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XLearning - AI on Hadoop
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 yarndata-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines
IPython Notebook(s) demonstrating deep learning functionality.IPython Notebook(s) demonstrating scikit-learn functionality.
machine-learning deep-learning data-science big-data aws tensorflow theano caffe scikit-learn kaggle spark mapreduce hadoop matplotlib pandas numpy scipy kerashave-fun-with-machine-learning - An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks
Also available in Chinese (Traditional). This is a hands-on guide to machine learning for programmers with no background in AI. Using a neural network doesn’t require a PhD, and you don’t need to be the person who makes the next breakthrough in AI in order to use what exists today. What we have now is already breathtaking, and highly usable. I believe that more of us need to play with this stuff like we would any other open source technology, instead of treating it like a research topic.
neural-network image-classification machine-learning caffe tutorialDIGITS - Deep Learning GPU Training System
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.
deep-learning machine-learning gpu caffe torchsphereface - Implementation for <SphereFace: Deep Hypersphere Embedding for Face Recognition> in CVPR'17
SphereFace is released under the MIT License (refer to the LICENSE file for details). 2018.8.14: We recommand an interesting ECCV 2018 paper that comprehensively evaluates SphereFace (A-Softmax) on current widely used face datasets and their proposed noise-controlled IMDb-Face dataset. Interested users can try to train SphereFace on their IMDb-Face dataset. Take a look here.
face-recognition caffe sphereface cvpr-2017 face-detection angular-softmax deep-learningSENet - Squeeze-and-Excitation Networks
By Jie Hu[1], Li Shen[2], Gang Sun[1]. Momenta[1] and University of Oxford[2].
senet caffe gpupolyaxon - An open source platform for reproducible machine learning and deep learning on kubernetes
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 k8sface_verification_experiment
The Deep Face Representation Experiment is based on Convolution Neural Network to learn a robust feature for face verification task. The popular deep learning framework caffe is used for training on face datasets such as CASIA-WebFace, VGG-Face and MS-Celeb-1M. And the feature extraction is realized by python code caffe_ftr.py. The single convolution net testing is evaluated on unsupervised setting only computing cosine similarity for lfw pairs.
face-recognition caffedeep-learning-model-convertor - The convertor/conversion of deep learning models for different deep learning frameworks/softwares
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 caffe2openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, and hands estimation
OpenPose represents the first real-time multi-person system to jointly detect human body, hand, and facial keypoints (in total 135 keypoints) on single images. For further details, check all released features and release notes.
openpose computer-vision machine-learning multi-threading cpp cpp11 caffe opencv human-pose-estimation real-time deep-learning human-behavior-understanding cvpr-2017interactive-deep-colorization - Deep learning software for colorizing black and white images with a few clicks
We first describe the system (0) Prerequisities and steps for (1) Getting started. We then describe the interactive colorization demo (2) Interactive Colorization (Local Hints Network). There are two demos: (a) a "barebones" version in iPython notebook and (b) the full GUI we used in our paper. We then provide an example of the (3) Global Hints Network. We provide a "barebones" demo in iPython notebook, which does not require QT. We also provide our full GUI demo.
colorization automatic-colorization deep-learning deep-learning-algorithms computer-vision caffe interactiveMobileNet-Caffe - Caffe Implementation of Google's MobileNets (v1 and v2)
We provide pretrained MobileNet models on ImageNet, which achieve slightly better accuracy rates than the original ones reported in the paper.
mobilenet caffe imagenet mobilenetv2 mobilnet-v2colorization - Automatic colorization using deep neural networks
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.
caffe colorization automatic-colorization deep-learning deep-learning-algorithms computer-visiondeepo - A series of Docker images (and their generator) that allows you to quickly set up your deep learning research environment
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 onnxRealtime_Multi-Person_Pose_Estimation - Code repo for realtime multi-person pose estimation in CVPR'17 (Oral)
By Zhe Cao, Tomas Simon, Shih-En Wei, Yaser Sheikh. Code repo for winning 2016 MSCOCO Keypoints Challenge, 2016 ECCV Best Demo Award, and 2017 CVPR Oral paper.
human-pose-estimation realtime caffe human-behavior-understanding deep-learning computer-vision matlab cpp11 cvpr-2017Awesome-CoreML-Models - Largest list of models for Core ML (for iOS 11+)
We've put up the largest collection of machine learning models in Core ML format, to help iOS, macOS, tvOS, and watchOS developers experiment with machine learning techniques. We've created a site with better visualization of the models CoreML.Store, and are working on more advance features. If you've converted a Core ML model, feel free to submit an issue.
coreml coreml-model apple machine-learning curated-list coreml-framework coreml-models coremltools awesome-list models model download awesome core-ml ml caffe caffemodel tensorflow-models ios ios11FeatherCNN - FeatherCNN is a high performance inference engine for convolutional neural networks.
FeatherCNN, developed by Tencent TEG AI Platform, is a high-performance lightweight CNN inference library. FeatherCNN is currently targeting at ARM CPUs, and is capable to extend to other devices in the future. Highly Performant FeatherCNN delivers state-of-the-art inference computing performance on a wide range of devices, including mobile phones (iOS/Android), embedded devices (Linux) as well as ARM-based servers (Linux).
convolutional-neural-networks inference-engine caffe android ios arm-neoncaffenet-benchmark - Evaluation of the CNN design choices performance on ImageNet-2012.
Welcome to evaluation of CNN design choises performance on ImageNet-2012. Here you can find prototxt's of tested nets and full train logs. **upd2.: Some of the pretrained models are in Releases section. They are licensed for unrestricted use.
convolutional-neural-networks convolutional-networks batch-size caffenet lr-policy architecture relu caffe benchmark activations dataset imagenetMMdnn - MMdnn is a set of tools to help users inter-operate among different deep learning frameworks
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 coremlpytorch-caffe-darknet-convert - convert between pytorch, caffe prototxt/weights and darknet cfg/weights
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
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