Show and tell: Ultralytics YOLOv8 deployment on embedded devices

Show and tell: Ultralytics YOLOv8 deployment on embedded devices#

At YOLO Vision 2023 (YV23), Lakshantha Dissanayake outlined the details of deploying Ultralytics YOLOv8 models on embedded devices, particularly on NVIDIA Jetson and MCU platforms. Here are the key points from his talk at the Google for Startups Campus in Madrid.

Meet Lakshantha Dissanayake#

Lakshantha Dissanayake, Application Engineer at Seeed Studio, works on the company's AIoT initiatives. His talk underscored Seeed Studio's commitment to fostering partnerships with developers, ISVs, and SIs, with an emphasis on making technology more accessible.

The edge evolution#

The move to the edge marks a shift in computing toward decentralized data processing. Running workloads on edge devices enables real-time processing, reduces latency, and makes systems more responsive across diverse industries.

During his presentation, Lakshantha discussed the challenges and evolution of edge devices and the role they play in making technology accessible. He covered the nuances of optimizing edge performance, particularly for video analytics applications.

Overcoming deployment challenges#

Numerous new GPU devices are entering the market, but their pricing is quite high. On the other hand, embedded devices like the Jetson series offer a range of deployment features that make it easier for end users to conduct the analytics they need. If you're interested in learning how to get started with Seeed Studio Jetson devices, read our post on upgrading any camera with Ultralytics YOLOv8 and Seeed Studio edge devices.

Addressing the deployment challenges of Ultralytics YOLOv8 on edge devices, Lakshantha shared practical solutions. From flashing the operating system (OS) to setting up the environment, the talk walked through each step, making the deployment process more accessible for developers.

TensorRT and DeepStream#

TensorRT is a high-performance inference engine for NVIDIA embedded devices. It can quantize and optimize the Ultralytics YOLOv8 model, improving its performance on edge devices.

Lakshantha further showed how TensorRT improves inference performance and how DeepStream increases the efficiency of multi-stream applications. Practical demonstrations showed how these tools help YOLO models run efficiently on embedded devices.

MCU Platform unveiling#

Another highlight was Lakshantha's live demonstration of deploying YOLO models on the MCU platform using the SenseCraft Model Assistant. The demo gave the audience a look at where edge AI is heading.

Wrapping up#

Today, much of the focus is on embedded devices, where customers seek cost-effective solutions with minimal maintenance. Seeed Studio embedded devices feature preboot functionality, facilitating easy operations for developers and end-users.

Overall, the session covered the technical details of edge deployment and highlighted the collaborative spirit within the AI community.

Watch the full Ultralytics YOLOv8 embedded device deployment talk.