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

IBM Code Model Asset Exchange: Show and Tell Image Caption Generator
https://github.com/IBM/MAX-Image-Caption-Generator

coco-dataset docker-image machine-learning machine-lerning-models

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IBM Code Model Asset Exchange: Show and Tell Image Caption Generator

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[![Build Status](https://travis-ci.org/IBM/MAX-Image-Caption-Generator.svg?branch=master)](https://travis-ci.org/IBM/MAX-Image-Caption-Generator) [![Website Status](https://img.shields.io/website/http/max-image-caption-generator.codait-prod-41208c73af8fca213512856c7a09db52-0000.us-east.containers.appdomain.cloud/swagger.json.svg?label=api+demo)](http://max-image-caption-generator.codait-prod-41208c73af8fca213512856c7a09db52-0000.us-east.containers.appdomain.cloud)



[](http://ibm.biz/max-to-ibm-cloud-tutorial)



# 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](http://cocodataset.org/#home). The model consists of an _encoder_ model - a deep convolutional net using the Inception-v3 architecture trained on [ImageNet-2012 data](http://www.image-net.org/challenges/LSVRC/2012/) - 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](https://github.com/tensorflow/models/tree/master/research/im2txt). The checkpoint files are hosted on [IBM Cloud Object Storage](https://max-cdn.cdn.appdomain.cloud/max-image-caption-generator/1.0.0/assets.tar.gz). 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](https://developer.ibm.com/code/exchanges/models/).



## Model Metadata

| Domain | Application | Industry  | Framework | Training Data | Input Data Format |

| ------------- | --------  | -------- | --------- | --------- | -------------- | 

| Vision | Image Caption Generator | General | TensorFlow | [COCO](http://cocodataset.org/#home) | Images | 



## References

* _O. Vinyals, A. Toshev, S. Bengio, D. Erhan._, ["Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge"](https://doi.org/10.1109/TPAMI.2016.2587640), IEEE transactions on Pattern Analysis and Machine Intelligence, 2017.

* [im2txt TensorFlow Model GitHub Page](https://github.com/tensorflow/models/tree/master/research/im2txt)

* [COCO Dataset Project Page](http://cocodataset.org/#home)



## Licenses



| Component | License | Link  |

| ------------- | --------  | -------- |

| This repository | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) | [LICENSE](LICENSE) |

| Model Weights | [MIT](https://opensource.org/licenses/MIT) | [Pretrained Show and Tell Model](https://github.com/KranthiGV/Pretrained-Show-and-Tell-model) |

| Model Code (3rd party) | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) | [im2txt](https://github.com/tensorflow/models/tree/master/research/im2txt) |

| Test assets | Various | [Sample README](samples/README.md) |



## Pre-requisites:



* `docker`: The [Docker](https://www.docker.com/) command-line interface. Follow the [installation instructions](https://docs.docker.com/install/) for your system.

* The minimum recommended resources for this model is 2GB Memory and 2 CPUs.

* If you are on x86-64/AMD64, your CPU must support [AVX](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions) at the minimum.



# Deployment options



* [Deploy from Quay](#deploy-from-quay)

* [Deploy on Red Hat OpenShift](#deploy-on-red-hat-openshift)

* [Deploy on Kubernetes](#deploy-on-kubernetes)

* [Run Locally](#run-locally)



## Deploy from Quay



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



```

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

```



This will pull a pre-built image from the Quay.io container registry (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](#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](https://developer.ibm.com/tutorials/deploy-a-model-asset-exchange-microservice-on-red-hat-openshift/), specifying `quay.io/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 Quay.



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](https://ibm.biz/Bdz2XM) can be found [here](http://ibm.biz/max-to-ibm-cloud-tutorial).



## Run Locally



1. [Build the Model](#1-build-the-model)

2. [Deploy the Model](#2-deploy-the-model)

3. [Use the Model](#3-use-the-model)

4. [Development](#4-development)

5. [Cleanup](#5-cleanup)



### 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.



![pic](/docs/swagger-screenshot.png "Swagger Screenshot")



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



```

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

```



```json

{

  "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](#1-build-the-model)).



### 5. Cleanup



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



## Links



* [Image Caption Generator Web App](https://developer.ibm.com/patterns/create-a-web-app-to-interact-with-machine-learning-generated-image-captions): A reference application created by the IBM CODAIT team that uses the Image Caption Generator



## Resources and Contributions

   

If you are interested in contributing to the Model Asset Exchange project or have any queries, please follow the instructions [here](https://github.com/CODAIT/max-central-repo).