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Kneron AI Training/Deployment Platform (mmTracking-based)

Introduction

kneron-mmtracking is a platform built upon the well-known mmtracking for tracking. We encourage you to start with ByteTrack: Multi-Object Tracking by Associating Every Detection Box to build basic knowledge of Kneron-Edition mmtracking, and read mmtracking docs for detailed mmtracking usage.

In this repository, we provide an end-to-end training/deployment flow to realize on Kneron's AI accelerators:

  1. Training/Evalulation:
  2. Converting to ONNX:
    • pytorch2onnx_kneron.py (beta)
    • Export optimized and Kneron-toolchain supported onnx
      • Automatically modify model for arbitrary data normalization preprocess
  3. Evaluation
    • test_kneron.py (beta)
    • Evaluate the model with pytorch checkpoint, onnx, and kneron-nef
  4. Testing
    • inference_kn (beta)
    • Verify the converted NEF model on Kneron USB accelerator with this API
  5. Converting Kneron-NEF: (toolchain feature)
    • Convert the trained pytorch model to Kneron-NEF model, which could be used on Kneron hardware platform.

License

This project is released under the Apache 2.0 license.

Changelog

N/A

Overview of Benchmark and Kneron Model Zoo

Model size Mem (GB) box AP Config Download
ByteTrack(YOLOX-s) (448, 800) 7.6 82.4 config model

Installation

Getting Started

Tutorial - Kneron Edition

Documents - Kneron Edition

Original mmtracking Documents

Contributing

kneron-mmtracking a platform built upon OpenMMLab-mmtracking

  • For issues regarding to the original mmtracking: We appreciate all contributions to improve OpenMMLab-mmtracking. Ongoing projects can be found in out GitHub Projects. Welcome community users to participate in these projects. Please refer to CONTRIBUTING.md for the contributing guideline.

  • For issues regarding to this repository kneron-mmtracking: Welcome to leave the comment or submit pull requests here to improve kneron-mmtracking

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