Yolo Models Preparation#

This page illustrates how to prepare models from the YOLO family for integration with the Intel® DL Streamer pipeline.

1. Setup#

The instructions assume Intel® DL Streamer framework is installed on the local system along with Intel® OpenVINO™ model downloader and converter tools, as described here: Tutorial.

For YoloV5, YoloV8 and YoloV9 models it is also necessary to install the Ultralytics python package:

pip install ultralytics

2. YoloV5u, YoloV8, YoloV9#

Python script converting the recent Ultralytics models to Intel® OpenVINO™ format (replace MODEL_NAME with yolov5su.pt, yolov8s.pt, yolov9c.pt etc.):

from ultralytics import YOLO
model = YOLO(MODEL_NAME)
model.info()
model.export(format='openvino')

3. YoloV7#

Model preparation:

git clone https://github.com/WongKinYiu/yolov7.git
cd yolov7
python3 export.py --weights yolov7.pt --grid
ovc yolov7.onnx

4. YoloV5 older versions#

Model preparation of YoloV5 7.0 from Ultralytics:

git clone https://github.com/ultralytics/yolov5
cd yolov5
wget https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt
python3 export.py --weights yolov5s.pt --include openvino

5. YoloX#

Intel® OpenVINO™ version of the model can be downloaded directly:

wget https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s_openvino.tar.gz
tar -xvf ./yolox_s_openvino.tar.gz

6. Model usage#

See Samples for detailed examples of Intel® DL Streamer pipelines using different Yolo models.

Yolo Models Preparation

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