Ultralytics
integrations/openvino/ #8086
giscus[bot]
bot
Replies: 3 comments 7 replies
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I wanna do inferences with my exported pre-trained yolov8 model using Raspberry PI with a Neural Network Computing 2 (NCS2) plugged in.. but its doesn't support opset 11 or above.. only opset10.. I specified onnx opset to 10 and convert it to Openvino but it didn't work. Anyway please to export openvino model directly in opset10 ? Does even yolov8 support this specified opset format ? Thank you. |
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@YouROS12 currently, YOLOv8 models are exported to ONNX with the latest opset version by default, which may not be compatible with certain versions of OpenVINO that support only up to opset 10. However, you can try exporting the YOLOv8 model to ONNX specifying opset version 10 and then convert it to OpenVINO format. Here's how you can do it:
Please note that while YOLOv8 supports exporting to ONNX with a specified opset, some operations used in YOLOv8 may not be supported in older opset versions, which could lead to errors during export or inference. If you encounter issues, it's recommended to check the compatibility of YOLOv8's operations with opset 10 or consider updating your OpenVINO toolkit to a version that supports the latest ONNX opset versions. |
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Where can I find documentation about YOLOv8's operations with opset 10 ? |
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@YouROS12 the documentation for ONNX opset versions and the operations they support can be found on the ONNX GitHub repository or the official ONNX documentation. However, there isn't specific documentation for YOLOv8's operations with opset 10, as this would be a combination of YOLOv8's implementation details and ONNX's opset specifications. To check which operations are supported in opset 10, you can refer to the ONNX Operators documentation for that specific version. This documentation lists all the operators available in opset 10 and their details. For YOLOv8, you would need to look at the model's architecture and identify which operations are used. Then, cross-reference these operations with the ONNX opset 10 documentation to see if they are supported. If certain operations are not supported in opset 10, you might need to modify the model or the export script to use alternative operations that are compatible with opset 10. Since updating OpenVINO is not an option for you, if you find that YOLOv8 uses operations not supported in opset 10, you might need to consider using a different model architecture that is compatible with opset 10, or reach out to the community or Ultralytics for further assistance on how to proceed with your specific use case and hardware constraints. |
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Hi Im getting the following error when attempting to convert the model to openVino. Tried with both 2024.1.0 and 2024.0.0. Im on ubuntu 20.04 with python 3.8 PyTorch: starting from 'runs/classify/train/weights/best.pt' with input shape (1, 3, 320, 320) BCHW and output shape(s) (1, 3) (2.8 MB) |
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@kanishkasw hi there, Thank you for reaching out and providing detailed information about the issue you're encountering. It looks like you're experiencing an error when attempting to export your model to OpenVINO format. Let's work through this together. First, let's ensure that you are using the latest versions of pip install --upgrade torch ultralyticsNext, it seems the error is related to the OpenVINO package. The error message pip install openvino==2023.0.1Once you've updated the packages, try exporting the model again: yolo export model=runs/classify/train/weights/best.pt format=openvinoIf the issue persists, could you please provide a minimum reproducible example? This will help us investigate the problem more effectively. You can find guidance on creating a minimum reproducible example here: Minimum Reproducible Example. Additionally, here's a Python example to ensure everything is set up correctly: from ultralytics import YOLO
# Load a YOLOv8n PyTorch model
model = YOLO("runs/classify/train/weights/best.pt")
# Export the model to OpenVINO format
model.export(format="openvino") # creates 'yolov8n_openvino_model/'
# Load the exported OpenVINO model
ov_model = YOLO("yolov8n_openvino_model/")
# Run inference
results = ov_model("https://ultralytics.com/images/bus.jpg")For more detailed instructions on using OpenVINO, please refer to our OpenVINO Integration Guide. Feel free to reach out if you have any further questions or run into any other issues. We're here to help! 😊 |
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hey i am working on object detection in videos and came across openvino that helps in increasing speed. how can i use openvino and yolov8 together to find objects in a video |
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@kimchi2020 hi there! It's great to hear that you're exploring OpenVINO to enhance the speed of your object detection tasks in videos. OpenVINO can indeed provide significant performance improvements, especially on Intel hardware. To get started with using YOLOv8 and OpenVINO together, follow these steps: Exporting YOLOv8 to OpenVINO Format
Running Inference on VideosTo process videos, you can modify the inference code to read frames from a video file and run the model on each frame. Here’s an example in Python: import cv2
from ultralytics import YOLO
# Load the exported OpenVINO model
model = YOLO("yolov8n_openvino_model/")
# Open the video file
video_path = "path/to/your/video.mp4"
cap = cv2.VideoCapture(video_path)
# Loop through the video frames
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Run inference on the frame
results = model(frame)
# Visualize the results on the frame
annotated_frame = results[0].plot()
# Display the annotated frame
cv2.imshow("YOLOv8 OpenVINO Inference", annotated_frame)
# Break the loop if 'q' is pressed
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()Additional ResourcesFor more detailed information and advanced usage, you can refer to the OpenVINO documentation. This guide covers everything from exporting models to running inference and optimizing performance. Feel free to reach out if you have any more questions. Happy coding! 🚀 |
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Traceback (most recent call last): I am getting this error, how to get rid of it and what does it mean |
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Hi @kimchi2020, Thank you for reaching out! The error you're encountering seems to be related to a circular import issue with the Steps to Resolve the Issue:
Example Code for Inference with OpenVINO:Here's a quick example of how to export your YOLOv8 model to OpenVINO and run inference on a video: import cv2
from ultralytics import YOLO
# Load a YOLOv8n PyTorch model
model = YOLO("yolov8n.pt")
# Export the model to OpenVINO format
model.export(format="openvino") # creates 'yolov8n_openvino_model/'
# Load the exported OpenVINO model
ov_model = YOLO("yolov8n_openvino_model/")
# Open the video file
video_path = "path/to/your/video.mp4"
cap = cv2.VideoCapture(video_path)
# Loop through the video frames
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Run inference on the frame
results = ov_model(frame)
# Visualize the results on the frame
annotated_frame = results[0].plot()
# Display the annotated frame
cv2.imshow("YOLOv8 OpenVINO Inference", annotated_frame)
# Break the loop if 'q' is pressed
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()For more detailed information on exporting and using OpenVINO, please refer to our OpenVINO Integration Guide. Feel free to reach out if you have any more questions or need further assistance. Happy coding! 🚀 |
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integrations/openvino/
Discover the power of deploying your Ultralytics YOLOv8 model using OpenVINO format for up to 10x speedup vs PyTorch.
https://docs.ultralytics.com/integrations/openvino/
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