# 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, refer to
this [document](https://docs.openedgeplatform.intel.com/2026.2/edge-ai-libraries/dlstreamer/dev_guide/yolo_models.html).

### Download

#### Step 1: Create virtual environment

```sh
python -m venv ov_env
```

#### Step 2: Activate virtual environment

```sh
source ov_env/bin/activate
```

#### Step 3: Upgrade pip to latest version

```sh
python -m pip install --upgrade pip
```

#### Step 4: Download and install packages

```sh
pip install openvino==2025.0.0 ultralytics
```

#### Step 5: Download YOLO11 model

Run the Python script from [here](https://docs.openedgeplatform.intel.com/2026.2/edge-ai-libraries/dlstreamer/dev_guide/yolo_models.html#ultralytics-model-preparation) to download and convert the YOLO11 model to OpenVINO™ format.

#### Step 6: Deactivate virtual environment

```sh
deactivate
```

### Run YOLO model

Volume mount YOLO model directory from host to DL Streamer Pipeline Server container by adding
the following lines to `[WORKDIR]/edge-ai-libraries/microservices/dlstreamer-pipeline-server/docker/docker-compose.yml`

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

Next, bring up DL Streamer Pipeline Server containers:

```sh
cd [WORKDIR]/edge-ai-libraries/microservices/dlstreamer-pipeline-server/docker
```

```sh
docker compose up
```

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

```sh
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"
        }
    }
}'
```