🎉 Welcome to my GitHub profile!
Never leave that till tomorrow which you can do today.
-- Benjamin Franklin
In 2024, I focused on contributing to the development of several software frameworks, including:
- Model training framework for autonomous driving
- Model deployment framework for edge computing
- MLOps framework
- Optimization toolkit
The Model Training Framework for Autonomous Driving I developed supports a variety of tasks, such as 2D/3D object detection, depth estimation, instance and semantic segmentation, and optical flow estimation. This framework is designed to facilitate efficient model training for autonomous driving applications, covering critical computer vision tasks.
On the deployment side, I worked on the Model Deployment Framework for Edge Computing, optimizing models to meet the constraints of edge devices. To achieve this, I applied techniques like quantization, pruning, and re-parameterization, all aimed at reducing model size and improving inference performance. These optimizations ensure that the models can be efficiently deployed on resource-constrained devices, making them suitable for real-time edge applications.
Additionally, I developed an Optimization Toolkit, which offers a set of tools and libraries designed to enhance program performance. The toolkit includes customized PyTorch operations, a lightweight visualizer, and a high-performance parser, and so on. All aimed at improving the efficiency of both model training and deployment workflows.
For more details about my work and projects, please visit my profile.