Overview
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 ResNet-50 architecture that was trained on the ImageNet-2012 data set. The input to the model is a 224×224 image, and the output is a list of estimated class probilities. The model is based on the Keras built-in model for ResNet-50.
Model Metadata
Domain | Application | Industry | Framework | Training Data | Input Data Format |
---|---|---|---|---|---|
Vision | Image Classification | General | Keras | ImageNet | Image (RGB/HWC) |
References
- K. He, X. Zhang, S. Ren and J. Sun, “Deep Residual Learning for Image Recognition”, CoRR (abs/1512.03385), 2015.
- Keras Applications
Licenses
Component | License | Link |
---|---|---|
Model GitHub Repository | Apache 2.0 | LICENSE |
Model Weights | MIT | Keras ResNet-50 |
Model Code (3rd party) | MIT | Keras LICENSE |
Test Assets | CC0 | Samples README |
Options available for deploying this model
This model can be deployed using the following mechanisms:
Deploy from Dockerhub:
docker run -it -p 5000:5000 codait/max-resnet-50
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-resnet-50
as the image name.Deploy on Kubernetes:
kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-ResNet-50/master/max-resnet-50.yaml
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:
curl -F "image=@samples/coffee.jpg" -XPOST http://localhost:5000/model/predict
You should see a JSON response like that below:
{
"status": "ok",
"predictions": [
{
"label_id": "n07920052",
"label": "espresso",
"probability": 0.9637148976326
},
{
"label_id": "n02877765",
"label": "bottlecap",
"probability": 0.007265966385603
},
{
"label_id": "n07930864",
"label": "cup",
"probability": 0.0059303143061697
},
{
"label_id": "n07693725",
"label": "bagel",
"probability": 0.0023403959348798
},
{
"label_id": "n04476259",
"label": "tray",
"probability": 0.0019735493697226
}
]
}
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.
Options available for training this model
This model can be trained using the following mechanisms:
- Train on IBM Cloud – Watson Machine Learning: follow the instructions in the model training README on GitHub.
Resources and Contributions
If you are interested in contributing to the Model Asset Exchange project or have any queries, please follow the instructions here.