# YOLO Models

This article describes how to prepare models from the **YOLO** family for
integration with the Deep Learning Streamer pipeline.

> **NOTE:** The instructions provided below are comprehensive, but for convenience,
>  it is recommended to use the
> [download_public_models.sh](https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/download_public_models.sh)
> script. This script will download all supported Yolo models and perform
> the necessary conversions automatically.
>
> See [download_public_models](./download_public_models.md) for more information.

## 1. Set up the framework

The instructions assume Deep Learning Streamer framework is installed on the
local system, along with Intel® OpenVINO™ model downloader and converter
tools, as described in the [Tutorial](../get_started/tutorial.md#setup).

For YOLOv5, YOLOv8, YOLOv9, YOLOv10, YOLO11 models you need to
install the Ultralytics Python package:

```bash
pip install ultralytics
```

## 2. YOLOv8, YOLOv9, YOLOv10, YOLO11

Below is a Python script that you can use to convert the recent Ultralytics
models to the Intel® OpenVINO™ format. Make sure to replace `model_name` and
`model_type` with the relevant models and types values.

Example for YOLO11 model:

```python
from ultralytics import YOLO
import openvino, sys, shutil, os

model_name = 'yolo11s'
model_type = 'yolo_v11'
weights = model_name + '.pt'
model = YOLO(weights)
model.info()

converted_path = model.export(format='openvino')
converted_model = converted_path + '/' + model_name + '.xml'

core = openvino.Core()

ov_model = core.read_model(model=converted_model)
if model_type in ["YOLOv8-SEG", "yolo_v11_seg"]:
    ov_model.output(0).set_names({"boxes"})
    ov_model.output(1).set_names({"masks"})
ov_model.set_rt_info(model_type, ['model_info', 'model_type'])

openvino.save_model(ov_model, './FP32/' + model_name + '.xml', compress_to_fp16=False)
openvino.save_model(ov_model, './FP16/' + model_name + '.xml', compress_to_fp16=True)

shutil.rmtree(converted_path)
os.remove(f"{model_name}.pt")
```

## 3. YOLOv7

Model preparation:

```bash
git clone https://github.com/WongKinYiu/yolov7.git
cd yolov7
python3 export.py --weights yolov7.pt --grid --dynamic-batch
# Convert the model to OpenVINO format FP32 precision
ovc yolov7.onnx --compress_to_fp16=False
# Convert the model to OpenVINO format FP16 precision
ovc yolov7.onnx
```

## 4. YOLOv5 latest version

Model preparation enabling the dynamic batch size:

``` python
from ultralytics import YOLO
from openvino.runtime import Core
from openvino.runtime import save_model
model = YOLO("yolov5s.pt")
model.info()
model.export(format='openvino', dynamic=True)  # creates 'yolov5su_openvino_model/'
core = Core()
model = core.read_model("yolov5su_openvino_model/yolov5su.xml")
model.reshape([-1, 3, 640, 640])
save_model(model, "yolov5su.xml")
```

## 5. YOLOv5 older versions

Model preparation of YOLOv5 7.0 from Ultralytics involves two steps.

1. Convert the PyTorch model to Intel® OpenVINO™ format :

   ```bash
   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 --dynamic
   ```

2. Then, reshape the model to enable the dynamic batch size and keep other
   dimensions fixed:

   ```python
   from openvino.runtime import Core
   from openvino.runtime import save_model
   core = Core()
   model = core.read_model("yolov5s_openvino_model/yolov5s.xml")
   model.reshape([-1, 3, 640, 640])
   save_model(model, "yolov5s.xml")
   ```

## 6. YOLOX

Intel® OpenVINO™ version of the model can be obtained from the ONNX
file:

```bash
wget https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.onnx
ovc yolox_s.onnx --compress_to_fp16=False
```

## 7. Model usage

See
[Samples](https://github.com/dlstreamer/dlstreamer/tree/master/samples/gstreamer/gst_launch/detection_with_yolo)
for detailed examples of Deep Learning Streamer pipelines using different
Yolo models.