Description

Run image classification engine on Apache PredictionIO using a Tensorflow model.

This template

• follows the workflow in official Tensorflow tutorial for image classification.
• uses a pre-trained model from the inception challenge. The purpose of the engine is to be able to deploy a Tensorflow model and do inference via HTTP.
• uses Tensorflow Java API

Version 1.0 Changes

• uses Tensorflow Java API

Workflow

Downloading the model

Run data/download.sh to download the pre-trained imagenet model.

Testing the engine

There are two ways of serving data to the engine.

1. Put the target image in data/images. You can change this path in engine.json. Then use image param as the filename of the target image such as curl -H "Content-Type: application/json" -d '{ "image":"cropped_panda.jpg" }' http://localhost:8000/queries.json.

2. Use data param to send a UTF-8 encoded string of the target image data.

If all goes well, the engine will return a JSON result such as {"predictions":[{"categories":"giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca","confidence":0.8910737037658691},{"categories":"indri, indris, Indri indri, Indri brevicaudatus","confidence":0.007790538016706705},{"categories":"lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens","confidence":0.002959122648462653},{"categories":"custard apple","confidence":0.0014657712308689952},{"categories":"earthstar","confidence":0.0011742385104298592}]}