Show and Tell: YOLOv8 Deployment on Embedded Devices

Show and Tell: YOLOv8 Deployment on Embedded Devices

At YOLO VISION 2023 (YV23), Lakshantha Dissanayake outlined the intricacies of deploying Ultralytics YOLOv8 models on embedded devices, particularly on NVIDIA Jetson and MCU platforms. Let's delve into the insightful journey he shared at the Google for Startups Campus in Madrid.‍

‍Meet Lakshantha Dissanayake

Application Engineer at Seeed Studio Lakshantha Dissanayake leads Seed Studio's charge in AIoT innovation. His talk underscored Seed Studio's commitment to fostering partnerships with developers, ISVs, and SIs, emphasizing the democratization of technology.

The Edge Evolution

The Edge Evolution signifies a pivotal shift in computing, emphasizing decentralized data processing. With a focus on edge devices, this evolution enhances real-time processing, reduces latency, and empowers local devices for efficient and responsive systems across diverse industries.

During his presentation, Lakshantha delved into the challenges and evolution of edge devices, recognizing the pivotal role they play in making technology accessible. He tackled the nuances of optimizing edge performance, particularly for video analytics applications, setting the stage for the audience.

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 Seeedstudio Jetson devices, you can visit our blog.

Navigating through the deployment challenges of YOLOv8 on-edge devices, Lakshantha shared practical solutions. From flashing OS (Operating System) to setting up the environment, the talk demystified complexities, making the deployment process more accessible for developers.

TensorRT and DeepStream Magic

TensorRT serves as a top-tier engine for inference on embedded devices. It quantizes and optimizes the Ultralytics YOLOv8 model, enhancing its performance specifically for edge devices.

Lakshantha further showcased the magic of TensorRT in enhancing inference performance and the efficiency of multi-stream applications using DeepStream. Practical demonstrations illustrated the power of these tools in maximizing the potential of YOLO models on embedded devices.

MCU Platform Unveiling

Another exciting highlight was Lakshantha's live demonstration of deploying YOLO models on the MCU platform using the SenseGraph model assistant. This glimpse into the future of edge AI left the audience eager to explore the possibilities.

Wrapping Up

In this era, the spotlight is primarily 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 not only illuminated the technical aspects but also showcased the collaborative spirit within the AI community, making it an enlightening experience for all attendees.

Learn more about YOLOv8 deployment on embedded devices here!