Update hardware recs

add detail to reset_admin_password setting

docs/docs/configuration/advanced.md

@@ -4,7 +4,9 @@ title: Advanced Options
 sidebar_label: Advanced Options
 ---
 
-### `logger`
+### Logging
+
+#### Frigate `logger`
 
 Change the default log level for troubleshooting purposes.
 
@@ -28,6 +30,18 @@ Examples of available modules are:
 - `watchdog.<camera_name>`
 - `ffmpeg.<camera_name>.<sorted_roles>` NOTE: All FFmpeg logs are sent as `error` level.
 
+#### Go2RTC Logging
+
+See [the go2rtc docs](for logging configuration)
+
+```yaml
+go2rtc:
+  streams:
+    ...
+  log:
+    exec: trace
+```
+
 ### `environment_vars`
 
 This section can be used to set environment variables for those unable to modify the environment of the container (ie. within HassOS)

docs/docs/configuration/authentication.md

@@ -24,6 +24,11 @@ On startup, an admin user and password are generated and printed in the logs. It
 
 In the event that you are locked out of your instance, you can tell Frigate to reset the admin password and print it in the logs on next startup using the `reset_admin_password` setting in your config file.
 
+```yaml
+auth:
+  reset_admin_password: true
+```
+
 ## Login failure rate limiting
 
 In order to limit the risk of brute force attacks, rate limiting is available for login failures. This is implemented with Flask-Limiter, and the string notation for valid values is available in [the documentation](https://flask-limiter.readthedocs.io/en/stable/configuration.html#rate-limit-string-notation).

docs/docs/configuration/objects.md

@@ -5,7 +5,7 @@ title: Available Objects
 
 import labels from "../../../labelmap.txt";
 
-Frigate includes the object models listed below from the Google Coral test data.
+Frigate includes the object labels listed below from the Google Coral test data.
 
 Please note:
 

docs/docs/configuration/reference.md

@@ -480,10 +480,12 @@ snapshots:
 # Uses https://github.com/AlexxIT/go2rtc (v1.9.2)
 go2rtc:
 
-# Optional: jsmpeg stream configuration for WebUI
+# Optional: Live stream configuration for WebUI.
+# NOTE: Can be overridden at the camera level
 live:
-  # Optional: Set the name of the stream that should be used for live view
-  # in frigate WebUI. (default: name of camera)
+  # Optional: Set the name of the stream configured in go2rtc
+  # that should be used for live view in frigate WebUI. (default: name of camera)
+  # NOTE: In most cases this should be set at the camera level only.
   stream_name: camera_name
   # Optional: Set the height of the jsmpeg stream. (default: 720)
   # This must be less than or equal to the height of the detect stream. Lower resolutions

docs/docs/frigate/hardware.md

@@ -13,20 +13,19 @@ Many users have reported various issues with Reolink cameras, so I do not recomm
 
 Here are some of the camera's I recommend:
 
-- <a href="https://amzn.to/3uFLtxB" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) T5442TM-AS-LED</a> (affiliate link)
-- <a href="https://amzn.to/3isJ3gU" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T5442TM-AS</a> (affiliate link)
-- <a href="https://amzn.to/2ZWNWIA" target="_blank" rel="nofollow noopener sponsored">Amcrest IP5M-T1179EW-28MM</a> (affiliate link)
+- <a href="https://amzn.to/4fwoNWA" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T549M-ALED-S3</a> (affiliate link)
+- <a href="https://amzn.to/3YXpcMw" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T54IR-AS</a> (affiliate link)
+- <a href="https://amzn.to/3AvBHoY" target="_blank" rel="nofollow noopener sponsored">Amcrest IP5M-T1179EW-AI-V3</a> (affiliate link)
 
 I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
 
 ## Server
 
-My current favorite is the Beelink EQ12 because of the efficient N100 CPU and dual NICs that allow you to setup a dedicated private network for your cameras where they can be blocked from accessing the internet. There are many used workstation options on eBay that work very well. Anything with an Intel CPU and capable of running Debian should work fine. As a bonus, you may want to look for devices with a M.2 or PCIe express slot that is compatible with the Google Coral. I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
+My current favorite is the Beelink EQ13 because of the efficient N100 CPU and dual NICs that allow you to setup a dedicated private network for your cameras where they can be blocked from accessing the internet. There are many used workstation options on eBay that work very well. Anything with an Intel CPU and capable of running Debian should work fine. As a bonus, you may want to look for devices with a M.2 or PCIe express slot that is compatible with the Google Coral. I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
 
-| Name                                                                                                          | Coral Inference Speed | Coral Compatibility | Notes                                                                                                                                   |
-| ------------------------------------------------------------------------------------------------------------- | --------------------- | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
-| Beelink EQ12 (<a href="https://amzn.to/3OlTMJY" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms                | USB                 | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras.                                               |
-| Intel NUC (<a href="https://amzn.to/3psFlHi" target="_blank" rel="nofollow noopener sponsored">Amazon</a>)    | 5-10ms                | USB                 | Overkill for most, but great performance. Can handle many cameras at 5fps depending on typical amounts of motion. Requires extra parts. |
+| Name                                                                                                          | Coral Inference Speed | Coral Compatibility | Notes                                                                                     |
+| ------------------------------------------------------------------------------------------------------------- | --------------------- | ------------------- | ----------------------------------------------------------------------------------------- |
+| Beelink EQ13 (<a href="https://amzn.to/4ejU0ew" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms                | USB                 | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras. |
 
 ## Detectors
 

docs/docs/guides/configuring_go2rtc.md

@@ -13,7 +13,15 @@ Use of the bundled go2rtc is optional. You can still configure FFmpeg to connect
 
 # Setup a go2rtc stream
 
-First, you will want to configure go2rtc to connect to your camera stream by adding the stream you want to use for live view in your Frigate config file. For the best experience, you should set the stream name under go2rtc to match the name of your camera so that Frigate will automatically map it and be able to use better live view options for the camera. Avoid changing any other parts of your config at this step. Note that go2rtc supports [many different stream types](https://github.com/AlexxIT/go2rtc/tree/v1.9.4#module-streams), not just rtsp.
+First, you will want to configure go2rtc to connect to your camera stream by adding the stream you want to use for live view in your Frigate config file. Avoid changing any other parts of your config at this step. Note that go2rtc supports [many different stream types](https://github.com/AlexxIT/go2rtc/tree/v1.9.4#module-streams), not just rtsp.
+
+:::tip
+
+For the best experience, you should set the stream name under `go2rtc` to match the name of your camera so that Frigate will automatically map it and be able to use better live view options for the camera.
+
+See [the live view docs](../configuration/live.md#setting-stream-for-live-ui) for more information.
+
+:::
 
 ```yaml
 go2rtc:

docs/docs/guides/getting_started.md

@@ -306,7 +306,9 @@ By default, Frigate will retain video of all events for 10 days. The full set of
 
 ### Step 7: Complete config
 
-At this point you have a complete config with basic functionality. You can see the [full config reference](../configuration/reference.md) for a complete list of configuration options.
+At this point you have a complete config with basic functionality. 
+- View [common configuration examples](../configuration/index.md#common-configuration-examples) for a list of common configuration examples.
+- View [full config reference](../configuration/reference.md) for a complete list of configuration options.
 
 ### Follow up
 

docs/docs/integrations/api.md

@@ -411,17 +411,37 @@ HTTP Live Streaming Video on Demand URL for the specified event. Can be viewed i
 
 HTTP Live Streaming Video on Demand URL for the camera with the specified time range. Can be viewed in an application like VLC.
 
+### `GET /api/exports`
+
+Fetch a list of all export recordings
+
+Sample response:
+```json
+[
+  {
+    "camera": "doorbell",
+    "date": 12800057,
+    "id": "doorbell_pjis54",
+    "in_progress": false,
+    "name": "2024-10-04 fox visit",
+    "thumb_path": "/media/frigate/clips/export/doorbell_pjis54.webp",
+    "video_path": "/media/frigate/exports/doorbell_pjis54.mp4"
+  }
+]
+```
+
 ### `POST /api/export/<camera>/start/<start-timestamp>/end/<end-timestamp>`
 
 Export recordings from `start-timestamp` to `end-timestamp` for `camera` as a single mp4 file. These recordings will be exported to the `/media/frigate/exports` folder.
 
-It is also possible to export this recording as a time-lapse.
+It is also possible to export this recording as a time-lapse using the "playback" key in the json body, or specify a custom export filename, using the "name" key.
 
 **Optional Body:**
 
 ```json
 {
-  "playback": "realtime" // playback factor: realtime or timelapse_25x
+  "playback": "realtime", // playback factor: realtime or timelapse_25x
+  "name": "custom export name" // override the default export filename with a custom name
 }
 ```
 

docs/docs/plus/first_model.md

@@ -5,7 +5,7 @@ title: Requesting your first model
 
 ## Step 1: Upload and annotate your images
 
-Before requesting your first model, you will need to upload at least 10 images to Frigate+. But for the best results, you should provide at least 100 verified images per camera. Keep in mind that varying conditions should be included. You will want images from cloudy days, sunny days, dawn, dusk, and night. Refer to the [integration docs](../integrations/plus.md#generate-an-api-key) for instructions on how to easily submit images to Frigate+ directly from Frigate.
+Before requesting your first model, you will need to upload and verify at least 10 images to Frigate+. The more images you upload, annotate, and verify the better your results will be. Most users start to see very good results once they have at least 100 verified images per camera. Keep in mind that varying conditions should be included. You will want images from cloudy days, sunny days, dawn, dusk, and night. Refer to the [integration docs](../integrations/plus.md#generate-an-api-key) for instructions on how to easily submit images to Frigate+ directly from Frigate.
 
 It is recommended to submit **both** true positives and false positives. This will help the model differentiate between what is and isn't correct. You should aim for a target of 80% true positive submissions and 20% false positives across all of your images. If you are experiencing false positives in a specific area, submitting true positives for any object type near that area in similar lighting conditions will help teach the model what that area looks like when no objects are present.
 
@@ -13,7 +13,7 @@ For more detailed recommendations, you can refer to the docs on [improving your
 
 ## Step 2: Submit a model request
 
-Once you have an initial set of verified images, you can request a model on the Models page. Each model request requires 1 of the 12 trainings that you receive with your annual subscription. This model will support all [label types available](./index.md#available-label-types) even if you do not submit any examples for those labels. Model creation can take up to 36 hours.
+Once you have an initial set of verified images, you can request a model on the Models page. For guidance on choosing a model type, refer to [this part of the documentation](./index.md#available-model-types). Each model request requires 1 of the 12 trainings that you receive with your annual subscription. This model will support all [label types available](./index.md#available-label-types) even if you do not submit any examples for those labels. Model creation can take up to 36 hours.
 ![Plus Models Page](/img/plus/plus-models.jpg)
 
 ## Step 3: Set your model id in the config

docs/docs/plus/improving_model.md

@@ -3,7 +3,7 @@ id: improving_model
 title: Improving your model
 ---
 
-You may find that Frigate+ models result in more false positives initially, but by submitting true and false positives, the model will improve. Because a limited number of users submitted images to Frigate+ prior to this launch, you may need to submit several hundred images per camera to see good results. With all the new images now being submitted, future base models will improve as more and more users (including you) submit examples to Frigate+. Note that only verified images will be used when training your model. Submitting an image from Frigate as a true or false positive will not verify the image. You still must verify the image in Frigate+ in order for it to be used in training.
+You may find that Frigate+ models result in more false positives initially, but by submitting true and false positives, the model will improve. With all the new images now being submitted by subscribers, future base models will improve as more and more examples are incorporated. Note that only images with at least one verified label will be used when training your model. Submitting an image from Frigate as a true or false positive will not verify the image. You still must verify the image in Frigate+ in order for it to be used in training.
 
 - **Submit both true positives and false positives**. This will help the model differentiate between what is and isn't correct. You should aim for a target of 80% true positive submissions and 20% false positives across all of your images. If you are experiencing false positives in a specific area, submitting true positives for any object type near that area in similar lighting conditions will help teach the model what that area looks like when no objects are present.
 - **Lower your thresholds a little in order to generate more false/true positives near the threshold value**. For example, if you have some false positives that are scoring at 68% and some true positives scoring at 72%, you can try lowering your threshold to 65% and submitting both true and false positives within that range. This will help the model learn and widen the gap between true and false positive scores.
@@ -13,7 +13,7 @@ You may find that Frigate+ models result in more false positives initially, but
 
 For the best results, follow the following guidelines.
 
-**Label every object in the image**: It is important that you label all objects in each image before verifying. If you don't label a car for example, the model will be taught that part of the image is _not_ a car and it will start to get confused.
+**Label every object in the image**: It is important that you label all objects in each image before verifying. If you don't label a car for example, the model will be taught that part of the image is _not_ a car and it will start to get confused. You can exclude labels that you don't want detected on any of your cameras.
 
 **Make tight bounding boxes**: Tighter bounding boxes improve the recognition and ensure that accurate bounding boxes are predicted at runtime.
 
@@ -21,7 +21,7 @@ For the best results, follow the following guidelines.
 
 **Label objects hard to identify as difficult**: When objects are truly difficult to make out, such as a car barely visible through a bush, or a dog that is hard to distinguish from the background at night, flag it as 'difficult'. This is not used in the model training as of now, but will in the future.
 
-**`amazon`, `ups`, and `fedex` should label the logo**: For a Fedex truck, label the truck as a `car` and make a different bounding box just for the Fedex logo. If there are multiple logos, label each of them.
+**Delivery logos such as `amazon`, `ups`, and `fedex` should label the logo**: For a Fedex truck, label the truck as a `car` and make a different bounding box just for the Fedex logo. If there are multiple logos, label each of them.
 
 ![Fedex Logo](/img/plus/fedex-logo.jpg)
 
@@ -36,18 +36,17 @@ Misidentified objects should have a correct label added. For example, if a perso
 
 ## Shortcuts for a faster workflow
 
-|Shortcut Key|Description|
-|-----|--------|
-|`?`|Show all keyboard shortcuts|
-|`w`|Add box|
-|`d`|Toggle difficult|
-|`s`|Switch to the next label|
-|`tab`|Select next largest box|
-|`del`|Delete current box|
-|`esc`|Deselect/Cancel|
-|`← ↑ → ↓`|Move box|
-|`Shift + ← ↑ → ↓`|Resize box|
-|`-`|Zoom out|
-|`=`|Zoom in|
-|`f`|Hide/show all but current box|
-|`spacebar`|Verify and save|
+| Shortcut Key      | Description                   |
+| ----------------- | ----------------------------- |
+| `?`               | Show all keyboard shortcuts   |
+| `w`               | Add box                       |
+| `d`               | Toggle difficult              |
+| `s`               | Switch to the next label      |
+| `tab`             | Select next largest box       |
+| `del`             | Delete current box            |
+| `esc`             | Deselect/Cancel               |
+| `← ↑ → ↓`         | Move box                      |
+| `Shift + ← ↑ → ↓` | Resize box                    |
+| `scrollwheel`     | Zoom in/out                   |
+| `f`               | Hide/show all but current box |
+| `spacebar`        | Verify and save               |

docs/docs/plus/index.md

@@ -15,25 +15,52 @@ With a subscription, 12 model trainings per year are included. If you cancel you
 
 Information on how to integrate Frigate+ with Frigate can be found in the [integration docs](../integrations/plus.md).
 
+## Available model types
+
+There are two model types offered in Frigate+, `mobiledet` and `yolonas`. Both of these models are object detection models and are trained to detect the same set of labels [listed below](#available-label-types).
+
+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).
+
+| 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. |
+
 ## Supported detector types
 
+Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVino (`openvino`), ONNX (`onnx`), and ROCm (`rocm`) detectors.
+
 :::warning
 
-Frigate+ models are not supported for TensorRT or OpenVino yet.
+Using Frigate+ models with `onnx` and `rocm` is only available with Frigate 0.15, which is still under development.
 
 :::
 
-Currently, Frigate+ models only support CPU (`cpu`) and Coral (`edgetpu`) models. OpenVino is next in line to gain support.
+| Hardware                                                                                                                     | Recommended Detector Type | Recommended Model Type |
+| ---------------------------------------------------------------------------------------------------------------------------- | ------------------------- | ---------------------- |
+| [CPU](/configuration/object_detectors.md#cpu-detector-not-recommended)                                                       | `cpu`                     | `mobiledet`            |
+| [Coral (all form factors)](/configuration/object_detectors.md#edge-tpu-detector)                                             | `edgetpu`                 | `mobiledet`            |
+| [Intel](/configuration/object_detectors.md#openvino-detector)                                                                | `openvino`                | `yolonas`              |
+| [NVidia GPU](https://deploy-preview-13787--frigate-docs.netlify.app/configuration/object_detectors#onnx)\*                   | `onnx`                    | `yolonas`              |
+| [AMD ROCm GPU](https://deploy-preview-13787--frigate-docs.netlify.app/configuration/object_detectors#amdrocm-gpu-detector)\* | `rocm`                    | `yolonas`              |
 
-The models are created using the same MobileDet architecture as the default model. Additional architectures will be added in future releases as needed.
+_\* Requires Frigate 0.15_
 
 ## Available label types
 
-Frigate+ models support a more relevant set of objects for security cameras. Currently, only the following objects are supported: `person`, `face`, `car`, `license_plate`, `amazon`, `ups`, `fedex`, `package`, `dog`, `cat`, `deer`. Other object types available in the default Frigate model are not available. Additional object types will be added in future releases.
+Frigate+ models support a more relevant set of objects for security cameras. Currently, the following objects are supported:
+
+- **People**: `person`, `face`
+- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `license_plate`
+- **Delivery Logos**: `amazon`, `usps`, `ups`, `fedex`, `dhl`, `an_post`, `purolator`, `postnl`, `nzpost`, `postnord`, `gls`, `dpd`
+- **Animals**: `dog`, `cat`, `deer`, `horse`, `bird`, `raccoon`, `fox`, `bear`, `cow`, `squirrel`, `goat`, `rabbit`
+- **Other**: `package`, `waste_bin`, `bbq_grill`, `robot_lawnmower`, `umbrella`
+
+Other object types available in the default Frigate model are not available. Additional object types will be added in future releases.
 
 ### Label attributes
 
-Frigate has special handling for some labels when using Frigate+ models. `face`, `license_plate`, `amazon`, `ups`, and `fedex` are considered attribute labels which are not tracked like regular objects and do not generate events. In addition, the `threshold` filter will have no effect on these labels. You should adjust the `min_score` and other filter values as needed.
+Frigate has special handling for some labels when using Frigate+ models. `face`, `license_plate`, and delivery logos such as `amazon`, `ups`, and `fedex` are considered attribute labels which are not tracked like regular objects and do not generate events. In addition, the `threshold` filter will have no effect on these labels. You should adjust the `min_score` and other filter values as needed.
 
 In order to have Frigate start using these attribute labels, you will need to add them to the list of objects to track:
 
@@ -56,6 +83,6 @@ When using Frigate+ models, Frigate will choose the snapshot of a person object
 
 ![Face Attribute](/img/plus/attribute-example-face.jpg)
 
-`amazon`, `ups`, and `fedex` labels are used to automatically assign a sub label to car objects.
+Delivery logos such as `amazon`, `ups`, and `fedex` labels are used to automatically assign a sub label to car objects.
 
 ![Fedex Attribute](/img/plus/attribute-example-fedex.jpg)

docs/docs/troubleshooting/edgetpu.md

@@ -49,7 +49,21 @@ The USB Coral can become stuck and need to be restarted, this can happen for a n
 
 ## PCIe Coral Not Detected
 
-The most common reason for the PCIe coral not being detected is that the driver has not been installed. See [the coral docs](https://coral.ai/docs/m2/get-started/#2-install-the-pcie-driver-and-edge-tpu-runtime) for how to install the driver for the PCIe based coral.
+The most common reason for the PCIe Coral not being detected is that the driver has not been installed. This process varies based on what OS and kernel that is being run. 
+
+- In most cases [the Coral docs](https://coral.ai/docs/m2/get-started/#2-install-the-pcie-driver-and-edge-tpu-runtime) show how to install the driver for the PCIe based Coral.
+- For Ubuntu 22.04+ https://github.com/jnicolson/gasket-builder can be used to build and install the latest version of the driver.
+
+### Not detected on Raspberry Pi5
+
+A kernel update to the RPi5 means an upate to config.txt is required, see [the raspberry pi forum for more info](https://forums.raspberrypi.com/viewtopic.php?t=363682&sid=cb59b026a412f0dc041595951273a9ca&start=25)
+
+Specifically, add the following to config.txt
+
+```
+dtoverlay=pciex1-compat-pi5,no-mip
+dtoverlay=pcie-32bit-dma-pi5
+```
 
 ## Only One PCIe Coral Is Detected With Coral Dual EdgeTPU
 

notebooks/YOLO_NAS_Pretrained_Export.ipynb

@@ -11,6 +11,18 @@
         "! pip install -q super_gradients==3.7.1"
       ]
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "! sed -i 's/sghub.deci.ai/sg-hub-nv.s3.amazonaws.com/' /usr/local/lib/python3.10/dist-packages/super_gradients/training/pretrained_models.py\n",
+        "! sed -i 's/sghub.deci.ai/sg-hub-nv.s3.amazonaws.com/' /usr/local/lib/python3.10/dist-packages/super_gradients/training/utils/checkpoint_utils.py"
+      ],
+      "metadata": {
+        "id": "NiRCt917KKcL"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -72,4 +84,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}