How to download and run YOLO models

Steps

This tutorial shows how to download YOLO models (YOLOv8, YOLOv9, YOLOv10, YOLO11) and run as part of object detection pipeline.

For downloading all supported YOLO models and converting them to OpenVINO IR format, please refer to this document.

Download

Step 1: Create virtual environment

python -m venv ov_env

Step 2: Activate virtual environment

source ov_env/bin/activate

Step 3: Upgrade pip to latest version

python -m pip install --upgrade pip

Step 4: Download and install packages

pip install openvino==2025.0.0 ultralytics

Step 5: Download Yolo11 model

Run the python script from here to download and convert yolo11 model in Intel OpenVINO format. Replace the model_name and model_type in the script with relevant value as required for other models.

Step 6: Deactivate virtual environment

deactivate

Run YOLO model

Volume mount YOLO model directory from host to DL Streamer Pipeline Server container by adding below lines to [WORKDIR]/docker/docker-compose.yml

    volumes:
      - "[Path to yolo11s model directory on host]:/home/pipeline-server/yolo_models/yolo11s"

Bring up DL Streamer Pipeline Server containers,

Next bring up the containers

cd [WORKDIR]/docker

docker compose up

The below CURL command runs the default pipeline with classroom.avi video as source and the downloaded Yolo model for object detection. Metadata is saved to file /tmp/results.jsonl and frames are streamed over RTSP accessible at rtsp://<SYSTEM_IP_ADDRESS>:8554/classroom-video-streaming.

curl localhost:8080/pipelines/user_defined_pipelines/pallet_defect_detection -X POST -H 'Content-Type: application/json' -d '{
    "source": {
        "uri": "file:///home/pipeline-server/resources/videos/classroom.avi",
        "type": "uri"
    },
    "destination": {
        "metadata": {
            "type": "file",
            "path": "/tmp/results.jsonl",
            "format": "json-lines"
        },
        "frame": {
            "type": "rtsp",
            "path": "classroom-video-streaming"
        }
    },
    "parameters": {
        "detection-properties": {
            "model": "/home/pipeline-server/yolo_models/yolo11s/FP32/yolo11s.xml",
            "device": "CPU"
        }
    }
}'