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Python scripts performing instance segmentation using the YOLOv5 model in ONNX.

Original image: https://www.flickr.com/photos/nicolelee/19041780

Important

• The input images are directly resized to match the input size of the model. I skipped adding the pad to the input image (image letterbox), it might affect the accuracy of the model if the input image has a different aspect ratio compared to the input size of the model. Always try to get an input size with a ratio close to the input images you will use.

Requirements

• Check the requirements.txt file.
• For ONNX, if you have a NVIDIA GPU, then install the onnxruntime-gpu, otherwise use the onnxruntime library.

Installation

git clone https://github.com/ibaiGorordo/ONNX-YOLOv5-Instance-Segmentation.git
cd ONNX-YOLOv5-Instance-Segmentation
pip install -r requirements.txt

ONNX Runtime

For Nvidia GPU computers: pip install onnxruntime-gpu

Otherwise: pip install onnxruntime

ONNX model

You can convert the Pytorch model to ONNX using the following Google Colab notebook:

• The License of the models is GPL-3.0 license: License

Original YOLOv5 model

The original YOLOv5 Instance Segmentation model can be found in this repository: YOLOv5 Instance Segmentation

Examples

• Image inference:

   python image_instance_segmentation.py
  

• Webcam inference:

   python webcam_instance_segmentation.py
  

• Video inference: https://youtu.be/p2iUTSiZBas

   python video_instance_segmentation.py
  

  

References:

• YOLOv5 model: https://github.com/ultralytics/yolov5
• yolov5-seg-opencv-onnxruntime-cpp: https://github.com/UNeedCryDear/yolov5-seg-opencv-onnxruntime-cpp
• PINTO0309’s model zoo: https://github.com/PINTO0309/PINTO_model_zoo
• PINTO0309’s model conversion tool: https://github.com/PINTO0309/openvino2tensorflow