IBM/MAX-Image-Caption-Generator

Name: MAX-Image-Caption-Generator

Owner: International Business Machines

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

Created: 2018-03-09 23:19:08.0

Updated: 2018-05-17 15:52:31.0

Pushed: 2018-04-29 22:04:15.0

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Size: 599

Language: Python

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README

IBM Code Model Asset Exchange: Show and Tell 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, 2016.
• 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 | Asset 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.

Steps

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

1. Build the Model

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

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

Change directory into the repository base folder:

 MAX-Image-Caption-Generator

To build the docker image locally, run:

cker build -t max-im2txt .

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:

cker run -it -p 5000:5000 max-im2txt

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:

rl -F "image=@assets/surfing.jpg" -X POST http://127.0.0.1:5000/model/predict

son

tatus": "ok",
redictions": [
{
  "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).