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

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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).

caffe darknet yolo yolo2 convert pytorch weight

XLearning - AI on Hadoop

•    Java

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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.

hadoop tensorflow caffe mxnet yarn

NetDissect - Network Dissection http://netdissect

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This repository contains the demo code for the CVPR'17 paper Network Dissection: Quantifying Interpretability of Deep Visual Representations. You can use this code with naive Caffe, with matcaffe and pycaffe compiled. We also provide a PyTorch wrapper to apply NetDissect to probe networks in PyTorch format. There are dissection results for several networks at the project page. Code to run network dissection on an arbitrary deep convolutional neural network provided as a Caffe deploy.prototxt and .caffemodel. The script rundissect.sh runs all the needed phases.

pytorch-faster-rcnn - 0.4 updated. Support cpu test and demo.

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The main differences between new and old master branch are in this two commits: 9d4c24e, c899ce7 The change is related to this issue; master now matches all the details in tf-faster-rcnn so that we can now convert pretrained tf model to pytorch model. A pytorch implementation of faster RCNN detection framework based on Xinlei Chen's tf-faster-rcnn. Xinlei Chen's repository is based on the python Caffe implementation of faster RCNN available here.

pytorch-yolo-v3 - A PyTorch implementation of the YOLO v3 object detection algorithm

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[UPDATE] : This repo serves as a driver code for my research. I just graduated college, and am very busy looking for research internship / fellowship roles before eventually applying for a masters. I won't have the time to look into issues for the time being. Thank you. This repository contains code for a object detector based on YOLOv3: An Incremental Improvement, implementedin PyTorch. The code is based on the official code of YOLO v3, as well as a PyTorch port of the original code, by marvis. One of the goals of this code is to improve upon the original port by removing redundant parts of the code (The official code is basically a fully blown deep learning library, and includes stuff like sequence models, which are not used in YOLO). I've also tried to keep the code minimal, and document it as well as I can.

yolov3 yolo object-detection pytorch

polyaxon - An open source platform for reproducible machine learning and deep learning on kubernetes

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Welcome to Polyaxon, a platform for building, training, and monitoring large scale deep learning applications. Polyaxon deploys into any data center, cloud provider, or can be hosted and managed by Polyaxon, and it supports all the major deep learning frameworks such as Tensorflow, MXNet, Caffe, Torch, etc.

deep-learning machine-learning artificial-intelligence data-science reinforcement-learning kubernetes tensorflow pytorch keras mxnet caffe ai dl ml k8s

LightCNN - A Light CNN for Deep Face Representation with Noisy Labels, TIFS 2018

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A pytorch implementation of A Light CNN for Deep Face Representation with Noisy Labels from the paper by Xiang Wu, Ran He, Zhenan Sun and Tieniu Tan. The official and original Caffe code can be found here. Download face dataset such as CASIA-WebFace, VGG-Face and MS-Celeb-1M.

pytorch face-recognition

MMdnn - MMdnn is a set of tools to help users inter-operate among different deep learning frameworks

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A comprehensive, cross-framework solution to convert, visualize and diagnosis deep neural network models. The "MM" in MMdnn stands for model management and "dnn" is an acronym for deep neural network.Across the industry and academia, there are a number of existing frameworks available for developers and researchers to design a model, where each framework has its own network structure definition and saving model format. The gaps between frameworks impede the inter-operation of the models.

cntk visualization tensorflow model-converter pytorch caffe keras mxnet coreml

deep-learning-model-convertor - The convertor/conversion of deep learning models for different deep learning frameworks/softwares

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Note: This is not one convertor for all frameworks, but a collection of different converters. Because github is an open source platform, I hope we can help each other here, gather everyone's strength. The sheet below is a overview of all convertors in github (not only contain official provided and more are user-self implementations). I just make a little work to collect these convertors. Also, hope everyone can support this project to help more people who're also crazy because of various frameworks.

deep-learning model neural-network convertor model-convertor awesome-list deep-learning-framework tensorflow caffe pytorch mxnet keras torch caffe2

NNPACK - Acceleration package for neural networks on multi-core CPUs

•    C

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NNPACK is an acceleration package for neural network computations. NNPACK aims to provide high-performance implementations of convnet layers for multi-core CPUs. NNPACK is not intended to be directly used by machine learning researchers; instead it provides low-level performance primitives leveraged in leading deep learning frameworks, such as PyTorch, Caffe2, MXNet, tiny-dnn, Caffe, Torch, and Darknet.

neural-network neural-networks convolutional-layers inference high-performance high-performance-computing simd cpu multithreading fast-fourier-transform winograd-transform matrix-multiplication

test-tube - Python library to easily log, track machine learning code, experiments and parallelize hyperparameter search

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Test tube is a python library to track and parallelize hyperparameter search for Deep Learning and ML experiments. It's framework agnostic and built on top of the python argparse API for ease of use. If you're a researcher, test-tube is highly encouraged as a way to post your paper's training logs to help add transparency and show others what you've tried that didn't work.

deep-learning machine-learning tensorflow hyperparameter-optimization neural-networks data-science keras pytorch caffe2 caffe chainer grid-search random-search

deepo - A series of Docker images (and their generator) that allows you to quickly set up your deep learning research environment

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If you want to share your data and configurations between the host (your machine or VM) and the container in which you are using Deepo, use the -v option, e.g. This will make /host/data from the host visible as /data in the container, and /host/config as /config. Such isolation reduces the chances of your containerized experiments overwriting or using wrong data.

deep-learning jupyter lasagne caffe tensorflow sonnet keras theano chainer torch pytorch mxnet cntk dockerfile-generator docker-image caffe2 onnx

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++

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###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.

Caffe-HRT - Heterogeneous Run Time version of Caffe

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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.

arm arm-compute-library caffe arm-neon arm-gpu machine-learning artificial-intelligence dnn cnn

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

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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

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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

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

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 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

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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.

cnn-model resnet imagenet alexnet batch-normalization caffe-framework vgg16 vgg19 vggnet vgg resnet-10 resnet-50 resnet-preact ilsvrc pretrained-models pre-trained fine-tune fine-tuning-cnns very-deep-cnn caffe