User Guide: Model Pipelining with DeGirum PySDK
Model pipelining is a versatile approach in AI, allowing multiple models to work in sequence. The output of one model is used as the input to another, enabling more sophisticated applications by combining specialized tasks. This guide introduces the concept using a practical example of face detection followed by gender classification.
Example: Face Detection and Gender Classification
In this example, we use two models:
- Face Detection Model: Detects faces in a video stream and generates bounding boxes around them.
- Gender Classification Model: Classifies the gender of each detected face.
The models are combined into a pipeline where the face detection model processes the input video, and its outputs (cropped face regions) are passed to the gender classification model for further analysis.
Code Reference
import degirum as dg, degirum_tools
# choose inference host address
inference_host_address = "@cloud"
# inference_host_address = "@local"
# choose zoo_url
zoo_url = "degirum/models_hailort"
# zoo_url = "../models"
# set token
token = degirum_tools.get_token()
# token = '' # leave empty for local inference
face_det_model_name = "yolov8n_relu6_face--640x640_quant_hailort_hailo8l_1"
gender_cls_model_name = "yolov8n_relu6_fairface_gender--256x256_quant_hailort_hailo8l_1"
video_source = "../assets/faces_and_gender.mp4"
# Load face detection and gender detection models
face_det_model = dg.load_model(
model_name=face_det_model_name,
inference_host_address=inference_host_address,
zoo_url=zoo_url,
token=degirum_tools.get_token(),
overlay_color=[(255,255,0),(0,255,0)]
)
gender_cls_model = dg.load_model(
model_name=gender_cls_model_name,
inference_host_address=inference_host_address,
zoo_url=zoo_url,
token=degirum_tools.get_token(),
)
# Create a compound cropping model with 30% crop extent
crop_model = degirum_tools.CroppingAndClassifyingCompoundModel(
face_det_model,
gender_cls_model,
30.0
)
# Run AI inference on video stream and display inference results
# Press 'x' or 'q' to stop
with degirum_tools.Display("Faces and Gender") as display:
for inference_result in degirum_tools.predict_stream(crop_model, video_source):
display.show(inference_result)
How It Works
-
Model Loading:
- The face detection and gender classification models are loaded using
dg.load_model. - The
overlay_colorparameter is used to differentiate detected faces visually.
- The face detection and gender classification models are loaded using
-
Pipeline Creation:
- A compound model is created using
degirum_tools.CroppingAndClassifyingCompoundModel, which combines face detection and gender classification. - The
crop_extentparameter (30% in this example) ensures that the face region is appropriately cropped for the second model.
- A compound model is created using
-
Inference Execution:
- The
predict_streamfunction runs the compound model on the input video source. - Cropped face regions are passed from the detection model to the classification model.
- The
-
Result Display:
- Detected faces and their classified genders are displayed in a dedicated window. Use the ‘x’ or ‘q’ keys to stop the display.
Applications
- Video analytics
- Security and surveillance
- Retail and customer insights
- Personalized user experiences
This pipelining approach allows you to extend workflows to include additional tasks, such as emotion detection or age estimation, making it a scalable and modular solution for complex AI applications.