487 Open source projects that are alternatives of or similar to MSG-Net

A Caffe-based implementation of very deep convolution network for image super-resolution

Caffe implementation of "Fast and Accurate Single Image Super-Resolution via Information Distillation Network" (CVPR 2018)

Deep learning software for colorizing black and white images with a few clicks.

ROS node for real-time FCNN depth reconstruction

Automatic image captioning model based on Caffe, using features from bottom-up attention.

Caffe2 on iOS Real-time Demo. Test with Your Own Model and Photos.

Implementation of RAISR (Rapid and Accurate Image Super Resolution) algorithm in Python 3.x by Jalali Laboratory at UCLA. The implementation presented here achieved performance results that are comparable to that presented in Google's research paper (with less than ± 0.1 dB in PSNR). Just-in-time (JIT) compilation employing JIT numba is used to …

Visualizer for neural network, deep learning, and machine learning models

Re-implementation of tripletloss function in FaceNet

Deep Learning API and Server in C++14 support for Caffe, Caffe2, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE

Implementation based on the paper Li et al., “A Convolutional Neural Network Cascade for Face Detection, ” 2015 CVPR

Use caffe to train your own data in just one click

Caffe for Sparse Convolutional Neural Network

基于RabbitMQ简单实现验证码识别平台，训练网络模型智能识别图形验证码

A windows caffe implementation of YOLO detection network

VGG16 architecture with BatchNorm

Supervised Semantics-preserving Deep Hashing (TPAMI18)

Generative Adversarial Network (GAN) for physically realistic enrichment of turbulent flow fields

MTCNN Face Detection & Alignment

在caffe应用到工程实现时，为了方便系统安装，需要尽可能减少软件的依赖库。 本项目以bash shell/PowerShell脚本实现将caffe依赖的所有第三方库与caffe静态编译一起，以满足全静态编译的要求。 通过本项目提供的脚本生成的caffe编译环境不需要在系统安装任何第三方库和软件，就可以自动完成caffe项目静态编译. 目前在centos6.5/ubuntu16/win7/win10上测试通过,windows上VS2013,VS2015,MinGW 5.2.0编译通过

The repository contains source code and models to use PixelNet architecture used for various pixel-level tasks. More details can be accessed at <http://www.cs.cmu.edu/~aayushb/pixelNet/>.

Compact Generalized Non-local Network (NIPS 2018)

Light field video applications (e.g. video refocusing, focus tracking, changing aperture and view)

A PyTorch implementation of ESPCN based on CVPR 2016 paper Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network.

Caffe implementation for "Hidden Two-Stream Convolutional Networks for Action Recognition"

The Convolutional Neural Network(CNN) for Android

A Caffe Re-Implementation of SENet

Automatic colorization using deep neural networks. "Colorful Image Colorization." In ECCV, 2016.

Caffe-to-Keras weight converter. Can also export weights as Numpy arrays for further processing.

Domain Guided Dropout for Person Re-identification

Semantic Image Segmentation by Scale-Adaptive Networks (TIP 2019)

ID-discriminative Embedding (IDE) for Person Re-identification

Python layer for the Caffe deep learning framework to compute the accuracy and the confusion matrix.

通用深度学习推理服务，可在生产环境中快速上线由TensorFlow、PyTorch、Caffe框架训练出的深度学习模型。

A Caffe Implementation of SRGAN

Oriented Response Networks, in CVPR 2017

将任何窗口放大至全屏

Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing (CVPR 2018).

SRDenseNet-pytorch（ICCV_2017）

MTCNN with Motion Detection, on Raspberry Pi with Love

A Big Awesome List CoreML Models.

Auto-ReID and Other Person Re-Identification Projects

Implemetation of Pyramid Scene Parsing Network in Pytorch

A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization, TPAMI 2020

Pytorch framework can easily implement srcnn algorithm with excellent performance

php wrapper for opencv

Crack Segmentation for Low-Resolution Images using Joint Learning with Super-Resolution (CSSR) was accepted to international conference on MVA2021 (oral), and selected for the Best Practical Paper Award.

Collective Knowledge workflow for Caffe to automate installation across diverse platforms and to collaboratively evaluate and optimize Caffe-based workloads across diverse hardware, software and data sets (compilers, libraries, tools, models, inputs):

A review of change detection methods, including codes and open data sets for deep learning. From paper: change detection based on artificial intelligence: state-of-the-art and challenges.

Toolbox for converting analog to spiking neural networks (ANN to SNN), and running them in a spiking neuron simulator.

The SimNets Architecture's Implementation in Caffe

Data Augmentation for Caffe

Squeeze-and-Excitation Networks

Video-friendly caffe -- comes with the most recent version of Caffe (as of Jan 2019), a video reader, 3D(ND) pooling layer, and an example training script for C3D network and UCF-101 data

Convert image folder to lmdb, adapted from Efficient-PyTorch

This repository was a fork of BVLC/caffe and includes the upsample, bn, dense_image_data and softmax_with_loss (with class weighting) layers of caffe-segnet (https://github.com/alexgkendall/caffe-segnet) to run SegNet with cuDNN version 5.

Simple implementation of kpzhang93's paper from Matlab to c++, and don't change models.

Superimposes your image into a 3D world using Metal+Arkit

Mxnet2Caffe_Tensor RT

🎯 保姆级深度学习从入门到放弃 🤪 🤪