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iOS-10-Sampler - Code examples for new APIs of iOS 10.
Code examples for new APIs of iOS 10. Just build with Xcode 8.
ios ios10 swift-3 swift-4 speech metal cnn image-recognition convolutional-neural-networks demo metal-performance-shaders metal-cnn uiviewpropertyanimatorfastai_deeplearn_part1 - Notes for Fastai Deep Learning Course
Notes for Fastai Deep Learning Course
deep-learning python3 pytorch image-recognitionjetson-inference - Guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson
Welcome to our training guide for inference and deep vision runtime library for NVIDIA DIGITS and Jetson Xavier/TX1/TX2. This repo uses NVIDIA TensorRT for efficiently deploying neural networks onto the embedded platform, improving performance and power efficiency using graph optimizations, kernel fusion, and half-precision FP16 on the Jetson.
deep-learning inference computer-vision embedded image-recognition object-detection segmentation jetson jetson-tx1 jetson-tx2sod - An Embedded Computer Vision & Machine Learning Library (CPU Optimized & IoT Capable)
SOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices. SOD was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well commercial products.
computer-vision library deep-learning image-processing object-detection cpu real-time convolutional-neural-networks recurrent-neural-networks face-detection facial-landmarks machine-learning-algorithms image-recognition image-analysis vision-framework embedded detection iot-device iotImageAI - A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities
A python library built to empower developers to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code. Built with simplicity in mind, ImageAI supports a list of state-of-the-art Machine Learning algorithms for image prediction, custom image prediction, object detection, video detection, video object tracking and image predictions trainings. ImageAI currently supports image prediction and training using 4 different Machine Learning algorithms trained on the ImageNet-1000 dataset. ImageAI also supports object detection, video detection and object tracking using RetinaNet, YOLOv3 and TinyYOLOv3 trained on COCO dataset. Eventually, ImageAI will provide support for a wider and more specialized aspects of Computer Vision including and not limited to image recognition in special environments and special fields.
artificial-intelligence machine-learning prediction image-prediction python3 offline-capable imageai artificial-neural-networks algorithm image-recognition object-detection squeezenet densenet video inceptionv3 detection gpu ai-practice-recommendationsKur - Descriptive Deep Learning
Kur is a system for quickly building and applying state-of-the-art deep learning models to new and exciting problems. Kur was designed to appeal to the entire machine learning community, from novices to veterans. It uses specification files that are simple to read and author, meaning that you can get started building sophisticated models without ever needing to code. Even so, Kur exposes a friendly and extensible API to support advanced deep learning architectures or workflows.
deep-learning deep-neural-networks speech-recognition deep-learning-tutorial machine-learning neural-networks neural-network image-recognition speech-to-textchannel-pruning - Channel Pruning for Accelerating Very Deep Neural Networks
Please have a look at AMC: AutoML for Model Compression and Acceleration on Mobile Devices ECCV'18, which combines channel pruning and reinforcement learning to further accelerate CNN.
image-recognition model-compression acceleration object-detection image-classification channel-pruning deep-neural-networkswhat_the_thing - :camera: Point camera at things to learn how to say them in a different language
Point camera at things to learn how to say them in a different language.Native Android App built with React Native.
image-recognition translation react-native android clarifai thing cameraOpenALPR - Automatic License Plate Recognition library
OpenALPR is an open source Automatic License Plate Recognition library written in C++ with bindings in C#, Java, Node.js, and Python. The library analyzes images and video streams to identify license plates. The output is the text representation of any license plate characters.
image-recognition license-plate carLargeMargin_Softmax_Loss - Implementation for <Large-Margin Softmax Loss for Convolutional Neural Networks> in ICML'16
We introduce a large-margin softmax (L-Softmax) loss for convolutional neural networks. L-Softmax loss can greatly improve the generalization ability of CNNs, so it is very suitable for general classification, feature embedding and biometrics (e.g. face) verification. We give the 2D feature visualization on MNIST to illustrate our L-Softmax loss. The paper is published in ICML 2016 and also available at arXiv.
l-softmax icml-2016 lsoftmax-loss caffe face-recognition image-recognition deep-learningsaliency - TensorFlow implementation for SmoothGrad, Grad-CAM, Guided backprop, Integrated Gradients and other saliency techniques
If the sign of the value given by the saliency mask is not important, then use VisualizeImageGrayscale, otherwise use VisualizeImageDiverging. See the SmoothGrad paper for more details on which visualization method to use. This example iPython notebook shows these techniques is a good starting place.
machine-learning deep-learning deep-neural-networks tensorflow convolutional-neural-networks saliency-map object-detection image-recognitionfashion - The Fashion-MNIST dataset and machine learning models.
Training AI machine learning models on the Fashion MNIST dataset. Fashion-MNIST is a dataset consisting of 70,000 images (60k training and 10k test) of clothing objects, such as shirts, pants, shoes, and more. Each example is a 28x28 grayscale image, associated with a label from 10 classes. The 10 classes are listed below.
mnist fashion dataset fashion-mnist machine-learning artificial-intelligence artificial-neural-networks support-vector-machines svm xgboost data-science r supervised-learning classification image-recognition image-classificationscanaerial - Fuzzy Select plugin for JOSM
Fuzzy Select plugin for JOSM
openstreetmap satellite-imagery image-recognitioneinstein-vision-node - Example image recognition web app using Einstein Vision Heroku Add-on
This Node.js sample app lets you upload an image to get predictions from Salesforce Einstein Vision general classifier using the Add-on. When deploying this app, a new Einstein Vision add-on will be created which includes an Einstein Vision account.
salesforce-einstein nodejs create-react-app heroku image-recognition deep-learning node express salesforce metamind reactcordova-plugin-tensorflow - On-device image recognition via TensorFlow/Inception
The plugin provides a TensorFlow class that can be used to initialize graphs and run the inference algorithm. To use a custom model, follow the steps to retrain the model and optimize it for mobile use. Put the .pb and .txt files in a HTTP-accessible zip file, which will be downloaded via the FileTransfer plugin. If you use the generic Inception model it will be downloaded from the TensorFlow website on first use.
cordova phonegap tensorflow inception image-recognition neural-network machine-learning ai inference classification imagerecognition neuralnetworks machinelearning ecosystem:cordova cordova-androidrostensorflow - TensorFlow ImageNet demo using ROS sensor_msgs/Image
Please read official guide. This is a only note for me.
tensorlow ros image-recognition image-classificationpowerai-transfer-learning - Image recognition training with TensorFlow Inception and transfer learning
Read this in other languages: 한국어. The pre-trained Inception-v3 model achieves state-of-the-art accuracy for recognizing general objects with 1000 classes. The model extracts general features from input images in the first part and classifies them based on those features in the second part. We will use this pre-trained model and re-train it it to classify houses with or without swimming pools.
jupyter-notebook nimbix-cloud-platform ibm nimbix cloud tensorflow inception image-recognition powerai ibmcodeSphereNet - Implementation for <Deep Hyperspherical Learning> in NIPS'17.
SphereNet is released under the MIT License (refer to the LICENSE file for details). The repository contains an example Tensorflow implementation for SphereNets. SphereNets are introduced in the NIPS 2017 paper "Deep Hyperspherical Learning" (arXiv). SphereNets are able to converge faster and more stably than its CNN counterparts, while yielding to comparable or even better classification accuracy.
hypersphereical-learning neural-network image-recognition nips-2017 deep-learningYSDA_deeplearning17 - Yandex SDA classes on deep learning. Version of year 2017
Yandex SDA classes on deep learning. Version of year 2017
seminar homework deep-learning neural-network image-recognitionvisualsearch - Visual Search is a little app to find and cluster similar images using Tagbox
Checking similarity bewteen images is done using Tagbox. Make sure you have it running on http://localhost:8080. You will need MB_KEY to run it visit https://machinebox.io to get it. Once you have Tagbox running you can do.
image-recognition similarity-search machinebox
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