Displaying 1 to 13 from 13 results

visual-recognition-coreml - Classify images offline using Watson Visual Recognition and Core ML

  •    Swift

Read this in other languages: 中国, 日本. Classify images with Watson Visual Recognition and Core ML. The images are classified offline using a deep neural network that is trained by Visual Recognition.

watson-calorie-counter - A mobile app that uses Watson Visual Recognition to provide nutritional analysis of captured food images

  •    Java

In this Code Pattern, we will create a calorie counter mobile app using Apache Cordova, Node.js and Watson Visual Recognition. This mobile app extracts nutritional information from captured images of food items. Currently this mobile app only runs on Android, but can be easily ported to iOS.

watson-waste-sorter - Create an iOS phone application that sorts waste into three categories (landfill, recycling, compost) using a Watson Visual Recognition custom classifier

  •    Swift

In this developer code pattern, we will create a mobile app, Python Server with Flask, and Watson Visual Recognition. This mobile app sends pictures of waste and garbage to be analyzed by a server app, using Watson Visual Recognition. The server application will use pictures of common trash to train Watson Visual Recognition to identify various categories of waste, e.g. recycle, compost, or landfill. A developer can leverage this to create their own custom Visual Recognition classifiers for their use cases. Create an IBM Cloud account and install the Cloud Foundry CLI on your machine.

snca.pytorch - Improving Generalization via Scalable Neighborhood Component Analysis

  •    Python

This repo constains the pytorch implementation for the ECCV 2018 paper (paper). We use deep networks to learn feature representations optimized for nearest neighbor classifiers, which could generalize better for new object categories. This project is a re-investigation of Neighborhood Component Analysis (NCA) with recent technologies to make it scalable to deep networks and large-scale datasets. Much of code is extended from the previous unsupervised learning project. Please refer to this repo for more details.

image-analysis-iot-alert - Using IBM Cloud Functions to process unstructured data for the Watson IoT Platform

  •    Javascript

Skill Level: Any Skill Level N.B: All services used in this repo are Lite plans. Build an IoT project with IBM Cloud Functions (serverless), Node-RED, Node.js and along with IoT Platform.

blue-cloud-mirror - Blue Cloud Mirror - IBM Cloud Technology Showcase

  •    Vue

This project contains a game where players need to show five specific emotions and do five specific poses in two levels. The fastest player wins. The game uses various key cloud technologies to demonstrate the value of a diverse, interconnected system, with both public and private cloud environments. Play the Game.

watson-deep-learning-tensorflow-lite - Deploying Watson Deep Learning Models to Edge Devices

  •    Python

This project includes sample code how to train a model with TensorFlow and the Deep Learning service within Watson Studio and how to deploy and access the model on iOS devices. Check out the video for a quick demo.

visual-recognition-ios - An iOS application written in Swift that uses Watson Visual Recognition

  •    Swift

In this code pattern, you will create an iOS app that showcases computer vision by labeling what the device's camera sees. You will provision a Visual Recognition service where you can either leverage a demo model or train your own custom model. As an alternative to steps 1 & 2 below, you can create this project as a starter kit on IBM Cloud, which automatically provisions required services, and injects service credentials into a custom fork of this pattern. Then, you can skip directly to step 3 below.

ibm-cloud-functions-refarch-serverless-image-recognition - Classify images as soon as you upload them in a database with serverless functions

  •    Javascript

The application demonstrates an IBM Cloud Functions (based on Apache OpenWhisk) that gets an image from the Cloudant database and classifies it through Watson Visual Recognition. The use case demonstrates how actions work with data services and execute logic in response to Cloudant events. One function, or action, is triggered by changes (in this use case, an upload of a document) in a Cloudant database. These documents are piped to another action that submits the image to Watson Visual recognition and upload a new document in Cloudant with the classifiers produced by Watson.

IoTWatsonTrainingandPredictionApp - Speeding up the VR Identification process using the IoT Platform

  •    Java

Watson Visual Recognition Image training app is standalone java based application which can be run independently on device which has valid jre installed such as MacBook or any linux/Windows based operating system. a. Train VR classifier to detect an object in an image. b. Recognize speech using Speech To Text(EN) service of Watson. c. User friendly interaction by playing WAV stream received by calling Text to Speech. d. Store Images on cloud using IBM Cloud Cloudant DB service. e. Leverage the IBM Cloud IoT platform.

Visual-Recognition-Tile-Localization - Node

  •    Javascript

The Visual-Recognition-Tile-Localization application leverages the Watson Visual Recognition service with image pre-processing techniques to deliver localized image classification. For example, "show me where there is rust on the bridge". The user drags & drops an image onto the applicaiton within the browser, and the image is uploaded to the Node.js application. Once uploaded, the image is "chopped up" into smaller images (tiles) and each individual tile is analyzed by the Watson Visual Recognition service. Once complete, all results are visualized within the browser in a heatmap-like visualization, where colorization is based on the confidence scores being returned by the Visual Recognition service's custom classifier.

G-SimCLR - This is the code base for paper "G-SimCLR : Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling" by Souradip Chakraborty, Aritra Roy Gosthipaty and Sayak Paul

  •    Jupyter

Official TensorFlow implementation of G-SimCLR (Guided-SimCLR), as described in the paper G-SimCLR: Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling by Souradip Chakraborty*, Aritra Roy Gosthipaty* and Sayak Paul*. *Equal contribution.

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