381b00157e
Hoped to investigate this with my dev board at some point. In the meantime, added a warning for others who may experience it when upgrading to the new stable release.
1.7 KiB
| id | title |
|---|---|
| detectors | Detectors |
By default, Frigate will use a single CPU detector. If you have a Coral, you will need to configure your detector devices in the config file. When using multiple detectors, they run in dedicated processes, but pull from a common queue of requested detections across all cameras.
Frigate supports edgetpu and cpu as detector types. The device value should be specified according to the Documentation for the TensorFlow Lite Python API.
Note: There is no support for Nvidia GPUs to perform object detection with tensorflow. It can be used for ffmpeg decoding, but not object detection.
Single USB Coral
detectors:
coral:
type: edgetpu
device: usb
Multiple USB Corals
detectors:
coral1:
type: edgetpu
device: usb:0
coral2:
type: edgetpu
device: usb:1
Native Coral (Dev Board)
warning: may have compatibility issues after v0.9.x
detectors:
coral:
type: edgetpu
device: ""
Multiple PCIE/M.2 Corals
detectors:
coral1:
type: edgetpu
device: pci:0
coral2:
type: edgetpu
device: pci:1
Mixing Corals
detectors:
coral_usb:
type: edgetpu
device: usb
coral_pci:
type: edgetpu
device: pci
CPU Detectors (not recommended)
detectors:
cpu1:
type: cpu
num_threads: 3
cpu2:
type: cpu
num_threads: 3
When using CPU detectors, you can add a CPU detector per camera. Adding more detectors than the number of cameras should not improve performance.