I'm excited to share my latest open-source project: an implementation of EfficientDet-Lite optimized with TensorRT to accelerate inference using NVIDIA GPUs. This work boosts the performance of object detection models, making them more practical for real-world applications that require both speed and precision.
What is EfficientDet-Lite?
EfficientDet-Lite is a family of object detection models designed for efficiency and speed, particularly on resource-constrained devices. Developed by Google Brain, these models maintain high accuracy while reducing computational costs, making them ideal for mobile and embedded applications. Leveraging TensorRT and NVIDIA GPUs
TensorRT is a high-performance deep learning inference optimizer and runtime developed by NVIDIA. It allows developers to maximize the performance of neural network models on NVIDIA GPUs. By integrating TensorRT with EfficientDet-Lite, I've harnessed the power of NVIDIA GPUs to achieve faster inference times without significantly compromising accuracy. Key Achievements The key achievements of this project are:
TensorRT Implementation: I've successfully converted EfficientDet-Lite models to run with TensorRT, significantly accelerating inference speeds on NVIDIA GPUs. This optimization takes full advantage of GPU acceleration, resulting in a more efficient object detection pipeline.
Open-Source Repository
The project is available on GitHub: EfficientDet-Lite with TensorRT. By sharing the code openly, I aim to contribute to the community, encouraging others to use, adapt, and enhance the implementation.
Performance Gains
Through optimization, the model achieves higher frames per second (FPS) during inference, making it suitable for applications that require real-time object detection.
Using the Repository:
To utilize the accelerated EfficientDet-Lite models:
Visit the Repository: EfficientDet-Lite on GitHub
Follow the Setup Instructions: The repository includes detailed steps on setting up the environment, installing dependencies, and running the model.
Run Inference: Test the model on sample images or integrate it into your application to experience the performance improvements firsthand.
Quantitative Comparison:
The following table shows the comparison of TensorRT accelerated EfficientDet-Lite models versus its ONNX variant.
Potential Applications
The optimized EfficientDet-Lite model can be applied across various fields:
Autonomous Vehicles: Enhance object detection for navigation and safety systems.
Surveillance Systems: Improve real-time monitoring with faster detection of objects and activities.
Robotics: Enable robots to perceive and react to their environment more effectively.
Augmented Reality: Integrate real-time object detection to create immersive AR experiences. By implementing EfficientDet-Lite with TensorRT on NVIDIA GPUs, this project demonstrates that it's possible to achieve high-speed inference without sacrificing accuracy, opening doors for innovative applications across various fields.
Thanks for reading!
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