Deploy YOLOv10 on NVIDIA Jetson Orin with a One-Line Command: Real-Time End-to-End Object Detection
š„ Highlights:
- YOLOv10 is a state-of-the-art real-time object detection model.
- NVIDIA Jetson Orin is a powerful embedded platform for real-time object detection.
- Jetson-examples is a toolkit designed to deploy containerized applications including computer vision and generative AI on NVIDIA Jetson devices.
Introduction to YOLOv10
Developed by researchers at Tsinghua University, YOLOv10 introduces a revolutionary approach to real-time object detection, addressing the limitations of previous YOLO versions in both post-processing and model architecture. By eliminating non-maximum suppression (NMS) and optimizing various model components, YOLOv10 achieves state-of-the-art performance and efficiency across different model scales. For instance, YOLOv10-S on COCO is 1.8 times faster than RT-DETR-R18 while reducing parameters and FLOPs by 2.8 times. Compared to YOLOv9-C, YOLOv10-B reduces latency by 46% and parameters by 25% with the same performance.
Model Architecture and Performance
YOLOv10, selected from YOLOv8 due to its commendable latency-accuracy balance and availability in various model sizes, utilizes consistent dual assignments for NMS-free training and holistic efficiency-accuracy-driven model design. This approach enhances the efficiency and accuracy of YOLOv10 models.
Key Comparisons:
- YOLOv10: Designed as a real-time end-to-end object detection model, YOLOv10 is significantly faster than its predecessors. It achieves high performance on benchmarks like the COCO dataset while maintaining a lower number of parameters, contributing to its efficiency.
- YOLOv8: Developed by Ultralytics, YOLOv8 is renowned for its state-of-the-art object detection and image segmentation capabilities. It excels in accuracy and robustness across various computer vision tasks but does not match the speed of YOLOv10.
Speed and Efficiency:
- YOLOv10: Its optimizations enable it to process images more quickly, making it ideal for real-time applications. This speed advantage is achieved without a significant increase in parameters, ensuring the model remains lightweight and efficient.
- YOLOv8: While efficient, it focuses more on high accuracy and robustness, sometimes at the cost of processing speed.
How Does YOLOv10 Achieve Its Fast Speed?
Key Features:
- NMS-Free Training: Utilizes consistent dual assignments to eliminate the need for NMS, reducing inference latency.
- Holistic Model Design: Comprehensive optimization of components from both efficiency and accuracy perspectives, including lightweight classification heads, spatial-channel decoupled-down sampling, and rank-guided block design.
- Enhanced Model Capabilities: Incorporates large-kernel convolutions and partial self-attention modules to improve performance without significant computational cost.
Deploy YOLOv10 at the Edge Made Easy, Within Just One Minute!
Jetson-example offers one-line deployment projects and edge AI applications, including generative AI models like Ollama and Llama3, as well as computer vision models like YOLOv8 and YOLOv10. We have pre-configured all environments for you to enable single-command deployment of projects.
Deploy with jetson-examples: https://github.com/Seeed-Projects/jetson-examples/blob/main/reComputer/scripts/yolov10/README.md
Steps to Get Started:
- Get a Jetson Orin device from Seeed; our full-system devices are pre-built with Jetpack.
- Install the jetson-examples toolkit using the command:
pip3 install jetson-examples. - Run the YOLOv10 model.
- Check real-time inferencing results through the web UI.
š Note: For optimal performance with your custom model, we highly recommend using TensorRT to accelerate your model to fit the task requirements in the physical world. Check out how to use TensorRT with DeepStream SDK support on Jetson: TensorRT with DeepStream SDK.
Deploy YOLOv10 on NVIDIA Jetson Orin easily and leverage its real-time, end-to-end object detection capabilities to power your edge AI applications efficiently!
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