🎁 Christmas Came Early This Year! 🎄
A huge thanks to @NVIDIARobotics team for sending over the brand-new NVIDIA Jetson Orin Nano Super Developer Kit! 🚀
Stay tuned—Official @ultralytics YOLO11 benchmarks on this device are coming soon! 📊💡
🚨 Missed the last NVIDIA Jetson AI Lab meeting? Catch up on my insights on running @ultralytics YOLOv8 models on @NVIDIARobotics Jetson devices!🚀
🔍 Watch the recording and dive into these advancements in the Edge AI space: 👉 https://t.co/LqiipfpR3g
#EdgeAI#Jetson
🚀 Exciting times ahead! After the release of the new, more affordable @Raspberry_Pi 5 2GB, we explored running simulated benchmarks of @ultralytics YOLOv8 models on the Raspberry Pi 5 4GB by limiting the memory.
🔍 Learn more: https://t.co/mzmvtZh6y2
It’s YOLO Vision season! 🚀 Register for Ultralytics’ hybrid event on September 27. Enjoy top speakers, product announcements, networking, and fun
📍 Google for Startups Campus, Madrid (150 seats only!)
💻 Livestream: YouTube & BiliBili
🔗 Register now! https://t.co/qUuwsiIKx4
#Event2Attend: Join @opencvweekly webinar at 9AM PST this Thursday, April 6th. @lakshanthad will share how #reComputer powered by Jetson Orin meets server-class AI performance.
📺 https://t.co/ARWc2B0Sba
Benchmarking all #YOLOv8 models using trtexec tool which comes with JetPack on #reComputer J4012. It seems that even the largest model can run with an impressive performance on a compact embedded device🚀
👉https://t.co/3ZqQ9d6B54
@seeedstudio@NVIDIAEmbedded@ultralytics
Let Jetson Orin handle all AI models. 🤓Deploy @ultralytics YOLOv8x with INT8 precision with #reComputer J4012 and achieve an✨46fps inference rate with a default input image size of 640x640! 👉Learn how at detailed wiki: https://t.co/OfJ20s6UCn
@NVIDIAEmbedded
How about inferencing @ultralytics YOLOv8 at the edge? #reComputer J4012 easily handle at 53FPS using @PyTorch inference on COCO dataset (480x640). By utilizing #TensorRT, the performance will be boosted with Orin NX, and meanwhile, work with a more complex system!