Miscellaneous fixes (#23651)

docs/docs/configuration/object_detectors.md

@@ -12,7 +12,7 @@ import objectDetectorsModels from '@site/data/object_detectors_models.yaml';
 
 ### Supported hardware
 
-Object detection is what allows Frigate to identify _what_ is in your camera's view — people, cars, animals, and more — rather than just reacting to pixel changes. When Frigate's motion detection finds activity in a frame, that region is sent to an **object detector**, which returns the objects it recognizes along with their location and a confidence score. These detections are what drive tracked objects, alerts, detections, and notifications.
+Object detection is what allows Frigate to identify _what_ is in your camera's view (people, cars, animals, and more) rather than just reacting to pixel changes. When Frigate's motion detection finds activity in a frame, that region is sent to an **object detector**, which returns the objects it recognizes along with their location and a confidence score. These detections are what drive tracked objects, alerts, detections, and notifications.
 
 Object detection is computationally intensive, so Frigate is designed to run it on a dedicated AI accelerator or GPU rather than the CPU. A **detector** is the specific hardware-and-model backend Frigate uses to run inference. Choosing a detector that matches your hardware is one of the most important steps in getting good performance, and the right choice depends on what device Frigate is running on.
 
@@ -79,15 +79,15 @@ This does not affect using hardware for accelerating other tasks such as [semant
 
 Along with picking a detector for your hardware, you will choose a model's **input resolution** (such as `320x320` or `640x640`) and, for model families like YOLOv9, a **variant size** (`tiny`, `small`, etc.). Both affect the balance between accuracy and the inference time your hardware can sustain.
 
-**Resolution (320x320 vs 640x640):** Frigate is optimized for `320x320` models, and `320x320` is the best choice for the vast majority of setups. Frigate is specifically designed to compensate for the smaller model by cropping a region of motion from the full frame and zooming into it before running detection, so a `320x320` model is actually _better_ at small and distant objects — not worse. A `640x640` model is slower and uses more resources, and its main benefit is fitting more objects into a single inference when many objects are spread across a large area. Recent versions of Frigate have improved support for `640x640` models, but `320x320` remains the recommended starting point for nearly all setups.
+**Resolution (320x320 vs 640x640):** Frigate is optimized for `320x320` models, and `320x320` is the best choice for the vast majority of setups. Frigate is specifically designed to compensate for the smaller model by cropping a region of motion from the full frame and zooming into it before running detection, so a `320x320` model is actually _better_ at small and distant objects, not worse. A `640x640` model is slower and uses more resources, and its main benefit is fitting more objects into a single inference when many objects are spread across a large area. Recent versions of Frigate have improved support for `640x640` models, but `320x320` remains the recommended starting point for nearly all setups.
 
-**Variant size (tiny/small/medium):** Larger variants are gradually more accurate but slower. Whether the difference is noticeable depends on your specific cameras and scenes. A good rule of thumb is to use the largest model your hardware can run without skipping detections, which you can monitor on the <NavPath path="System > Metrics > Cameras" /> page in the UI — better accuracy only helps if your detector keeps up with the detection load across all cameras.
+**Variant size (tiny/small/medium):** Larger variants are gradually more accurate but slower. Whether the difference is noticeable depends on your specific cameras and scenes. A good rule of thumb is to use the largest model your hardware can run without skipping detections, which you can monitor on the <NavPath path="System > Metrics > Cameras" /> page in the UI. Better accuracy only helps if your detector keeps up with the detection load across all cameras.
 
 **Acceptable inference time depends on your hardware.** Inference time alone does not tell the whole story, because different hardware has different capacity. A GPU can run multiple instances of the same model concurrently, so an inference time around 30ms can still keep up with several cameras. A Google Coral runs only a single instance of the model, so it needs a much lower inference time (around 10ms) to keep up.
 
 :::tip
 
-The best detection accuracy comes from a model trained on images that look like what Frigate actually sees — security camera footage cropped to regions of interest. You can train or fine-tune your own model on images like this and run it as a custom model (see the per-detector sections below), but [Frigate+](/plus) makes this much easier by handling the training for you on images submitted from your own cameras. For YOLOv9, the `s` (small) variant at `320x320` resolution is a good place to start.
+The best detection accuracy comes from a model trained on images that look like what Frigate actually sees: security camera footage cropped to regions of interest. You can train or fine-tune your own model on images like this and run it as a custom model (see the per-detector sections below), but [Frigate+](/plus) makes this much easier by handling the training for you on images submitted from your own cameras. For YOLOv9, the `s` (small) variant at `320x320` resolution is a good place to start.
 
 :::