https://github.com/mlpack/onnx-mlpack

onnx to mlpack converter (Under construction)
https://github.com/mlpack/onnx-mlpack

mlpack mlpack-library mobilenet-ssd mobilenetv2 neural-networks object-detection onnx onnx-models

Last synced: 6 months ago
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onnx to mlpack converter (Under construction)

• Host: GitHub
• URL: https://github.com/mlpack/onnx-mlpack
• Owner: mlpack
• License: mit
• Created: 2024-06-03T03:42:15.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2025-02-23T14:13:54.000Z (7 months ago)
• Last Synced: 2025-03-27T19:21:40.804Z (6 months ago)
• Topics: mlpack, mlpack-library, mobilenet-ssd, mobilenetv2, neural-networks, object-detection, onnx, onnx-models
• Language: C++
• Homepage:
• Size: 156 MB
• Stars: 5
• Watchers: 2
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          

# ONNX-mlpack Translator



  

    

  

  


  
Unlock the Power of Other Frameworks in mlpack



This repository contains a converter for certain machine learning models from

onnx to mlpack format. Currently this repository is still under construction

and might undergo some major refactoring, please use with cautios.

### The repository is in developing phase and its been tested on the following models.

| Models              | Graph Generation | Weight Transfer |

| ------------------- | ---------------- | --------------- |

| mobileNet           | ✔️               | ✔️              |

| yolo-tiny v2        | ✔️               | ✔️              |

| Iris classification | ✔️               | ✔️              |

## ONNX-mlpack Repository Setup Guide:

### Prerequisites

1. **MLPack Installation:**

    - Ensure that MLPack is installed on your local system. Follow the [official MLPack build instructions](https://github.com/mlpack/mlpack#:~:text=3.%20Installing%20and%20using%20mlpack%20in%20C%2B%2B) to complete this step.

    

1. **ONNX Installation:**

    - If you don't have Protobuf installed, ONNX will internally download and build Protobuf during its build process. You only need to build ONNX. Refer to the [official ONNX build instructions](https://github.com/onnx/onnx#:~:text=conda%2Dforge%20onnx-,Build%20ONNX%20from%20Source,-Before%20building%20from) for more details.

    - However, to avoid potential version issues in the future, we have provided a zipped format of ONNX in the `build_onnx` repository along with a script that will directly install ONNX on your system.

    - 

follow the below instruction to build onnx and make the repository running:

### Steps to Build ONNX

1. **Clone the Repository:**

    - Clone the `onnx-mlpack` repository to your local system and navigate to the repository directory.

    

2. **Build ONNX:**

    - Run the following commands to build ONNX:

        `chmod +x run.sh` 

	    `./run.sh`

        

    - This will generate all the necessary build files for ONNX inside the `build_onnx` folder.

    

3. **Verify mlpack and ONNX Build:**

    - With both ONNX and mlpack built, it's time to test the setup with an example repository.

### Running the Example Repository

1. Go to the `example/iris-classification` folder.

    

2. In the Makefile, update the mlpack header path to match your mlpack build path. For example:

      `-I/home/your_username/mlpack/build/installdir/include`

        

3. Run the make command and check the console output to verify that everything is working correctly.

   If everythig goes fine you can similarly run the other example as well.