administrator/frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2026-01-22 20:18:30 +03:00
Code Issues Actions 2 Packages Projects Releases Wiki Activity

remove footnote about 0.17

Browse Source
This commit is contained in:
Josh Hawkins 2025-12-22 17:53:11 -06:00
parent bf4007d66a
commit bcc2d37a4c
1 changed files with 5 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
docs/docs/plus/index.md
View File

@ -16,12 +16,10 @@ There are three model types offered in Frigate+, `mobiledet`, `yolonas`, and `yo
Not all model types are supported by all detectors, so it's important to choose a model type to match your detector as shown in the table under [supported detector types](#supported-detector-types). You can test model types for compatibility and speed on your hardware by using the base models.
| Model Type | Description |
| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `mobiledet` | Based on the same architecture as the default model included with Frigate. Runs on Google Coral devices and CPUs. |
| `yolonas` | A newer architecture that offers slightly higher accuracy and improved detection of small objects. Runs on Intel, NVidia GPUs, and AMD GPUs. |
| `yolov9` | A leading SOTA (state of the art) object detection model with similar performance to yolonas, but on a wider range of hardware options. Runs on Intel, NVidia GPUs, AMD GPUs, Hailo, MemryX\*, Apple Silicon\*, and Rockchip NPUs. |
_\* Support coming in 0.17_
| `yolov9` | A leading SOTA (state of the art) object detection model with similar performance to yolonas, but on a wider range of hardware options. Runs on Intel, NVidia GPUs, AMD GPUs, Hailo, MemryX, Apple Silicon, and Rockchip NPUs. |
### YOLOv9 Details