seeed studio Coral USB Accelerator Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers

I am thoroughly impressed with the Coral USB Accelerator—it's truly a game-changer for AI camera systems! As a developer working extensively with AI applications for surveillance and monitoring, this device has exceeded all my expectations. Firstly, the setup was incredibly straightforward. I simply plugged the Coral USB Accelerator into my existing camera system and connected it to my development environment. Within minutes, I was up and running, ready to integrate powerful AI capabilities into my cameras. The performance is exceptional. The Coral USB Accelerator significantly boosts the processing power of my cameras, allowing for real-time object detection, classification, and tracking. Even with multiple cameras running simultaneously, the Coral USB Accelerator handles the workload effortlessly, ensuring smooth and accurate AI inference. What I appreciate most is the versatility of this device. It supports various AI models, allowing me to choose the best one for my specific application. Whether I'm detecting intruders, monitoring traffic patterns, or identifying wildlife, the Coral USB Accelerator adapts seamlessly to different use cases. The compact size of the Coral USB Accelerator is also a big plus. It's portable and doesn't take up much space, making it ideal for both indoor and outdoor installations. The low power consumption is another bonus, ensuring that my camera systems remain energy-efficient. Overall, the Coral USB Accelerator has revolutionized the way I approach AI camera systems. It has enhanced the intelligence and responsiveness of my cameras, enabling more sophisticated and effective surveillance solutions. If you're looking to integrate AI into your camera network, I highly recommend the Coral USB Accelerator—it's a must-have for AI developers and security professionals alike.

I ordered this Coral accelerator because it was $12USD less than the official Google product. After installing the drivers, it was not recognized by the USB hub. There was no power light when plugged in. I sent it back and ordered one directly from Google. It works fine. Amazon needs to deep 6 whatever seeed studio is. I'll never purchase from them again.

This think works great. It's expensive, but it does what I need it to do. Using this with a mini-PC, running Frigate under Ubuntu for video security cameras. It found the device, and offloads the image detection to this, and takes a massive load off the PC. The processing is very quick, so I can get real-time detection and push alerts to my phone within seconds.

Using this with Frigate NVR and it is a friggin awesome little device! Camera detections are off the chart in quality--1 false identification (tagged my dog as a horse, but still an alert). I am using a TrueNAS server, plugged this in and used to help with the docker config. I also have a Nvidia P1000 running 8 cameras. The CPU utilization is about 2%, as is the GPU and the Coral TPU. Rain is no problem, squirrels are ignored and detections are just so good. Highly recommended for this application. Dropped this to 4 stars because my first device was DOA, returned it, re-ordered it and the second one is fine. Minor delay and easy to correct with Amazon returns.

Arrive quite quickly. Plugged it in using supplied cable to USB port on back of a Topton fanless 4 port mini PC (n5105) and passed through USB port from Proxmox to Frigate docker container (running in Docker LXC). Set up camera (Reolink RL-810A) and checked Inference Speed of Coral TPU and it was not good at 29.3 ms. Checked USB port specifications of Topton mini-PC and remembered that the back USB ports are all just USB gen2. Both USB A type ports on front of the Topton are gen3 but both were already in use so plugged Coral cable into a USB-A to USB-C adapter and then plugged adapter into front USB-C port on Topton mini PC. Re-configured USB pass through from Proxmox to container and restarted. Inference Speed dropped to 9.2 ms - should have read the section on using fastest USB port! Added 3 more cameras (one more Reolink RL-810A, one Eufy 2K indoor and one Eufy 2k Pan and Tilt indoor). Inference Speed looks stable at about 8.9 ms (not as good as some reports in frigate forums but still good given Coral is passed through from Proxmox to Docker LXC then Frigate docker container inside that). Coral TPU has been working well for past week and false positives cut down markedly from Eufy cameras (mostly cat detection) compared to native camera app's 'pet' detection while notifications from Frigate NVR via Home Assistant to phone are a few seconds faster than the notifications from the native camera apps but not much in it. Person detection from Reolink itself seems on a par with frigate/coral however but Frigate + Coral is work well with Person, cat, dog, bicycle and motorbike objects while Reolink natively only has Person, Vehicle and Pet. Overall pleased with Coral unit (even though seems expensive) and very pleased with speed of dispatch as well as delivery time from Seeed Studio Store.

The Coral is working just as expected! I have it currently running on a Frigate Docker instance to detect objects and its doing a great job of that. Initial testing using various models and images show that the Coral is very versatile and is easily hidden behind some other items. It does not draw too much power to overtax a Raspberry Pi power supply and is well within tolerance for all the different USB ports I've tried on different computers. I'm well pleased and will be ordering another in the coming weeks to add to another project as it looks like it'll fit into several other projects I have in mind.

Dos unidades he pedido, las dos vienen muertas, ni se enciende el led al conectarlo al usb ni lo reconoce ninguno de los 3 pcs en los que he probado, tanto windows como Linux. No se cual es el problema porque ambos venian precintados pero no funcionan. Devueltos.

Produto excelente. Usando o Frigate em meu MiniPC i5, antes do Coral ficava com 85% de consumo de CPU. Com o Coral, caiu para 25~30% de consumo. Recomendo bastante aos que usam Frigate.

Le Google Coral n'est pas reconnu sur Aucun de mes OS, j'ai changé de cable, même combat.

El tpu funciona como dice pero lo hace de manera lenta, es decir, dota a la raspberry de la capacidad de procesar informacion como el algoritmo YOLO pero eso mismo hace que el procesamiento disminuya, realembete lo hace pero lo hace lento.

Works with frigate