Yolo Models Preparation

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

Note

The steps outlined below enable the creation of basic model versions, ready for use in Intel® DL Streamer. However, for YoloV5 (both versions) and YoloV7, additional operations not covered here are required if the batch size is to exceed 1.

It is recommended to use the download_public_models.sh script, which automatically downloads all supported Yolo models and carries out a complete conversion, allowing for the utilization of their full potential.

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.