marvin

ConvNet framework

A minimalistic GPU-only framework for building N-dimensional ConvNet neural networks

Marvin: A Minimalist GPU-only N-Dimensional ConvNets Framework

GitHub

421 stars
44 watching
137 forks
Language: C++
last commit: over 6 years ago

Related projects:

Repository Description Stars
xuzhenqi/cnn Provides an implementation of convolutional neural networks in MATLAB. 95
jimmy-ren/vcnn_double-bladed A GPU-enabled vectorized implementation of CNNs for computer vision tasks 136
ahmedfgad/numpycnn Builds convolutional neural networks from scratch using NumPy 572
marvinteichmann/convcrf An implementation of a convolutional Conditional Random Field model for semantic segmentation tasks. 564
vlfeat/matconvnet A MATLAB toolbox implementing Convolutional Neural Networks for computer vision applications. 1,402
hannes-brt/hebel A Python library for GPU-accelerated deep learning 1,169
torontodeeplearning/convnet A high-performance GPU implementation of neural networks using C++ 506
hagaygarty/mdcnn A 3D convolutional neural network framework supporting volumetric inputs and various features like dropout and batch normalization. 52
donnyyou/pytorchcv A PyTorch-based framework for building and training deep learning models in computer vision. 47
tobypde/frrn A software framework for training and evaluating full-resolution residual networks for semantic image segmentation tasks 280
jihongju/keras-fcn A library implementing a Fully Convolutional Network architecture with Keras support 202
marvinteichmann/tensorflow-fcn An implementation of a fully convolutional network architecture for image segmentation using VGG weights. 1,101
mmlab-cu/polynet An implementation of a pursuit of structural diversity in very deep neural networks 82
eladhoffer/convnet.pytorch A PyTorch implementation of various deep convolutional networks for efficient training and evaluation on diverse datasets. 347
d-li14/psconv A deep learning framework module implementing a compact multi-scale convolutional layer for feature extraction in object detection models. 174