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Age and Gender Recognition via REST API {#ovms_demo_age_gender_guide}

This article describes how to use OpenVINO™ Model Server to execute inference requests sent over the REST API interface. The demo uses a pretrained model from the Open Model Zoo repository.

Prerequisites

Model preparation: Python 3.9 or higher with pip

Model Server deployment: Installed Docker Engine or OVMS binary package according to the baremetal deployment guide

Download the pretrained model for age and gender recognition

Download both components of the model (xml and bin file) using curl in the model directory

curl --create-dirs https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.1/models_bin/2/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013.bin https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.1/models_bin/2/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013.xml -o model/1/age-gender-recognition-retail-0013.bin -o model/1/age-gender-recognition-retail-0013.xml

Server Deployment

:::{dropdown} Deploying with Docker Start OVMS container with image pulled in previous step and mount model directory :

chmod -R 755 model
docker run --rm -d -u $(id -u):$(id -g) -v $(pwd)/model:/models/age_gender -p 9000:9000 -p 8000:8000 openvino/model_server:latest --model_path /models/age_gender --model_name age_gender --port 9000 --rest_port 8000

::: :::{dropdown} Deploying on Bare Metal Assuming you have unpacked model server package, make sure to:

  • On Windows: run setupvars script
  • On Linux: set LD_LIBRARY_PATH and PATH environment variables

as mentioned in deployment guide, in every new shell that will start OpenVINO Model Server.

ovms --model_path model --model_name age_gender --port 9000 --rest_port 8000

:::

Requesting the Service

Clone the repository

git clone https://github.com/openvinotoolkit/model_server.git

Enter age_gender_recognition python demo directory:

cd model_server/demos/age_gender_recognition/python

Download sample image using the command :

curl https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/2022.1.0/models/intel/age-gender-recognition-retail-0013/assets/age-gender-recognition-retail-0001.jpg -o age-gender-recognition-retail-0001.jpg

Install python dependencies:

pip3 install -r requirements.txt

Run age_gender_recognition.py script to make an inference:

python age_gender_recognition.py --image_input_path age-gender-recognition-retail-0001.jpg --rest_port 8000

Sample Output :

age-gender-recognition-retail-0001.jpg (1, 3, 62, 62) ; data range: 0 : 239
{'outputs': {'prob': [[[[0.9874807]], [[0.0125193456]]]], 'age_conv3': [[[[0.25190413]]]]}}

Output format :

Output Name Shape Description
age_conv3 [1, 1, 1, 1] Estimated age divided by 100
prob [1, 2, 1, 1] Softmax output across 2 type classes [female, male]