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Yolov5s onnx model inference
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Question
Hi , I am unable to get bounding boxes from the output i got by running yolov5s onnx model in onnx runtime. The output is list of arrays of the shape (3,52,52,85) , (3,26,26,85) , (3,13,13,85) respectively . it will be helpful if you can provide me with the postprocess code to define the bounding boxes.
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👋 Hello @anazkhan, thank you for your interest in YOLOv5 🚀! Please check out our ⭐️ Tutorials for guidance on various tasks such as ONNX Export and Inference.
If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it. For your specific issue, ensuring you have the correct post-processing steps is key when working with ONNX outputs.
Also, make sure you meet the following requirements:
Requirements
Python>=3.8.0 with all requirements.txt installed. To get started:
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install
Environments
YOLOv5 can be run in any of the following environments:
- Notebooks with free GPU:
- Google Cloud, AWS, Docker: See respective Quickstart Guides
Status
Check our CI Status:
If the badge is green, all tests are passing 👍.
Introducing YOLOv8 🚀
Explore our state-of-the-art YOLOv8 here for enhanced capabilities in object detection and image processing tasks. Install with:
pip install ultralytics
This is an automated response. An Ultralytics engineer will assist you further soon. Thanks for your patience! 😊
@anazkhan to extract bounding boxes from the YOLOv5s ONNX model output, you'll need to apply non-max suppression and decode the predictions. You can refer to the post-processing steps in the YOLOv5 repository's detect.py script, which includes functions for these tasks. If you need further guidance, please check the YOLOv5 documentation for detailed instructions.
