Ihr Warenkorb ist leer
Ihr Warenkorb ist leerDas integrierte Edge-TPU ist ein kleiner ASIC, der von Google entwickelt wurde, der TensorFlow Lite-Modelle energieeffizient beschleunigt: Es ist in der Lage, 4 Billionen Operationen pro Sekunde (4 TOPS) durchzuführen, wobei 2 Watt Leistung verbraucht – das sind 2 TOPS pro Watt. Zum Beispiel kann One Edge TPU hochmoderne mobile Vision-Modelle wie MobileNet v2 mit fast 400 Bildern pro Sekunde ausführen. Diese ML-Verarbeitung auf dem Gerät reduziert die Latenz, erhöht den Datenschutz und macht eine ständige Internetverbindung überflüssig. Der Coral USB Accelerator fügt Ihrem System einen Edge-TPU-Coprozessor hinzu, der High-Speed-Maschine-Learning-Inferenzen auf einer Vielzahl von Systemen ermöglicht, indem Sie ihn einfach an einen USB-Anschluss anschließen. Führt High-Speed-ML-Inferenzen durch. Der integrierte Edge-TPU-Coprozessor ist in der Lage, 4 Billionen Operationen (Tera-Operationen) pro Sekunde (TOPS) durchzuführen, wobei 0,5 Watt für jedes TOPS (2 TOPS pro Watt) verwendet werden. Zum Beispiel kann es hochmoderne mobile Vision-Modelle wie MobileNet v2 mit fast 400 FPS energieeffizient ausführen. Weitere Leistungs-Benchmarks Unterstützt alle wichtigen Plattformen. Verbindet sich über USB mit jedem System mit Debian Linux (einschließlich Raspberry Pi), macOS oder Windows 10. Unterstützt TensorFlow Lite, keine Notwendigkeit, Modelle von Grund auf zu bauen. TensorFlow Lite Modelle können kompiliert werden, um auf dem Edge TPU zu laufen.
Ahmad F Al-Twaijiry
Bewertet in Saudi-Arabien am 30. August 2024
Works with frigate
Ross Crowhurst
Bewertet in den USA am29. Februar 2024
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.
Bobby R.
Bewertet in den USA am26. Mai 2024
Fits right into an m.2 slot and out of the way. Runs as it should and the documentation is pretty extensive.
T@Alasmari
Bewertet in Saudi-Arabien am 13. Oktober 2024
not working at all
justin tackett
Bewertet in den USA am13. Dezember 2024
This was a huge pain in the A$$ to set up. I purchased it as an upgrade from my coral USB accelorator. Only to find it out was a nightmare to get up and running in on my raspberry pi. Mainly because Google abandoned the project 3 years ago. Meaning they havnt updated anything. One of those things is the version of python. The libraries you need run on an older version of python and Raspberry Pis OS comes with the latest version. I had to find an older version of debian LINUX that still used it and run a docker container just to set everything up. WAS NOT fun and I essentially was so disappointed, havnt used it much since. You can still use it to increase your inference rate for training your Ai model, but keep running into so many issues it's really not worth thr hassle I woukd not suggest buying one unless support and development has inprocd
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