publicly available, and is able to process more than 40 million images per day on a single NVIDIA K40 GPU (or 20 million per day on a K20)*. Caffe also provides seamless switching between CPU and GPU, which allows one to train models with fast GPUs and then deploy them on non-GPU clusters with one line of code: Caffe::set_mode(Caffe::CPU). Even in CPU mode, computing predictions on an image takes only 20 ms when images are processed in batch mode. * When measured with the SuperVision model that won the ImageNet Large Scale Visual Recognition Challenge 2012.