Great video! I'm curious, how does running inference on the Edge TPU compare in real-world performance and power efficiency to other popular AI accelerators for Raspberry Pi? Would love to see some benchmarks or practical examples showing the difference, especially for projects in remote or battery-powered environments.
Thanks for watching! 😊 The Edge TPU is designed for high efficiency, offering fast inference speeds with low power consumption, making it ideal for remote or battery-powered projects. While I don't have specific benchmarks here, you can explore our detailed guide on using the Coral Edge TPU with Raspberry Pi for more insights: Edge TPU on Raspberry Pi Guide docs.ultralytics.com/guides/coral-edge-tpu-on-raspberry-pi/. This guide includes setup instructions and performance tips. For comparisons with other accelerators, you might want to check community forums or specific project case studies.
Good stuff, the Python API is excellent. I know you guys are probably still figuring this out with Hailo, but it would be awesome if a future video covered how to use the API with .hef models compiled for the AI Kit chips.
Thanks for the feedback! 😊 We appreciate your suggestion. While we don't have a video on that yet, you can explore our current documentation and stay tuned for future updates. If you have any other questions, feel free to ask!
Hi ultralytics, Jt here, dont know if you remember Im the professor of advance robotics who is using yolo and the full ultralytics repo, to teach edge computing in academia level here in Universidad del desarrollo Chile, we've been developing small edge PI4 with google coral last week, and we are gonna make a camp to deploy the models and leave it recording traffic data, about pedestrians, parking lot and its fluxes. It would be an honor if one of your team could review our 6 group project at the end of the month in a 30 min zoom meeting, so you could see our progress in class with the kids. It would be an amazing experience to my student to be in such pioneers and innovators.
Great, you should try with RKNN AI (Rockchips Chip) for example using Orange Pi board that have slightly better specifications than Raspberry Pi board 👍👍
That's a great suggestion! Using RKNN AI with an Orange Pi board could indeed offer enhanced performance. If you're interested in exploring this, you might want to check out the RKNN Toolkit for model conversion and deployment. Let us know how it goes! 😊
Bro, so if I strap a GoPro to my Labrador, could I use the Edge TPU on a Pi to differentiate between squirrels and Jeep trails? Asking for Lagging Louie. Curious if this setup can handle the wild AI needs of fieldwork.
Absolutely! 🐾 You can use the Edge TPU on a Raspberry Pi to run models that differentiate between squirrels and Jeep trails. Just make sure your model is trained with relevant data. The Edge TPU will help accelerate inference, making it suitable for real-time fieldwork. Check out our guide for setup details: docs.ultralytics.com/guides/coral-edge-tpu-on-raspberry-pi/ 🚀
Currently, our focus is on Raspberry Pi with Coral Edge TPU. For Ubuntu or Windows, you might need to adapt the Raspberry Pi guide. Check out the detailed setup here: docs.ultralytics.com/guides/coral-edge-tpu-on-raspberry-pi/. If you have specific questions, feel free to ask! 😊
Synaesthesia groove on this topic 🔥-I'm vibing hard. If I'm using my Edge TPU on Raspberry Pi for something heavy like real-time video, how does it realmente handle the heat both física e metafórica? Curious if leaving AI estressada leads to actual system stress down the road. Morro de vontade to push its boundaries! 🎶
Great to hear you're vibing with the Edge TPU! 🎶 The Edge TPU is designed for efficiency, but real-time video can push it. Make sure your Raspberry Pi is well-ventilated and consider a heat sink or fan to manage the heat. Over time, consistent high loads can stress the system, so monitor performance and temperature. Check out our guide for more tips: docs.ultralytics.com/guides/coral-edge-tpu-on-raspberry-pi/ 🚀
Thanks for the suggestion! We'll consider it for future content. In the meantime, you can explore our Raspberry Pi guide here: docs.ultralytics.com/guides/raspberry-pi/ 🚀
Hey! For running AI on Raspberry Pi with the Hailo AI kit, you'll want to check out Hailo's specific documentation and tools. Unfortunately, we don't have a guide for Hailo, but you can explore our Raspberry Pi setup guide here: docs.ultralytics.com/guides/raspberry-pi/ for general tips. 😊
This is mind-blowing! 🌱 But how energy-efficient is running these AI models on a Raspberry Pi with Edge TPU compared to traditional cloud computing? Could this be a more eco-friendly solution for smaller projects? #SaveThePlanet #GreenTech
Absolutely! Running AI models on a Raspberry Pi with an Edge TPU is much more energy-efficient than traditional cloud computing. It reduces data transfer and processing energy, making it a greener choice for smaller projects. 🌍 For more details, check out our guide: docs.ultralytics.com/guides/coral-edge-tpu-on-raspberry-pi/
✨ Yo, pondering the implications of cramming the genius of Edge TPU into the modest Raspberry Pi... how does running inference here compare with traditional cloud-based models in terms of latency and energy munching? Could this spark an "Edge vs Cloud" AI inferno? Dive deep, peeps! 🔍🔥 Bonus: Any thoughts on security when pushing AI decisions to the edge? 🚨🔍
Great question! Running inference on Edge TPU with Raspberry Pi significantly reduces latency and energy consumption compared to cloud-based models. This is because data processing happens locally, minimizing data transfer time and energy use. 🌟 As for security, edge deployment enhances privacy since data stays local, reducing exposure to potential breaches. However, it's crucial to implement strong access controls and encryption to safeguard data and model integrity. For more insights, check out our model deployment practices guide docs.ultralytics.com/guides/model-deployment-practices/. 🔒
Heyyy, this is WILD! If Raspberry Pi + Edge TPU is such an AI powerhouse now, do u think it could handle, like, real-time sports analytics at local games? Or r we still splinted to data centers for that kinda speed? Drop some wisdom, peeps!!! 🏃♂️🤖
Hey! 🌟 Raspberry Pi with Edge TPU can definitely handle some real-time sports analytics, especially for tasks like object detection and tracking. However, for more complex analytics or higher frame rates, data centers might still be needed. It’s all about balancing the workload! Check out our guide for more insights: docs.ultralytics.com/guides/coral-edge-tpu-on-raspberry-pi/ 🚀
Great video! I'm curious, how does running inference on the Edge TPU compare in real-world performance and power efficiency to other popular AI accelerators for Raspberry Pi? Would love to see some benchmarks or practical examples showing the difference, especially for projects in remote or battery-powered environments.
Thanks for watching! 😊 The Edge TPU is designed for high efficiency, offering fast inference speeds with low power consumption, making it ideal for remote or battery-powered projects. While I don't have specific benchmarks here, you can explore our detailed guide on using the Coral Edge TPU with Raspberry Pi for more insights: Edge TPU on Raspberry Pi Guide docs.ultralytics.com/guides/coral-edge-tpu-on-raspberry-pi/. This guide includes setup instructions and performance tips. For comparisons with other accelerators, you might want to check community forums or specific project case studies.
Good stuff, the Python API is excellent. I know you guys are probably still figuring this out with Hailo, but it would be awesome if a future video covered how to use the API with .hef models compiled for the AI Kit chips.
Thanks for the feedback! 😊 We appreciate your suggestion. While we don't have a video on that yet, you can explore our current documentation and stay tuned for future updates. If you have any other questions, feel free to ask!
Hi ultralytics, Jt here, dont know if you remember Im the professor of advance robotics who is using yolo and the full ultralytics repo, to teach edge computing in academia level here in Universidad del desarrollo Chile, we've been developing small edge PI4 with google coral last week, and we are gonna make a camp to deploy the models and leave it recording traffic data, about pedestrians, parking lot and its fluxes.
It would be an honor if one of your team could review our 6 group project at the end of the month in a 30 min zoom meeting, so you could see our progress in class with the kids. It would be an amazing experience to my student to be in such pioneers and innovators.
Hi Jt! It's fantastic. Thanks for watching! 😊
Great, you should try with RKNN AI (Rockchips Chip) for example using Orange Pi board that have slightly better specifications than Raspberry Pi board 👍👍
That's a great suggestion! Using RKNN AI with an Orange Pi board could indeed offer enhanced performance. If you're interested in exploring this, you might want to check out the RKNN Toolkit for model conversion and deployment. Let us know how it goes! 😊
Bro, so if I strap a GoPro to my Labrador, could I use the Edge TPU on a Pi to differentiate between squirrels and Jeep trails? Asking for Lagging Louie. Curious if this setup can handle the wild AI needs of fieldwork.
Absolutely! 🐾 You can use the Edge TPU on a Raspberry Pi to run models that differentiate between squirrels and Jeep trails. Just make sure your model is trained with relevant data. The Edge TPU will help accelerate inference, making it suitable for real-time fieldwork. Check out our guide for setup details: docs.ultralytics.com/guides/coral-edge-tpu-on-raspberry-pi/ 🚀
is there any tutorial of how to run yolo with coral tpu usb in ubuntu or windows? thanks
Currently, our focus is on Raspberry Pi with Coral Edge TPU. For Ubuntu or Windows, you might need to adapt the Raspberry Pi guide. Check out the detailed setup here: docs.ultralytics.com/guides/coral-edge-tpu-on-raspberry-pi/. If you have specific questions, feel free to ask! 😊
Synaesthesia groove on this topic 🔥-I'm vibing hard. If I'm using my Edge TPU on Raspberry Pi for something heavy like real-time video, how does it realmente handle the heat both física e metafórica? Curious if leaving AI estressada leads to actual system stress down the road. Morro de vontade to push its boundaries! 🎶
Great to hear you're vibing with the Edge TPU! 🎶 The Edge TPU is designed for efficiency, but real-time video can push it. Make sure your Raspberry Pi is well-ventilated and consider a heat sink or fan to manage the heat. Over time, consistent high loads can stress the system, so monitor performance and temperature. Check out our guide for more tips: docs.ultralytics.com/guides/coral-edge-tpu-on-raspberry-pi/ 🚀
Interesting video please create video on rasberry pi5 with hailo AI accelerator kit with ultralytics yolov8
Thanks for the suggestion! We'll consider it for future content. In the meantime, you can explore our Raspberry Pi guide here: docs.ultralytics.com/guides/raspberry-pi/ 🚀
Can you please do this but with raspberry pi AI kit (Hailo). thanks!
Hey! For running AI on Raspberry Pi with the Hailo AI kit, you'll want to check out Hailo's specific documentation and tools. Unfortunately, we don't have a guide for Hailo, but you can explore our Raspberry Pi setup guide here: docs.ultralytics.com/guides/raspberry-pi/ for general tips. 😊
This is mind-blowing! 🌱 But how energy-efficient is running these AI models on a Raspberry Pi with Edge TPU compared to traditional cloud computing? Could this be a more eco-friendly solution for smaller projects? #SaveThePlanet #GreenTech
Absolutely! Running AI models on a Raspberry Pi with an Edge TPU is much more energy-efficient than traditional cloud computing. It reduces data transfer and processing energy, making it a greener choice for smaller projects. 🌍 For more details, check out our guide: docs.ultralytics.com/guides/coral-edge-tpu-on-raspberry-pi/
✨ Yo, pondering the implications of cramming the genius of Edge TPU into the modest Raspberry Pi... how does running inference here compare with traditional cloud-based models in terms of latency and energy munching? Could this spark an "Edge vs Cloud" AI inferno? Dive deep, peeps! 🔍🔥
Bonus: Any thoughts on security when pushing AI decisions to the edge? 🚨🔍
Great question! Running inference on Edge TPU with Raspberry Pi significantly reduces latency and energy consumption compared to cloud-based models. This is because data processing happens locally, minimizing data transfer time and energy use. 🌟
As for security, edge deployment enhances privacy since data stays local, reducing exposure to potential breaches. However, it's crucial to implement strong access controls and encryption to safeguard data and model integrity. For more insights, check out our model deployment practices guide docs.ultralytics.com/guides/model-deployment-practices/. 🔒
Heyyy, this is WILD! If Raspberry Pi + Edge TPU is such an AI powerhouse now, do u think it could handle, like, real-time sports analytics at local games? Or r we still splinted to data centers for that kinda speed? Drop some wisdom, peeps!!! 🏃♂️🤖
Hey! 🌟 Raspberry Pi with Edge TPU can definitely handle some real-time sports analytics, especially for tasks like object detection and tracking. However, for more complex analytics or higher frame rates, data centers might still be needed. It’s all about balancing the workload! Check out our guide for more insights: docs.ultralytics.com/guides/coral-edge-tpu-on-raspberry-pi/ 🚀