In my previous post, " Text Analysis with IBM Cloud Code Engine" you learned how to create an IBM Cloud™ Code Engine project, select the project and deploy Code Engine entities - applications and jobs to the project. You also learned how to bind IBM Cloud services (e.g., IBM Cloud Object Storage and Natural Language Understanding) to your Code Engine entities to analyze your text files uploaded to Cloud Object Storage.
In this post, you will deploy an image classification application, upload images to IBM Cloud Object Storage and then classify the uploaded images using a pre-defined MobileNet Tensorflow.js model without any training. The images are classified with labels from the ImageNet database.
On your machine, launch a terminal or command prompt and run the below commands to clone the GitHub repository and then move it to the cloned repo folder:
git clone https://github.com/VidyasagarMSC/image-classification-code-engine
cd image-classification-code-engine
Before building and pushing your container images, plan your image registry:
<DOCKER_ACCOUNT_NAME>
with your own Docker account name:./deploy.sh <DOCKER_ACCOUNT_NAME>
vidyasagarmsc/*
. For example: docker pull vidyasagarmsc/frontend
.Follow the steps in the solution tutorial and use this code sample to learn about IBM Cloud Code Engine by deploying an image classification application.
Use the container images built from this code sample. Replace ibmcom/*
with <ACCOUNT_NAME>/*
.
Instead of uploading a text file, upload an image (.jpeg, .png) to COS. For sample images, check the images folder in this repo.
#tensorflow #ibm cloud #image classification