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🚀 Elevate your edge AI game with Google Coral USB Accelerator — power, speed, and efficiency in your pocket!
The Seeed Studio Google Coral USB Accelerator is a compact USB 3.0 device featuring Google's custom Edge TPU ASIC, designed to deliver high-speed, low-power machine learning inference on Raspberry Pi and other Linux systems. Fully compatible with TensorFlow Lite models and Google Cloud, it supports popular architectures like MobileNet and Inception, enabling developers to add powerful AI capabilities to existing hardware effortlessly.
| Asin | B084TBYKL9 |
| Best Sellers Rank | #26,087 in Mobile Phones & Communication Products (See Top 100 in Mobile Phones & Communication Products) #10,741 in Mobile Phone Basic Cases |
| Brand | Seeed Studio |
| Built In Media | Seeed Studio Google Coral USB-Beschleuniger und Kabel |
| Compatible Devices | Raspberry Pi, Other Single-Board Computers, Google Cloud Connected Devices |
| Connectivity Technology | USB |
| Cpu Model | 80386 |
| Customer Reviews | 4.4 4.4 out of 5 stars (46) 4.4 out of 5 stars |
| Manufacturer | seeed studio |
| Mfr Part Number | 114991790U |
| Model Name | Google Coral |
| Model Number | 114991790U |
| Number Of Items | 1 |
| Number Of Packs | 1 |
| Operating System | Linux |
| Processor Brand | Microchip Technology |
| Processor Count | 1 |
| Ram Memory Installed | 1 GB |
| Ram Memory Technology | LPDDR3 |
| Total Usb Ports | 1 |
| Unit Count | 1.0 Count |
| Upc | 886268616142 |
| Wireless Compability | Bluetooth |
User
Must have hardware for frigate
Fast delivery and product is as described.Works really well with Frigate!
User
Parfait
Je l'utilise avec Frigate.Ca fait parfaitement le job avec 6 caméras.
User
Great addition for Frigate
Having purchased a Coral USB from a UK supplier that was DOA, it was with a little bit of trepidation that I ordered via Amazon's Global Store. However, the Amazon device worked out of the box and is a fabulous addition to a Frigate installation. My mini-PC's fan is no longer on 24/7. Inference is down from above 40ms to below 9ms. CPU usage is down from high 40% to below 12%. Seeing how difficult these are to get hold of, I might buy another, just in case.
User
Awesome
Awesome. Helps me with my project. Have to tinker a bit with the command line on Linux, which ai helps with. A great hobbyist tool. Happy with the purchase
User
era molto che volevo acquistarlo
ma nessuno lo vendeva su amazon....per darvi un'idea, in un video 640x480, l'algoritmo object detection (SSD Mobilenet) allenato su COCO dataset impiega 2 millisecondi a frame, indipendentemente dal numero di oggetti presenti... pazzesco, se pensate che RPI4 fa a malapena 3 frame al secondo. chiaramente tira da bestia se usato su usb3, e senza aumentarne il clock (si può fare per farlo andare ancora più veloce).il riconoscimento dei visi è spettacolare e velocissimo.
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