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DeployableObject Detection in Images
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By IBM Developer Staff | Published September 21, 2018
Artificial intelligenceDeep learningVisual recognitionObject Detection in Images
This model detects faces in an image, extracts facial features for each face detected and finally predicts the age of each face. The model uses a coarse-to-fine strategy to perform multi-class classification and regression for age estimation.
The input to the model is an image and the output is a list of estimated ages and bounding box coordinates of each face detected in the image. The format of the bounding box coordinates is [ymin, xmin, ymax, xmax], where each coordinate is normalized by the height and width of the image dimension for y and x, respectively. Each coordinate is therefore in the range [0, 1].
The model is based on the SSR-Net model.
[ymin, xmin, ymax, xmax]
This model can be deployed using the following mechanisms:
docker run -it -p 5000:5000 codait/max-facial-age-estimator
kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Facial-Age-Estimator/master/max-facial-age-estimator.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=@samples/tom_cruise.jpg" -XPOST http://localhost:5000/model/predict
Complete the node-red-contrib-model-asset-exchange module setup instructions and import the facial-age-estimator getting started flow.
Learn how to send an image to the model and how to render the results in CodePen.
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