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Add a community supported badge to specific detectors in the info summaries to better separate

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Nicolas Mowen 2025-11-22 10:19:10 -07:00
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3 changed files with 50 additions and 20 deletions
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14
docs/docs/configuration/object_detectors.md
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@ -3,6 +3,8 @@ id: object_detectors
title: Object Detectors title: Object Detectors
--- ---
import CommunityBadge from '@site/src/components/CommunityBadge';
# Supported Hardware # Supported Hardware
:::info :::info
@ -13,8 +15,8 @@ Frigate supports multiple different detectors that work on different types of ha
- [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB and m.2 format allowing for a wide range of compatibility with devices. - [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB and m.2 format allowing for a wide range of compatibility with devices.
- [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices. - [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
- [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms. - <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
- [DeGirum](#degirum): Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on [their website](https://hub.degirum.com). - <CommunityBadge /> [DeGirum](#degirum): Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on [their website](https://hub.degirum.com).
**AMD** **AMD**
@ -34,16 +36,16 @@ Frigate supports multiple different detectors that work on different types of ha
- [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt` Frigate image when a supported ONNX model is configured. - [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt` Frigate image when a supported ONNX model is configured.
**Nvidia Jetson** **Nvidia Jetson** <CommunityBadge />
- [TensortRT](#nvidia-tensorrt-detector): TensorRT can run on Jetson devices, using one of many default models. - [TensortRT](#nvidia-tensorrt-detector): TensorRT can run on Jetson devices, using one of many default models.
- [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt-jp6` Frigate image when a supported ONNX model is configured. - [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt-jp6` Frigate image when a supported ONNX model is configured.
**Rockchip** **Rockchip** <CommunityBadge />
- [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs. - [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs.
**Synaptics** **Synaptics** <CommunityBadge />
- [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs. - [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs.
@ -988,7 +990,7 @@ model:
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model. # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
# path: /config/yolox.zip # path: /config/yolox.zip
# The .zip file must contain: # The .zip file must contain:
# ├── yolox.dfp (a file ending with .dfp) # ├── yolox.dfp (a file ending with .dfp)
``` ```
#### SSDLite MobileNet v2 #### SSDLite MobileNet v2

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docs/docs/frigate/hardware.md
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@ -3,6 +3,8 @@ id: hardware
title: Recommended hardware title: Recommended hardware
--- ---
import CommunityBadge from '@site/src/components/CommunityBadge';
## Cameras ## Cameras
Cameras that output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. It is also helpful if your camera supports multiple substreams to allow different resolutions to be used for detection, streaming, and recordings without re-encoding. Cameras that output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. It is also helpful if your camera supports multiple substreams to allow different resolutions to be used for detection, streaming, and recordings without re-encoding.
@ -59,7 +61,7 @@ Frigate supports multiple different detectors that work on different types of ha
- [Supports primarily ssdlite and mobilenet model architectures](../../configuration/object_detectors#edge-tpu-detector) - [Supports primarily ssdlite and mobilenet model architectures](../../configuration/object_detectors#edge-tpu-detector)
- [MemryX](#memryx-mx3): The MX3 M.2 accelerator module is available in m.2 format allowing for a wide range of compatibility with devices. - <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 M.2 accelerator module is available in m.2 format allowing for a wide range of compatibility with devices.
- [Supports many model architectures](../../configuration/object_detectors#memryx-mx3) - [Supports many model architectures](../../configuration/object_detectors#memryx-mx3)
- Runs best with tiny, small, or medium-size models - Runs best with tiny, small, or medium-size models
@ -84,32 +86,26 @@ Frigate supports multiple different detectors that work on different types of ha
**Nvidia** **Nvidia**
- [TensortRT](#tensorrt---nvidia-gpu): TensorRT can run on Nvidia GPUs and Jetson devices. - [TensortRT](#tensorrt---nvidia-gpu): TensorRT can run on Nvidia GPUs to provide efficient object detection.
- [Supports majority of model architectures via ONNX](../../configuration/object_detectors#onnx-supported-models) - [Supports majority of model architectures via ONNX](../../configuration/object_detectors#onnx-supported-models)
- Runs well with any size models including large - Runs well with any size models including large
**Rockchip** - <CommunityBadge /> [Jetson](#nvidia-jetson): Jetson devices are supported via the TensorRT or ONNX detectors when running Jetpack 6.
**Rockchip** <CommunityBadge />
- [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs to provide efficient object detection. - [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs to provide efficient object detection.
- [Supports limited model architectures](../../configuration/object_detectors#choosing-a-model) - [Supports limited model architectures](../../configuration/object_detectors#choosing-a-model)
- Runs best with tiny or small size models - Runs best with tiny or small size models
- Runs efficiently on low power hardware - Runs efficiently on low power hardware
**Synaptics** **Synaptics** <CommunityBadge />
- [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs to provide efficient object detection. - [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs to provide efficient object detection.
::: :::
### Synaptics
- **Synaptics** Default model is **mobilenet**
| Name | Synaptics SL1680 Inference Time |
| ---------------- | ------------------------------- |
| ssd mobilenet | ~ 25 ms |
| yolov5m | ~ 118 ms |
### Hailo-8 ### Hailo-8
Frigate supports both the Hailo-8 and Hailo-8L AI Acceleration Modules on compatible hardware platforms—including the Raspberry Pi 5 with the PCIe hat from the AI kit. The Hailo detector integration in Frigate automatically identifies your hardware type and selects the appropriate default model when a custom model isn’t provided. Frigate supports both the Hailo-8 and Hailo-8L AI Acceleration Modules on compatible hardware platforms—including the Raspberry Pi 5 with the PCIe hat from the AI kit. The Hailo detector integration in Frigate automatically identifies your hardware type and selects the appropriate default model when a custom model isn’t provided.
@ -261,7 +257,7 @@ Inference speeds may vary depending on the host platform. The above data was mea
### Nvidia Jetson ### Nvidia Jetson
Frigate supports all Jetson boards, from the inexpensive Jetson Nano to the powerful Jetson Orin AGX. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration_video#nvidia-jetson-orin-agx-orin-nx-orin-nano-xavier-agx-xavier-nx-tx2-tx1-nano) when configured with the [appropriate presets](/configuration/ffmpeg_presets#hwaccel-presets), and will make use of the Jetson's GPU and DLA for object detection when configured with the [TensorRT detector](/configuration/object_detectors#nvidia-tensorrt-detector). Jetson devices are supported via the TensorRT or ONNX detectors when running Jetpack 6. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration_video#nvidia-jetson-orin-agx-orin-nx-orin-nano-xavier-agx-xavier-nx-tx2-tx1-nano) when configured with the [appropriate presets](/configuration/ffmpeg_presets#hwaccel-presets), and will make use of the Jetson's GPU and DLA for object detection when configured with the [TensorRT detector](/configuration/object_detectors#nvidia-tensorrt-detector).
Inference speed will vary depending on the YOLO model, jetson platform and jetson nvpmodel (GPU/DLA/EMC clock speed). It is typically 20-40 ms for most models. The DLA is more efficient than the GPU, but not faster, so using the DLA will reduce power consumption but will slightly increase inference time. Inference speed will vary depending on the YOLO model, jetson platform and jetson nvpmodel (GPU/DLA/EMC clock speed). It is typically 20-40 ms for most models. The DLA is more efficient than the GPU, but not faster, so using the DLA will reduce power consumption but will slightly increase inference time.
@ -282,6 +278,15 @@ Frigate supports hardware video processing on all Rockchip boards. However, hard
The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms for yolo-nas s. The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms for yolo-nas s.
### Synaptics
- **Synaptics** Default model is **mobilenet**
| Name | Synaptics SL1680 Inference Time |
| ------------- | ------------------------------- |
| ssd mobilenet | ~ 25 ms |
| yolov5m | ~ 118 ms |
## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version) ## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity. This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.

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docs/src/components/CommunityBadge/index.jsx Normal file
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@ -0,0 +1,23 @@
import React from "react";
export default function CommunityBadge() {
return (
<span
title="This detector is maintained by community members who provide code, maintenance, and support. See the contributing boards documentation for more information."
style={{
display: "inline-block",
backgroundColor: "#f1f3f5",
color: "#24292f",
fontSize: "11px",
fontWeight: 600,
padding: "2px 6px",
borderRadius: "3px",
border: "1px solid #d1d9e0",
marginLeft: "4px",
cursor: "help",
}}
>
Community Supported
</span>
);
}