This repository contains code to instantiate and deploy an image classification model. This model recognizes the 1000 different classes of objects in the ImageNet 2012 Large Scale Visual Recognition Challenge. The model consists of a deep convolutional net using the Inception-ResNet-v2 architecture that was trained on the ImageNet-2012 data set. The input to the model is a 299x299 image, and the output is a list of estimated class probilities.

The model is based on the Keras built-in model for Inception-ResNet-v2. The model 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.

• C. Szegedy, S. Ioffe, V. Vanhoucke, A. Alemi, "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning", CoRR (abs/1602.07261), 2016.
• Keras Applications

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

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

$ git clone https://github.com/IBM/MAX-Inception-ResNet-v2.git

Change directory into the repository base folder:

$ cd MAX-Inception-ResNet-v2

To build the docker image locally, run:

$ docker build -t max-keras-inceptionresnetv2 .

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).

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

$ docker run -it -p 5000:5000 max-keras-inceptionresnetv2

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 image (you can use one of the test images from the assets folder) and get predicted labels for the image from the API.

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

$ curl -F "image=@assets/dog.jpg" -X POST http://127.0.0.1:5000/model/predict

You should see a JSON response like that below:

{
  "status": "ok",
  "predictions": [
    {
      "label_id": "n02088364",
      "label": "beagle",
      "probability": 0.44505545496941
    },
    {
      "label_id": "n02089867",
      "label": "Walker_hound",
      "probability": 0.3902231156826
    },
    {
      "label_id": "n02089973",
      "label": "English_foxhound",
      "probability": 0.02027696929872
    },
    {
      "label_id": "n02088632",
      "label": "bluetick",
      "probability": 0.010103852488101
    },
    {
      "label_id": "n02088238",
      "label": "basset",
      "probability": 0.001649746671319
    }
  ]
}

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).