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Model Export & Model Infer

Model Inference Backend Pre/Post-processing Implementation File Implemented
YOLOv5 ONNX Runtime PyTorch yolov5_runtime_w_torch.py
YOLOv5 ONNX Runtime NumPy yolov5_runtime_w_numpy.py
YOLOv5 TensorRT NumPy yolov5_tensorrt_w_numpy.py
YOLOv5 Triton Server NumPy yolov5_triton_w_numpy.py
YOLOv8 ONNX Runtime PyTorch yolov8_runtime_w_torch.py
YOLOv8 ONNX Runtime NumPy yolov8_runtime_w_numpy.py
YOLOv8 TensorRT NumPy yolov8_tensorrt_w_numpy.py
YOLOv8 Triton Server NumPy yolov8_triton_w_numpy.py
YOLOv8-seg ONNX Runtime PyTorch yolov8_seg_runtime_w_torch.py
YOLOv8-seg ONNX Runtime NumPy yolov8_seg_runtime_w_numpy.py
YOLOv8-seg TensorRT NumPy yolov8_seg_tensorrt_w_numpy.py
YOLOv8-seg Triton Server NumPy yolov8_seg_triton_w_numpy.py

Table of Contents

Background

The purpose of this repository is to better deploy computer vision algorithms, especially the implementation of object classification, object detection, and instance segmentation algorithms.

At first, I wanted to design a unified architecture that would adapt to different pre-processing and post-processing, network module implementations, and inference engines through a modular paradigm, similar to commonly used popular repositories. But this approach is difficult to push forward. Every time I want to add a new algorithm, I need to repeatedly think about how to split the algorithm according to the current architecture and how to adapt the input and output of each module. These issues have left me exhausted and even lost my passion for warehouse optimization for a period of time.

I have thought for a long time and confirmed that I may have fallen into some kind of development dilemma. In order to pursue design perfection, the complexity of engineering development has been infinitely increased. After realizing the problem of Over-Engineering, I plan to start over. In the new development, I will focus as much as possible on the goals of this repository: model transformation and model inference, and minimize the content of architecture design. Focus the main energy on algorithm deployment.

Note: The previous implementation was in v0.1.0.

Maintainers

  • zhujian - Initial work - zjykzj

Contributing

Anyone's participation is welcome! Open an issue or submit PRs.

License

Apache License 2.0 © 2021 zjykzj