DeployableObject Detection in Images
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By IBM Developer Staff | Updated September 21, 2018 - Published March 20, 2018
Artificial intelligenceVisionImage 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:
docker run -it -p 5000:5000 codait/max-object-detector
kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Object-Detector/master/max-object-detector.yaml
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=@assets/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.
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Artificial intelligenceDeep Learning+
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