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Deployable, TrainableObject Detection in Images
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By IBM Developer Staff | Updated September 21, 2018 - Published March 20, 2018
Artificial intelligenceDeep learningVisual recognitionImage ClassificationObject Detection in Images
This model recognizes the objects present in an image from the 80 different high-level classes of objects in the COCO Dataset. The model consists of a deep convolutional net base model for image feature extraction, together with additional convolutional layers specialized for the task of object detection, that was trained on the COCO data set. The input to the model is an image, and the output is a list of estimated class probabilities for the objects detected in the image. The model is based on the SSD Mobilenet V1 object detection model for TensorFlow.
This model can be deployed using the following mechanisms:
Deploy from Dockerhub:
docker run -it -p 5000:5000 codait/max-object-detector
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-object-detector as the image name.
Deploy on Kubernetes:
kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Object-Detector/master/max-object-detector.yaml
Locally: follow the instructions in the model README on GitHub
You can test or use this model
Once deployed, you can test the model from the command line. For example if running locally:
curl -F "image=@samples/dog-human.jpg" -XPOST http://127.0.0.1:5000/model/predict
You should see a JSON response like that below:
Complete the node-red-contrib-model-asset-exchange module setup instructions and import the object-detector getting started flow.
Learn how to send an image to the model and how to render the results in CodePen.
This model can be trained using the following mechanisms:
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