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IBM Code Model Asset Exchange: Show and Tell Image Caption Generator
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api Update MAX model to use MAX Framework (#24) Feb 7, 2019
core Update MAX model to use MAX Framework (#24) Feb 7, 2019
docs Add Swagger UI screenshot to README (#10) Aug 3, 2018
.dockerignore added basic intergration tests and switched to max base image (#11) Aug 13, 2018
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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



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 Asset README


  • 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.


  1. Deploy from Docker Hub
  2. Deploy on Kubernetes
  3. 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 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

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

Run Locally

  1. Build the Model
  2. Deploy the Model
  3. Use the Model
  4. Development
  5. Cleanup

1. Build the Model

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

$ git clone

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=@assets/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 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.


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