Yolo Models

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

Note

The instructions provided below are comprehensive, but for convenience, it is recommended to use the download_public_models.sh script. This script will download all supported Yolo models and perform the necessary conversions automatically.

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, YoloV9 and YoloV10 models it is also necessary to install the Ultralytics Python package:

pip install ultralytics

2. YoloV8, YoloV9, YoloV10

Python script converting the recent Ultralytics models to Intel® OpenVINO™ format (replace MODEL_NAME with “yolov8s.pt”, “yolov9c.pt” or “yolov10s.pt”):

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

For model YoloV10 modify model_type as below:

from ultralytics import YOLO
import openvino, sys, shutil
model = YOLO(MODEL_NAME)
model.info()
converted_path = model.export(format='openvino')
converted_model = converted_path + '/yolov10s.xml'
core = openvino.Core()
ov_model = core.read_model(model=converted_model)
ov_model.set_rt_info("yolo_v10", ['model_info', 'model_type'])
openvino.save_model(ov_model, './FP32/yolov10s.xml', compress_to_fp16=False)
openvino.save_model(ov_model, './FP16/yolov10s.xml', compress_to_fp16=True)
shutil.rmtree(converted_path)

3. YoloV7

Model preparation:

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

4. YoloV5 latest version

Model preparation enabling the dynamic batch size:

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. First, convert the PyTorch model to Intel® OpenVINO™ format :

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

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

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:

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