Fine tune ONNX Models (MMSegemetation) Inference for NVIDIA Jetson(2) 调优语义分割 ONNX 通用模型，使其适合NVIDIA Jetson 平台部署(2)

[OpenMMLab-Assistant-004]

Fine tune ONNX Models (MMSegemetation) Inference for NVIDIA Jetson(2)

调优语义分割 ONNX 通用模型，使其适合NVIDIA Jetson 平台部署(2)

To deploy on the NVIDIA Jetson platform, you can follow these steps to optimize generic ONNX models to meet the specific requirements of the platform:
[https://platform.openmmlab.com/deploee/onnx-list](https://platform.openmmlab.com/deploee/onnx-list](https://platform.openmmlab.com/deploee/onnx-list))
Model Selection: Choose a generic ONNX model suitable for the Jetson platform as the base model.

• Dataset : Collect a dataset relevant to your application scenario. For optimization tasks, you may need an annotated dataset for model fine-tuning.
• Model Fine-tuning and Performance Optimization: Optimize the model on the Jetson platform to improve inference speed and efficiency.
• Deployment and Testing: Deploy the optimized model on the Jetson platform and ensure that the model performs as expected in real-world environments.

Choose one of semanic segmentation models to deploy:

MMSegmentation:](https://mmdeploy.readthedocs.io/en/latest/04-supported-codebases/mmseg.html):) rtmpose, simcc, yolox-pose

Project requirements:

• Submit the project's GitHub project link.
• Provide a deployment tutorial that adheres to code standards and development requirements, including the following:
• Introduce the dataset used for model optimization and testing, and explain the data preprocessing process.
• Model Fine-tuning: Explain in detail the process of model fine-tuning, including the chosen base model, fine-tuning strategies, and performance optimization methods.
• Model Deployment: Describe how to deploy the optimized model on the Jetson platform.
• Summary and Evaluation: Summarize the project's achievements and evaluate the model's performance, efficiency, and adaptability on the Jetson platform.
• Produce relevant test results and benchmarks after optimization.

请从 语义分割 MMSegmentation](https://mmdeploy.readthedocs.io/en/latest/04-supported-codebases/mmseg.html)) 模型中选择 1 个：MMSegementation（分割）: fastscnn, bisenetv2, stdc ，在 NVIDIA Jetson 平台上进行部署，可通过以下几个步骤来调优模型：

• 模型选择：选择适合 Jetson 平台的通用 ONNX 模型作为基础模型。
• 数据集准备：收集与您的应用场景相关的数据集。对于调优任务，您可能需要有标注的数据集，以便进行模型微调。
• 模型微调，性能优化：在 Jetson 平台上，优化模型以提高推理速度和效率。
• 部署和测试：将调优后的模型部署到 Jetson 平台上，确保模型在实际环境中的性能符合预期。

产出要求：

1. 提交项目 Github 项目链接
2. 产出部署教程，符合代码规范和开发要求，包含以下内容：
   数据集和数据处理：介绍用于模型调优和测试的数据集，并解释数据的预处理过程。
   模型调优：详细说明模型调优的过程，包括选择的基础模型、微调策略和性能优化方法。
   模型部署：说明如何将调优后的模型部署到Jetson平台上
   总结和评估：总结项目的成果，并评估模型在Jetson平台上的性能、效率和适应性。
3. 对算法进行调优，产出相关测试结果和benchmark

硬件的使用可通过部署平台](https://platform.openmmlab.com/deploee?lang=zh-CN)) ，使用 Jetson Orin

Task Information

Repository：Other

Difficulty：Hard

Points：50

Estimated Time Consumption：21 days

Task Status

Claimant's Github ID：None

Claim Time：2023年08月23日

PR Link：None

Points to Note

To claim the task, please reply Claim the mission in the comment section. After claiming the task, please add our assistant bot called MeowMewo on WeChat at: openmmlabwx.

After completing the task, please submit a PR with the title starting with [MMSIG]. After submission, please reply with your PR link in the comments section of this Discussion. We will review your work as soon as possible.

在评论区回复 Claim the mission 领取任务，领取任务后请及时添加小助手喵喵微信: openmmlabwx

完成任务后提交相关链接（如是PR，标题请以[MMSIG]开头）回复到本Disucssion评论区，我们会第一时间审核您的贡献。