Mirrors/blakeblackshear.frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2025-03-04 00:17:22 +01:00
Code Issues Projects Releases Wiki Activity

config docs fix

Browse Source
This commit is contained in:
OmriAx 2025-03-03 14:18:04 +02:00
parent 2bdbad72aa
commit f99bb8ec14
2 changed files with 36 additions and 24 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

47
docs/docs/configuration/object_detectors.md
View File

@ -131,7 +131,8 @@ detectors:
``` ```
--- ---
## Hailo-8 / Hailo-8L Detector
## Hailo-8 Detector
This detector is available for use with both Hailo-8 and Hailo-8L AI Acceleration Modules. The integration automatically detects your hardware architecture via the Hailo CLI and selects the appropriate default model if no custom model is specified. This detector is available for use with both Hailo-8 and Hailo-8L AI Acceleration Modules. The integration automatically detects your hardware architecture via the Hailo CLI and selects the appropriate default model if no custom model is specified.
@ -139,11 +140,14 @@ See the [installation docs](../frigate/installation.md#hailo-8l) for information
### Configuration ### Configuration
When configuring the Hailo detector, you have two options to specify the model: a local **path** or a **URL**.
If both are provided, the detector will first check for the model at the given local path. If the file is not found, it will download the model from the specified URL. The model file is cached under `/config/model_cache/hailo`.
#### YOLO (Recommended) #### YOLO (Recommended)
Use this configuration for YOLO-based models. When no custom model path or URL is provided, the detector checks for a cached model at `/config/model_cache/hailo` and automatically downloads the default model based on the detected hardware: Use this configuration for YOLO-based models. When no custom model path or URL is provided, the detector automatically downloads the default model based on the detected hardware:
- **Hailo-8 hardware:** Uses `yolov8s.hef` - **Hailo-8 hardware:** Uses **YOLOv8s** (default: `yolov8s.hef`)
- **Hailo-8L hardware:** Uses `yolov6n.hef` - **Hailo-8L hardware:** Uses **YOLOv6n** (default: `yolov6n.hef`)
```yaml ```yaml
detectors: detectors:
@ -157,15 +161,23 @@ model:
input_tensor: nhwc input_tensor: nhwc
input_pixel_format: rgb input_pixel_format: rgb
input_dtype: int input_dtype: int
model_type: hailoyolo model_type: hailo-yolo
# The detector automatically selects the default model based on your hardware: # The detector automatically selects the default model based on your hardware:
# - For Hailo-8 hardware: YOLOv8s (default: yolov8s.hef) # - For Hailo-8 hardware: YOLOv8s (default: yolov8s.hef)
# - For Hailo-8L hardware: YOLOv6n (default: yolov6n.hef) # - For Hailo-8L hardware: YOLOv6n (default: yolov6n.hef)
#
# Optionally, you can specify a local model path to override the default.
# If a local path is provided and the file exists, it will be used instead of downloading.
# Example:
# path: /config/model_cache/hailo/yolov8s.hef
#
# You can also override using a custom URL:
# url: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8/yolov8s.hef
``` ```
#### SSD #### SSD
For SSD-based models, provide the model path (or URL) to your compiled SSD model: For SSD-based models, provide either a model path or URL to your compiled SSD model. The integration will first check the local path before downloading if necessary.
```yaml ```yaml
detectors: detectors:
@ -179,19 +191,28 @@ model:
input_tensor: nhwc input_tensor: nhwc
input_pixel_format: bgr input_pixel_format: bgr
model_type: ssd model_type: ssd
path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef # Specify the local model path (if available) or URL for SSD MobileNet v1.
# Example with a local path:
# path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
#
# Or override using a custom URL:
# url: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8l/ssd_mobilenet_v1.hef
``` ```
#### Custom Models #### Custom Models
The Hailo detector supports all YOLO models compiled for Hailo hardware that include post-processing. You can specify a custom URL to download your model directly. If provided, the detector will use the custom model instead of the default one. The Hailo detector supports all YOLO models compiled for Hailo hardware that include post-processing. You can specify a custom URL or a local path to download or use your model directly. If both are provided, the detector checks the local path first.
```yaml ```yaml
detectors: detectors:
hailo8l: hailo8l:
type: hailo8l type: hailo8l
device: PCIe device: PCIe
url: https://custom-model-url.com/path/to/model.hef # Optional: Specify a local model path.
# path: /config/model_cache/hailo/custom_model.hef
#
# Alternatively, or as a fallback, provide a custom URL:
# url: https://custom-model-url.com/path/to/model.hef
model: model:
width: 640 width: 640
@ -199,14 +220,18 @@ model:
input_tensor: nhwc input_tensor: nhwc
input_pixel_format: rgb input_pixel_format: rgb
input_dtype: int input_dtype: int
model_type: hailoyolo model_type: hailo-yolo
``` ```
> **Note:** > **Note:**
> If both a model path and URL are provided, the detector will first check the local model path. If the file is not found, it will download the model from the URL. > If both a model **path** and **URL** are provided, the detector will first check the local model path. If the file is not found, it will download the model from the URL.
> >
> *Tested custom models include: yolov5, yolov8, yolov9, yolov11.* > *Tested custom models include: yolov5, yolov8, yolov9, yolov11.*
---
This guide now clearly explains how the model is chosen based on the presence of a local file path versus a URL, ensuring users know which model will be used by the integration.
## OpenVINO Detector ## OpenVINO Detector

13
docs/docs/frigate/hardware.md
View File

@ -104,19 +104,6 @@ Additionally, the heavier **YOLOv8m** model has been tested on Hailo-8 hardware
In real-world deployments, even with multiple cameras running concurrently, Frigate has demonstrated consistent performance. Testing on x86 platforms—with dual PCIe lanes—yields further improvements in FPS, throughput, and latency compared to the Raspberry Pi setup. In real-world deployments, even with multiple cameras running concurrently, Frigate has demonstrated consistent performance. Testing on x86 platforms—with dual PCIe lanes—yields further improvements in FPS, throughput, and latency compared to the Raspberry Pi setup.
#### Supported Models & Inference Times
| Model Type | Hardware | Inference Time (RPi) | Inference Time (x86) | Resolution |
|--------------------|------------------|----------------------|----------------------|------------|
| SSD MobileNet V1 | Hailo-8L | 17–21 ms | 12–15 ms | 300×300 |
| SSD MobileNet V1 | Hailo-8 | 10–13 ms | – | 300×300 |
| YOLOv6n (Default) | Hailo-8L | 16–20 ms | 10–13 ms | 640×640 |
| YOLOv8s (Default) | Hailo-8 | 15–19 ms | 12–18 ms | 640×640 |
| YOLOv8m (Tested) | Hailo-8 | 18–25 ms | 16–22 ms | 640×640 |
*Note: Inference times may vary based on system configuration and operating conditions.*
This documentation is part of Frigate’s internal integration guide, ensuring that users get the optimal performance by automatically adapting to the available Hailo hardware.
## Community Supported Detectors ## Community Supported Detectors