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https://github.com/blakeblackshear/frigate.git
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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.
80 lines
1.7 KiB
Markdown
80 lines
1.7 KiB
Markdown
---
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id: detectors
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title: Detectors
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---
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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.
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Frigate supports `edgetpu` and `cpu` as detector types. The device value should be specified according to the [Documentation for the TensorFlow Lite Python API](https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api).
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**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.
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### Single USB Coral
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```yaml
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detectors:
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coral:
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type: edgetpu
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device: usb
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```
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### Multiple USB Corals
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```yaml
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detectors:
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coral1:
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type: edgetpu
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device: usb:0
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coral2:
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type: edgetpu
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device: usb:1
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```
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### Native Coral (Dev Board)
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_warning: may have [compatibility issues](https://github.com/blakeblackshear/frigate/issues/1706) after `v0.9.x`_
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```yaml
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detectors:
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coral:
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type: edgetpu
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device: ""
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```
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### Multiple PCIE/M.2 Corals
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```yaml
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detectors:
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coral1:
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type: edgetpu
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device: pci:0
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coral2:
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type: edgetpu
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device: pci:1
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```
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### Mixing Corals
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```yaml
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detectors:
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coral_usb:
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type: edgetpu
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device: usb
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coral_pci:
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type: edgetpu
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device: pci
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```
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### CPU Detectors (not recommended)
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```yaml
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detectors:
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cpu1:
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type: cpu
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num_threads: 3
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cpu2:
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type: cpu
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num_threads: 3
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```
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When using CPU detectors, you can add a CPU detector per camera. Adding more detectors than the number of cameras should not improve performance.
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