IBM Developer Model Asset Exchange: Image Segmentation

This repository contains code to instantiate and deploy an image segmentation model. This model takes an image file as an input and returns a segmentation map containing a predicted class for each pixel in the input image.

This repository contains 2 models trained on PASCAL VOC 2012. One model is trained using the xception architecture and produces very accurate results but takes a few seconds to run and the other model is trained on MobileNetV2 and is faster but less accurate. You can specify which model you wish to use when you start the Docker image. See below for more details.

The segmentation map returns an integer between 0 and 20 that corresponds to one of the labels below for each pixel in the input image. The first nested array corresponds to the top row of pixels in the image and the first element in that array corresponds to the pixel at the top left hand corner of the image. NOTE: the image will be resized and the segmentation map refers to pixels in the resized image, not the original input image.

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 and the public API is powered by IBM Cloud.

Model Metadata

References

• Haozhi Qi, Zheng Zhang, Bin Xiao, Han Hu, Bowen Cheng, Yichen Wei, Jifeng Dai, Deformable Convolutional Networks -- COCO Detection and Segmentation Challenge 2017 Entry. ICCV COCO Challenge Workshop, 2017.

• Mark Everingham, S. M. Ali Eslami, Luc Van Gool, Christopher K. I. Williams, John M. Winn, Andrew Zisserman, The Pascal Visual Object Classes Challenge: A Retrospective. IJCV, 2014.

• Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, Piotr Dollar, Microsoft COCO: Common Objects in Context. In the Proc. of ECCV, 2014.

Licenses

Prerequisites

• 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.
• If you are on x86-64/AMD64, your CPU must support AVX at the minimum.

Deployment options

• Deploy from Quay
• Deploy on Red Hat OpenShift
• Deploy on Kubernetes
• 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-segmenter

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 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 quay.io/codait/max-image-segmenter 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-Segmenter/master/max-image-segmenter.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.

Run Locally

To build and deploy the model to a REST API using Docker, follow these steps:

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

1. Build the Model

Clone the MAX-Image-Segmenter repository locally. In a terminal, run the following command:

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

Change directory into the repository base folder:

$ cd MAX-Image-Segmenter

To build the docker image locally, run:

$ docker build -t max-image-segmenter .

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-segmenter

If you would like to specify what model or image size to load into the model, use -e flags to pass the API environmental variables:

$ docker run -it -e MODEL_TYPE='mobile' -e IMAGE_SIZE=333 -p 5000:5000 max-image-segmenter

By default, Cross-Origin Resource Sharing (CORS) is disabled. To enable CORS support, include the following -e flag with your run command:

$ docker run -it -e CORS_ENABLE='true' -p 5000:5000 max-image-segmenter

Note extra parameter info:

• Model types available: 'mobile', 'full' (default: mobile)
• Image size range: 16 to 1024 pixels (default: 513)
• CORS_ENABLE accepts either 'true' or 'false' (default: 'false')

Note that the image size parameter controls to what size the image will be resized to before it is processed by the model. Smaller images run faster but generate less accurate segmentation maps.

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 image (you can use one of the test images from the samples folder) and get predicted segmentation map for the image from the API.

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

$ curl -F "image=@samples/stc.jpg" -XPOST http://localhost:5000/model/predict

You should see a JSON response like that below:

{
  "status": "ok",
  "image_size": [
    256,
    128
  ],
  "seg_map": [
    [
      0,
      0,
      0,
      ...,
      15,
      15,
      15,
      ...,
      0,
      0,
      0
    ],
    ...,
    ...,
    ...,
    [
      0,
      0,
      0,
      ...,
      15,
      15,
      15,
      ...,
      0,
      0,
      0
    ]
  ]
}

4. Run the Notebook

Once the model server is running, you can see how to use it by walking through the demo notebook. Note the demo requires jupyter, numpy, Pillow, matplotlib and pycurl.

Run the following command from the model repo base folder, in a new terminal window (leaving the model server running in the other terminal window):

jupyter notebook

This will start the notebook server. You can open the demo notebook by clicking on demo.ipynb.

5. 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).

6. Cleanup

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

Train this Model on Watson Machine Learning

This model supports both fine-tuning with transfer learning and training from scratch on a custom model. Please follow the steps listed under the training readme to retrain the model on Watson Machine Learning, a deep-learning as a service (DLaaS) offering of IBM Cloud.

Resources and Contributions

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