The table below presents the performance benchmark results for the popular AI models and tasks such as object detection, face detection, and more on the raspberry pi 5 and Hailo-8L with batch size 8.
Network | Rpi5 Performance | Input resolution |
---|---|---|
yolov5s_personface | 150.21 | 640x640x3 |
yolov6n | 354.07 | 640x640x3 |
yolov8s | 127.85 | 640x640x3 |
Yolov8s_pose | 123.43 | 640x640x3 |
yolox_s_leaky | 110.26 | 640x640x3 |
Yolov5n_seg | 103.57 | 640x640x3 |
ssd_mobilenet_v2 | 145.42 | 300x300x3 |
efficientnet_edgetpu_l | 97.48 | 300x300x3 |
efficientnet_edgetpu_m | 242.92 | 240x240x3 |
efficientnet_edgetpu_s | 373.96 | 224x224x3 |
Resnet_v1_50 | 257.56 | 224x224x3 |
clip_resnet_50 | 127.85 | 224x224x3 |
The results show the RPi5 and Hailo-8L offers excellent performance that should be more than sufficient for deploying these types of AI applications.
The frame rates are high enough to support real-time processing for most common use cases like security camers, smart home devices, robotics, etc. And these popular models are very efficient, so you still have headroom to run additional software on the Pi alongside them.
Please keep in mind that the performance results may vary depending on the software version being used.
The results presented here were generated using PCIe Gen 3 technology.
It’s worth noting that the difference in performance between the Hailo-8L with Raspberry Pi 5 and the Explorer model is due to the fact that the Raspberry Pi 5 and Hailo-8L are connected using a single-lane PCIe interface.