administrator/frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2026-04-15 19:42:08 +03:00
Code Issues Actions 2 Packages Projects Releases Wiki Activity

Dumb copy/paste

Browse Source
This commit is contained in:
laviddichterman 2025-09-07 17:10:07 -07:00 committed by GitHub
parent f19b02638b
commit bdba7561d9
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 5 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

7
docs/docs/configuration/object_detectors.md
View File

@ -697,9 +697,8 @@ Replace `<your_codeproject_ai_server_ip>` and `<port>` with the IP address and p
To verify that the integration is working correctly, start Frigate and observe the logs for any error messages related to CodeProject.AI. Additionally, you can check the Frigate web interface to see if the objects detected by CodeProject.AI are being displayed and tracked properly. To verify that the integration is working correctly, start Frigate and observe the logs for any error messages related to CodeProject.AI. Additionally, you can check the Frigate web interface to see if the objects detected by CodeProject.AI are being displayed and tracked properly.
# Community Supported Detectors # Community Supported Detectors
## NVidia TensorRT Detector
laviddichterman marked this conversation as resolved.
## NVidia TensorRT Detector
Nvidia Jetson devices may be used for object detection using the TensorRT libraries. Due to the size of the additional libraries, this detector is only provided in images with the `-tensorrt-jp6` tag suffix, e.g. `ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp6`. This detector is designed to work with Yolo models for object detection. Nvidia Jetson devices may be used for object detection using the TensorRT libraries. Due to the size of the additional libraries, this detector is only provided in images with the `-tensorrt-jp6` tag suffix, e.g. `ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp6`. This detector is designed to work with Yolo models for object detection.
### Generate Models ### Generate Models
@ -717,6 +716,7 @@ If your GPU does not support FP16 operations, you can pass the environment varia
Specific models can be selected by passing an environment variable to the `docker run` command or in your `docker-compose.yml` file. Use the form `-e YOLO_MODELS=yolov4-416,yolov4-tiny-416` to select one or more model names. The models available are shown below. Specific models can be selected by passing an environment variable to the `docker run` command or in your `docker-compose.yml` file. Use the form `-e YOLO_MODELS=yolov4-416,yolov4-tiny-416` to select one or more model names. The models available are shown below.
<details> <details>
<summary>Available Models</summary> <summary>Available Models</summary>
``` ```
yolov3-288 yolov3-288
@ -747,7 +747,9 @@ yolov7x-640
yolov7x-320 yolov7x-320
``` ```
</details> </details>
An example `docker-compose.yml` fragment that converts the `yolov4-608` and `yolov7x-640` models would look something like this: An example `docker-compose.yml` fragment that converts the `yolov4-608` and `yolov7x-640` models would look something like this:
```yml ```yml
frigate: frigate:
environment: environment:
@ -777,6 +779,7 @@ model:
width: 320 # MUST match the chosen model i.e yolov7-320 -> 320, yolov4-416 -> 416 width: 320 # MUST match the chosen model i.e yolov7-320 -> 320, yolov4-416 -> 416
height: 320 # MUST match the chosen model i.e yolov7-320 -> 320 yolov4-416 -> 416 height: 320 # MUST match the chosen model i.e yolov7-320 -> 320 yolov4-416 -> 416
``` ```
## Rockchip platform ## Rockchip platform
Hardware accelerated object detection is supported on the following SoCs: Hardware accelerated object detection is supported on the following SoCs: