mtcnn-caffe - Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks

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Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks. This project provide you a method to update multi-task-loss for multi-input source.



Related Projects

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 ( 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.

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.

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

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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.

caffe-tensorflow - Caffe models in TensorFlow

  •    Python

Convert Caffe models to TensorFlow. Run 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

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DQN-in-the-Caffe is an implementation of Deep Q-Network using Caffe. See 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

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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.

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

  •    Matlab

This is an implementation of "Accurate Image Super-Resolution Using Very Deep Convolutional Networks" (CVPR 2016 Oral Paper) in caffe. VDSR (Very Deep network for Super-Resolution) is an end-to-end network with 20 convolutional layers for single image super-resolution. The performance of VDSR is better than other state-of-the-art SISR methods, such as SRCNN, A+ and CSCN (My implementation of CSCN).

loadcaffe - Load Caffe networks in Torch7

  •    Protocol

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

colorization - Automatic colorization using deep neural networks

  •    Jupyter

Richard Zhang, Phillip Isola, Alexei A. Efros. In ECCV, 2016. This code requires a working installation of Caffe and basic Python libraries (numpy, pyplot, skimage, scipy). For guidelines and help with installation of Caffe, consult the installation guide and Caffe users group.

DenseNet-Caffe - DenseNet Caffe Models, converted from


We manually converted the original torch models into caffe format from Update (July 27, 2017): for your convenience, we also provide a link to these models on Baidu Disk.

Android-Object-Detection - :coffee: Fast-RCNN and Scene Recognition using Caffe

  •    Java

Get the Caffe model and push it to Phone SDCard. For object detection, network(*.prototxt) should use ROILayer, you can refer to Fast-RCNN. For scene recognition(object recognition), it can use any caffe network and weight with memory input layer. Scene recognition - Convolutional neural networks trained on Places Input a picture of a place or scene and predicts it.


  •    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).

XLearning - AI on Hadoop

  •    Java

XLearning is a convenient and efficient scheduling platform combined with the big data and artificial intelligence, support for a variety of machine learning, deep learning frameworks. XLearning is running on the Hadoop Yarn and has integrated deep learning frameworks such as TensorFlow, MXNet, Caffe, Theano, PyTorch, Keras, XGBoost. XLearning has the satisfactory scalability and compatibility.Besides the distributed mode of TensorFlow and MXNet frameworks, XLearning supports the standalone mode of all deep learning frameworks such as Caffe, Theano, PyTorch. Moreover, XLearning allows the custom versions and multi-version of frameworks flexibly.

deepdraw - Notebook example of how to generate class visualizations with Caffe

  •    Jupyter

DeepDraw is a ipython notebook example of generating class visualizations, such as the one above, from deep neural networks using Caffe. The examples and settings in this notebook was based on the pretrained GoogLeNet model available with Caffe, but it's easy to modify to use other networks, such as AlexNet. For some more detailed information about how these class visualizations are generated, check out this blogpost, and for some more examples of generated images, see this album of highlights or this album with all 1000 imagenet classes. The repository also includes some code examples of drawing with the class visualizations, as described in this blogpost, in the folder "/other".

pynetbuilder - pyNetBuilder is a modular pytonic interface with builtin modules for generating popular caffe prototxt network file definitions

  •    Python

pyNetBuilder is a modular pytonic interface with builtin modules for generating popular caffe networks. A neural network is a Directed acyclic graph (DAG) of layers. The caffe layers and the network is represented using prototxt format. As we go deeper, and add more layers or build more complex DAG's using basic layers, writing the prototxt files becomes tedious. This tool aims to provide a pytonic interface to generate prototxt files.

caffe-fast-rcnn - Caffe fork that supports Fast R-CNN

  •    C++

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors. and step-by-step examples.