Inferencing time in milli-seconds for the for MobileNet v2 model (left hand bars, blue) and the MobileNet v1 SSD 0.75 depth model (right hand bars, green), trained using the Common Objects in Context (COCO) dataset with an input size of 300×300.
| Board | Framework | Connection | MobileNet v2 (ms) | MobileNet v1 (ms) |
|---|---|---|---|---|
| Jetson Nano | TensorFlow | 309.3 | 276.0 | |
| Jetson Nano | TensorRT | 72.3 | 61.6 | |
| Coral Dev Board | Edge TPU | 20.9 | 15.7 | |
| Coral USB Accelerator | Edge TPU | USB2 | 58.1 | 49.3 |
| Coral USB Accelerator | Edge TPU | USB3 | 18.2 | 14.9 |
| Movidius NCS | OpenVINO | USB2 | 204.5 | 115.7 |
| Movidius NCS | OpenVINO | USB3 | 176.4 | 88.4 |
| Intel NCS2 | OpenVIINO | USB2 | 118.6 | 87.2 |
| Intel NCS2 | OpenVINO | USB3 | 80.4 | 52.8 |
| Raspberry Pi 3, Model B+ | TensorFlow | 654.0 | 480.3 | |
| Raspberry Pi 4 | TensorFlow | 483.5 | 263.9 | |
| Raspberry Pi 5 | TensorFlow | 148.9 | 66.2 | |
| Raspberry Pi 3, Model B+ | TensorFlow Lite | 379.6 | 271.5 | |
| Raspberry Pi 4 | TensorFlow Lite | 112.6 | 82.7 | |
| Raspberry Pi 5 | TensorFlow Lite | 23.5 | 16.9 |
The latest results are presented in the article benchmarking the Raspberry Pi 5.
NOTE: See the documentation directory for instructions on how to install TensorFlow and TensorFlow Lite, and how to run the benchmarking scripts on your hardware.
- Hands on with the Coral Dev Board
- How to use a Raspberry Pi to flash the new firmware onto the Coral Dev Board
- Hands on with the Coral USB Accelerator
NOTE: These guides are likely to be out of date and are in need of updating.
NOTE: These guides are likely to be out of date and are in need of updating.
NOTE: These guides are likely to be out of date and are in need of updating.
- The Big Benchmark Roundup
- Benchmarking Edge Computing
- Benchmarking TensorFlow and TensorFlow Lite on the Raspberry Pi
- Benchmarking the Xnor AI2GO Platform on the Raspberry Pi
- Benchmarking Machine Learning on the new Raspberry Pi 4
- Benchmarking TensorFlow Lite on the new Raspberry Pi 4
- Benchmarking the Intel Neural Compute Stick on the new Raspberry Pi 4
- Benchmarking TensorFlow and TensorFlow Lite on Raspberry Pi 5
The benchmark code need to be updated to run the latest versions of the inferenecing frameworks:
- benchmark_edgetpu.py - Script for Edge TPU (Coral) hardware
- benchmark_intel.py - Script for Intel (Movidius) hardware using OpenVINO
- benchmark_tf.py - Script for TensorFlow on generic hardware (CPU and GPU)
- benchmark_tf_lite.py - Script for TensorFlow Lite on generic hardware
- benchmark_tf_trt.py - Script for Nvidia Jetson hardware using TensorRT
NOTE: The benchmark_edgetpu.py script currently uses the deprecated edgetpu library, and needs to be updated to use the pycoral library. However even that library is no longer properly supported by Google.
The code in this repository is licensed under the MIT licence.
Copyright © 2019-2024 Alasdair Allan <alasdair@babilim.co.uk>
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