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Image Classifier – Inception ResNet v2


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 299×299 image, and the output is a list of estimated class probabilities.

Model Metadata

Domain Application Industry Framework Training Data Input Data Format
Vision Image Classification General Keras ImageNet Image (RGB/HWC)



Component License Link
Model GitHub Repository Apache 2.0 LICENSE
Model Weights Apache 2.0 Keras Inception-ResNet-v2
Model Code (3rd party) MIT Keras LICENSE
Test assets Various Samples README

Options available for deploying this model

  • Deploy from Dockerhub:

    docker run -it -p 5000:5000 codait/max-inception-resnet-v2
  • Deploy on Red Hat OpenShift:

    Follow the instructions for the OpenShift web console or the OpenShift Container Platform CLI in this tutorial and specify codait/max-inception-resnet-v2 as the image name.

  • Deploy on Kuberneters:

    kubectl apply -f

    A more elaborate tutorial on how to deploy this MAX model to production on IBM Cloud can be found here.

  • Locally: follow the instructions in the model README on GitHub

Example Usage

You can test or use this model

Test the model using cURL

Once deployed, you can test the model from the command line. For example if running locally:

curl -F "image=@samples/dog.jpg" -X POST http://localhost:5000/model/predict
  "status": "ok",
  "predictions": [
      "label_id": "n02088364",
      "label": "beagle",
      "probability": 0.44505545496941
      "label_id": "n02089867",
      "label": "Walker_hound",
      "probability": 0.3902231156826

Test the model in a Node-RED flow

Complete the node-red-contrib-model-asset-exchange module setup instructions and import the inception-resnet-v2 getting started flow.

Test the model in a serverless app

You can utilize this model in a serverless application by following the instructions in the Leverage deep learning in IBM Cloud Functions tutorial.

Resources and Contributions

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