SENet - Squeeze-and-Excitation Networks

  •        62

By Jie Hu[1], Li Shen[2], Gang Sun[1]. Momenta[1] and University of Oxford[2].

https://github.com/hujie-frank/SENet

Tags
Implementation
License
Platform

   




Related Projects

LightNet - LightNet: Light-weight Networks for Semantic Image Segmentation (Cityscapes and Mapillary Vistas Dataset)

  •    Python

This repository contains the code (in PyTorch) for: "LightNet: Light-weight Networks for Semantic Image Segmentation " (underway) by Huijun Liu @ TU Braunschweig. Semantic Segmentation is a significant part of the modern autonomous driving system, as exact understanding the surrounding scene is very important for the navigation and driving decision of the self-driving car. Nowadays, deep fully convolutional networks (FCNs) have a very significant effect on semantic segmentation, but most of the relevant researchs have focused on improving segmentation accuracy rather than model computation efficiency. However, the autonomous driving system is often based on embedded devices, where computing and storage resources are relatively limited. In this paper we describe several light-weight networks based on MobileNetV2, ShuffleNet and Mixed-scale DenseNet for semantic image segmentation task, Additionally, we introduce GAN for data augmentation[17] (pix2pixHD) concurrent Spatial-Channel Sequeeze & Excitation (SCSE) and Receptive Field Block (RFB) to the proposed network. We measure our performance on Cityscapes pixel-level segmentation, and achieve up to 70.72% class mIoU and 88.27% cat. mIoU. We evaluate the trade-offs between mIoU, and number of operations measured by multiply-add (MAdd), as well as the number of parameters.

Caffe-HRT - Heterogeneous Run Time version of Caffe

  •    C++

Caffe-HRT is a project that is maintained by OPEN AI LAB, it uses heterogeneous computing infrastructure framework to speed up Caffe and provide utilities to debug, profile and tune application performance. The Caffe based version is 793bd96351749cb8df16f1581baf3e7d8036ac37.

DIGITS - Deep Learning GPU Training System

  •    HTML

DIGITS (the Deep Learning GPU Training System) is a webapp for training deep learning models. The currently supported frameworks are: Caffe, Torch, and Tensorflow. Once you have installed DIGITS, visit docs/GettingStarted.md for an introductory walkthrough.

gpu-rest-engine - A REST API for Caffe using Docker and Go

  •    C++

This repository shows how to implement a REST server for low-latency image classification (inference) using NVIDIA GPUs. This is an initial demonstration of the GRE (GPU REST Engine) software that will allow you to build your own accelerated microservices. This repository is a demo, it is not intended to be a generic solution that can accept any trained model. Code customization will be required for your use cases.


loadcaffe - Load Caffe networks in Torch7

  •    Protocol

NN support means both CPU and GPU backends. You can also use Caffe inside Torch with this: https://github.com/szagoruyko/torch-caffe-binding However you can't use both loadcaffe and caffe in one torch session.

CaffeOnSpark

  •    Jupyter

CaffeOnSpark brings deep learning to Hadoop and Spark clusters. By combining salient features from deep learning framework Caffe and big-data frameworks Apache Spark and Apache Hadoop, CaffeOnSpark enables distributed deep learning on a cluster of GPU and CPU servers.As a distributed extension of Caffe, CaffeOnSpark supports neural network model training, testing, and feature extraction. Caffe users can now perform distributed learning using their existing LMDB data files and minorly adjusted network configuration (as illustrated).

Knet.jl - Koç University deep learning framework.

  •    Julia

Knet uses dynamic computational graphs generated at runtime for automatic differentiation of (almost) any Julia code. This allows machine learning models to be implemented by defining just the forward calculation (i.e. the computation from parameters and data to loss) using the full power and expressivity of Julia. The implementation can use helper functions, loops, conditionals, recursion, closures, tuples and dictionaries, array indexing, concatenation and other high level language features, some of which are often missing in the restricted modeling languages of static computational graph systems like Theano, Torch, Caffe and Tensorflow. GPU operation is supported by simply using the KnetArray type instead of regular Array for parameters and data. Knet builds a dynamic computational graph by recording primitive operations during forward calculation. Only pointers to inputs and outputs are recorded for efficiency. Therefore array overwriting is not supported during forward and backward passes. This encourages a clean functional programming style. High performance is achieved using custom memory management and efficient GPU kernels. See Under the hood for more details.

OpenCL-caffe - This is a Experimental version of OpenCL by AMD Research, we now recommend you to use The official BVLC Caffe OpenCL branch is over at Caffe branch now at https://github

  •    C++

###OpenCL Caffe Experimental branch by AMD Reserach- No new development is happing on it. This is an OpenCL implementation of Caffe, a mainstream DNN framework (https://github.com/BVLC/caffe). It includes a largely complete Caffe feature set as of August 2015. The project is under active development to improve performance and add new features. Contributions from the community are welcome.

leaf - Open Machine Intelligence Framework for Hackers. (GPU/CPU)

  •    Rust

Leaf is a open Machine Learning Framework for hackers to build classical, deep or hybrid machine learning applications. It was inspired by the brilliant people behind TensorFlow, Torch, Caffe, Rust and numerous research papers and brings modularity, performance and portability to deep learning. Leaf has one of the simplest APIs, is lean and tries to introduce minimal technical debt to your stack.

tf-faster-rcnn - Tensorflow Faster RCNN for Object Detection

  •    Python

For a good and more up-to-date implementation for faster/mask RCNN with multi-gpu support, please see the example in TensorPack here. A Tensorflow implementation of faster RCNN detection framework by Xinlei Chen (xinleic@cs.cmu.edu). This repository is based on the python Caffe implementation of faster RCNN available here.

fb-caffe-exts - Some handy utility libraries and tools for the Caffe deep learning framework.

  •    C++

fb-caffe-exts is a collection of extensions developed at FB while using Caffe in (mainly) production scenarios.A simple C++ library that wraps the common pattern of running a caffe::Net in multiple threads while sharing weights. It also provides a slightly more convenient usage API for the inference case.

caffe-tutorial - DIY Deep Learning for Vision: a Hands-On Tutorial with Caffe

  •    Shell

This site holds the materials for the ECCV '14 on deep learning for vision with Caffe. Everything has been merged to Caffe master as of the rc release, so refer to the latest BVLC/caffe.

caffe - This fork of BVLC/Caffe is dedicated to improving performance of this deep learning framework when running on CPU, in particular Intel® Xeon processors (HSW+) and Intel® Xeon Phi processors

  •    C++

Build procedure is the same as on bvlc-caffe-master branch, see section "Caffe". Both Make and CMake can be used. When OpenMP is available will be used automatically. Run procedure is the same as on bvlc-caffe-master branch.

Frank - Automated acceptance tests for native iOS apps

  •    Objective-C

The official repository for Frank has moved to TestingWithFrank/Frank. Please submit all pull requests to the new location. The frank-cucumber gem will continue to work.

caffe-tensorflow - Caffe models in TensorFlow

  •    Python

Convert Caffe models to TensorFlow. Run convert.py to convert an existing Caffe model to TensorFlow.

pytorch-caffe-darknet-convert - convert between pytorch, caffe prototxt/weights and darknet cfg/weights

  •    Python

This repository is specially designed for pytorch-yolo2 to convert pytorch trained model to any platform. It can also be used as a common model converter between pytorch, caffe and darknet. MIT License (see LICENSE file).

dqn-in-the-caffe - An implementation of Deep Q-Network using Caffe

  •    C++

DQN-in-the-Caffe is an implementation of Deep Q-Network using Caffe. See http://www.cs.toronto.edu/~vmnih/docs/dqn.pdf for the details of DQN.

cnn-models - ImageNet pre-trained models with batch normalization for the Caffe framework

  •    Python

This repository contains convolutional neural network (CNN) models trained on ImageNet by Marcel Simon at the Computer Vision Group Jena (CVGJ) using the Caffe framework as published in the accompanying technical report. Each model is in a separate subfolder and contains everything needed to reproduce the results. This repository focuses currently contains the batch-normalization-variants of AlexNet and VGG19 as well as the training code for Residual Networks (Resnet). No mean subtraction is required for the pre-trained models! We have a batch-normalization layer which basically does the same.

nlpcaffe - natural language processing with Caffe

  •    C++

NLP-Caffe is a pull request [1] on the Caffe framework developed by Yangqing Jia and Evan Shelhamer, among other members of the BVLC lab at Berkeley and a large number of independent online contributers. This fork makes it easier for NLP users to get started without merging C++ code. The current example constructs a language model for a small subset of Google's Billion Word corpus. It uses a two-layer LSTM architecture that processes in excess of 15,000 words per second [2], and achieves a perplexity of 79. More examples for Machine Translation using the encoder-decoder model and character-level RNNs are in the works. This code will eventually be merged into the Caffe master branch. This work was funded by the Stanford NLP Group, under the guidance of Chris Manning, and with the invaluable expertise of Thang Luong.