Sold by: RocketMLÂ
Deployed on AWS
Model is a using a sequence of neural networks for Emotion and head pose detection
Overview
Model generates output of face bounding boxes from face detection network, labels of head pose, emotion information, and facial landmarks positions for each detected face
Highlights
- The output and labels from the model can be used with visualization frameworks
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.c5.xlarge Inference (Batch) Recommended | Model inference on the ml.c5.xlarge instance type, batch mode | $0.15 |
ml.c5.large Inference (Real-Time) Recommended | Model inference on the ml.c5.large instance type, real-time mode | $0.15 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $0.15 |
ml.c5.18xlarge Inference (Batch) | Model inference on the ml.c5.18xlarge instance type, batch mode | $0.15 |
ml.c5.9xlarge Inference (Batch) | Model inference on the ml.c5.9xlarge instance type, batch mode | $0.15 |
ml.c5.4xlarge Inference (Batch) | Model inference on the ml.c5.4xlarge instance type, batch mode | $0.15 |
ml.c5.2xlarge Inference (Real-Time) | Model inference on the ml.c5.2xlarge instance type, real-time mode | $0.15 |
ml.c5.xlarge Inference (Real-Time) | Model inference on the ml.c5.xlarge instance type, real-time mode | $0.15 |
ml.c5.9xlarge Inference (Real-Time) | Model inference on the ml.c5.9xlarge instance type, real-time mode | $0.15 |
ml.c5.4xlarge Inference (Real-Time) | Model inference on the ml.c5.4xlarge instance type, real-time mode | $0.15 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
This release contains updates for realtime inference using sagemaker endpoint configuration.
Additional details
Inputs
- Summary
Real-time inference Example:
#content-type: application/x-image
#Convert image to bytearray with open(face_image_path, "rb") as image: b = bytearray(image.read())
#Perform a prediction emotion_detection_result = predictor_emotion_headpose_detection.predict(b).decode('utf-8')
#result [{'confidence': '0.999840', 'emotion': 'happy', 'height': '97', 'pitch': '3.576530', 'roll': '0.184108', 'width': '74', 'x_ll': '77', 'y_ll': '33', 'yaw': '-11.587129'}]
- Input MIME type
- application/x-image
See Input Summary
See Input Summary
Resources
Vendor resources
Support
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