IBM Developer Model Asset Exchange: Image Caption Generator

This repository contains code to instantiate and deploy an image caption generation model. This model generates captions from a fixed vocabulary that describe the contents of images in the COCO Dataset. The model consists of an encoder model - a deep convolutional net using the Inception-v3 architecture trained on ImageNet-2012 data - and a decoder model - an LSTM network that is trained conditioned on the encoding from the image encoder model. The input to the model is an image, and the output is a sentence describing the image content.

The model is based on the Show and Tell Image Caption Generator Model. The checkpoint files are hosted on IBM Cloud Object Storage. The code in this repository deploys the model as a web service in a Docker container. This repository was developed as part of the IBM Code Model Asset Exchange.

Model Metadata

Domain	Application	Industry	Framework	Training Data	Input Data Format
Vision	Image Caption Generator	General	TensorFlow	COCO	Images

References

O. Vinyals, A. Toshev, S. Bengio, D. Erhan., "Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge", IEEE transactions on Pattern Analysis and Machine Intelligence, 2017.
im2txt TensorFlow Model GitHub Page
COCO Dataset Project Page

Licenses

Component	License	Link
This repository	Apache 2.0	LICENSE
Model Weights	MIT	Pretrained Show and Tell Model
Model Code (3rd party)	Apache 2.0	im2txt
Test assets	Various	Sample README

Pre-requisites:

docker: The Docker command-line interface. Follow the installation instructions for your system.
The minimum recommended resources for this model is 2GB Memory and 2 CPUs.

Deployment options

Deploy from Docker Hub
Deploy on Red Hat OpenShift
Deploy on Kubernetes
Run Locally

Deploy from Docker Hub

To run the docker image, which automatically starts the model serving API, run:

$ docker run -it -p 5000:5000 codait/max-image-caption-generator

This will pull a pre-built image from Docker Hub (or use an existing image if already cached locally) and run it. If you'd rather checkout and build the model locally you can follow the run locally steps below.

Deploy on Red Hat OpenShift

You can deploy the model-serving microservice on Red Hat OpenShift by following the instructions for the OpenShift web console or the OpenShift Container Platform CLI in this tutorial, specifying codait/max-image-caption-generator as the image name.

Deploy on Kubernetes

You can also deploy the model on Kubernetes using the latest docker image on Docker Hub.

On your Kubernetes cluster, run the following commands:

$ kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Image-Caption-Generator/master/max-image-caption-generator.yaml

The model will be available internally at port 5000, but can also be accessed externally through the NodePort.

A more elaborate tutorial on how to deploy this MAX model to production on IBM Cloud can be found here.

1. Build the Model

Clone this repository locally. In a terminal, run the following command:

$ git clone https://github.com/IBM/MAX-Image-Caption-Generator.git

Change directory into the repository base folder:

$ cd MAX-Image-Caption-Generator

To build the docker image locally, run:

$ docker build -t max-image-caption-generator .

All required model assets will be downloaded during the build process. Note that currently this docker image is CPU only (we will add support for GPU images later).

2. Deploy the Model

To run the docker image, which automatically starts the model serving API, run:

$ docker run -it -p 5000:5000 max-image-caption-generator

3. Use the Model

The API server automatically generates an interactive Swagger documentation page. Go to http://localhost:5000 to load it. From there you can explore the API and also create test requests.

Use the model/predict endpoint to load a test file and get captions for the image from the API.

You can also test it on the command line, for example:

$ curl -F "image=@samples/surfing.jpg" -X POST http://localhost:5000/model/predict

{
  "status": "ok",
  "predictions": [
    {
      "index": "0",
      "caption": "a man riding a wave on top of a surfboard .",
      "probability": 0.038827644239537
    },
    {
      "index": "1",
      "caption": "a person riding a surf board on a wave",
      "probability": 0.017933410519265
    },
    {
      "index": "2",
      "caption": "a man riding a wave on a surfboard in the ocean .",
      "probability": 0.0056628732021868
    }
  ]
}

4. Development

To run the Flask API app in debug mode, edit config.py to set DEBUG = True under the application settings. You will then need to rebuild the docker image (see step 1).

5. Cleanup

To stop the Docker container, type CTRL + C in your terminal.

Resources and Contributions

If you are interested in contributing to the Model Asset Exchange project or have any queries, please follow the instructions here.