mirror of
https://github.com/alexhopeoconnor/frigate.git
synced 2026-10-21 18:52:37 +10:00
call out avx2 requirement (#22305)
This commit is contained in:
Josh Hawkins
@@ -11,7 +11,7 @@ Object classification models are lightweight and run very fast on CPU.
|
||||
|
||||
Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
|
||||
|
||||
A CPU with AVX instructions is required for training and inference.
|
||||
A CPU with AVX + AVX2 instructions is required for training and inference.
|
||||
|
||||
## Classes
|
||||
|
||||
@@ -27,7 +27,6 @@ For object classification:
|
||||
### Classification Type
|
||||
|
||||
- **Sub label**:
|
||||
|
||||
- Applied to the object’s `sub_label` field.
|
||||
- Ideal for a single, more specific identity or type.
|
||||
- Example: `cat` → `Leo`, `Charlie`, `None`.
|
||||
|
||||
@@ -11,7 +11,7 @@ State classification models are lightweight and run very fast on CPU.
|
||||
|
||||
Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
|
||||
|
||||
A CPU with AVX instructions is required for training and inference.
|
||||
A CPU with AVX + AVX2 instructions is required for training and inference.
|
||||
|
||||
## Classes
|
||||
|
||||
|
||||
@@ -32,7 +32,7 @@ All of these features run locally on your system.
|
||||
|
||||
## Minimum System Requirements
|
||||
|
||||
A CPU with AVX instructions is required to run Face Recognition.
|
||||
A CPU with AVX + AVX2 instructions is required to run Face Recognition.
|
||||
|
||||
The `small` model is optimized for efficiency and runs on the CPU, most CPUs should run the model efficiently.
|
||||
|
||||
@@ -145,17 +145,14 @@ Start with the [Usage](#usage) section and re-read the [Model Requirements](#mod
|
||||
1. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Recent Recognitions tab in the Frigate UI's Face Library.
|
||||
|
||||
If you are using a Frigate+ or `face` detecting model:
|
||||
|
||||
- Watch the debug view (Settings --> Debug) to ensure that `face` is being detected along with `person`.
|
||||
- You may need to adjust the `min_score` for the `face` object if faces are not being detected.
|
||||
|
||||
If you are **not** using a Frigate+ or `face` detecting model:
|
||||
|
||||
- Check your `detect` stream resolution and ensure it is sufficiently high enough to capture face details on `person` objects.
|
||||
- You may need to lower your `detection_threshold` if faces are not being detected.
|
||||
|
||||
2. Any detected faces will then be _recognized_.
|
||||
|
||||
- Make sure you have trained at least one face per the recommendations above.
|
||||
- Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
|
||||
|
||||
|
||||
@@ -30,7 +30,7 @@ In the default mode, Frigate's LPR needs to first detect a `car` or `motorcycle`
|
||||
|
||||
## Minimum System Requirements
|
||||
|
||||
License plate recognition works by running AI models locally on your system. The YOLOv9 plate detector model and the OCR models ([PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)) are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM and a CPU with AVX instructions is required.
|
||||
License plate recognition works by running AI models locally on your system. The YOLOv9 plate detector model and the OCR models ([PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)) are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM and a CPU with AVX + AVX2 instructions is required.
|
||||
|
||||
## Configuration
|
||||
|
||||
@@ -375,7 +375,6 @@ Use `match_distance` to allow small character mismatches. Alternatively, define
|
||||
Start with ["Why isn't my license plate being detected and recognized?"](#why-isnt-my-license-plate-being-detected-and-recognized). If you are still having issues, work through these steps.
|
||||
|
||||
1. Start with a simplified LPR config.
|
||||
|
||||
- Remove or comment out everything in your LPR config, including `min_area`, `min_plate_length`, `format`, `known_plates`, or `enhancement` values so that the only values left are `enabled` and `debug_save_plates`. This will run LPR with Frigate's default values.
|
||||
|
||||
```yaml
|
||||
@@ -386,7 +385,6 @@ Start with ["Why isn't my license plate being detected and recognized?"](#why-is
|
||||
```
|
||||
|
||||
2. Enable debug logs to see exactly what Frigate is doing.
|
||||
|
||||
- Enable debug logs for LPR by adding `frigate.data_processing.common.license_plate: debug` to your `logger` configuration. These logs are _very_ verbose, so only keep this enabled when necessary. Restart Frigate after this change.
|
||||
|
||||
```yaml
|
||||
@@ -399,18 +397,15 @@ Start with ["Why isn't my license plate being detected and recognized?"](#why-is
|
||||
3. Ensure your plates are being _detected_.
|
||||
|
||||
If you are using a Frigate+ or `license_plate` detecting model:
|
||||
|
||||
- Watch the debug view (Settings --> Debug) to ensure that `license_plate` is being detected.
|
||||
- View MQTT messages for `frigate/events` to verify detected plates.
|
||||
- You may need to adjust your `min_score` and/or `threshold` for the `license_plate` object if your plates are not being detected.
|
||||
|
||||
If you are **not** using a Frigate+ or `license_plate` detecting model:
|
||||
|
||||
- Watch the debug logs for messages from the YOLOv9 plate detector.
|
||||
- You may need to adjust your `detection_threshold` if your plates are not being detected.
|
||||
|
||||
4. Ensure the characters on detected plates are being _recognized_.
|
||||
|
||||
- Enable `debug_save_plates` to save images of detected text on plates to the clips directory (`/media/frigate/clips/lpr`). Ensure these images are readable and the text is clear.
|
||||
- Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the `car` or `motorcycle` label will change to the recognized plate when LPR is enabled and working.
|
||||
- Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
|
||||
|
||||
@@ -13,7 +13,7 @@ Semantic Search is accessed via the _Explore_ view in the Frigate UI.
|
||||
|
||||
Semantic Search works by running a large AI model locally on your system. Small or underpowered systems like a Raspberry Pi will not run Semantic Search reliably or at all.
|
||||
|
||||
A minimum of 8GB of RAM is required to use Semantic Search. A CPU with AVX instructions is required to run Semantic Search. A GPU is not strictly required but will provide a significant performance increase over CPU-only systems.
|
||||
A minimum of 8GB of RAM is required to use Semantic Search. A CPU with AVX + AVX2 instructions is required to run Semantic Search. A GPU is not strictly required but will provide a significant performance increase over CPU-only systems.
|
||||
|
||||
For best performance, 16GB or more of RAM and a dedicated GPU are recommended.
|
||||
|
||||
|
||||
Reference in New Issue
Block a user