Detail XDNA2 community detector (#24480)

* Update hardware.md

Fix stray qualification

* docs: add XDNA2 detector configuration

* docs: add XDNA2 ZMQ model config

.cspell/frigate-dictionary.txt

@@ -8,6 +8,7 @@ amdgpu
 analyzeduration
 Annke
 apexcharts
+Aqara
 arange
 argmax
 argmin
@@ -64,6 +65,7 @@ dsize
 dtype
 ECONNRESET
 edgetpu
+Eufy
 facenet
 fastapi
 faststart
@@ -82,6 +84,7 @@ frontdoor
 fstype
 fullchain
 fullscreen
+gatekeep
 genai
 generativeai
 genpts

.github/DISCUSSION_TEMPLATE/beta-support.yml

@@ -10,8 +10,11 @@ body:
 
         Before submitting, read the [beta documentation][docs].
 
+        By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
         [docs]: https://docs-dev.frigate.video/
         [discussions]: https://github.com/blakeblackshear/frigate/discussions
+        [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
   - type: textarea
     id: description
     attributes:

.github/DISCUSSION_TEMPLATE/camera-support.yml

@@ -8,9 +8,12 @@ body:
 
         Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
 
+        By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
         [discussions]: https://www.github.com/blakeblackshear/frigate/discussions
         [docs]: https://docs.frigate.video
         [faq]: https://github.com/blakeblackshear/frigate/discussions/12724
+        [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
   - type: textarea
     id: description
     attributes:

.github/DISCUSSION_TEMPLATE/config-support.yml

@@ -8,9 +8,12 @@ body:
 
         Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
 
+        By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
         [discussions]: https://www.github.com/blakeblackshear/frigate/discussions
         [docs]: https://docs.frigate.video
         [faq]: https://github.com/blakeblackshear/frigate/discussions/12724
+        [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
   - type: textarea
     id: description
     attributes:

.github/DISCUSSION_TEMPLATE/detector-support.yml

@@ -8,9 +8,12 @@ body:
 
         Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
 
+        By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
         [discussions]: https://www.github.com/blakeblackshear/frigate/discussions
         [docs]: https://docs.frigate.video
         [faq]: https://github.com/blakeblackshear/frigate/discussions/12724
+        [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
   - type: textarea
     id: description
     attributes:

.github/DISCUSSION_TEMPLATE/general-support.yml

@@ -8,9 +8,12 @@ body:
 
         Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
 
+        By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
         [discussions]: https://www.github.com/blakeblackshear/frigate/discussions
         [docs]: https://docs.frigate.video
         [faq]: https://github.com/blakeblackshear/frigate/discussions/12724
+        [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
   - type: textarea
     id: description
     attributes:

.github/DISCUSSION_TEMPLATE/hardware-acceleration-support.yml

@@ -8,9 +8,12 @@ body:
 
         Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
 
+        By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
         [discussions]: https://www.github.com/blakeblackshear/frigate/discussions
         [docs]: https://docs.frigate.video
         [faq]: https://github.com/blakeblackshear/frigate/discussions/12724
+        [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
   - type: textarea
     id: description
     attributes:

.github/DISCUSSION_TEMPLATE/question.yml

@@ -10,9 +10,12 @@ body:
 
         **If you are looking for support, start a new discussion and use a support category.**
 
+        By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
         [discussions]: https://www.github.com/blakeblackshear/frigate/discussions
         [docs]: https://docs.frigate.video
         [faq]: https://github.com/blakeblackshear/frigate/discussions/12724
+        [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
   - type: textarea
     id: description
     attributes:

.github/DISCUSSION_TEMPLATE/report-a-bug.yml

@@ -12,11 +12,14 @@ body:
 
         **If you are unsure if your issue is actually a bug or not, please submit a support request first.**
 
+        By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
         [discussions]: https://www.github.com/blakeblackshear/frigate/discussions
         [prs]: https://www.github.com/blakeblackshear/frigate/pulls
         [docs]: https://docs.frigate.video
         [faq]: https://github.com/blakeblackshear/frigate/discussions/12724
         [ai]: https://docs.frigate.video
+        [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
   - type: checkboxes
     attributes:
       label: Checklist

.github/ISSUE_TEMPLATE/feature_request.md

@@ -7,6 +7,13 @@ assignees: ''
 
 ---
 
+<!--
+  By posting here you agree to follow our AI policy:
+  https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
+
+  Requests that appear to be written by an AI on your behalf may be closed without a response.
+-->
+
 **Describe what you are trying to accomplish and why in non technical terms**
 I want to be able to ... so that I can ... 
 

.github/pull_request_template.md

@@ -1,4 +1,4 @@
-_Please read the [contributing guidelines](https://github.com/blakeblackshear/frigate/blob/dev/CONTRIBUTING.md) before submitting a PR._
+_Please read the [contributing guidelines](https://github.com/blakeblackshear/frigate/blob/dev/CONTRIBUTING.md) and the [AI policy](https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md) before submitting a PR. Every PR must be read and submitted by a person, and PRs that appear to be unreviewed AI output will be closed without review._
 
 ## Proposed change
 

AI_POLICY.md

@@ -0,0 +1,126 @@
+# Frigate AI Policy
+
+## TL;DR
+
+- **Use AI tools if they help you.** We do too. This is about what you post, not which tools you use to write it.
+- **A person has to read it and send it.** Don't wire a bot or an agent up to post on your behalf.
+- **Write your posts yourself.** Your own words, the template filled in, and you answering maintainers rather than your assistant.
+- **Don't paste an AI's guess at the cause as though it were a diagnosis.** Tell us what you actually observed.
+- **Read your code before you submit it.** Disclose that AI was used, and be ready to explain every line.
+- **If we misjudge something you wrote, just say so.** We'll take you at your word.
+
+The rest of this document explains each of these, and why.
+
+## Scope
+
+AI tools are a reality of modern development and we're not opposed to their use. You are responsible for anything you submit, however it was produced, and we are responsible for anything we merge and release. We hold a high bar for both.
+
+This policy applies everywhere this project is discussed: issues, discussions, pull requests, code reviews, and commit comments.
+
+## Why this exists
+
+Frigate is built and supported by a small group of maintainers and a community of volunteers who read every post and review every pull request. Nobody here is paid to do it, and time spent reading a post is time not spent fixing bugs or building features.
+
+We're not opposed to AI tools. We use them too. But content generated by an AI and submitted without review costs a real person real time, and usually gives them less to work with than a few honest sentences would have. That is the problem this policy addresses.
+
+## A person has to be in the loop
+
+Every issue, discussion, comment, and pull request here must be read and submitted by a person. Using an AI tool to help you write is fine. Wiring one up to post on your behalf is not.
+
+Specifically, do not:
+
+- Connect a bot or agent to GitHub that opens issues, discussions, or pull requests without you reading them first
+- Post output from a tool you have not read
+- Use tooling to file bulk or drive-by contributions across the repository
+
+We will close anything we believe was posted without a person reading it, and we may mark it as spam. Posts that skip the templates are the most common sign of this.
+
+## Issues, discussions, and comments
+
+We do not mind if you use AI tools to help you write. Do not have tools post unreviewed content on your behalf. We may hide any comment we believe to be unreviewed AI output.
+
+Keep posts to what is needed to communicate your point. A long, confidently written, AI-padded post is harder to help with than a short direct one, not easier, and it is usually obvious.
+
+**Describe your actual problem in your own words.** Tell us what you did, what you expected, and what actually happened. That is the information we need, and only you have it.
+
+**Do not paste an AI's guess at the cause as though it were a diagnosis.** It is frequently wrong in ways that send everyone down the wrong path, and it buries the details that would have led to the real answer. We would rather see what you observed than what a model inferred.
+
+**Fill in the template completely.** The templates ask for logs, config, version, and hardware because those are the things needed to help you. An AI cannot supply them for you, and a post missing them cannot be acted on.
+
+**Answer maintainers yourself.** If we ask you a question, we are asking _you_, not your AI assistant. These are the spaces where we build trust and understanding with the community, and that only works if we're talking to each other. Using AI to fix your grammar or clarity is fine, but the substance has to be yours.
+
+This applies to pull request descriptions and review replies as much as it does to bug reports and discussions.
+
+### Quoting AI output
+
+If you want to include something an AI told you, it must be:
+
+- In a quote block, using `>`
+- Disclosed as AI output, saying which tool it came from
+- Accompanied by your own comment explaining why you think it is relevant
+
+Keep the excerpt short. Do not paste long transcripts.
+
+### Non-native English speakers
+
+AI is genuinely useful for participating in a project that operates in English, and we would rather hear from you through a translation tool than not hear from you at all. Using AI to improve the grammar or clarity of something you wrote yourself is fine.
+
+If you are translating your posts, make sure the translation says what you meant. Including your original text in a `<details>` block helps us verify the translation if something reads oddly, and keeps the thread readable.
+
+## Code contributions
+
+We need to understand your relationship with the code you're submitting. The more AI was involved, the more important it is that you've genuinely reviewed, tested, and understood what it produced.
+
+Because of the long-term maintenance burden every merged change creates, we require a human in the loop who understands the work the AI produced. Pull requests that appear to be unreviewed AI output will be closed without review.
+
+### Requirements when AI is used
+
+If AI is used to generate any portion of the code, contributors must adhere to the following requirements:
+
+1. **Explicitly disclose the manner in which AI was employed.** The PR template asks for this. Be honest, this won't automatically disqualify your PR. We'd rather have an honest disclosure than find out later. Trust matters more than method.
+2. **Perform a comprehensive manual review prior to submitting the pull request.** Don't submit code you haven't read carefully and tested locally.
+3. **Be prepared to explain every line of code you submitted when asked about it by a maintainer.** If you can't explain why something works the way it does, you're not ready to submit it.
+4. **Check for an existing pull request addressing the same change.** If one exists, comment there and work with its author instead of opening a duplicate.
+5. **It is strictly prohibited to use AI to write your posts for you** (bug reports, feature requests, pull request descriptions, GitHub discussions, responding to humans, etc.). We need to hear from _you_, not your AI assistant. These are the spaces where we build trust and understanding with contributors, and that only works if we're talking to each other.
+
+### Established contributors
+
+Contributors with a long history of thoughtful, quality contributions to Frigate have earned trust through that track record. The level of scrutiny we apply to AI usage naturally reflects that trust. This isn't a formal exemption, it's just how trust works. If you've been around, we know how you think and how you work. If you're new, we're still getting to know you, and clear disclosure helps build that relationship.
+
+### What this means in practice
+
+We're not trying to gatekeep how you write code. Use whatever tools make you productive. But there's a difference between using AI as a tool to implement something you understand and handing a feature request to an AI and submitting whatever comes back. The former is fine. The latter creates maintenance risk for the project.
+
+Some honest context: when we review a PR, we're not just evaluating whether the code works today. We're evaluating whether we can maintain it, debug it, and extend it long-term, often without the original author's involvement. Code that the author doesn't deeply understand is code that nobody understands, and that's a liability.
+
+One more thing worth saying directly: most maintainers already have access to the same AI tools you do. A PR that's entirely AI-generated, where the author can't explain the design, debug issues independently, or engage substantively in design discussions, doesn't offer something we couldn't produce ourselves. What makes a contribution genuinely valuable is the human judgment and domain understanding behind it, as well as the engagement during review that shapes it into something we can confidently take on long-term.
+
+## Our use of AI
+
+The Frigate documentation site has an "Ask AI" search that answers questions from the docs, and we may use AI tooling to help with triage and project management. Like any automated tooling, it is not always right.
+
+If an AI tool leaves a comment on your contribution, treat it the way you would any other comment. If you think it is wrong, say so, and a brief explanation is enough. Maintainers always have the final say.
+
+## Enforcement
+
+Contributions and posts that do not follow this policy will be closed. Depending on the situation, maintainers may also:
+
+- Hide or delete comments that appear to be unreviewed AI output
+- Mark automated content as spam
+- Close an issue, discussion, or pull request without further review
+- Lock a conversation
+- Temporarily or permanently block an account from participating in the project
+
+Repeated violations may result in being blocked from contributing to Frigate.
+
+### When we get it wrong
+
+There is no reliable way to detect this, and we're not going to pretend otherwise. Whether something reads as unreviewed AI output is a judgment call, usually made quickly, by a volunteer with limited time and no way to know for certain. These calls are subjective and we won't always get them right.
+
+If it happens to you, just say so. A short reply telling us you wrote it yourself is enough, and we'll take you at your word and pick the conversation back up. We would much rather occasionally reopen something we misjudged than treat everyone who posts here as a suspect.
+
+We'd ask for some understanding in return. These calls get made quickly because the volume is real, and time spent second-guessing them is time not spent helping the person in the next thread.
+
+## Attribution
+
+Portions of this policy are adapted from the [Open Home Foundation AI Policy](https://developers.home-assistant.io/docs/ai_policy/).

CONTRIBUTING.md

@@ -2,6 +2,8 @@
 
 Thank you for your interest in contributing to Frigate. This document covers the expectations and guidelines for contributions. Please read it before submitting a pull request.
 
+All participation in this project, including pull requests, issues, and discussions, is covered by our [AI policy](AI_POLICY.md).
+
 ## Before you start
 
 ### Bugfixes
@@ -21,28 +23,16 @@ Before writing code for a new feature:
 
 ## AI usage policy
 
-AI tools are a reality of modern development and we're not opposed to their use. But we need to understand your relationship with the code you're submitting. The more AI was involved, the more important it is that you've genuinely reviewed, tested, and understood what it produced.
+AI tools are a reality of modern development and we're not opposed to their use. But we need to understand your relationship with the code you're submitting, and we need to hear from you rather than from your AI assistant.
 
-### Requirements when AI is used
+**Read the [AI policy](AI_POLICY.md) before you open a pull request.** It is short, and it applies to everything you post here. The parts that most often catch people out:
 
-If AI is used to generate any portion of the code, contributors must adhere to the following requirements:
+- A person has to be in the loop. Don't wire a bot or agent up to open pull requests, issues, or discussions on your behalf.
+- Disclose how AI was used. The PR template asks for this. Be honest, it won't automatically disqualify your PR.
+- Review and test everything you submit, and be prepared to explain every line when asked.
+- Don't use AI to write your PR description or your replies to maintainers.
 
-1. **Explicitly disclose the manner in which AI was employed.** The PR template asks for this. Be honest — this won't automatically disqualify your PR. We'd rather have an honest disclosure than find out later. Trust matters more than method.
-2. **Perform a comprehensive manual review prior to submitting the pull request.** Don't submit code you haven't read carefully and tested locally.
-3. **Be prepared to explain every line of code they submitted when asked about it by a maintainer.** If you can't explain why something works the way it does, you're not ready to submit it.
-4. **It is strictly prohibited to use AI to write your posts for you** (bug reports, feature requests, pull request descriptions, GitHub discussions, responding to humans, etc.). We need to hear from _you_, not your AI assistant. These are the spaces where we build trust and understanding with contributors, and that only works if we're talking to each other.
-
-### Established contributors
-
-Contributors with a long history of thoughtful, quality contributions to Frigate have earned trust through that track record. The level of scrutiny we apply to AI usage naturally reflects that trust. This isn't a formal exemption — it's just how trust works. If you've been around, we know how you think and how you work. If you're new, we're still getting to know you, and clear disclosure helps build that relationship.
-
-### What this means in practice
-
-We're not trying to gatekeep how you write code. Use whatever tools make you productive. But there's a difference between using AI as a tool to implement something you understand and handing a feature request to an AI and submitting whatever comes back. The former is fine. The latter creates maintenance risk for the project.
-
-Some honest context: when we review a PR, we're not just evaluating whether the code works today. We're evaluating whether we can maintain it, debug it, and extend it long-term — often without the original author's involvement. Code that the author doesn't deeply understand is code that nobody understands, and that's a liability.
-
-One more thing worth saying directly: most maintainers already have access to the same AI tools you do. A PR that's entirely AI-generated — where the author can't explain the design, debug issues independently, or engage substantively in design discussions — doesn't offer something we couldn't produce ourselves. What makes a contribution genuinely valuable is the human judgment and domain understanding behind it, as well as the engagement during review that shapes it into something we can confidently take on long-term.
+Pull requests that appear to be unreviewed AI output will be closed without review.
 
 ## Pull request guidelines
 

Makefile

@@ -1,7 +1,7 @@
 default_target: local
 
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.18.0
+VERSION = 0.18.1
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
 BOARDS= #Initialized empty

audio-labelmap.txt

@@ -24,7 +24,7 @@ yell
 sigh
 singing
 choir
-sodeling
+yodeling
 chant
 mantra
 child_singing

docker/main/build_ov_model.py

@@ -1,10 +1,14 @@
 """Convert the default SSDLite MobileNet v2 model to OpenVINO IR.
 
 Replaces the legacy openvino-dev Model Optimizer conversion. The TensorFlow
-frontend converts the Object Detection API frozen graph natively; the four TF
-outputs are then repacked into the single [1, 1, 100, 7] DetectionOutput-style
-tensor that Frigate's OpenVINO detector expects, and the input is flipped to
-BGR to match the legacy reverse_input_channels behavior.
+frontend translates the Object Detection API pre and post processors literally,
+producing per-class NonMaxSuppression, NonZero ops and map loops with data
+dependent shapes that the GPU plugin handles very badly. Both are cut out the
+way ssd_v2_support.json used to do it: the preprocessor is an identity at the
+native 300x300 input, and the postprocessor becomes a single fused
+DetectionOutput. The result is the [1, 1, 100, 7] tensor that Frigate's
+OpenVINO detector expects, with the input flipped to BGR to match the legacy
+reverse_input_channels behavior.
 """
 
 import numpy as np
@@ -12,31 +16,91 @@ import openvino as ov
 from openvino import opset8 as ops
 from openvino.preprocess import PrePostProcessor
 
+MODEL_DIR = "/models/ssdlite_mobilenet_v2_coco_2018_05_09"
+OUTPUT_PATH = "/models/ssdlite_mobilenet_v2.xml"
+INPUT_SHAPE = [1, 300, 300, 3]
+
+# faster_rcnn_box_coder divides the deltas by pipeline.config's y/x/height/width
+# scales of 10/10/5/5, which DetectionOutput expresses as per-prior variances.
+BOX_VARIANCES = np.float32([0.1, 0.1, 0.2, 0.2])
+
 model = ov.convert_model(
-    "/models/ssdlite_mobilenet_v2_coco_2018_05_09/frozen_inference_graph.pb",
-    input=[("image_tensor:0", [1, 300, 300, 3])],
+    f"{MODEL_DIR}/frozen_inference_graph.pb",
+    input=[("image_tensor:0", INPUT_SHAPE)],
 )
 
-# rows of (image_id, class_id, score, xmin, ymin, xmax, ymax)
-boxes = model.output("detection_boxes:0").get_node().input_value(0)
-classes = model.output("detection_classes:0").get_node().input_value(0)
-scores = model.output("detection_scores:0").get_node().input_value(0)
+nodes = {op.get_friendly_name(): op for op in model.get_ordered_ops()}
+parameter = model.get_parameters()[0]
 
-# (ymin,xmin,ymax,xmax) -> (xmin,ymin,xmax,ymax)
-boxes = ops.gather(boxes, [1, 0, 3, 2], 2)
-classes = ops.unsqueeze(classes, 2)
-scores = ops.unsqueeze(scores, 2)
-image_id = ops.multiply(scores, np.float32(0.0))
+preprocessor = nodes["Preprocessor/map/TensorArrayStack/TensorArrayGatherV3"]
+box_deltas = nodes["Postprocessor/Reshape_1"].output(0)
+class_scores = nodes["Postprocessor/convert_scores"].output(0)
+anchors_output = nodes["Postprocessor/Reshape"].output(0)
 
-detections = ops.concat([image_id, classes, scores, boxes], 2)
-detections = ops.unsqueeze(detections, 1)
+# The anchors only depend on the static input shape, so fold them into a
+# constant and drop the generator subgraph with the rest of the postprocessor.
+probe = ov.Core().compile_model(
+    ov.Model([anchors_output, preprocessor.output(0)], [parameter], "probe"), "CPU"
+)
+probe_input = np.random.default_rng(0).integers(0, 255, INPUT_SHAPE, dtype=np.uint8)
+anchors, resized = (out.copy() for out in probe([probe_input]).values())
+
+assert np.allclose(resized, probe_input, atol=1e-3), (
+    "preprocessor is not an identity at 300x300, it cannot be bypassed"
+)
+
+image = ops.convert(parameter, "f32")
+
+for consumer in list(preprocessor.output(0).get_target_inputs()):
+    consumer.replace_source_output(image.output(0))
+
+# (ymin, xmin, ymax, xmax) -> (xmin, ymin, xmax, ymax)
+priors = anchors[:, [1, 0, 3, 2]].astype(np.float32).reshape(-1)
+variances = np.tile(BOX_VARIANCES, len(anchors))
+proposals = ops.constant(np.stack([priors, variances])[np.newaxis])
+
+# (ty, tx, th, tw) -> (dx, dy, dw, dh) for the CENTER_SIZE decode
+box_logits = ops.reshape(ops.gather(box_deltas, [1, 0, 3, 2], 1), [1, -1], False)
+class_preds = ops.reshape(class_scores, [1, -1], False)
+
+detections = ops.detection_output(
+    box_logits,
+    class_preds,
+    proposals,
+    {
+        "background_label_id": 0,
+        "top_k": 100,
+        "keep_top_k": [100],
+        "nms_threshold": 0.6,
+        "confidence_threshold": 0.3,
+        "code_type": "caffe.PriorBoxParameter.CENTER_SIZE",
+        "share_location": True,
+        "variance_encoded_in_target": False,
+        "normalized": True,
+        "clip_before_nms": False,
+        "clip_after_nms": True,
+        "decrease_label_id": False,
+    },
+)
 detections.output(0).get_tensor().set_names({"detection_out"})
 
-model = ov.Model([detections], model.get_parameters(), "ssdlite_mobilenet_v2")
+model = ov.Model([detections], [parameter], "ssdlite_mobilenet_v2")
 
 ppp = PrePostProcessor(model)
 ppp.input().tensor().set_layout(ov.Layout("NHWC"))
 ppp.input().preprocess().reverse_channels()
 model = ppp.build()
 
-ov.save_model(model, "/models/ssdlite_mobilenet_v2.xml", compress_to_fp16=True)
+# Fail the build rather than silently ship the dynamically shaped graph again.
+op_types = [op.get_type_name() for op in model.get_ordered_ops()]
+assert op_types.count("DetectionOutput") == 1, "postprocessor was not fused"
+
+for dynamic_op in ("NonMaxSuppression", "NonZero", "Loop", "TensorIterator"):
+    assert dynamic_op not in op_types, f"{dynamic_op} left in the graph"
+
+output_shape = model.outputs[0].get_partial_shape()
+assert output_shape.is_static and list(output_shape) == [1, 1, 100, 7], (
+    f"unexpected detector output shape {output_shape}"
+)
+
+ov.save_model(model, OUTPUT_PATH, compress_to_fp16=True)

docker/main/requirements-wheels.txt

@@ -79,7 +79,5 @@ sherpa-onnx==1.12.*
 faster-whisper==1.1.*
 librosa==0.11.*
 soundfile==0.13.*
-# DeGirum detector
-degirum == 0.16.*
 # Memory profiling
 memray == 1.15.*

docker/main/rootfs/usr/local/nginx/conf/nginx.conf

@@ -150,7 +150,9 @@ http {
             include auth_request.conf;
             types {
                 video/mp4 mp4;
-                image/jpeg jpg;
+                image/jpeg jpg jpeg;
+                image/png png;
+                image/webp webp;
             }
 
             expires 7d;

docs/data/object_detectors_models.yaml

@@ -894,6 +894,41 @@ deepstack:
             api_url: http://<your_codeproject_ai_server_ip>:<port>/v1/vision/detection
             type: deepstack
             api_timeout: 0.1 # seconds
+xdna2:
+  title: AMD XDNA2
+  models:
+    - key: yolov9
+      label: YOLOv9
+      recommended: true
+      download: |-
+        Prepare the model using the frigate-xdna setup instructions linked above. For local YOLO models, Frigate must have access to the same ONNX file bytes as the sidecar. The example below uses YOLOv9-C at 320x320. Frigate+ models may instead use the same `plus://MODEL_ID` in Frigate and the sidecar.
+      ui: |-
+        Navigate to **Settings > System > Detectors and model** and select **ZMQ IPC** from the detector type dropdown and click **Add**, then set the endpoint to `tcp://xdna:5555`. Then on the same page, in the **Custom Model** tab, configure:
+
+        | Field                                    | Value                                      |
+        | ---------------------------------------- | ------------------------------------------ |
+        | **Custom object detector model path**    | `/config/models/yolov9-c-320.onnx`       |
+        | **Label map for custom object detector** | `/labelmap/coco-80.txt`                   |
+        | **Object detection model input width**   | `320`                                    |
+        | **Object detection model input height**  | `320`                                    |
+        | **Model Input Pixel Color Format**       | `rgb` (Frigate's default value)          |
+        | **Model Input Tensor Shape**             | `nchw`                                   |
+        | **Model Input D Type**                   | `float`                                  |
+        | **Object Detection Model Type**          | `yolo-generic`                           |
+      yaml: |-
+        detectors:
+          xdna:
+            type: zmq
+            endpoint: tcp://xdna:5555
+
+        model:
+          model_type: yolo-generic
+          width: 320
+          height: 320
+          input_tensor: nchw
+          input_dtype: float
+          path: /config/models/yolov9-c-320.onnx
+          labelmap_path: /labelmap/coco-80.txt
 memryx:
   title: MemryX
   models:
@@ -1100,7 +1135,7 @@ synaptics:
     - key: ssd
       label: SSD MobileNet
       recommended: true
-      download: A synap model is provided in the container at `/mobilenet.synap` and is used by this detector type by default. The model comes from the [Synap-release Github](https://github.com/synaptics-astra/synap-release/tree/v1.5.0/models/dolphin/object_detection/coco/model/mobilenet224_full80).
+      download: A synap model is provided in the container at `/synaptics/mobilenet.synap` and is used by this detector type by default. The model comes from the [Synap-release Github](https://github.com/synaptics-astra/synap-release/tree/v1.5.0/models/dolphin/object_detection/coco/model/mobilenet224_full80).
       ui: |-
         Navigate to **Settings > System > Detectors and model** and select **Synaptics** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
 
@@ -1269,78 +1304,3 @@ axengine:
           input_dtype: int
           input_pixel_format: bgr
           labelmap_path: /labelmap/coco-80.txt
-degirumAiServer:
-  title: DeGirum AI Server
-  models:
-    - key: ai-server-inference
-      label: AI Server Inference
-      recommended: true
-      download: |-
-        Launch a DeGirum AI server as a Docker container, then point the detector at it. Add this to your `docker-compose.yml`:
-
-        ```yaml
-        degirum_detector:
-          container_name: degirum
-          image: degirum/aiserver:latest
-          privileged: true
-          ports:
-            - "8778:8778"
-        ```
-
-        Set `location` to the server's service name, container name, or `host:port`.
-      ui: |
-        Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
-
-        | Field | Value |
-        | --- | --- |
-        | **Location** | `degirum` |
-        | **Zoo** | `degirum/public` |
-        | **Token** | your AI Hub token (optional for the public zoo) |
-      yaml: |
-        degirum_detector:
-          type: degirum
-          location: degirum
-          zoo: degirum/public
-          token: dg_example_token
-degirumLocal:
-  title: DeGirum Local
-  models:
-    - key: local-inference
-      label: Local Inference
-      recommended: true
-      download: Run hardware directly inside the Frigate container with `@local`, removing the AI server hop. The matching device runtime (e.g. the Hailo runtime) must be installed in the container; confirm it with `degirum sys-info`.
-      ui: |
-        Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
-
-        | Field | Value |
-        | --- | --- |
-        | **Location** | `@local` |
-        | **Zoo** | `degirum/public` |
-        | **Token** | your AI Hub token (optional for the public zoo) |
-      yaml: |
-        degirum_detector:
-          type: degirum
-          location: @local
-          zoo: degirum/public
-          token: dg_example_token
-degirumCloud:
-  title: DeGirum AI Hub Cloud
-  models:
-    - key: ai-hub-cloud-inference
-      label: AI Hub Cloud Inference
-      recommended: true
-      download: Run inferences on DeGirum's [AI Hub](https://hub.degirum.com) cloud with `@cloud`. Sign up, create an access token, and set it as `token`. Network latency may require lowering your detection fps.
-      ui: |
-        Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
-
-        | Field | Value |
-        | --- | --- |
-        | **Location** | `@cloud` |
-        | **Zoo** | `degirum/public` |
-        | **Token** | your AI Hub token (optional for the public zoo) |
-      yaml: |
-        degirum_detector:
-          type: degirum
-          location: @cloud
-          zoo: degirum/public
-          token: dg_example_token

docs/docs/configuration/advanced/reference.md

@@ -286,7 +286,7 @@ ffmpeg:
     # Optional: output args for detect streams (default: shown below)
     detect: -threads 2 -f rawvideo -pix_fmt yuv420p
     # Optional: output args for record streams (default: shown below)
-    record: preset-record-generic
+    record: preset-record-generic-audio-aac
   # Optional: Time in seconds to wait before ffmpeg retries connecting to the camera. (default: shown below)
   # If set too low, frigate will retry a connection to the camera's stream too frequently, using up the limited streams some cameras can allow at once
   # If set too high, then if a ffmpeg crash or camera stream timeout occurs, you could potentially lose up to a maximum of retry_interval second(s) of footage
@@ -981,7 +981,9 @@ cameras:
       # Optional: Adjust sort order of cameras in the UI. Larger numbers come later (default: shown below)
       # By default the cameras are sorted alphabetically.
       order: 0
-      # Optional: Whether or not to show the camera in the Frigate UI (default: shown below)
+      # Optional: Whether or not to show the camera on the default All Cameras live dashboard.
+      # The camera is still available everywhere else, including camera groups and settings
+      # (default: shown below)
       dashboard: True
       # Optional: Whether this camera is visible in review (the review page and its camera
       # filter, motion review, and the history view) (default: shown below)

docs/docs/configuration/advanced/system.md

@@ -293,6 +293,10 @@ networking:
 
 This setting is for advanced users. For the majority of use cases it's recommended to change the `ports` section of your Docker compose file or use the Docker `run` `--publish` option instead, e.g. `-p 443:8971`. Changing Frigate's ports may break some integrations.
 
+The internal and external ports must be different port numbers, and Frigate will refuse to start otherwise. Requests arriving on the internal port are treated as authenticated admins, so pointing both at the same port would remove authentication from the external one.
+
+Nginx binds these ports when it starts, so port changes only take effect after Frigate restarts.
+
 :::
 
 ### Customizing the Nginx configuration

docs/docs/configuration/audio_detectors.md

@@ -256,7 +256,7 @@ The only field that is valid at the camera level is `enabled`.
 
 #### Live transcription
 
-The single camera Live view in the Frigate UI supports live transcription of audio for streams defined with the `audio` role. Use the Enable/Disable Live Audio Transcription button/switch to toggle transcription processing. When speech is heard, the UI will display a black box over the top of the camera stream with text. The MQTT topic `frigate/<camera_name>/audio/transcription` will also be updated in real-time with transcribed text.
+The single camera Live view in the Frigate UI supports live transcription of audio for streams defined with the `audio` role. Use the Enable/Disable Live Audio Transcription button/switch to toggle transcription processing, or toggle it outside of the UI with the [`frigate/<camera_name>/audio_transcription/set`](/integrations/mqtt#frigatecamera_nameaudio_transcriptionset) MQTT topic or the HTTP API. When speech is heard, the UI will display a black box over the top of the camera stream with text. The MQTT topic `frigate/<camera_name>/audio/transcription` will also be updated in real-time with transcribed text.
 
 Results can be error-prone due to a number of factors, including:
 

docs/docs/configuration/cameras.md

@@ -50,6 +50,31 @@ Connect each stream to get a live preview, an estimated bandwidth figure, and a
 
 Other features, including [hardware acceleration](hardware_acceleration_video.md), [two way talk](/configuration/live#two-way-talk), and audio transcoding, is configured after the camera has been added. For camera model specific quirks, see the [camera specific](camera_specific.md) docs.
 
+## Deleting a camera
+
+Click **Delete Camera** in <NavPath path="Settings > Global configuration > Camera management" />, choose the camera, and confirm. Deleting a camera requires the `admin` role and cannot be undone.
+
+:::warning
+
+Deleting a camera permanently removes its recordings, tracked objects, and configuration. If you only want to stop processing a camera, set its state to **Off** or **Disabled** in <NavPath path="Settings > Global configuration > Camera management" /> instead. See [camera state](/configuration/live#camera-state).
+
+:::
+
+Deleting a camera removes:
+
+- The camera's section of your config file, along with its entries in any [role](authentication.md#user-roles) camera list. A custom role left with no cameras is removed as well.
+- Every database record for the camera: tracked objects, review items, recordings, previews, timeline entries, the saved region grid, and [triggers](semantic_search.md#triggers).
+- Every media file for the camera: recordings, snapshots, thumbnails, and preview clips.
+
+[Exports](/usage/exports) are kept by default, so saved footage survives the deletion of the camera it came from. Turn on **Also delete exports for this camera** in the confirmation step to remove those too.
+
+The camera's processes are stopped and the change takes effect immediately, so no restart is required. If the resulting config cannot be parsed, Frigate restores the previous config and reports an error instead of leaving Frigate in a broken state.
+
+Two things are not cleaned up for you:
+
+- **go2rtc streams.** Frigate makes a best effort to stop a running [go2rtc](go2rtc.md) stream named after the camera, but stream entries in your config file remain and are recreated on the next restart. Remove them in <NavPath path="Settings > System > go2rtc streams" /> or in your config file.
+- **Camera groups.** A deleted camera stays listed in any [camera group](#setting-up-camera-groups) that referenced it. The group skips the missing camera, so this is harmless, but you can edit the group to drop the stale entry.
+
 ## Setting Up Camera Inputs
 
 Several inputs can be configured for each camera and the role of each input can be mixed and matched based on your needs. This allows you to use a lower resolution stream for object detection, but create recordings from a higher resolution stream, or vice versa.

docs/docs/configuration/custom_classification/object_classification.md

@@ -11,7 +11,7 @@ Object classification allows you to train a custom MobileNetV2 classification mo
 
 :::info
 
-Training a custom object classification model requires a one-time internet connection to download MobileNetV2 base weights. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+Training a custom object classification model requires an internet connection to download MobileNetV2 base weights. By default these weights are not cached in `/config/`, so they are downloaded again after the container is recreated. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
 
 :::
 

docs/docs/configuration/custom_classification/state_classification.md

@@ -11,7 +11,7 @@ State classification allows you to train a custom MobileNetV2 classification mod
 
 :::info
 
-Training a custom state classification model requires a one-time internet connection to download MobileNetV2 base weights. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+Training a custom state classification model requires an internet connection to download MobileNetV2 base weights. By default these weights are not cached in `/config/`, so they are downloaded again after the container is recreated. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
 
 :::
 

docs/docs/configuration/genai/config.md

@@ -6,6 +6,7 @@ title: Configuring Generative AI
 import ConfigTabs from "@site/src/components/ConfigTabs";
 import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";
+import FaqItem from "@site/src/components/FaqItem";
 
 ## Configuration
 
@@ -13,6 +14,18 @@ A Generative AI provider can be configured in the global config, which will make
 
 `genai` is a map of named providers. Each key under `genai` is a name you choose, and its value is that provider's settings:
 
+<ConfigTabs>
+<TabItem value="ui">
+
+1. Navigate to <NavPath path="Settings > Enrichments > Generative AI" />.
+   - Click **Add** and enter a **Provider name**. Any name of letters, numbers, hyphens, and underscores is accepted, but it cannot be changed from the UI after the provider is created.
+   - Set **Provider** to the service you are using (e.g., `ollama`)
+   - Set **Base URL**, **API key**, and **Model** as required by that provider
+   - Set **Roles** to the roles this provider should handle.
+
+</TabItem>
+<TabItem value="yaml">
+
 ```yaml
 genai:
   my_provider: # any name you like
@@ -25,6 +38,9 @@ genai:
       - chat
 ```
 
+</TabItem>
+</ConfigTabs>
+
 The examples on this page all use `my_provider`, but the name is arbitrary and is only used to reference the provider elsewhere in the config (for example, `semantic_search.model`).
 
 Each provider handles one or more **roles**: `chat`, `descriptions`, and `embeddings`. A provider handles all three by default, and each role may be assigned to exactly one provider. Define a single provider if you want it to do everything, or split the roles across several providers using the `roles` option.
@@ -43,14 +59,23 @@ Running Generative AI models on CPU is not recommended, as high inference times
 
 ### Recommended Local Models
 
-You must use a vision-capable model with Frigate. The following models are recommended for local deployment:
+#### Vision models
 
-| Model      | Notes                                                                                                                                                                |
-| ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `qwen3-vl` | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object.                                              |
-| `qwen3.5`  | Strong situational understanding, but missing DeepStack from qwen3-vl leading to worse performance for identifying objects in people's hand and other small details. |
-| `qwen3.6`  | Strong situational understanding, similar to qwen3-vl                                                                                                                |
-| `gemma4`   | Strong situational understanding, sometimes resorts to more vague terms like 'interacts' instead of assigning a specific action.                                     |
+You must use a vision-capable model with Frigate. The following models are recommended for local deployment of the `descriptions` and `chat` roles:
+
+| Model               | Notes                                                                                                                                                                |
+| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `qwen3-vl`          | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object.                                              |
+| `qwen3.6`/`qwen3.8` | Strong situational understanding, but missing DeepStack from qwen3-vl leading to worse performance for identifying objects in people's hand and other small details. |
+| `gemma4`            | Strong situational understanding, sometimes resorts to more vague terms like 'interacts' instead of assigning a specific action.                                     |
+
+#### Embedding models
+
+The `embeddings` role needs a different kind of model. Text queries are matched against the stored image embeddings, so the model must be trained to place images and text into the same vector space. A chat or description model will still return vectors when asked, but those vectors are not trained for retrieval and text searches will return poor matches with no error to indicate why.
+
+| Model                | Notes                                                                                                                                                               |
+| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `qwen3-vl-embedding` | Multimodal embeddings for [Semantic Search](/configuration/semantic_search#genai-provider). Must be served by llama.cpp started with `--embeddings` and `--mmproj`. |
 
 :::info
 
@@ -416,3 +441,82 @@ genai:
 
 </TabItem>
 </ConfigTabs>
+
+## FAQ
+
+<FaqItem id="how-do-i-debug-genai-issues" question="How do I debug GenAI issues?">
+
+Frigate's Generative AI features are configured and enabled separately. [Review descriptions and summaries](/configuration/genai/genai_review) live under `review.genai`, and [object descriptions](/configuration/genai/genai_objects) live under `objects.genai`. Configuring a provider on this page does not enable either feature, and enabling one does not enable the other. Decide which of the two is not working, then work through the steps below.
+
+1. Confirm a provider is available and holds the `descriptions` role.
+   - Review descriptions, review summaries, and object descriptions all use the provider that has the `descriptions` role assigned in <NavPath path="Settings > Enrichments > Generative AI > Roles" /> (`genai.<provider>.roles`).
+   - A provider is contacted the first time one of its roles is actually used. A provider holding the `embeddings` role for semantic search is initialized during startup, while a `descriptions` provider is not initialized until the first description is requested, which may be well after boot.
+   - In <NavPath path="Settings > Enrichments > Generative AI" />, use **Refresh models** next to the model field. It queries the provider for its model list and is a quick way to verify that the base URL, API key, and network path between Frigate and your provider are correct.
+
+2. Confirm the feature you expect is actually enabled.
+   - Object descriptions are disabled by default. Turn on <NavPath path="Settings > Global configuration > Objects > GenAI object config > Enable GenAI" /> (`objects.genai.enabled`), either globally or per camera. This is the most common reason custom prompts appear to be ignored while review summaries are still being generated.
+   - Review descriptions are disabled by default. Turn on <NavPath path="Settings > Global configuration > Review > GenAI config > Enable GenAI descriptions" /> (`review.genai.enabled`). Once enabled, alerts are described by default but detections are not, so a detection-only review item will never get a summary unless **Enable GenAI for detections** (`review.genai.detections`) is also on.
+
+3. If object descriptions are never requested, check the filters that skip generation.
+   - <NavPath path="Settings > Global configuration > Objects > GenAI object config > GenAI objects" /> (`objects.genai.objects`) limits generation to specific labels, and **Required zones** (`objects.genai.required_zones`) requires the object to have entered one of those zones. If either is set and does not match, Frigate skips the request silently.
+   - Thumbnails are only collected while an object is moving. Objects that go stationary early contribute fewer frames.
+   - **Use snapshots** (`objects.genai.use_snapshot`) requires snapshots to be enabled for the camera. If the snapshot cannot be read, Frigate logs `Cannot load snapshot for <id>, file not found` and no description is generated.
+   - **Send on end** (`objects.genai.send_triggers.tracked_object_end`) is on by default. If you have turned it off in favor of **Early GenAI trigger** (`objects.genai.send_triggers.after_significant_updates`), descriptions are only requested once that number of updates is reached.
+
+4. Enable debug logs to see exactly what Frigate is doing. Restart Frigate after this change. The next step also requires a restart, so turn both on at the same time to avoid restarting twice.
+
+   ```yaml
+   logger:
+     default: info
+     logs:
+       # highlight-start
+       frigate.genai: debug
+       frigate.data_processing.post.object_descriptions: debug
+       frigate.data_processing.post.review_descriptions: debug
+       # highlight-end
+   ```
+
+5. Save the exact images and prompts that were sent to your provider.
+   - Turn on **Save thumbnails** for the feature you are debugging (`review.genai.debug_save_thumbnails` or `objects.genai.debug_save_thumbnails`). Both features write to `/media/frigate/clips/genai-requests/`, and these files are admin-only.
+   - Review descriptions write `genai-requests/<review_id>/` containing the numbered frames that were sent, plus `prompt.txt` and `response.txt` with the exact prompt and the raw, unparsed model response.
+   - Review summary reports write `genai-requests/<start_ts>-<end_ts>/prompt.txt` and `response.txt`. No images are involved, since a report summarizes existing review descriptions.
+   - Object descriptions write `genai-requests/<event_id>/` containing the numbered thumbnails. The prompt for object descriptions is not written to a file, it is only visible in the debug logs from step 4.
+   - Look at the saved images before blaming the model. If the object is small, blurry, or out of frame, no prompt will fix the result. For object descriptions, consider turning on **Use snapshots** (`objects.genai.use_snapshot`) to send a higher quality image. For review items, consider setting **Review image source** (`review.genai.image_source`) to `recordings` for 480p frames instead of the lower resolution preview frames.
+
+<ConfigTabs>
+<TabItem value="ui">
+
+For review descriptions, navigate to <NavPath path="Settings > Global configuration > Review" /> and set **GenAI config > Save thumbnails** to on.
+
+For object descriptions, navigate to <NavPath path="Settings > Global configuration > Objects" />, expand **GenAI object config**, and set **Save thumbnails** to on.
+
+</TabItem>
+<TabItem value="yaml">
+
+```yaml
+review:
+  genai:
+    enabled: true
+    # highlight-next-line
+    debug_save_thumbnails: true
+
+objects:
+  genai:
+    enabled: true
+    # highlight-next-line
+    debug_save_thumbnails: true
+```
+
+</TabItem>
+</ConfigTabs>
+
+6. Verify the prompt is what you think it is.
+   - Object description prompts are the ones you control directly. A camera-level <NavPath path="Settings > Camera configuration > Objects > GenAI object config > Caption prompt" /> (`objects.genai.prompt`) overrides the global one, and an entry in **Object prompts** (`objects.genai.object_prompts`) for a label overrides both for that label. Only `{label}`, `{sub_label}`, and `{camera}` are substituted.
+   - Review description prompts are built by Frigate and request a structured JSON response, so they are not fully replaceable. The parts you control are <NavPath path="Settings > Global configuration > Review > GenAI config > Activity context prompt" /> (`review.genai.activity_context_prompt`) and **Additional concerns** (`review.genai.additional_concerns`). Keep the activity context prompt general, since overly specific rules will sway the model's threat level scoring.
+
+7. If descriptions are generated but the results are poor or inconsistent, look at the model and the context window.
+   - Empty fields, missing `shortSummary` values, or `Failed to parse review description` errors usually mean the model is not following the requested JSON schema. Smaller models struggle with structured output. Try a larger parameter size or one of the [recommended models](#recommended-local-models).
+   - Frigate calculates how many frames to send from the context size the provider reports. If your server reports a different value than it is actually running with, frames will be truncated or the request will fail. Pin the value by adding `context_size` under <NavPath path="Settings > Enrichments > Generative AI > Provider options" /> (`genai.<provider>.provider_options`), and for Ollama also confirm `options.num_ctx` there matches the context you have configured.
+   - Check **Review Description Speed** and **Object Description Speed** in <NavPath path="System metrics > Enrichments" />. If inference takes tens of seconds, requests will queue behind each other and descriptions will appear to stop. For Ollama, review `OLLAMA_NUM_PARALLEL`, `OLLAMA_MAX_QUEUE`, and `OLLAMA_MAX_LOADED_MODELS` so that concurrent requests from Frigate are handled the way you expect.
+
+</FaqItem>

docs/docs/configuration/genai/objects.md

@@ -113,3 +113,7 @@ Many providers also have a public facing chat interface for their models. Downlo
 - OpenAI - [ChatGPT](https://chatgpt.com)
 - Gemini - [Google AI Studio](https://aistudio.google.com)
 - Ollama - [Open WebUI](https://docs.openwebui.com/)
+
+## Troubleshooting
+
+If descriptions are not being generated, or the generated descriptions are not what you expect, see [How do I debug GenAI issues?](/configuration/genai/genai_config#how-do-i-debug-genai-issues).

docs/docs/configuration/genai/review_summaries.md

@@ -201,3 +201,7 @@ Along with individual review item summaries, Generative AI can also produce a si
 Review reports can be requested via the [API](/integrations/api/generate-review-summary-review-summarize-start-start-ts-end-end-ts-post) by sending a POST request to `/api/review/summarize/start/{start_ts}/end/{end_ts}` with Unix timestamps.
 
 For Home Assistant users, there is a built-in service (`frigate.review_summarize`) that makes it easy to request review reports as part of automations or scripts. This allows you to automatically generate daily summaries, vacation reports, or custom time period reports based on your specific needs.
+
+## Troubleshooting
+
+If summaries are not being generated, or the generated summaries are not what you expect, see [How do I debug GenAI issues?](/configuration/genai/genai_config#how-do-i-debug-genai-issues).

docs/docs/configuration/go2rtc.md

@@ -67,4 +67,6 @@ If your stream won't play, has no audio, uses excessive CPU, or otherwise misbeh
 
 ## Homekit Configuration
 
-To add camera streams to Homekit Frigate must be configured in docker to use `host` networking mode. Once that is done, you can use the go2rtc WebUI (accessed via port 1984, which is disabled by default) to export a camera to Homekit. Any changes made will automatically be saved to `/config/go2rtc_homekit.yml`.
+To export camera streams to HomeKit, Frigate must be configured in docker to use `host` networking mode. HomeKit settings are stored in `/config/go2rtc_homekit.yml` rather than in your Frigate config, and are edited through the go2rtc config editor at `http://<frigate_host>:1984/editor.html`. Pairings are saved back to that file automatically.
+
+See the [HomeKit integration docs](/integrations/homekit) for the full setup, including the video and audio requirements HomeKit places on the stream.

docs/docs/configuration/hardware_acceleration_video.md

@@ -498,7 +498,7 @@ cameras:
 
 ## Synaptics
 
-Hardware accelerated video de-/encoding is supported on Synpatics SL-series SoC.
+Hardware accelerated video de-/encoding is supported on Synaptics SL-series SoC.
 
 ### Prerequisites
 

docs/docs/configuration/license_plate_recognition.md

@@ -8,7 +8,7 @@ import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";
 import FaqItem from "@site/src/components/FaqItem";
 
-Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) name as a `sub_label` to tracked objects of type `car` or `motorcycle`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
+Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) name as a `sub_label` to tracked objects of type `car`, `motorcycle`, `bus`, `truck`, `school_bus`, or `garbage_truck`, depending on which of those labels your model detects. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
 
 LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. When a vehicle becomes stationary, LPR continues to run for a short time after to attempt recognition.
 
@@ -24,7 +24,7 @@ When a plate is recognized, the details are:
 - Viewable in the Details pane in Review/History.
 - Viewable in the Tracked Object Details pane in Explore (sub labels and recognized license plates).
 - Filterable through the More Filters menu in Explore.
-- Published via the `frigate/events` MQTT topic as a `sub_label` ([known](#matching)) or `recognized_license_plate` (unknown) for the `car` or `motorcycle` tracked object.
+- Published via the `frigate/events` MQTT topic as a `sub_label` ([known](#matching)) or `recognized_license_plate` (unknown) for the vehicle tracked object.
 - Published via the `frigate/tracked_object_update` MQTT topic with `name` (if [known](#matching)) and `plate`.
 
 ## Model Requirements
@@ -35,7 +35,7 @@ Users without a model that detects license plates can still run LPR. Frigate use
 
 :::note
 
-In the default mode, Frigate's LPR needs to first detect a `car` or `motorcycle` before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a `car` or `motorcycle` will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.
+In the default mode, Frigate's LPR needs to first detect a vehicle before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a vehicle will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.
 
 :::
 
@@ -86,7 +86,7 @@ cameras:
 </TabItem>
 </ConfigTabs>
 
-For non-dedicated LPR cameras, ensure that your camera is configured to detect objects of type `car` or `motorcycle`, and that a car or motorcycle is actually being detected by Frigate. Otherwise, LPR will not run.
+For non-dedicated LPR cameras, ensure that your camera is configured to detect vehicle objects, and that a vehicle is actually being detected by Frigate. Otherwise, LPR will not run. The object types that can carry a plate are defined by your model's `attributes_map`, so if your model detects other vehicle labels, you can add them there.
 
 Like the other real-time processors in Frigate, license plate recognition runs on the camera stream defined by the `detect` role in your config. To ensure optimal performance, select a suitable resolution for this stream in your camera's firmware that fits your specific scene and requirements.
 
@@ -158,7 +158,7 @@ lpr:
 
 Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
 
-- **Known plates**: Assign custom `sub_label` values to `car` and `motorcycle` objects when a recognized plate matches a known value. These labels appear in the UI, filters, and notifications. Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
+- **Known plates**: Assign custom `sub_label` values to vehicle objects when a recognized plate matches a known value. These labels appear in the UI, filters, and notifications. Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
 - **Match distance**: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate. For example, setting to `1` allows a plate `ABCDE` to match `ABCBE` or `ABCD`. This parameter will _not_ operate on known plates that are defined as regular expressions.
 
 </TabItem>
@@ -316,7 +316,7 @@ lpr:
 
 :::note
 
-If a camera is configured to detect `car` or `motorcycle` but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:
+If a camera is configured to detect vehicles but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:
 
 <ConfigTabs>
 <TabItem value="ui">
@@ -378,10 +378,10 @@ Navigate to <NavPath path="Settings > Camera configuration > Object detection" /
 
 Navigate to <NavPath path="Settings > Camera configuration > Objects" />.
 
-| Field                                          | Description         |
-| ---------------------------------------------- | ------------------- |
-| **Objects to track**                           | Add `license_plate` |
-| **Object filters > License Plate > Threshold** | Set to `0.7`        |
+| Field                                                     | Description         |
+| --------------------------------------------------------- | ------------------- |
+| **Objects to track**                                      | Add `license_plate` |
+| **Object filters > License Plate > Confidence threshold** | Set to `0.7`        |
 
 Navigate to <NavPath path="Settings > Camera configuration > Motion detection" />.
 
@@ -456,7 +456,7 @@ With this setup:
 - Snapshots will have license plate bounding boxes on them.
 - The `frigate/events` MQTT topic will publish tracked object updates.
 - Debug view will display `license_plate` bounding boxes.
-- If you are using a Frigate+ model and want to submit images from your dedicated LPR camera for model training and fine-tuning, annotate both the `car` / `motorcycle` and the `license_plate` in the snapshots on the Frigate+ website, even if the car is barely visible.
+- If you are using a Frigate+ model and want to submit images from your dedicated LPR camera for model training and fine-tuning, annotate both the vehicle and the `license_plate` in the snapshots on the Frigate+ website, even if the vehicle is barely visible.
 
 ### Using the Secondary LPR Pipeline (Without Frigate+)
 
@@ -611,9 +611,9 @@ If you are still having issues detecting plates, start with a basic configuratio
 
 </FaqItem>
 
-<FaqItem id="can-i-run-lpr-without-detecting-car-or-motorcycle-objects" question={<>Can I run LPR without detecting <code>car</code> or <code>motorcycle</code> objects?</>}>
+<FaqItem id="can-i-run-lpr-without-detecting-car-or-motorcycle-objects" question={<>Can I run LPR without detecting vehicle objects?</>}>
 
-In normal LPR mode, Frigate requires a `car` or `motorcycle` to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
+In normal LPR mode, Frigate requires a vehicle to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
 
 </FaqItem>
 
@@ -699,7 +699,7 @@ lpr:
 4. Ensure the characters on detected plates are being _recognized_.
    - Check the **Plate recognition** inference time in Enrichment metrics (<NavPath path="System metrics > Enrichments" />). High inference times (> 100ms) could lead to poor recognition results, especially for dedicated LPR cameras where the plate crosses the frame quickly.
    - 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.
+   - Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the vehicle's label will change to the recognized plate when LPR is enabled and working.
    - Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
 
 </FaqItem>
@@ -714,13 +714,13 @@ LPR's performance impact depends on your hardware. Ensure you have at least 4GB
 
 The YOLOv9 license plate detector model will run (and the metric will appear) if you've enabled LPR but haven't defined `license_plate` as an object to track, either at the global or camera level.
 
-If you are detecting `car` or `motorcycle` on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
+If you are detecting vehicles on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
 
 </FaqItem>
 
 <FaqItem id="it-looks-like-frigate-picked-up-my-cameras-timestamp-or-overlay-text-as-the-license-plate-how-can-i-prevent-this" question="It looks like Frigate picked up my camera's timestamp or overlay text as the license plate. How can I prevent this?">
 
-This could happen if cars or motorcycles travel close to your camera's timestamp or overlay text. You could either move the text through your camera's firmware, or apply a mask to it in Frigate.
+This could happen if vehicles travel close to your camera's timestamp or overlay text. You could either move the text through your camera's firmware, or apply a mask to it in Frigate.
 
 If you are using a model that natively detects `license_plate`, add an _object mask_ of type `license_plate` and a _motion mask_ over your text.
 

docs/docs/configuration/live.md

@@ -334,7 +334,7 @@ When your browser runs into problems playing back your camera streams, it will l
 
 - **stalled**
   - What it means: Playback has stalled because the player has fallen too far behind live (extended buffering or no data arriving).
-  - What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval: shorter intervals make playback start and recover faster. You can also try increasing the timeout value in the UI pane of Frigate's settings.
+  - What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval: shorter intervals make playback start and recover faster. You can also try increasing the timeout value in <NavPath path="Settings > UI" /> .
 
   - Possible console messages from the player code:
     - `Buffer time (10 seconds) exceeded, browser may not be playing media correctly.`

docs/docs/configuration/notifications.md

@@ -6,6 +6,7 @@ title: Notifications
 import ConfigTabs from "@site/src/components/ConfigTabs";
 import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";
+import FaqItem from "@site/src/components/FaqItem";
 
 # Notifications
 
@@ -21,7 +22,7 @@ Push notifications require internet access from the Frigate server to the browse
 
 In order to use notifications the following requirements must be met:
 
-- Frigate must be accessed via a secure `https` connection ([see the authorization docs](/configuration/authentication)).
+- Frigate must be accessed via a secure `https` connection while signed in as a Frigate user ([see the authorization docs](/configuration/authentication)).
 - A supported browser must be used. Currently Chrome, Firefox, and Safari are known to be supported.
 - In order for notifications to be usable externally, Frigate must be accessible externally.
 - For iOS devices, some users have also indicated that the Notifications switch needs to be enabled in iOS Settings --> Apps --> Safari --> Advanced --> Features.
@@ -85,7 +86,13 @@ cameras:
 
 ### Registration
 
-Once notifications are enabled, press the `Register for Notifications` button on all devices that you would like to receive notifications on. This will register the background worker. After this Frigate must be restarted and then notifications will begin to be sent.
+Once notifications are enabled, press the `Register This Device` button on all devices that you would like to receive notifications on. This will register the background worker. After this Frigate must be restarted and then notifications will begin to be sent.
+
+:::warning
+
+Each registration is attached to the Frigate user account you are signed in as, so you must register over a secure connection to the authenticated port (`8971`). Reverse proxies and tunnels should point at port `8971`.
+
+:::
 
 ## Supported Notifications
 
@@ -104,3 +111,62 @@ Different platforms handle notifications differently, some settings changes may
 ### Android
 
 Most Android phones have battery optimization settings. To get reliable Notification delivery the browser (Chrome, Firefox) should have battery optimizations disabled. If Frigate is running as a PWA then the Frigate app should have battery optimizations disabled as well.
+
+## Notifications FAQ
+
+<FaqItem id="how-do-i-debug-notifications-issues" question="How do I debug notifications issues?">
+
+Push notifications involve Frigate, your browser, and your browser vendor's push service, so it helps to work from the server outward.
+
+1. Enable debug logs for the push client by adding `frigate.comms.webpush: debug` to your `logger` configuration. Restart Frigate after this change.
+
+   ```yaml
+   logger:
+     default: info
+     logs:
+       # highlight-next-line
+       frigate.comms.webpush: debug
+   ```
+
+   These logs show exactly where a notification stopped, including:
+   - `Email must be provided for push notifications to be sent` means the global `email` field is empty and nothing will ever be sent.
+   - `Sending test notification` and `Sending push notification for <camera>, review ID <id>` mean Frigate handed the message off to the push service.
+   - `Skipping notification for <camera> - in global cooldown period` (or `camera-specific cooldown period`) means your [cooldown](#configuration) values suppressed it.
+   - `Notifications for <camera> are currently suspended` means notifications were suspended from <NavPath path="Settings > Notifications" /> or MQTT.
+   - `Notification endpoint expired for <user>, received 410` means that device's subscription is no longer valid and it must be re-registered.
+   - `Failed to send notification to <user> :: <status>` means the push service rejected the message. A `401` or `403` usually points at a VAPID or `email` problem, and a `5xx` is a problem on the push service's end.
+   - If you see no messages at all when an alert occurs, the notification was never queued. Confirm an actual **alert** was created (notifications are not sent for detections), and that notifications are enabled both globally and for that camera.
+
+2. Verify the basics that most reports come down to:
+   - Frigate must be reached over `https` with a certificate your device trusts. Browsers silently refuse to register a service worker otherwise, and a self-signed certificate that is not installed as trusted on the device will fail.
+   - On iOS, notifications only work when Frigate has been installed to the Home Screen via **Share > Add to Home Screen** and opened from that icon. Safari and Chrome tabs cannot receive web push on iOS.
+   - Each device must be registered individually, and Frigate must be restarted after registering before anything can be sent, including test notifications.
+   - The Frigate server needs outbound internet access to the browser vendor's push service. See [Network Requirements](/frigate/network_requirements#push-notifications).
+
+3. Test from the UI. Use the `Send a test notification` button in <NavPath path="Settings > Notifications" />. If the log shows `Sending test notification` but nothing arrives on the device, the problem is between the push service and your device rather than in Frigate.
+
+4. Check the browser side on the device that is not receiving notifications:
+   - Confirm the site's notification permission is set to **Allow** in your browser or OS settings, and that a focus/do not disturb mode is not hiding them.
+   - In desktop browsers, open Developer Tools > Application > Service Workers and confirm `notifications-worker.js` is registered and activated. Unregistering it and registering the device again will rebuild a broken subscription.
+   - Check the browser console and your reverse proxy logs for failures loading `/notifications-worker.js` or errors on `/api/notifications/register`.
+
+</FaqItem>
+
+<FaqItem id="why-did-notifications-stop-arriving-after-working-for-a-while" question="Why did notifications stop arriving after working for a while?">
+
+Push subscriptions are issued by the browser vendor and can be revoked, most often after a browser update, after clearing site data, or when a device has been offline for an extended period. When this happens the device still appears registered in Frigate, but the push service rejects the message. The debug logs will show `Notification endpoint expired` with a `404` or `410` status.
+
+Unregister and re-register the affected device from <NavPath path="Settings > Notifications" />, then restart Frigate.
+
+</FaqItem>
+
+<FaqItem id="why-am-i-not-getting-notifications-for-one-specific-camera" question="Why am I not getting notifications for one specific camera?">
+
+Work through these in order:
+
+- Notifications are only sent for **alerts**. If the camera is producing detections instead, adjust the camera's `review > alerts > labels` so the objects you care about are classified as alerts.
+- Confirm notifications are enabled for that camera in <NavPath path="Settings > Camera configuration > Notifications" />.
+- Check the camera's `cooldown` value, and remember that the global cooldown applies across all cameras. A busy camera can consume the global cooldown and suppress a quieter one.
+- If [authentication](/configuration/authentication) is enabled with roles, users only receive notifications for the cameras their role grants access to.
+
+</FaqItem>

docs/docs/configuration/object_detectors.md

@@ -24,12 +24,12 @@ Frigate supports multiple different detectors that work on different types of ha
 - [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB, Mini PCIe, and m.2 formats allowing for a wide range of compatibility with devices.
 - [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
 - <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
-- <CommunityBadge /> [DeGirum](#degirum): Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on [their website](https://hub.degirum.com).
 
 **AMD**
 
 - [ROCm](#amdrocm-gpu-detector): ROCm can run on AMD Discrete GPUs to provide efficient object detection.
 - [ONNX](#onnx): ROCm will automatically be detected and used as a detector in the `-rocm` Frigate image when a supported ONNX model is configured.
+- <CommunityBadge /> [XDNA2](#amd-xdna2): AMD Ryzen AI / XDNA2 NPUs can run object detection through the community-maintained `frigate-xdna` ZMQ sidecar.
 
 **Apple Silicon**
 
@@ -298,6 +298,14 @@ detectors:
 
 :::
 
+### Intel NPU host requirements {#intel-npu-requirements}
+
+The NPU firmware is loaded by the host kernel and is not part of the Frigate image. Everything else the NPU needs is bundled in the container, so host NPU libraries should never be mounted in.
+
+Frigate bundles a specific version of Intel's [linux-npu-driver](https://github.com/intel/linux-npu-driver/releases), and the host firmware must come from that release or a newer one. Firmware older than the bundled driver may fail with `MAPPED_INFERENCE_VERSION is NOT compatible with the ELF`, where `Expected` is the version the firmware supports and `received` is the version the bundled compiler produced. Distributions often package older firmware than the driver Frigate ships, so check the build date on the host with `sudo dmesg | grep -i vpu` and update it there if needed.
+
+Intel NPUs cannot be used under Home Assistant OS, which does not include the NPU firmware.
+
 ### Configuration {#configuration-openvino}
 
 <ModelConfigDropdown detectorTitle="OpenVINO" models={objectDetectorsModels.openvino.models} />
@@ -501,6 +509,28 @@ To verify that the integration is working correctly, start Frigate and observe t
 
 # Community Supported Detectors
 
+## AMD XDNA2
+
+AMD Ryzen AI / XDNA2 NPUs can be used through the community-maintained
+[frigate-xdna](https://github.com/mitchins/frigate-xdna) detector sidecar.
+The sidecar runs separately from Frigate and connects using Frigate's ZMQ
+detector interface.
+
+Currently qualified on **Ryzen AI Max 300 / Strix Halo**. Other XDNA2 devices
+are not yet qualified; XDNA1 is unsupported.
+
+Follow the frigate-xdna setup instructions to prepare and start the sidecar
+before starting Frigate.
+
+### Configuration {#configuration-xdna2}
+
+Using the detector config below will connect Frigate to the sidecar:
+
+<ModelConfigDropdown detectorTitle="AMD XDNA2" models={objectDetectorsModels.xdna2.models} />
+
+The example assumes Frigate and the sidecar share a Docker network where the
+sidecar is named `xdna`.
+
 ## MemryX MX3
 
 This detector is available for use with the MemryX MX3 accelerator M.2 module. Frigate supports the MX3 on compatible hardware platforms, providing efficient and high-performance object detection.
@@ -755,87 +785,6 @@ Explanation of the parameters:
   - **example**: Specifying `output_name = "frigate-{quant}-{input_basename}-{soc}-v{tk_version}"` could result in a model called `frigate-i8-my_model-rk3588-v2.3.0.rknn`.
 - `config`: Configuration passed to `rknn-toolkit2` for model conversion. For an explanation of all available parameters have a look at section "2.2. Model configuration" of [this manual](https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.2/03_Rockchip_RKNPU_API_Reference_RKNN_Toolkit2_V2.3.2_EN.pdf).
 
-## DeGirum
-
-DeGirum is a detector that can use any type of hardware listed on [their website](https://hub.degirum.com). DeGirum can be used with local hardware through a DeGirum AI Server, or through the use of `@local`. You can also connect directly to DeGirum's AI Hub to run inferences. **Please Note:** This detector _cannot_ be used for commercial purposes.
-
-### Configuration {#configuration-degirum}
-
-#### AI Server Inference
-
-Before starting with the config file for this section, you must first launch an AI server. DeGirum has an AI server ready to use as a docker container. Add this to your `docker-compose.yml` to get started:
-
-```yaml
-degirum_detector:
-  container_name: degirum
-  image: degirum/aiserver:latest
-  privileged: true
-  ports:
-    - "8778:8778"
-```
-
-All supported hardware will automatically be found on your AI server host as long as relevant runtimes and drivers are properly installed on your machine. Refer to [DeGirum's docs site](https://docs.degirum.com/pysdk/runtimes-and-drivers) if you have any trouble.
-
-Once completed, configure the detector as follows:
-
-<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumAiServer.models} />
-
-Setting up a model in the `config.yml` is similar to setting up an AI server.
-You can set it to:
-
-- A model listed on the [AI Hub](https://hub.degirum.com), given that the correct zoo name is listed in your detector
-  - If this is what you choose to do, the correct model will be downloaded onto your machine before running.
-- A local directory acting as a zoo. See DeGirum's docs site [for more information](https://docs.degirum.com/pysdk/user-guide-pysdk/organizing-models#model-zoo-directory-structure).
-- A path to some model.json.
-
-```yaml
-model:
-  path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1 # directory to model .json and file
-  width: 300 # width is in the model name as the first number in the "int"x"int" section
-  height: 300 # height is in the model name as the second number in the "int"x"int" section
-  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
-```
-
-#### Local Inference
-
-It is also possible to eliminate the need for an AI server and run the hardware directly. The benefit of this approach is that you eliminate any bottlenecks that occur when transferring prediction results from the AI server docker container to the frigate one. However, the method of implementing local inference is different for every device and hardware combination, so it's usually more trouble than it's worth. A general guideline to achieve this would be:
-
-1. Ensuring that the frigate docker container has the runtime you want to use. So for instance, running `@local` for Hailo means making sure the container you're using has the Hailo runtime installed.
-2. To double check the runtime is detected by the DeGirum detector, make sure the `degirum sys-info` command properly shows whatever runtimes you mean to install.
-3. Create a DeGirum detector in your configuration.
-
-<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumLocal.models} />
-
-Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
-
-```yaml
-model:
-  path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
-  width: 300 # width is in the model name as the first number in the "int"x"int" section
-  height: 300 # height is in the model name as the second number in the "int"x"int" section
-  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
-```
-
-#### AI Hub Cloud Inference
-
-If you do not possess whatever hardware you want to run, there's also the option to run cloud inferences. Do note that your detection fps might need to be lowered as network latency does significantly slow down this method of detection. For use with Frigate, we highly recommend using a local AI server as described above. To set up cloud inferences,
-
-1. Sign up at [DeGirum's AI Hub](https://hub.degirum.com).
-2. Get an access token.
-3. Create a DeGirum detector in your configuration.
-
-<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumCloud.models} />
-
-Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
-
-```yaml
-model:
-  path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
-  width: 300 # width is in the model name as the first number in the "int"x"int" section
-  height: 300 # height is in the model name as the second number in the "int"x"int" section
-  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
-```
-
 ## AXERA
 
 Hardware accelerated object detection is supported on the following SoCs:

docs/docs/configuration/object_filters.md

@@ -45,10 +45,10 @@ Any detection below `min_score` will be immediately thrown out and never tracked
 
 Navigate to <NavPath path="Settings > Global configuration > Objects" /> to set score filters globally.
 
-| Field                                   | Description                                                      |
-| --------------------------------------- | ---------------------------------------------------------------- |
-| **Object filters > Person > Min Score** | Minimum score for a single detection to initiate tracking        |
-| **Object filters > Person > Threshold** | Minimum computed (median) score to be considered a true positive |
+| Field                                              | Description                                                      |
+| -------------------------------------------------- | ---------------------------------------------------------------- |
+| **Object filters > Person > Minimum confidence**   | Minimum score for a single detection to initiate tracking        |
+| **Object filters > Person > Confidence threshold** | Minimum computed (median) score to be considered a true positive |
 
 To override score filters for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Objects" /> and select the camera.
 
@@ -103,12 +103,12 @@ Conceptually, a ratio of 1 is a square, 0.5 is a "tall skinny" box, and 2 is a "
 
 Navigate to <NavPath path="Settings > Global configuration > Objects" /> to set shape filters globally.
 
-| Field                                   | Description                                                              |
-| --------------------------------------- | ------------------------------------------------------------------------ |
-| **Object filters > Person > Min Area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
-| **Object filters > Person > Max Area**  | Maximum bounding box area in pixels (or decimal for percentage of frame) |
-| **Object filters > Person > Min Ratio** | Minimum width/height ratio of the bounding box                           |
-| **Object filters > Person > Max Ratio** | Maximum width/height ratio of the bounding box                           |
+| Field                                              | Description                                                              |
+| -------------------------------------------------- | ------------------------------------------------------------------------ |
+| **Object filters > Person > Minimum object area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Maximum object area**  | Maximum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Minimum aspect ratio** | Minimum width/height ratio of the bounding box                           |
+| **Object filters > Person > Maximum aspect ratio** | Maximum width/height ratio of the bounding box                           |
 
 To override shape filters for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Objects" /> and select the camera.
 

docs/docs/configuration/objects.md

@@ -70,14 +70,14 @@ Object filters help reduce false positives by constraining the size, shape, and
 
 Navigate to <NavPath path="Settings > Global configuration > Objects" />.
 
-| Field                                   | Description                                                              |
-| --------------------------------------- | ------------------------------------------------------------------------ |
-| **Object filters > Person > Min Area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
-| **Object filters > Person > Max Area**  | Maximum bounding box area in pixels (or decimal for percentage of frame) |
-| **Object filters > Person > Min Ratio** | Minimum width/height ratio of the bounding box                           |
-| **Object filters > Person > Max Ratio** | Maximum width/height ratio of the bounding box                           |
-| **Object filters > Person > Min Score** | Minimum score for the object to initiate tracking                        |
-| **Object filters > Person > Threshold** | Minimum computed score to be considered a true positive                  |
+| Field                                              | Description                                                              |
+| -------------------------------------------------- | ------------------------------------------------------------------------ |
+| **Object filters > Person > Minimum object area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Maximum object area**  | Maximum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Minimum aspect ratio** | Minimum width/height ratio of the bounding box                           |
+| **Object filters > Person > Maximum aspect ratio** | Maximum width/height ratio of the bounding box                           |
+| **Object filters > Person > Minimum confidence**   | Minimum score for the object to initiate tracking                        |
+| **Object filters > Person > Confidence threshold** | Minimum computed score to be considered a true positive                  |
 
 To override filters for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Objects" />.
 

docs/docs/configuration/profiles.md

@@ -126,7 +126,7 @@ Only the fields you explicitly set in a profile override are applied. All other
 
 ## Activating Profiles
 
-Profiles can be activated and deactivated via the Frigate UI, [MQTT](/integrations/mqtt#frigateprofileset), or the Home Assistant integration.
+Profiles can be activated and deactivated via the Frigate UI, [MQTT](/integrations/mqtt#frigateprofileset), the [HTTP API](../integrations/api/camera-set-camera-camera-name-set-feature-sub-command-put.api.mdx), or the Home Assistant integration.
 
 In the Frigate UI, open the Settings cog and select **Profiles** from the submenu to see all defined profiles. From there you can activate any profile or deactivate the current one. The active profile is indicated in the UI so you always know which profile is in effect.
 
@@ -191,14 +191,12 @@ cameras:
     detect:
       enabled: false
     record:
-      enabled: false
+      enabled: true
     profiles:
       away:
         enabled: true
         detect:
           enabled: true
-        record:
-          enabled: true
       home:
         enabled: false
 ```
@@ -251,6 +249,12 @@ Leaving the `objects` section empty (or omitting `track`) does not clear the lis
 
 Fields that require a Frigate restart to take effect cannot be overridden by profiles, since profiles are applied at runtime without restarting. Those fields are hidden when editing a profile override and can only be changed on the base configuration.
 
+### Why can't a profile enable recording when it's disabled in the base config?
+
+Frigate only sets up a camera's recording stream at startup when recording is enabled in the base config, so enabling it later from a profile has no effect. The same applies to turning recording on from the UI or MQTT.
+
+To keep recording off by default, leave `record.enabled: true` in the base config and create a profile that sets `record.enabled: false`. Activate that profile and it will be restored automatically when Frigate starts.
+
 ### Can I schedule profiles to be enabled or disabled at certain times?
 
 Not within Frigate itself. Frigate is an NVR, not an automation platform, so it intentionally does not include a scheduler for activating profiles. Instead, activate profiles from an automation platform that already handles time- and event-based triggers well, such as [Home Assistant](https://www.home-assistant.io/) or [Node-RED](https://nodered.org/). These integrate with Frigate and give you far more robust and flexible scheduling than a built-in scheduler could.

docs/docs/configuration/record.md

@@ -9,7 +9,7 @@ import NavPath from "@site/src/components/NavPath";
 
 Recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM-DD/HH/<camera_name>/MM.SS.mp4` in **UTC time**. These recordings are written directly from your camera stream without re-encoding. Each camera supports a configurable retention policy. Frigate chooses the largest matching retention value between the recording retention and the tracked object retention when determining if a recording should be removed.
 
-New recording segments are written from the camera stream to cache, they are only moved to disk if they match the setup recording retention policy.
+New recording segments are written from the camera stream to cache, they are only moved to disk if they pass a validation check and match the setup recording retention policy.
 
 :::tip
 
@@ -291,7 +291,7 @@ For advanced use cases, the [custom export HTTP API](../integrations/api/export-
 POST /export/custom/{camera_name}/start/{start_time}/end/{end_time}
 ```
 
-The request body accepts `ffmpeg_input_args` and `ffmpeg_output_args` to control encoding, frame rate, filters, and other FFmpeg options. If neither is provided, Frigate defaults to time-lapse output settings (25x speed, 30 FPS).
+The request body accepts `ffmpeg_input_args` and `ffmpeg_output_args` to control encoding, frame rate, filters, and other FFmpeg options. If neither is provided, Frigate defaults to time-lapse output settings (25x speed, 30 FPS) with audio removed (`-an`). When providing your own `ffmpeg_input_args`, include `-an` if you want audio stripped from the export.
 
 The following example exports a time-lapse at 60x speed with 25 FPS:
 

docs/docs/configuration/restream.md

@@ -197,7 +197,7 @@ For cameras that support two-way talk, go2rtc will automatically establish an au
 To prevent this, you must configure two separate stream instances:
 
 1. One stream instance with `#backchannel=0` for Frigate's viewing, recording, and detection (prevents go2rtc from establishing the blocking backchannel)
-2. A second stream instance without `#backchannel=0` for two-way talk functionality (can be used by Frigate's WebRTC viewer or other applications)
+2. A second stream instance with no `#` parameters at all for two-way talk functionality (can be used by Frigate's WebRTC viewer or other applications)
 
 Configuration example:
 
@@ -215,6 +215,8 @@ In this configuration:
 - `front_door` stream is used by Frigate for viewing, recording, and detection. The `#backchannel=0` parameter prevents go2rtc from establishing the audio output backchannel, so it won't block two-way talk access.
 - `front_door_twoway` stream is used for two-way talk functionality. This stream can be used by Frigate's WebRTC viewer when two-way talk is enabled, or by other applications (like Home Assistant Advanced Camera Card) that need access to the camera's audio output channel.
 
+Any `#` parameter on a bare `rtsp://` source disables the backchannel unless the URL explicitly contains `#backchannel=1`. A two-way talk stream with something like `#video=h264` on it silently loses two-way audio, and Frigate will report that two-way talk is unavailable for that stream.
+
 ## Security: Restricted Stream Sources
 
 For security reasons, the `echo:`, `expr:`, and `exec:` stream sources are disabled by default in go2rtc. These sources allow arbitrary command execution and can pose security risks if misconfigured.

docs/docs/configuration/review.md

@@ -121,6 +121,31 @@ cameras:
 </TabItem>
 </ConfigTabs>
 
+## Categorizing manual events
+
+Events created with the [create manual event API](../integrations/api/create-event-events-camera-name-label-create-post.api.mdx) are categorized with the same label lists, using the label from the request path:
+
+1. If alerts are enabled and the label is listed in `review -> alerts -> labels`, the review item is an alert.
+2. Otherwise, if detections are enabled and the label is listed in `review -> detections -> labels`, the review item is a detection.
+3. If the label is in neither list, the review item is an alert, or no review item is created if alerts are disabled.
+
+This means manual events are alerts unless you explicitly list their label as a detection label. For example, to have PIR sensors create detections instead of alerts, post to `/api/events/front_door/pir_sensor/create` with the following config:
+
+```yaml {5-7}
+cameras:
+  front_door:
+    review:
+      detections:
+        labels:
+          - pir_sensor
+```
+
+:::note
+
+Required zones do not apply to manual events, since they are created through the API rather than by the object tracker. Setting `review -> alerts -> labels` to an empty list also does not stop manual events from becoming alerts, as a label in neither list still falls back to an alert.
+
+:::
+
 ## Restricting review items to specific zones
 
 By default a review item will be created if any `review -> alerts -> labels` and `review -> detections -> labels` are detected anywhere in the camera frame. You will likely want to configure review items to only be created when the object enters an area of interest, [see the zone docs for more information](./zones.md#restricting-alerts-and-detections-to-specific-zones)

docs/docs/configuration/semantic_search.md

@@ -245,8 +245,8 @@ Triggers are best configured through the Frigate UI.
 1. Navigate to <NavPath path="Settings > Enrichments > Triggers" /> and select a camera from the dropdown menu.
 2. Click **Add Trigger** to create a new trigger or use the pencil icon to edit an existing one.
 3. In the **Create Trigger** wizard:
-   - Enter a **Name** for the trigger (e.g., "Red Car Alert").
-   - Enter a descriptive **Friendly Name** for the trigger (e.g., "Red car on the driveway camera").
+   - Enter a **Name** for the trigger (e.g., "Red Car Alert"). Frigate derives the trigger's
+     internal **ID** from this name, which can be revealed and edited with the show/hide toggle.
    - Select the **Type** (`Thumbnail` or `Description`).
    - For `Thumbnail`, select an image to trigger this action when a similar thumbnail image is detected, based on the threshold.
    - For `Description`, enter text to trigger this action when a similar tracked object description is detected.

docs/docs/configuration/zones.md

@@ -28,7 +28,7 @@ During testing, enable the Zones option for the [Debug view](/usage/live#the-sin
 1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
 2. Under the **Zones** section, click the plus icon to add a new zone.
 3. Click on the camera's latest image to create the points for the zone boundary. Click the first point again to close the polygon.
-4. Configure zone options such as **Friendly name**, **Objects**, **Loitering time**, and **Inertia** in the zone editor.
+4. Configure zone options such as **Name**, **Objects**, **Loitering Time**, and **Inertia** in the zone editor.
 5. Press **Save** when finished.
 
 </TabItem>
@@ -200,7 +200,7 @@ When using loitering zones, a review item will behave in the following way:
 
 1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
 2. Edit or create the zone (e.g., `sidewalk`).
-   - Set **Loitering time** to the desired number of seconds (e.g., `4`)
+   - Set **Loitering Time** to the desired number of seconds (e.g., `4`)
    - Under **Objects**, add the relevant object types (e.g., `person`)
 
 </TabItem>
@@ -291,7 +291,7 @@ Accurate real-world distance measurements are required to estimate speeds. These
 
 1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
 2. Create or edit a zone with exactly 4 points aligned to the ground plane.
-3. In the zone editor, enter the real-world **Distances** between each pair of consecutive points.
+3. In the zone editor, enable **Speed Estimation** and enter the real-world **Line A distance**, **Line B distance**, **Line C distance**, and **Line D distance** between each pair of consecutive points.
    - For example, if the distance between the first and second points is 10 meters, between the second and third is 12 meters, etc.
 4. Distances are measured in meters (metric) or feet (imperial), depending on the **Unit system** setting.
 
@@ -358,7 +358,7 @@ Zones can be configured with a minimum speed requirement, meaning an object must
 
 1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
 2. Edit or create the zone with distances configured.
-   - Set **Speed threshold** to the desired minimum speed (e.g., `20`)
+   - Set **Speed Threshold** to the desired minimum speed (e.g., `20`)
    - The unit is kph or mph, depending on the **Unit system** setting
 
 </TabItem>

docs/docs/frigate/glossary.md

@@ -54,7 +54,7 @@ An object filter mask drops any [bounding box](#bounding-box) whose bottom cente
 
 ## Min Score
 
-The lowest score a detected object can have to be kept during tracking. Anything scoring below the minimum is assumed to be a [false positive](#false-positive) and discarded.
+The lowest score a detected object can have to be kept during tracking. Anything scoring below the minimum is assumed to be a [false positive](#false-positive) and discarded. Set with `min_score` in the config, shown as **Minimum confidence** in the settings UI.
 
 ## Model
 
@@ -86,7 +86,7 @@ A more specific identity assigned to a [tracked object](#tracked-object-event-in
 
 ## Threshold
 
-The median score an object must reach to be considered a true positive.
+The median score an object must reach to be considered a true positive. Set with `threshold` in the config, shown as **Confidence threshold** in the settings UI.
 
 ## Top Score
 

docs/docs/frigate/hardware.md

@@ -70,6 +70,9 @@ Frigate supports multiple different detectors that work on different types of ha
 - [ROCm](#rocm---amd-gpu): ROCm can run on AMD Discrete GPUs to provide efficient object detection
   - [Supports limited model architectures](../../configuration/object_detectors#amdrocm-gpu-detector)
   - Runs best on discrete AMD GPUs
+- <CommunityBadge /> [XDNA2 (Ryzen AI)](#amd-xdna2): AMD XDNA2 NPU (sub-watt power AI/ML processor separate to the GPU) inside Strix and other "AI" branded AMD platforms
+  - Has only been tested with YOLOv9, in theory other graphs may be compiled too.
+  - Runs via ZMQ proxy which adds some latency, only recommended for local connection
 
 **Apple Silicon**
 
@@ -296,6 +299,32 @@ The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms fo
 | ---------------- | ----------------------------------- |
 | yolov9-tiny      | ~ 4 ms                              |
 
+### AMD Ryzen AI / XDNA2
+
+Frigate supports AMD XDNA2 NPUs through the community-maintained
+frigate-xdna ZMQ sidecar. It works with stock Frigate and supports
+Frigate+ models or compatible local YOLO ONNX models. Models are compiled
+once on the target system and cached for subsequent use.
+
+Currently qualified on **Ryzen AI Max 300 / Strix Halo**. Other XDNA2
+devices are not yet qualified; XDNA1 is unsupported.
+
+Measured YOLOv9 detector latency on Strix Halo:
+
+| Model | 320 | 640 |
+| ----- | ---: | ---: |
+| YOLOv9-T | ~7.4 ms | unsupported |
+| YOLOv9-S | ~9.0 ms | ~20.0 ms |
+| YOLOv9-M | ~13.1 ms | ~34.4 ms |
+| YOLOv9-C | ~14.1 ms | ~35.2 ms |
+| YOLOv9-E | ~69.4 ms | ~224.8 ms |
+
+**YOLOv9-C at 320 is the recommended quality/performance balance.**
+C at 640 is also usable where the lower throughput is acceptable.
+
+Setup, model preparation, and compatibility details are available
+[in the frigate-xdna documentation](https://github.com/mitchins/frigate-xdna).
+
 ## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
 
 This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.

docs/docs/frigate/network_requirements.md

@@ -34,6 +34,12 @@ The following models are downloaded automatically the first time their associate
 | [Custom classification](/configuration/custom_classification/state_classification) (training) | MobileNetV2 ImageNet base weights (via Keras)                              | Google storage       |
 | [Audio transcription](/configuration/advanced/system)                                         | Whisper or Sherpa-ONNX streaming model                                     | HuggingFace / OpenAI |
 
+:::note
+
+The MobileNetV2 base weights are the one exception to the `/config/model_cache/` rule. They are also the only entry that is not downloaded when the feature is enabled: Frigate fetches them when a training run actually starts.
+
+:::
+
 ### Hardware-Specific Detector Models
 
 If you are using one of the following hardware detectors and have not provided your own model file, a default model will be downloaded on first startup:
@@ -75,7 +81,7 @@ If your Frigate instance has restricted internet access, you can point model dow
 | `HF_ENDPOINT`                       | `https://huggingface.co`            | Semantic search, Sherpa-ONNX, AXEngine models |
 | `GITHUB_ENDPOINT`                   | `https://github.com`                | Face recognition, LPR, RKNN models            |
 | `GITHUB_RAW_ENDPOINT`               | `https://raw.githubusercontent.com` | Bird classification                           |
-| `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` | Google storage (Keras default)      | Custom classification training                |
+| `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` | Unset (Keras uses its own default)  | Custom classification training                |
 
 ## Optional Cloud Services
 
@@ -147,9 +153,23 @@ When running as a Home Assistant App, the go2rtc startup script queries the loca
 To run Frigate in an air-gapped or offline environment:
 
 1. **Pre-download models**: Start Frigate with internet access once with all desired features enabled. Models will be cached in `/config/model_cache/`.
-2. **Disable version check**: Set `telemetry.version_check: false` in your configuration.
-3. **Block outbound model requests**: Set the `HF_HUB_OFFLINE=1` and `TRANSFORMERS_OFFLINE=1` environment variables to prevent HuggingFace and Transformers from attempting any network requests.
-4. **Avoid cloud features**: Do not configure Frigate+, Generative AI providers that require internet, or cloud MQTT brokers.
-5. **Use local model mirrors**: If limited internet is available, set the `HF_ENDPOINT`, `GITHUB_ENDPOINT`, and `GITHUB_RAW_ENDPOINT` environment variables to point to local mirrors.
+2. **Pre-download the training base weights**: If you plan to train custom classification models, set `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` before training, then run one training job while online. Without this variable the base weights are cached outside `/config/` and are lost whenever the container is recreated, so a later training run will fail offline. If the machine never has internet access, copy the weights in manually as described below.
+3. **Disable version check**: Set `telemetry.version_check: false` in your configuration.
+4. **Block outbound model requests**: Set the `HF_HUB_OFFLINE=1` and `TRANSFORMERS_OFFLINE=1` environment variables to prevent HuggingFace and Transformers from attempting any network requests.
+5. **Avoid cloud features**: Do not configure Frigate+, Generative AI providers that require internet, or cloud MQTT brokers.
+6. **Use local model mirrors**: If limited internet is available, set the `HF_ENDPOINT`, `GITHUB_ENDPOINT`, `GITHUB_RAW_ENDPOINT`, and `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` environment variables to point to local mirrors.
 
 After these steps, Frigate will operate with no outbound internet connections.
+
+### Manually Copying the Training Base Weights
+
+On a machine with internet access, download the weights:
+
+```bash
+curl -L -o mobilenet_v2_weights.h5 \
+  "https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_0.35_224_no_top.h5"
+```
+
+Copy the file into your Frigate config volume as `/config/model_cache/MobileNet/mobilenet_v2_weights.h5`, keeping that exact filename, then set the environment variable `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` in your Docker compose file to the URL above and restart Frigate.
+
+The variable must be set even though the URL is never contacted. If it is unset, Frigate ignores the copied file and asks Keras to download the weights instead.

docs/docs/integrations/homekit.md

@@ -3,35 +3,100 @@ id: homekit
 title: HomeKit
 ---
 
-Frigate cameras can be integrated with Apple HomeKit through go2rtc. This allows you to view your camera streams directly in the Apple Home app on your iOS, iPadOS, macOS, and tvOS devices.
+Frigate cameras can be exported to Apple HomeKit through go2rtc. Each exported camera appears as an accessory in the Apple Home app on your iOS, iPadOS, macOS, and tvOS devices.
 
 ## Overview
 
-HomeKit integration is handled entirely through go2rtc, which is embedded in Frigate. go2rtc provides the necessary HomeKit Accessory Protocol (HAP) server to expose your cameras to HomeKit.
+Exporting cameras is handled entirely through go2rtc, which is embedded in Frigate. go2rtc provides the necessary HomeKit Accessory Protocol (HAP) server, so your camera is published to HomeKit as an accessory in its own right.
 
-## Setup
+:::note
 
-All HomeKit configuration and pairing should be done through the **go2rtc WebUI**.
+This is the opposite of importing a HomeKit camera. go2rtc can also pair with an existing HomeKit camera (Aqara, Eve, Eufy, and similar) and use it as a stream source, which is what the `add` page of the go2rtc WebUI is for. That page discovers HomeKit accessories on your network and will not list your Frigate cameras. It is not used for exporting.
 
-### Accessing the go2rtc WebUI
-
-The go2rtc WebUI is available at:
-
-```
-http://<frigate_host>:1984
-```
-
-Replace `<frigate_host>` with the IP address or hostname of your Frigate server.
-
-### Pairing Cameras
-
-1. Navigate to the go2rtc WebUI at `http://<frigate_host>:1984`
-2. Use the `add` section to add a new camera to HomeKit
-3. Follow the on-screen instructions to generate pairing codes for your cameras
+:::
 
 ## Requirements
 
-- Frigate must be accessible on your local network using host network_mode
-- Your iOS device must be on the same network as Frigate
-- Port 1984 must be accessible for the go2rtc WebUI
-- For detailed go2rtc configuration options, refer to the [go2rtc documentation](https://github.com/AlexxIT/go2rtc)
+- Frigate must be running with `network_mode: host` so that HomeKit can discover your cameras over mDNS
+- Your Apple device must be on the same network as Frigate
+- Port 1984 must be accessible so you can reach the go2rtc WebUI
+
+HomeKit also places strict limits on the stream itself. go2rtc passes your stream through without resizing or re-encoding it, so the stream you export must already meet these requirements:
+
+- **Video:** H.264 at 1920x1080, 1280x720, or 320x240
+- **Audio:** Opus, mono, 16 kHz
+
+A camera's full resolution stream usually does not qualify. See [Exporting a compatible stream](#exporting-a-compatible-stream) below.
+
+## Configuration
+
+HomeKit settings are stored in `/config/go2rtc_homekit.yml`. This is a separate file from your Frigate config, because go2rtc needs to write your pairings back to it when you pair a device.
+
+Edit it using the go2rtc config editor, which writes to that file directly:
+
+```
+http://<frigate_host>:1984/editor.html
+```
+
+Replace `<frigate_host>` with the IP address or hostname of your Frigate server. The editor will be empty until you add a HomeKit section, since this file holds only your HomeKit settings and not the rest of your go2rtc config.
+
+:::warning
+
+Do not put the `homekit:` section in the `go2rtc:` section of your Frigate config.
+
+Frigate regenerates that config on every startup, so go2rtc cannot save your pairings to it. Pairing will appear to succeed and then fail after the next restart with `PairVerify with unknown client_id`. If the section exists in both places, your saved pairings are erased on every restart.
+
+:::
+
+Add an entry for each camera you want to export. The key must match the name of a go2rtc stream, and the pin must be 8 digits. This is the number the Home app calls the setup code:
+
+```yaml
+homekit:
+  front_door:
+    name: Front Door
+    pin: "12345678"
+```
+
+If the key does not match a go2rtc stream, go2rtc logs `[homekit] missing stream:` at startup and the camera will not appear in the Home app.
+
+:::note
+
+go2rtc derives each accessory's HomeKit identity from this key, so renaming it later means the camera appears as a new accessory and has to be paired again. Settle on the name before you pair.
+
+:::
+
+Frigate keeps only the `homekit:` section of this file when it starts, so do not store streams or other go2rtc settings in it.
+
+### Exporting a compatible stream
+
+If a camera's stream does not meet the requirements listed above, define a scaled restream in your Frigate config and point HomeKit at that stream instead of the original:
+
+```yaml
+go2rtc:
+  streams:
+    front_door:
+      - rtsp://user:password@192.168.1.50:554/stream
+    front_door_homekit:
+      - "ffmpeg:front_door#video=h264#width=1280#height=720#audio=opus/16000"
+```
+
+```yaml
+# /config/go2rtc_homekit.yml
+homekit:
+  front_door_homekit:
+    name: Front Door
+    pin: "12345678"
+```
+
+Add `#hardware=cuda`, `#hardware=vaapi`, or the appropriate value for your system to transcode using your GPU. Note that NVENC cannot encode H.264 wider than 4096 pixels, so very wide streams must be scaled down as shown above rather than only re-encoded.
+
+## Pairing Cameras
+
+1. Restart Frigate after adding the `homekit:` section
+2. In the Apple Home app, choose **Add Accessory**, then **More options** to enter a code manually
+3. Select your camera and enter the pin you configured as the setup code
+4. Confirm that a `pairings:` list now appears under the camera in `/config/go2rtc_homekit.yml`
+
+Pairings are saved back to that file automatically. If step 4 shows no `pairings:` list, check the Frigate log for `[homekit] can't save`, which means the `homekit:` section is missing from `/config/go2rtc_homekit.yml`.
+
+For detailed go2rtc configuration options, refer to the [go2rtc documentation](https://github.com/AlexxIT/go2rtc).

docs/docs/integrations/mqtt.md

@@ -11,6 +11,12 @@ MQTT requires a network connection to your broker. This is typically local, but
 
 :::
 
+:::note
+
+Wherever a topic below includes a camera, mask, or zone name, use its `ID` from the config, not its `friendly_name`. For example, a camera with `friendly_name: "Back Yard"` and ID `back_yard` publishes to `frigate/back_yard/...`, not `frigate/Back Yard/...`.
+
+:::
+
 ## General Frigate Topics
 
 ### `frigate/available`
@@ -292,7 +298,9 @@ Topic with the currently active profile name. Published value is the profile nam
 
 ### `frigate/notifications/set`
 
-Topic to turn notifications on and off. Expected values are `ON` and `OFF`.
+Topic to turn notifications on and off for all cameras. Expected values are `ON` and `OFF`.
+
+Only available when notifications are enabled in the config. Not persisted across Frigate restarts.
 
 ### `frigate/notifications/state`
 
@@ -308,6 +316,8 @@ Publishes the current health status of each role that is enabled (`audio`, `dete
 - `offline`: Stream is offline and is being restarted
 - `disabled`: Camera is currently turned off (either at runtime via the `enabled/set` topic, or persistently via the configuration file). See [Camera state](/configuration/live#camera-state) for the distinction.
 
+These reflect the state of Frigate's process for that role, not the camera's reachability, so an unreachable camera alternates between `offline` and `online` as the watchdog restarts ffmpeg. Wait for the status to hold steady (for example with Home Assistant's `for:`) rather than acting on a single message.
+
 ### `frigate/<camera_name>/<object_name>`
 
 Publishes the count of objects for the camera for use as a sensor in Home Assistant.
@@ -390,6 +400,18 @@ Topic to turn audio detection for a camera on and off. Expected values are `ON`
 
 Topic with current state of audio detection for a camera. Published values are `ON` and `OFF`.
 
+### `frigate/<camera_name>/audio_transcription/set`
+
+Topic to turn [live audio transcription](/configuration/audio_detectors#live-transcription) for a camera on and off. Expected values are `ON` and `OFF`. Transcribed text is published to `frigate/<camera_name>/audio/transcription`.
+
+`ON` is ignored unless audio transcription is enabled in the config for the camera. Unlike the other camera toggles, this one is not persisted across Frigate restarts.
+
+**NOTE:** Requires audio detection and transcription to be enabled
+
+### `frigate/<camera_name>/audio_transcription/state`
+
+Topic with current state of live audio transcription for a camera. Published values are `ON` and `OFF`.
+
 ### `frigate/<camera_name>/recordings/set`
 
 Topic to turn recordings for a camera on and off. Expected values are `ON` and `OFF`. The change is persisted across Frigate restarts (see [Runtime toggle persistence](/configuration/live#runtime-toggle-persistence)).
@@ -556,16 +578,20 @@ Topic with current state of the Birdseye mode for a camera. Published values are
 
 ### `frigate/<camera_name>/notifications/set`
 
-Topic to turn notifications on and off. Expected values are `ON` and `OFF`.
+Topic to turn notifications for a camera on and off. Expected values are `ON` and `OFF`.
+
+`ON` is ignored unless notifications are enabled in the config for the camera. This is not persisted across Frigate restarts. It is the same control the UI labels **Suspend until restart**.
 
 ### `frigate/<camera_name>/notifications/state`
 
-Topic with current state of notifications. Published values are `ON` and `OFF`.
+Topic with current state of notifications. Published values are `ON` and `OFF`. This is the authoritative topic for whether a camera will notify.
 
 ### `frigate/<camera_name>/notifications/suspend`
 
-Topic to suspend notifications for a certain number of minutes. Expected value is an integer.
+Topic to suspend notifications for a certain number of minutes. Expected value is an integer. Separate from `notifications/set`: it does not change `notifications/state`, and is ignored while notifications are off.
 
 ### `frigate/<camera_name>/notifications/suspended`
 
-Topic with timestamp that notifications are suspended until. Published value is a UNIX timestamp, or 0 if notifications are not suspended.
+Topic with timestamp that notifications are suspended until. Published value is a UNIX timestamp, or 0 if there is no timed suspension.
+
+`0` does not mean notifications are enabled: `notifications/set` `OFF` clears the timed suspension, so this publishes `0` while `notifications/state` is `OFF`.

docs/docs/plus/faq.md

@@ -21,7 +21,13 @@ Yes. Models and metadata are stored in the `model_cache` directory within the co
 
 ### Can I keep using my Frigate+ models even if I do not renew my subscription?
 
-Yes. Subscriptions to Frigate+ provide access to the infrastructure used to train the models. Models trained with your subscription are yours to keep and use forever. However, do note that the terms and conditions prohibit you from sharing, reselling, or creating derivative products from the models.
+Yes. Subscriptions to Frigate+ provide access to the infrastructure used to train the models. Models you train during an active subscription remain licensed for your continued use even after your subscription ends — models already in your model cache will keep working indefinitely. An active subscription is required to train new models and download new versions.
+
+### Can I use Frigate+ models commercially?
+
+A standard subscription covers use on camera systems you own or operate, including for your business. A shop, restaurant, warehouse, or office running Frigate+ at its own locations (including multiple locations) is exactly the kind of use the subscription is for.
+What the standard subscription does not cover is using Frigate+ models to provide a product or service to others. If you're deploying models at your customers' sites, bundling them with hardware you sell, or running them as part of a hosted or managed service, even if your customers never receive the model files themselves, you'll need a commercial license.
+Note that professional installers are fine under standard subscriptions when each customer holds their own Frigate+ subscription. The commercial license is for cases where your license powers your customers' sites.
 
 ### Why can't I submit images to Frigate+?
 

docs/docs/plus/first_model.md

@@ -64,20 +64,20 @@ Frigate+ models generally have much higher scores than the default model provide
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > Global configuration > Objects" />. Under **Object filters**, set **Min Score** and **Threshold** for each object type, then click **Save**.
+Navigate to <NavPath path="Settings > Global configuration > Objects" />. Under **Object filters**, set **Minimum confidence** and **Confidence threshold** for each object type, then click **Save**.
 
-| Object            | Min Score | Threshold |
-| ----------------- | --------- | --------- |
-| **dog**           | .7        | .9        |
-| **cat**           | .65       | .8        |
-| **face**          | .7        |           |
-| **package**       | .65       | .9        |
-| **license_plate** | .6        |           |
-| **amazon**        | .75       |           |
-| **ups**           | .75       |           |
-| **fedex**         | .75       |           |
-| **person**        | .65       | .85       |
-| **car**           | .65       | .85       |
+| Object            | Minimum confidence | Confidence threshold |
+| ----------------- | ------------------ | -------------------- |
+| **dog**           | .7                 | .9                   |
+| **cat**           | .65                | .8                   |
+| **face**          | .7                 |                      |
+| **package**       | .65                | .9                   |
+| **license_plate** | .6                 |                      |
+| **amazon**        | .75                |                      |
+| **ups**           | .75                |                      |
+| **fedex**         | .75                |                      |
+| **person**        | .65                | .85                  |
+| **car**           | .65                | .85                  |
 
 </TabItem>
 <TabItem value="yaml">

docs/docs/plus/index.md

@@ -65,11 +65,11 @@ Some users may find that Frigate+ models result in more false positives initiall
 
 Frigate+ models support a more relevant set of objects for security cameras. The labels for annotation in Frigate+ are configurable by editing the camera in the Cameras section of Frigate+. Currently, the following objects are supported:
 
-- **People**: `person`, `face`
-- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `school_bus`, `license_plate`
+- **People**: `person`, `face`, `baby`
+- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `school_bus`, `garbage truck`, `license_plate`
 - **Delivery Logos**: `amazon`, `usps`, `ups`, `fedex`, `dhl`, `an_post`, `purolator`, `postnl`, `nzpost`, `postnord`, `gls`, `dpd`, `canada_post`, `royal_mail`
-- **Animals**: `dog`, `cat`, `deer`, `horse`, `bird`, `raccoon`, `fox`, `bear`, `cow`, `squirrel`, `goat`, `rabbit`, `skunk`, `kangaroo`
-- **Other**: `package`, `waste_bin`, `bbq_grill`, `robot_lawnmower`, `umbrella`
+- **Animals**: `dog`, `cat`, `deer`, `horse`, `bird`, `raccoon`, `fox`, `bear`, `cow`, `squirrel`, `goat`, `rabbit`, `skunk`, `kangaroo`, `possum`, `rodent`
+- **Other**: `package`, `waste_bin`, `bbq_grill`, `robot_lawnmower`, `umbrella`, `baby_stroller`
 
 Other object types available in the default Frigate model are not available. Additional object types will be added in future releases.
 
@@ -77,9 +77,12 @@ Other object types available in the default Frigate model are not available. Add
 
 Candidate labels are also available for annotation. These labels don't have enough data to be included in the model yet, but using them will help add support sooner. You can enable these labels by editing the camera settings.
 
-Where possible, these labels are mapped to existing labels during training. For example, any `baby` labels are mapped to `person` until support for new labels is added.
+Where possible, these labels are mapped to existing labels during training. For example, any `duck` labels are mapped to `bird` until support for new labels is added.
 
-The candidate labels are: `baby`, `bpost`, `badger`, `possum`, `rodent`, `chicken`, `groundhog`, `boar`, `hedgehog`, `tractor`, `golf cart`, `garbage truck`, `bus`, `sports ball`, `la_poste`, `lawnmower`, `heron`, `rickshaw`, `wombat`, `auspost`, `aramex`, `bobcat`, `mustelid`, `transoflex`, `airplane`, `drone`, `mountain_lion`, `crocodile`, `turkey`, `baby_stroller`, `monkey`, `coyote`, `porcupine`, `parcelforce`, `sheep`, `snake`, `helicopter`, `lizard`, `duck`, `hermes`, `cargus`, `fan_courier`, `sameday`
+- **Vehicles**: `tractor`, `golf_cart`, `bus`, `airplane`, `helicopter`, `rickshaw`, `scooter`
+- **Delivery Logos**: `bpost`, `auspost`, `aramex`, `transoflex`, `parcelforce`, `hermes`, `cargus`, `fan_courier`, `sameday`, `la_poste`
+- **Animals**: `badger`, `chicken`, `duck`, `turkey`, `groundhog`, `boar`, `hedgehog`, `wombat`, `bobcat`, `mustelid`, `mountain_lion`, `crocodile`, `monkey`, `coyote`, `porcupine`, `sheep`, `snake`, `lizard`, `heron`, `elk`, `moose`, `pig`, `donkey`, `civet`
+- **Other**: `sports_ball`, `drone`, `lawnmower`
 
 Candidate labels are not available for automatic suggestions.
 

docs/docs/troubleshooting/common_errors.md

@@ -34,11 +34,15 @@ The detect FFmpeg process exited on its own. This message is only the notificati
 
 </FaqItem>
 
-<FaqItem id="non-monotonically-increasing-dts" question="Application provided invalid, non monotonically increasing dts to muxer">
+<FaqItem id="non-monotonically-increasing-dts" question="Non-monotonic DTS / non monotonically increasing dts to muxer / Queue input is backward in time">
 
-An FFmpeg message meaning the camera sent packets with out-of-order timestamps. Because recordings are copied without re-encoding, FFmpeg cannot fix them, and the segment muxer often splits early, producing one-second segments and a cache backlog. The usual cause is a camera "Smart Codec" / H.264+ / H.265+ mode or a camera clock that jumps.
+These are FFmpeg messages indicating the camera sent packets with out-of-order timestamps, either on the video or the audio stream. Timestamp jitter like this is common with WiFi cameras and restreamed or proxied sources; other causes are a camera "Smart Codec" / H.264+ / H.265+ mode or a camera clock that jumps. A sustained flood of these messages usually precedes the stream stalling and the watchdog restarting FFmpeg.
 
-See [Recordings: segments are only 1 second long](/troubleshooting/recordings#segments-are-only-1-second-long).
+In most cases, the fix is to improve the network, reduce system resource usage, or switch to non-WiFi cameras. In general, WiFi cameras are [not recommended](https://ipcamtalk.com/threads/multiple-cameras-high-bandwidth.77100/#post-861110).
+
+On the video stream, this can affect recordings: because they are copied without re-encoding, FFmpeg cannot fix the timestamps, and the segment muxer often splits early, producing one-second segments and a cache backlog. See [Recordings: segments are only 1 second long](/troubleshooting/recordings#segments-are-only-1-second-long).
+
+On the audio stream, the messages can come from the output's audio encoding. If the audio stream is the problem, it may help to have go2rtc transcode it by adding `#audio=aac` to the camera's go2rtc stream to produce clean timestamps for everything consuming the restream.
 
 </FaqItem>
 
@@ -62,17 +66,19 @@ An FFmpeg message meaning it probed the stream but never saw enough decodable vi
 
 ## Recording
 
-<FaqItem id="no-new-recording-segments" question="No new recording segments were created for <camera> in the last 120s">
+<FaqItem id="no-new-recording-segments" question="No new recording segments were created (or: No new valid recording segments were created / No valid segments created since last invalid segment) for <camera> in the last 120s">
 
-Frigate's record watchdog is restarting the record FFmpeg process because no valid segment has reached the cache. This means the record stream is not connecting or the segments are being rejected (see the audio-codec entry below).
+Frigate's record watchdog is restarting the record FFmpeg process because the camera stopped producing usable recordings. The wording distinguishes the cases: `No new recording segments` means no new segment file reached the cache, so ffmpeg isn't getting video out of the record stream; the two `valid` variants mean recordings are arriving but keep failing validation. Either way the fault is on the camera or network side, and the restart is Frigate trying to recover.
 
-See [Recordings: the record stream isn't connecting](/troubleshooting/recordings#the-record-stream-isnt-connecting).
+See [Recordings: no new recording segments were created](/troubleshooting/recordings#no-new-recording-segments-were-created).
 
 </FaqItem>
 
-<FaqItem id="invalid-or-missing-video-stream-in-segment" question="Invalid or missing video stream in segment. Discarding.">
+<FaqItem id="invalid-or-missing-video-stream-in-segment" question="Invalid or missing video stream in segment. Discarding. / Discarding a corrupt recording segment / Failed to probe corrupt segment / Invalid recording segment detected">
 
-A cached recording segment failed validation (no readable video stream) and was deleted. The most common cause is a segment that was truncated because the record FFmpeg process was killed mid-write, so this often appears alongside, and as a consequence of, the record-stream restarts above. A segment containing only audio triggers it too.
+A cached recording segment failed validation and was deleted, either because it had no readable video stream or because its length was impossible. This nearly always means the camera stopped sending usable video partway through the segment: a camera that rebooted, dropped the connection, or ran out of simultaneous connections, or an unreliable link such as WiFi or a failing switch port. Broken camera timestamps (a "Smart Codec" / H.264+ mode) cause the corrupt-segment variants. The same stream failure trips the record watchdog, so the restarts above usually appear alongside these messages.
+
+See [Recordings: invalid or missing video stream in segment](/troubleshooting/recordings#invalid-or-missing-video-stream-in-segment).
 
 </FaqItem>
 

docs/docs/troubleshooting/cpu.md

@@ -3,7 +3,31 @@ id: cpu
 title: High CPU Usage
 ---
 
-High CPU usage can impact Frigate's performance and responsiveness. This guide outlines the most effective configuration changes to help reduce CPU consumption and optimize resource usage.
+High CPU usage can impact Frigate's performance and responsiveness. This guide explains how to interpret the CPU values Frigate reports and outlines the most effective configuration changes to help reduce CPU consumption and optimize resource usage.
+
+## Understanding Frigate's Reported CPU Usage
+
+Frigate's CPU percentages often look much higher than what the host reports. Usually both numbers are correct and are simply measured against different denominators, so confirm you actually have a problem before tuning anything.
+
+### Per-process values are relative to a single core
+
+The values Frigate reports for FFmpeg, capture, detect, detector, and other processes follow the same convention as `top`: 100% means one CPU core is fully saturated, not that the whole system is saturated. A multithreaded process such as FFmpeg can legitimately report well over 100%.
+
+Host and hypervisor tools instead report a percentage of the machine's total capacity across all cores. This includes `docker stats`, the `htop` summary, the Proxmox summary graph, the Unraid dashboard, Synology Resource Monitor, and Home Assistant's system monitor sensors. To reconcile the two:
+
+```
+host percentage ≈ (sum of Frigate's process percentages) / (number of cores)
+```
+
+On a 4 core system, an FFmpeg process reporting 100% is consuming one quarter of the machine, so the host will show roughly 25 to 30% once the remaining Frigate processes are included. That same 100% on a 16 core system is about 6%. Frigate's own warning thresholds use the per-core convention as well, so an FFmpeg process is flagged at 20% of a single core, not 20% of the system.
+
+### Instantaneous samples and averages measure different things
+
+Frigate collects stats every 15 seconds, and the `cpu` value covers only the interval since the previous collection. The `cpu_average` value in the stats API and MQTT payload is the average across the entire life of the process, and it is what the high CPU usage warnings are based on. Host dashboards generally plot data averaged over a longer window, so a single Frigate sample can show a peak that a host graph never displays. A process that has just started, such as FFmpeg after a camera reconnect, reports 0 until it has been sampled twice.
+
+### The system-wide value depends on what the container can see
+
+The system CPU value is read from `/proc/stat`. Under Docker that file belongs to the host, so the value covers the entire machine including workloads unrelated to Frigate, and it will not match `docker stats` for the Frigate container. Under an LXC container, lxcfs virtualizes `/proc/stat` and the value reflects only the cores assigned to the container. In a virtual machine, the guest sees only its assigned vCPUs while the hypervisor divides by every physical thread on the node, so guest and host percentages will not agree even when both are accurate.
 
 ## 1. Hardware Acceleration for Video Decoding
 
@@ -72,3 +96,19 @@ The model you use significantly impacts detector performance. Frigate provides d
 - Larger models (640x640): Slower inference, can sometimes have higher accuracy on very large objects that take up a majority of the frame.
 
 For more detail on picking the right size, see [Choosing a model size](../configuration/object_detectors.md#choosing-a-model-size).
+
+## 3. Reducing Detector CPU Usage
+
+**Priority: High**
+
+The **Detector CPU Usage** metric measures the CPU spent converting frames into the tensor format the model expects and post-processing the model's output. It does not include inference, so this value can be high even when you've configured a GPU, NPU, or Coral for object detection.
+
+This metric scales with how many detections per second Frigate runs and how expensive each one is to prepare. Tuning [motion detection](../configuration/motion_detection) is usually the first recommendation to reduce the number of detections. Additionally, you can:
+
+- **Lower `detect -> fps`.** 5 is the recommended value for nearly all cameras. Running at 10 doubles the frames eligible for detection and is one of the largest contributors to this metric.
+- **Use a 320x320 model.** A 640x640 model has 4 times as many pixels to transpose, convert, and copy on every inference.
+- **Prefer a model that takes integer input.** Models configured with `input_dtype: float` require each frame to be converted to float32 and normalized on the CPU first. Models taking `int` input, such as the tflite models used by the Edge TPU, skip that step.
+- **Do not match the detect resolution to the model resolution.** The detect stream should match your camera's aspect ratio, for example `1280x720`, not the model's input size. Frigate crops and scales regions of motion itself, so an oversized detect stream only adds work.
+- **Tune stationary object behavior.** Objects that never settle into a stationary state are re-detected continuously. Raising `detect -> stationary -> interval` reduces how often detection runs on objects that are already parked. See [stationary objects](../configuration/stationary_objects).
+
+Adding [more detector instances](#multiple-detector-instances) spreads this work across more CPU cores, but does not reduce the total CPU used.

docs/docs/troubleshooting/dummy-camera.md

@@ -39,7 +39,7 @@ The per-clip variation is typically quite low and is mostly an artifact of keyfr
 
 Debug Replay lets you re-run Frigate's detection pipeline against a section of recorded video without manually configuring a dummy camera. It automatically extracts the recording, creates a temporary camera with the same detection settings as the original, and loops the clip through the pipeline so you can observe detections in real time.
 
-The replay camera behaves like a live camera feed rather than History's video player: it loops the clip continuously as Frigate analyzes it and has no playback controls, so you cannot pause, scrub, or step through it frame by frame.
+The replay camera behaves like a live camera feed rather than History's video player: it loops the clip continuously as Frigate analyzes it and has no playback controls, so you cannot pause, scrub, or step through it frame by frame. The Debug Replay camera does not save recordings or snapshots or surface anything in Explore, but it otherwise behaves like a regular camera, including running enrichments such as Face Recognition, LPR, and custom classification.
 
 Debug Replay isn't intended to be a one-stop pane for all Frigate diagnostics or a comprehensive debugging environment for every Frigate feature. It merely makes it easier to spin up a "dummy camera" and perform some common adjustments in real time. You'll still need to use the normal tools (logs, an MQTT client, etc) to debug your feature.
 

docs/docs/troubleshooting/faqs.md

@@ -39,6 +39,20 @@ To do this efficiently the following setup is required:
 
 When this is done correctly, the GPU will do the decoding and scaling which will result in a small increase in CPU usage but with better results.
 
+### How can I rotate my camera's video feed?
+
+Rotation is best done in the camera's firmware settings (usually called rotate, flip, or corridor mode) so the video arrives already rotated and no extra processing is needed. Check there first.
+
+If your camera does not support rotation, go2rtc's ffmpeg module can rotate the stream with the `#rotate` parameter (`90`, `180`, `270`, or `-90`), but this is not recommended: rotation requires transcoding (re-encoding) the video, which significantly increases CPU usage, especially for high resolution streams.
+
+```yaml
+go2rtc:
+  streams:
+    my_camera: "ffmpeg:rtsp://user:password@192.168.1.10:554/stream#video=h264#hardware#rotate=90"
+```
+
+Point the camera's inputs at the restream as described in the [restream docs](/configuration/restream.md), and swap `detect -> width` and `detect -> height` to match the rotated resolution.
+
 ### My mjpeg stream or snapshots look green and crazy
 
 This almost always means that the width/height defined for your camera are not correct. Double check the resolution with VLC or another player. Also make sure you don't have the width and height values backwards.
@@ -65,9 +79,17 @@ This is because Frigate does not run in host mode so localhost points to the Fri
 
 ### How do I know if my camera is offline
 
-A camera being offline can be detected via MQTT or /api/stats, the camera_fps for any offline camera will be 0.
+Frigate publishes a per-role health status to [`frigate/<camera_name>/status/<role>`](/integrations/mqtt#frigatecamera_namestatusrole), where `<role>` is each enabled role on the camera (`detect`, `record`, and `audio`). The published value is one of:
 
-Also, Home Assistant will mark any offline camera as being unavailable when the camera is offline.
+- `online`: Frigate's process for that role is running normally
+- `offline`: the process is down and Frigate is restarting it
+- `disabled`: the camera is turned off, either at runtime or in the configuration file
+
+These reflect the state of Frigate's process for that role, not the camera's reachability, so an unreachable camera alternates between `offline` and `online` as the watchdog restarts ffmpeg. Wait for the status to hold steady (for example with Home Assistant's `for:`) rather than acting on a single message.
+
+Because the status is per role, a camera whose substream is fine but whose recording stream has dropped will report `online` for `detect` and `offline` for `record`. The status is republished whenever it changes.
+
+You can also detect an offline camera through `/api/stats`, where `camera_fps` will be 0.
 
 ### How can I view the Frigate log files without using the Web UI?
 
@@ -125,6 +147,12 @@ cameras:
       height: 720
 ```
 
+### What is the `version` key in my config file?
+
+`version` records the config format that your config was last migrated to. On startup Frigate compares it against the format the running version expects, and if it is older it copies your config to `/config/backup_config.yaml`, rewrites it to the new format, and updates `version` as the final step. A config with no `version` key is assumed to predate 0.14 and is migrated from there.
+
+Frigate manages this key for you, so do not set or edit it. Raising it makes Frigate skip migrations your config still needs, and lowering it re-runs migrations against config that has already been converted. Either can leave you with a config that no longer validates.
+
 ### Why does Frigate keep creating new tracked objects for my parked car?
 
 Stationary tracking is designed to _prevent_ this: a parked car should remain a single tracked object rather than generating new ones. If you're repeatedly getting new tracked objects for the same car, it's likely that Frigate is losing the object and re-detecting it as a new one.

docs/docs/troubleshooting/go2rtc.md

@@ -78,7 +78,9 @@ go2rtc:
 
 :::warning
 
-The `#`-modifiers (`#video=`, `#audio=`, `#hardware`, `#backchannel=0`, …) **only take effect on a source that is prefixed with `ffmpeg:`**. Adding them to a bare `rtsp://…#audio=opus` source does nothing: go2rtc ignores them. Likewise, when a source references another stream by name (e.g. `ffmpeg:back#audio=aac`), the name must match the stream key **exactly** (it is case sensitive), or the transcode is silently never produced. This is the single most common configuration mistake. In the Frigate UI, the **Use compatibility mode (ffmpeg)** toggle adds the `ffmpeg:` prefix for you.
+The transcoding modifiers (`#video=`, `#audio=`, `#hardware`, …) **only take effect on a source that is prefixed with `ffmpeg:`**. Adding them to a bare `rtsp://…#audio=opus` source does nothing: go2rtc ignores them. Likewise, when a source references another stream by name (e.g. `ffmpeg:back#audio=aac`), the name must match the stream key **exactly** (it is case sensitive), or the transcode is silently never produced. This is the single most common configuration mistake. In the Frigate UI, the **Use compatibility mode (ffmpeg)** toggle adds the `ffmpeg:` prefix for you.
+
+A bare `rtsp://` source reads a different set of modifiers: `#backchannel=`, `#media=`, `#timeout=`, and `#transport=`. These do nothing on an `ffmpeg:` source. Adding **any** modifier to a bare `rtsp://` source also disables the camera's backchannel unless the URL explicitly contains `#backchannel=1`, so a stream dedicated to two-way talk should carry no modifiers at all.
 
 :::
 
@@ -153,7 +155,7 @@ WebRTC is only attempted when MSE fails or when using a camera's two-way talk fe
 
 - **Codec mismatch**: WebRTC cannot carry H.265 or AAC. The stream backing the WebRTC view must provide Opus (or PCMA/PCMU) audio and H.264 video. Add an `ffmpeg:back#audio=opus` source as shown above.
 - **Port `8555` not reachable, or no candidates set**: WebRTC needs port `8555` (both TCP and UDP) open and a reachable candidate advertised. On Docker installs running on a custom/overlay network, go2rtc may advertise unreachable container IPs as ICE candidates; setting `webrtc.filters.candidates: []` and supplying only your host's LAN IP resolves this. See [WebRTC extra configuration](/configuration/live#webrtc-extra-configuration).
-- **Two-way talk** additionally requires a secure context (HTTPS or the authenticated port `8971`, because browsers block microphone access on plain HTTP). The camera's RTSP backchannel must also be handled correctly: go2rtc seizes the backchannel by default, which blocks two-way audio for other consumers and can inject static. Disable it on the primary stream with `#backchannel=0` and use a separate dedicated stream for talk, as documented in [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream).
+- **Two-way talk** additionally requires a secure context (HTTPS or the authenticated port `8971`, because browsers block microphone access on plain HTTP). The camera's RTSP backchannel must also be handled correctly: go2rtc seizes the backchannel by default, which blocks two-way audio for other consumers and can inject static. Disable it on the primary stream with `#backchannel=0` and use a separate dedicated stream for talk, carrying no `#` modifiers of any kind, as documented in [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream).
 
 ## High CPU usage
 

docs/docs/troubleshooting/recordings.md

@@ -209,6 +209,50 @@ If the record stream uses a "Smart Codec"/H.264+ mode or changes encoding parame
 
 </FaqItem>
 
+<FaqItem id="invalid-or-missing-video-stream-in-segment" question="I see the message: WARNING : Invalid or missing video stream in segment ... Discarding.">
+
+Every recording segment is validated before it leaves the cache. Frigate probes each finished `.mp4` in `/tmp/cache` and requires a readable video stream and a valid duration before moving to storage. A segment that fails is deleted, so those ~10 seconds of footage are lost. Three messages come from this check:
+
+- `Invalid or missing video stream in segment <path>. Discarding.` The segment holds no video, or could not be read at all.
+- `Failed to probe corrupt segment <path>` followed by `Discarding a corrupt recording segment: <path>`. The segment was read, but its length could not be determined.
+- `Discarding a corrupt recording segment: <path>` on its own. The segment's length is impossible (empty, or longer than ten minutes), which points at broken timestamps coming from the camera.
+
+For each one, the camera watchdog also logs `Invalid recording segment detected for <camera> at <timestamp>`.
+
+:::warning
+
+This is almost always a **camera or network problem**, not a Frigate one. A segment is only complete once ffmpeg has finished writing it, so anything that interrupts the stream partway through leaves behind a file that cannot be saved. Frigate is reporting the interruption, not causing it.
+
+:::
+
+#### Start with the camera and the network
+
+- **The camera dropped the connection.** Cameras reboot, reinitialize their stream when switching to night mode, and cut clients off when they are overloaded or out of simultaneous connections. Count everything pulling from the camera at once: Frigate's detect and record streams, go2rtc, a phone app, and any other NVR each use one. Routing all roles through a single [RTSP restream](/configuration/restream#reduce-connections-to-camera) so the camera only ever sees one connection often resolves this by itself.
+- **The link to the camera is unreliable.** WiFi cameras, powerline adapters, a saturated uplink, a failing switch port, or a marginal cable all produce this pattern, and usually only on one camera at a time. WiFi cameras are [not recommended](https://ipcamtalk.com/threads/multiple-cameras-high-bandwidth.77100/#post-861110).
+- **The camera cannot reliably send what it is being asked for.** A high bitrate 4K stream can be more than the camera's own hardware can encode and push out under load. Lower the bitrate, or record a lower-resolution profile.
+- **The camera is using a "Smart Codec", H.264+, or H.265+ mode.** These change encoding parameters mid-stream and produce the broken timestamps behind the corrupt-segment variant. Turn the mode off and set the camera's keyframe interval equal to its frame rate. See [Segments are only ~1 second long](#segments-are-only-1-second-long).
+
+Read the rest of the Frigate and/or go2rtc log around the **first** occurrence. When the camera or the network is at fault, other messages show up with it, such as `No frames received from <camera> in 20 seconds`, `Non-monotonic DTS`, `RTP: PT=xx: bad cseq`, `error while decoding MB`, or a connection timeout. Each of those is explained in [Common error messages](/troubleshooting/common_errors). To confirm the camera is the source, open its stream in the [go2rtc web interface](/troubleshooting/go2rtc) on port `1984` or play the same URL in VLC, and leave it running long enough for the failures to happen again.
+
+#### If the camera and network check out
+
+- **Audio the recording cannot store.** Some cameras send G.711 audio, which cannot be saved in an MP4 and stops segments from finalizing. See [Incompatible audio codec](#incompatible-audio-codec-recordings-silently-fail-to-save).
+- **Frigate itself was stopped or restarted.** A single warning per camera around a restart is expected and needs no action.
+- **The system ran out of room or memory.** A full `/tmp/cache`, or the host killing Frigate for using too much memory, cuts off the segment being written. Both leave other errors in the log alongside this one. See [No space left on device](#errno-28-no-space-left-on-device).
+
+</FaqItem>
+
+<FaqItem id="no-new-recording-segments-were-created" question="I see the message: ERROR : No new recording segments were created for <camera> in the last 120s. Restarting the ffmpeg record process...">
+
+When a camera stops producing usable recordings for two minutes, Frigate restarts that camera's record process to try to recover. The wording tells you how far the recordings got:
+
+- **`No new recording segments were created`**: no new segment file showed up in the cache at all, so ffmpeg isn't getting video out of the record stream. The camera is unreachable or refusing the connection, the stream URL, path, or credentials are wrong, or the camera accepted the connection and then sent nothing. See [The record stream isn't connecting](#the-record-stream-isnt-connecting).
+- **`No new valid recording segments were created`** and **`No valid segments created since last invalid segment`**: recordings are arriving, but they keep failing validation, so the camera is sending video that cannot be saved. See [Invalid or missing video stream in segment](#invalid-or-missing-video-stream-in-segment) above.
+
+The restart is Frigate recovering from a problem, not causing one. One of these after a camera reboot or a brief network drop is normal. Seeing them repeat every couple of minutes means the camera or the network is still failing, and the restarts can extend the damage, because each one cuts off the segment that was being written. Work from the earliest failure in that camera's log rather than from the restarts.
+
+</FaqItem>
+
 <FaqItem id="i-see-the-message-warning--unable-to-keep-up-with-recording-segments-in-cache-for-camera-keeping-the-5-most-recent-segments-out-of-6-and-discarding-the-rest" question="I see the message: WARNING : Unable to keep up with recording segments in cache for camera. Keeping the 5 most recent segments out of 6 and discarding the rest...">
 
 This warning means the recording maintainer cannot move recording segments from the RAM cache to disk fast enough. When the cache fills up, Frigate discards the oldest segments to avoid running out of memory and crashing, so you lose recorded footage. This is almost always a storage throughput or system resource problem. Work through the steps below to identify which.
@@ -353,19 +397,11 @@ dmesg | grep -i -E "gpu|drm|reset|hang"
 
 Messages like `trying reset from guc_exec_queue_timedout_job` or similar GPU reset/hang messages indicate a driver or hardware issue. Ensure your kernel and GPU drivers (especially Intel) are up to date.
 
-#### Step 6: Verify hardware acceleration configuration
-
-An incorrect `hwaccel_args` preset can cause ffmpeg to fail silently or consume excessive CPU, starving the detector of resources.
-
-- After upgrading Frigate, verify your preset matches your hardware (e.g., `preset-intel-qsv-h264` instead of the deprecated `preset-vaapi`).
-- For h265 cameras, use the corresponding h265 preset (e.g., `preset-intel-qsv-h265`).
-- Note that `hwaccel_args` are only relevant for the detect stream. Frigate does not decode the record stream.
-
-#### Step 7: Verify go2rtc stream configuration
+#### Step 6: Verify go2rtc stream configuration
 
 Ensure that the ffmpeg source names in your go2rtc configuration match the correct camera stream. A misconfigured stream name (e.g., copying a config from one camera to another without updating the stream reference) will cause the wrong stream to be used or the stream to fail entirely.
 
-#### Step 8: Check system resources
+#### Step 7: Check system resources
 
 If none of the above apply, the issue may be a general resource constraint. Monitor the following on your host:
 

docs/docusaurus.config.ts

@@ -3,6 +3,9 @@ import * as path from "node:path";
 import type { Config, PluginConfig } from "@docusaurus/types";
 import type * as OpenApiPlugin from "docusaurus-plugin-openapi-docs";
 
+// Bump when a new stable release ships
+const STABLE_VERSION = "0.18";
+
 const config: Config = {
   title: "Frigate",
   tagline: "NVR With Realtime Object Detection for IP Cameras",
@@ -23,17 +26,17 @@ const config: Config = {
     mermaid: true,
   },
   i18n: {
-    defaultLocale: 'en',
-    locales: ['en'],
+    defaultLocale: "en",
+    locales: ["en"],
     localeConfigs: {
       en: {
-        label: 'English',
-      }
+        label: "English",
+      },
     },
   },
   themeConfig: {
     announcementBar: {
-      id: 'frigate_plus',
+      id: "frigate_plus",
       content: `
         <span style="margin-right: 8px; display: inline-block; animation: pulse 2s infinite;">🚀</span>
         Get more relevant and accurate detections with Frigate+ models.
@@ -45,8 +48,8 @@ const config: Config = {
             50%       { transform: scale(1.1); }
           }
         </style>`,
-      backgroundColor: '#005f73',
-      textColor: '#e0fbfc',
+      backgroundColor: "#005f73",
+      textColor: "#e0fbfc",
       isCloseable: false,
     },
     docs: {
@@ -83,15 +86,15 @@ const config: Config = {
       },
     },
     prism: {
-      magicComments:[
+      magicComments: [
         {
-          className: 'theme-code-block-highlighted-line',
-          line: 'highlight-next-line',
-          block: {start: 'highlight-start', end: 'highlight-end'},
+          className: "theme-code-block-highlighted-line",
+          line: "highlight-next-line",
+          block: { start: "highlight-start", end: "highlight-end" },
         },
         {
-          className: 'code-block-error-line',
-          line: 'highlight-error-line',
+          className: "code-block-error-line",
+          line: "highlight-error-line",
         },
       ],
       additionalLanguages: ["bash", "json"],
@@ -131,6 +134,11 @@ const config: Config = {
         srcDark: "img/branding/logo-dark.svg",
       },
       items: [
+        {
+          href: "https://github.com/blakeblackshear/frigate/releases",
+          label: `${STABLE_VERSION}`,
+          position: "left",
+        },
         {
           to: "/",
           activeBasePath: "docs",
@@ -148,19 +156,19 @@ const config: Config = {
           position: "right",
         },
         {
-          type: 'localeDropdown',
-          position: 'right',
+          type: "localeDropdown",
+          position: "right",
           dropdownItemsAfter: [
             {
-              label: '简体中文（社区翻译）',
-              href: 'https://docs.frigate-cn.video',
-            }
-          ]
+              label: "简体中文（社区翻译）",
+              href: "https://docs.frigate-cn.video",
+            },
+          ],
         },
         {
-          href: 'https://github.com/blakeblackshear/frigate',
-          label: 'GitHub',
-          position: 'right',
+          href: "https://github.com/blakeblackshear/frigate",
+          label: "GitHub",
+          position: "right",
         },
       ],
     },

docs/static/frigate-api.yaml

@@ -693,6 +693,43 @@ paths:
         **Access:** Admin role required.
 
         Set a camera feature state. Use camera_name='*' to target all cameras.
+
+        The value to set is sent in the request body as `{"value": "<value>"}`.
+
+        | Feature | Accepted values |
+        | --- | --- |
+        | `enabled` | `ON`, `OFF` |
+        | `detect` | `ON`, `OFF` |
+        | `motion` | `ON`, `OFF` |
+        | `recordings` | `ON`, `OFF` |
+        | `snapshots` | `ON`, `OFF` |
+        | `audio` | `ON`, `OFF` |
+        | `audio_transcription` | `ON`, `OFF` |
+        | `notifications` | `ON`, `OFF` |
+        | `review_alerts` | `ON`, `OFF` |
+        | `review_detections` | `ON`, `OFF` |
+        | `object_descriptions` | `ON`, `OFF` |
+        | `review_descriptions` | `ON`, `OFF` |
+        | `improve_contrast` | `ON`, `OFF` |
+        | `ptz_autotracker` | `ON`, `OFF` |
+        | `birdseye` | `ON`, `OFF` |
+        | `birdseye_mode` | `CONTINUOUS`, `MOTION`, `OBJECTS` |
+        | `motion_contour_area` | integer |
+        | `motion_threshold` | integer |
+        | `motion_mask` | `ON`, `OFF` |
+        | `object_mask` | `ON`, `OFF` |
+        | `zone` | `ON`, `OFF` |
+        | `profile` | a profile name, or `none` to deactivate |
+
+        `motion_mask`, `object_mask`, and `zone` require the `sub_command` path
+        parameter to be set to the name of the mask or zone. All other features
+        reject a sub-command.
+
+        `profile` applies globally rather than per camera, so it requires
+        `camera_name` to be `*`.
+
+        These features map to the equivalent MQTT topics, which document the
+        behavior of each value in more detail.
       operationId: 
         camera_set_camera__camera_name__set__feature___sub_command__put
       parameters:
@@ -746,6 +783,43 @@ paths:
         **Access:** Admin role required.
 
         Set a camera feature state. Use camera_name='*' to target all cameras.
+
+        The value to set is sent in the request body as `{"value": "<value>"}`.
+
+        | Feature | Accepted values |
+        | --- | --- |
+        | `enabled` | `ON`, `OFF` |
+        | `detect` | `ON`, `OFF` |
+        | `motion` | `ON`, `OFF` |
+        | `recordings` | `ON`, `OFF` |
+        | `snapshots` | `ON`, `OFF` |
+        | `audio` | `ON`, `OFF` |
+        | `audio_transcription` | `ON`, `OFF` |
+        | `notifications` | `ON`, `OFF` |
+        | `review_alerts` | `ON`, `OFF` |
+        | `review_detections` | `ON`, `OFF` |
+        | `object_descriptions` | `ON`, `OFF` |
+        | `review_descriptions` | `ON`, `OFF` |
+        | `improve_contrast` | `ON`, `OFF` |
+        | `ptz_autotracker` | `ON`, `OFF` |
+        | `birdseye` | `ON`, `OFF` |
+        | `birdseye_mode` | `CONTINUOUS`, `MOTION`, `OBJECTS` |
+        | `motion_contour_area` | integer |
+        | `motion_threshold` | integer |
+        | `motion_mask` | `ON`, `OFF` |
+        | `object_mask` | `ON`, `OFF` |
+        | `zone` | `ON`, `OFF` |
+        | `profile` | a profile name, or `none` to deactivate |
+
+        `motion_mask`, `object_mask`, and `zone` require the `sub_command` path
+        parameter to be set to the name of the mask or zone. All other features
+        reject a sub-command.
+
+        `profile` applies globally rather than per camera, so it requires
+        `camera_name` to be `*`.
+
+        These features map to the equivalent MQTT topics, which document the
+        behavior of each value in more detail.
       operationId: camera_set_camera__camera_name__set__feature__put
       parameters:
         - name: camera_name
@@ -1402,7 +1476,7 @@ paths:
         - Classification
       summary: Get custom classification attributes
       description: |-
-        **Access:** Admin role required.
+        **Access:** Authenticated user with access to all cameras.
 
         Returns custom classification attributes for a given object type.
             Only includes models with classification_type set to 'attribute'.
@@ -1436,8 +1510,8 @@ paths:
               schema:
                 $ref: '#/components/schemas/HTTPValidationError'
       security:
-        - frigateAdminAuth: []
-      x-required-role: admin
+        - frigateUserAuth: []
+      x-required-role: all_cameras
   /classification/{name}/train:
     get:
       tags:
@@ -2234,15 +2308,15 @@ paths:
                 $ref: '#/components/schemas/HTTPValidationError'
       security:
         - frigateUserAuth: []
-      x-required-role: any
-      description: '**Access:** Any authenticated user.'
+      x-required-role: camera
+      description: '**Access:** Authenticated user with access to the referenced camera.'
   /review/summarize/start/{start_ts}/end/{end_ts}:
     post:
       tags:
         - Review
       summary: Generate Review Summary
       description: |-
-        **Access:** Admin role required.
+        **Access:** Authenticated user with access to all cameras.
 
         Use GenAI to summarize review items over a period of time.
       operationId: 
@@ -2273,8 +2347,8 @@ paths:
               schema:
                 $ref: '#/components/schemas/HTTPValidationError'
       security:
-        - frigateAdminAuth: []
-      x-required-role: admin
+        - frigateUserAuth: []
+      x-required-role: all_cameras
   /:
     get:
       tags:
@@ -3999,6 +4073,16 @@ paths:
               - type: 'null'
             default: 100
             title: Limit
+        - name: offset
+          in: query
+          required: false
+          schema:
+            anyOf:
+              - type: integer
+                minimum: 0
+              - type: 'null'
+            default: 0
+            title: Offset
         - name: after
           in: query
           required: false
@@ -4304,6 +4388,16 @@ paths:
               - type: 'null'
             default: 50
             title: Limit
+        - name: offset
+          in: query
+          required: false
+          schema:
+            anyOf:
+              - type: integer
+                minimum: 0
+              - type: 'null'
+            default: 0
+            title: Offset
         - name: cameras
           in: query
           required: false
@@ -5019,6 +5113,7 @@ paths:
             NOTES:
             - Creating a manual event does not trigger an update to /events MQTT topic.
             - If a duration is set to null, the event will need to be ended manually by calling /events/{event_id}/end.
+            - The review item is an alert unless the label is listed in the camera's review -> detections -> labels config.
       operationId: create_event_events__camera_name___label__create_post
       parameters:
         - name: camera_name
@@ -7034,7 +7129,9 @@ paths:
               schema:
                 $ref: '#/components/schemas/DebugReplayStartResponse'
         '400':
-          description: Invalid camera, time range, or no recordings
+          description: Invalid camera or time range
+        '404':
+          description: No recordings in the requested time range
         '409':
           description: A replay session is already active
         '422':

frigate/api/app.py

@@ -31,7 +31,10 @@ from frigate.api.auth import (
     get_allowed_cameras_for_filter,
     require_role,
 )
-from frigate.api.config_util import swap_runtime_config
+from frigate.api.config_util import (
+    publish_camera_section_updates,
+    swap_runtime_config,
+)
 from frigate.api.defs.query.app_query_parameters import AppTimelineHourlyQueryParameters
 from frigate.api.defs.request.app_body import (
     AppConfigSetBody,
@@ -963,6 +966,17 @@ def config_set(request: Request, body: AppConfigSetBody):
                             body.update_topic, settings
                         )
 
+                        # a config/cameras/* topic publishes camera copies, a
+                        # global topic the global object. FrigateConfig.parse
+                        # folds some global sections down into every camera,
+                        # and workers read both objects, so any such section
+                        # needs its camera copies sent alongside the global
+                        # publish above.
+                        if body.update_topic == "config/birdseye":
+                            publish_camera_section_updates(
+                                request.app, config, CameraConfigUpdateEnum.birdseye
+                            )
+
             return JSONResponse(
                 content=(
                     {

frigate/api/auth.py

@@ -31,7 +31,7 @@ from frigate.api.media_auth import (
     deny_response_for_media_uri,
     is_role_restricted,
 )
-from frigate.config import AuthConfig, NetworkingConfig, ProxyConfig
+from frigate.config import AuthConfig, ProxyConfig
 from frigate.const import CONFIG_DIR, JWT_SECRET_ENV_VAR, PASSWORD_HASH_ALGORITHM
 from frigate.models import User
 
@@ -85,6 +85,7 @@ def require_admin_by_default():
         "/sub_labels",
         "/plus/models",
         "/recognized_license_plates",
+        "/classification/attributes",
         "/timeline",
         "/timeline/hourly",
         "/recordings/storage",
@@ -620,18 +621,18 @@ def resolve_role(
 def auth(request: Request):
     auth_config: AuthConfig = request.app.frigate_config.auth
     proxy_config: ProxyConfig = request.app.frigate_config.proxy
-    networking_config: NetworkingConfig = request.app.frigate_config.networking
 
     success_response = Response("", status_code=202)
 
-    # handle case where internal port is a string with ip:port
-    internal_port = networking_config.listen.internal
-    if type(internal_port) is str:
-        internal_port = int(internal_port.split(":")[-1])
-
     # dont require auth if the request is on the internal port
-    # this header is set by Frigate's nginx proxy, so it cant be spoofed
-    if int(request.headers.get("x-server-port", default=0)) == internal_port:
+    # this header is set by Frigate's nginx proxy, so it cant be spoofed.
+    # the port is the boot-time snapshot rather than the live config value:
+    # nginx's listeners are fixed at container start, so an in-memory config
+    # change must never move the port that is trusted here
+    if (
+        int(request.headers.get("x-server-port", default=0))
+        == request.app.auth_internal_port
+    ):
         success_response.headers["remote-user"] = "anonymous"
         success_response.headers["remote-role"] = "admin"
         return success_response
@@ -971,6 +972,7 @@ def delete_user(request: Request, username: str):
     summary="Update user password",
     description="Updates a user's password. Users can only change their own password unless they have admin role. Requires the current password to verify identity for non-admin users. Password must be at least 12 characters long. If user changes their own password, a new JWT cookie is automatically issued.",
 )
+@limiter.limit(limit_value=rateLimiter.get_limit)
 async def update_password(
     request: Request,
     username: str,
@@ -984,10 +986,11 @@ async def update_password(
     current_username = current_user.get("username")
     current_role = current_user.get("role")
 
-    # viewers can only change their own password
-    if current_role == "viewer" and current_username != username:
+    # Only admins may target another account. This has to cover every non-admin
+    # role rather than just viewer, since custom roles are arbitrary names
+    if current_role != "admin" and current_username != username:
         raise HTTPException(
-            status_code=403, detail="Viewers can only update their own password"
+            status_code=403, detail="Users can only update their own password"
         )
 
     HASH_ITERATIONS = request.app.frigate_config.auth.hash_iterations
@@ -1251,3 +1254,23 @@ async def get_allowed_cameras_for_filter(request: Request):
     all_camera_names = set(request.app.frigate_config.cameras.keys())
     roles_dict = request.app.frigate_config.auth.roles
     return User.get_allowed_cameras(role, roles_dict, all_camera_names)
+
+
+async def require_full_camera_access(
+    request: Request,
+    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
+):
+    """Dependency for endpoints returning data that spans every camera.
+
+    Some responses cannot be meaningfully scoped to a subset of cameras, so
+    rather than filter them the endpoint is limited to callers who can already
+    see every camera. Admin and viewer always qualify; a custom role qualifies
+    only when its camera list covers all configured cameras.
+    """
+    all_camera_names = set(request.app.frigate_config.cameras.keys())
+
+    if not all_camera_names.issubset(allowed_cameras):
+        raise HTTPException(
+            status_code=403,
+            detail="Access to all cameras is required for this endpoint",
+        )

frigate/api/camera.py

@@ -1328,7 +1328,45 @@ def camera_set(
     body: CameraSetBody,
     sub_command: str | None = None,
 ):
-    """Set a camera feature state. Use camera_name='*' to target all cameras."""
+    """Set a camera feature state. Use camera_name='*' to target all cameras.
+
+    The value to set is sent in the request body as `{"value": "<value>"}`.
+
+    | Feature | Accepted values |
+    | --- | --- |
+    | `enabled` | `ON`, `OFF` |
+    | `detect` | `ON`, `OFF` |
+    | `motion` | `ON`, `OFF` |
+    | `recordings` | `ON`, `OFF` |
+    | `snapshots` | `ON`, `OFF` |
+    | `audio` | `ON`, `OFF` |
+    | `audio_transcription` | `ON`, `OFF` |
+    | `notifications` | `ON`, `OFF` |
+    | `review_alerts` | `ON`, `OFF` |
+    | `review_detections` | `ON`, `OFF` |
+    | `object_descriptions` | `ON`, `OFF` |
+    | `review_descriptions` | `ON`, `OFF` |
+    | `improve_contrast` | `ON`, `OFF` |
+    | `ptz_autotracker` | `ON`, `OFF` |
+    | `birdseye` | `ON`, `OFF` |
+    | `birdseye_mode` | `CONTINUOUS`, `MOTION`, `OBJECTS` |
+    | `motion_contour_area` | integer |
+    | `motion_threshold` | integer |
+    | `motion_mask` | `ON`, `OFF` |
+    | `object_mask` | `ON`, `OFF` |
+    | `zone` | `ON`, `OFF` |
+    | `profile` | a profile name, or `none` to deactivate |
+
+    `motion_mask`, `object_mask`, and `zone` require the `sub_command` path
+    parameter to be set to the name of the mask or zone. All other features
+    reject a sub-command.
+
+    `profile` applies globally rather than per camera, so it requires
+    `camera_name` to be `*`.
+
+    These features map to the equivalent MQTT topics, which document the
+    behavior of each value in more detail.
+    """
     dispatcher = request.app.dispatcher
     frigate_config: FrigateConfig = request.app.frigate_config
 

frigate/api/classification.py

@@ -11,11 +11,10 @@ from typing import Any
 import cv2
 from fastapi import APIRouter, Depends, Request, UploadFile
 from fastapi.responses import JSONResponse
-from pathvalidate import sanitize_filename
 from peewee import DoesNotExist
 from playhouse.shortcuts import model_to_dict
 
-from frigate.api.auth import require_role
+from frigate.api.auth import require_full_camera_access, require_role
 from frigate.api.defs.request.classification_body import (
     AudioTranscriptionBody,
     DeleteFaceImagesBody,
@@ -43,12 +42,21 @@ from frigate.util.classification import (
     write_training_metadata,
 )
 from frigate.util.file import get_event_snapshot
+from frigate.util.path import safe_join, sanitize_path_component
 
 logger = logging.getLogger(__name__)
 
 router = APIRouter(tags=[Tags.classification])
 
 
+def invalid_name_response(value: str) -> JSONResponse:
+    """Response for a name that cannot be used as a path component."""
+    return JSONResponse(
+        content={"success": False, "message": f"Invalid name: {value}"},
+        status_code=400,
+    )
+
+
 @router.get(
     "/faces",
     response_model=FacesResponse,
@@ -98,9 +106,7 @@ def reclassify_face(request: Request, body: dict = None):
         )
 
     json: dict[str, Any] = body or {}
-    training_file = os.path.join(
-        FACE_DIR, f"train/{sanitize_filename(json.get('training_file', ''))}"
-    )
+    training_file = safe_join(FACE_DIR, "train", json.get("training_file", ""))
 
     if not training_file or not os.path.isfile(training_file):
         return JSONResponse(
@@ -150,8 +156,10 @@ def train_face(request: Request, name: str, body: dict = None):
         )
 
     json: dict[str, Any] = body or {}
-    training_file_name = sanitize_filename(json.get("training_file", ""))
-    training_file = os.path.join(FACE_DIR, f"train/{training_file_name}")
+    training_file_name = json.get("training_file", "")
+    training_file = (
+        safe_join(FACE_DIR, "train", training_file_name) if training_file_name else None
+    )
     event_id = json.get("event_id")
 
     if not training_file_name and not event_id:
@@ -165,7 +173,9 @@ def train_face(request: Request, name: str, body: dict = None):
             status_code=400,
         )
 
-    if training_file_name and not os.path.isfile(training_file):
+    if training_file_name and (
+        training_file is None or not os.path.isfile(training_file)
+    ):
         return JSONResponse(
             content=(
                 {
@@ -176,9 +186,13 @@ def train_face(request: Request, name: str, body: dict = None):
             status_code=404,
         )
 
-    sanitized_name = sanitize_filename(name)
+    sanitized_name = sanitize_path_component(name)
+    new_file_folder = safe_join(FACE_DIR, name)
+
+    if sanitized_name is None or new_file_folder is None:
+        return invalid_name_response(name)
+
     new_name = f"{sanitized_name}-{datetime.datetime.now().timestamp()}.webp"
-    new_file_folder = os.path.join(FACE_DIR, f"{sanitized_name}")
 
     os.makedirs(new_file_folder, exist_ok=True)
 
@@ -261,9 +275,12 @@ async def create_face(request: Request, name: str):
             content={"message": "Face recognition is not enabled.", "success": False},
         )
 
-    os.makedirs(
-        os.path.join(FACE_DIR, sanitize_filename(name.replace(" ", "_"))), exist_ok=True
-    )
+    face_folder = safe_join(FACE_DIR, name.replace(" ", "_"))
+
+    if face_folder is None:
+        return invalid_name_response(name)
+
+    os.makedirs(face_folder, exist_ok=True)
     return JSONResponse(
         status_code=200,
         content={"success": False, "message": "Successfully created face folder."},
@@ -287,6 +304,9 @@ def register_face(request: Request, name: str, file: UploadFile):
             content={"message": "Face recognition is not enabled.", "success": False},
         )
 
+    if sanitize_path_component(name) is None:
+        return invalid_name_response(name)
+
     context: EmbeddingsContext = request.app.embeddings
     result = None if context is None else context.register_face(name, file.file.read())
 
@@ -356,8 +376,8 @@ def reclassify_face_image(request: Request, name: str, body: dict = None):
         )
 
     json: dict[str, Any] = body or {}
-    image_id = sanitize_filename(json.get("id", ""))
-    new_name = sanitize_filename(json.get("new_name", ""))
+    image_id = sanitize_path_component(json.get("id", ""))
+    new_name = sanitize_path_component(json.get("new_name", ""))
 
     if not image_id or not new_name:
         return JSONResponse(
@@ -381,7 +401,12 @@ def reclassify_face_image(request: Request, name: str, body: dict = None):
             status_code=400,
         )
 
-    source_folder = os.path.join(FACE_DIR, sanitize_filename(name))
+    source_folder = safe_join(FACE_DIR, name)
+    target_folder = safe_join(FACE_DIR, new_name)
+
+    if source_folder is None or target_folder is None:
+        return invalid_name_response(name)
+
     source_file = os.path.join(source_folder, image_id)
 
     if not os.path.isfile(source_file):
@@ -396,7 +421,6 @@ def reclassify_face_image(request: Request, name: str, body: dict = None):
         )
 
     target_filename = f"{new_name}-{datetime.datetime.now().timestamp()}.webp"
-    target_folder = os.path.join(FACE_DIR, new_name)
 
     os.makedirs(target_folder, exist_ok=True)
     shutil.move(source_file, os.path.join(target_folder, target_filename))
@@ -430,8 +454,19 @@ def deregister_faces(request: Request, name: str, body: DeleteFaceImagesBody):
             content={"message": "Face recognition is not enabled.", "success": False},
         )
 
+    sanitized_name = sanitize_path_component(name)
+
+    if sanitized_name is None:
+        return invalid_name_response(name)
+
+    sanitized_ids = [
+        component
+        for component in map(sanitize_path_component, body.ids)
+        if component is not None
+    ]
+
     context: EmbeddingsContext = request.app.embeddings
-    context.delete_face_ids(name, map(lambda file: sanitize_filename(file), body.ids))
+    context.delete_face_ids(sanitized_name, sanitized_ids)
     return JSONResponse(
         content=({"success": True, "message": "Successfully deleted faces."}),
         status_code=200,
@@ -642,7 +677,11 @@ def transcribe_audio(request: Request, body: AudioTranscriptionBody):
 def get_classification_dataset(name: str):
     dataset_dict: dict[str, list[str]] = {}
 
-    dataset_dir = os.path.join(CLIPS_DIR, sanitize_filename(name), "dataset")
+    sanitized_name = sanitize_path_component(name)
+    dataset_dir = safe_join(CLIPS_DIR, name, "dataset")
+
+    if sanitized_name is None or dataset_dir is None:
+        return invalid_name_response(name)
 
     if not os.path.exists(dataset_dir):
         return JSONResponse(
@@ -664,8 +703,8 @@ def get_classification_dataset(name: str):
             dataset_dict[category_name].append(file)
 
     # Get training metadata
-    metadata = read_training_metadata(sanitize_filename(name))
-    current_image_count = get_dataset_image_count(sanitize_filename(name))
+    metadata = read_training_metadata(sanitized_name)
+    current_image_count = get_dataset_image_count(sanitized_name)
 
     if metadata is None:
         training_metadata = {
@@ -702,6 +741,7 @@ def get_classification_dataset(name: str):
 
 @router.get(
     "/classification/attributes",
+    dependencies=[Depends(require_full_camera_access)],
     summary="Get custom classification attributes",
     description="""Returns custom classification attributes for a given object type.
     Only includes models with classification_type set to 'attribute'.
@@ -729,8 +769,8 @@ def get_custom_attributes(
         if object_type is not None and object_type not in model_objects:
             continue
 
-        dataset_dir = os.path.join(CLIPS_DIR, sanitize_filename(model_key), "dataset")
-        if not os.path.exists(dataset_dir):
+        dataset_dir = safe_join(CLIPS_DIR, model_key, "dataset")
+        if dataset_dir is None or not os.path.exists(dataset_dir):
             continue
 
         attributes = []
@@ -760,7 +800,10 @@ def get_custom_attributes(
     The name must exist in the classification models. Returns a success message or an error if the name is invalid.""",
 )
 def get_classification_images(name: str):
-    train_dir = os.path.join(CLIPS_DIR, sanitize_filename(name), "train")
+    train_dir = safe_join(CLIPS_DIR, name, "train")
+
+    if train_dir is None:
+        return invalid_name_response(name)
 
     if not os.path.exists(train_dir):
         return JSONResponse(status_code=200, content=[])
@@ -831,15 +874,17 @@ def delete_classification_dataset_images(
 
     json: dict[str, Any] = body or {}
     list_of_ids = json.get("ids", "")
-    folder = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "dataset", sanitize_filename(category)
-    )
+    sanitized_name = sanitize_path_component(name)
+    folder = safe_join(CLIPS_DIR, name, "dataset", category)
+
+    if sanitized_name is None or folder is None:
+        return invalid_name_response(name)
 
     deleted_count = 0
     for id in list_of_ids:
-        file_path = os.path.join(folder, sanitize_filename(id))
+        file_path = safe_join(folder, id)
 
-        if os.path.isfile(file_path):
+        if file_path and os.path.isfile(file_path):
             os.unlink(file_path)
             deleted_count += 1
 
@@ -850,7 +895,6 @@ def delete_classification_dataset_images(
     # This ensures the dataset is marked as changed after deletion
     # (even if the total count happens to be the same after adding and deleting)
     if deleted_count > 0:
-        sanitized_name = sanitize_filename(name)
         metadata = read_training_metadata(sanitized_name)
         if metadata:
             last_count = metadata.get("last_training_image_count", 0)
@@ -888,8 +932,8 @@ def reclassify_classification_image(
         )
 
     json: dict[str, Any] = body or {}
-    image_id = sanitize_filename(json.get("id", ""))
-    new_category = sanitize_filename(json.get("new_category", ""))
+    image_id = sanitize_path_component(json.get("id", ""))
+    new_category = sanitize_path_component(json.get("new_category", ""))
 
     if not image_id or not new_category:
         return JSONResponse(
@@ -913,10 +957,13 @@ def reclassify_classification_image(
             status_code=400,
         )
 
-    sanitized_name = sanitize_filename(name)
-    source_folder = os.path.join(
-        CLIPS_DIR, sanitized_name, "dataset", sanitize_filename(category)
-    )
+    sanitized_name = sanitize_path_component(name)
+    source_folder = safe_join(CLIPS_DIR, name, "dataset", category)
+    target_folder = safe_join(CLIPS_DIR, name, "dataset", new_category)
+
+    if sanitized_name is None or source_folder is None or target_folder is None:
+        return invalid_name_response(name)
+
     source_file = os.path.join(source_folder, image_id)
 
     if not os.path.isfile(source_file):
@@ -933,7 +980,6 @@ def reclassify_classification_image(
     random_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
     timestamp = datetime.datetime.now().timestamp()
     new_name = f"{new_category}-{timestamp}-{random_id}.png"
-    target_folder = os.path.join(CLIPS_DIR, sanitized_name, "dataset", new_category)
 
     os.makedirs(target_folder, exist_ok=True)
 
@@ -983,7 +1029,7 @@ def rename_classification_category(
         )
 
     json: dict[str, Any] = body or {}
-    new_category = sanitize_filename(json.get("new_category", ""))
+    new_category = sanitize_path_component(json.get("new_category", ""))
 
     if not new_category:
         return JSONResponse(
@@ -996,12 +1042,12 @@ def rename_classification_category(
             status_code=400,
         )
 
-    old_folder = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "dataset", sanitize_filename(old_category)
-    )
-    new_folder = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "dataset", new_category
-    )
+    sanitized_name = sanitize_path_component(name)
+    old_folder = safe_join(CLIPS_DIR, name, "dataset", old_category)
+    new_folder = safe_join(CLIPS_DIR, name, "dataset", new_category)
+
+    if sanitized_name is None or old_folder is None or new_folder is None:
+        return invalid_name_response(name)
 
     if not os.path.exists(old_folder):
         return JSONResponse(
@@ -1030,7 +1076,6 @@ def rename_classification_category(
 
         # Mark dataset as ready to train by resetting training metadata
         # This ensures the dataset is marked as changed after renaming
-        sanitized_name = sanitize_filename(name)
         write_training_metadata(sanitized_name, 0)
 
         return JSONResponse(
@@ -1078,13 +1123,20 @@ def categorize_classification_image(request: Request, name: str, body: dict = No
         )
 
     json: dict[str, Any] = body or {}
-    category = sanitize_filename(json.get("category", ""))
-    training_file_name = sanitize_filename(json.get("training_file", ""))
-    training_file = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "train", training_file_name
+    category = sanitize_path_component(json.get("category", ""))
+    training_file_name = json.get("training_file", "")
+    training_file = (
+        safe_join(CLIPS_DIR, name, "train", training_file_name)
+        if training_file_name
+        else None
     )
 
-    if training_file_name and not os.path.isfile(training_file):
+    if category is None:
+        return invalid_name_response(json.get("category", ""))
+
+    if training_file_name and (
+        training_file is None or not os.path.isfile(training_file)
+    ):
         return JSONResponse(
             content=(
                 {
@@ -1098,9 +1150,10 @@ def categorize_classification_image(request: Request, name: str, body: dict = No
     random_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
     timestamp = datetime.datetime.now().timestamp()
     new_name = f"{category}-{timestamp}-{random_id}.png"
-    new_file_folder = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "dataset", category
-    )
+    new_file_folder = safe_join(CLIPS_DIR, name, "dataset", category)
+
+    if new_file_folder is None:
+        return invalid_name_response(name)
 
     os.makedirs(new_file_folder, exist_ok=True)
 
@@ -1138,9 +1191,10 @@ def create_classification_category(request: Request, name: str, category: str):
             status_code=404,
         )
 
-    category_folder = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "dataset", sanitize_filename(category)
-    )
+    category_folder = safe_join(CLIPS_DIR, name, "dataset", category)
+
+    if category_folder is None:
+        return invalid_name_response(category)
 
     os.makedirs(category_folder, exist_ok=True)
 
@@ -1179,12 +1233,15 @@ def delete_classification_train_images(request: Request, name: str, body: dict =
 
     json: dict[str, Any] = body or {}
     list_of_ids = json.get("ids", "")
-    folder = os.path.join(CLIPS_DIR, sanitize_filename(name), "train")
+    folder = safe_join(CLIPS_DIR, name, "train")
+
+    if folder is None:
+        return invalid_name_response(name)
 
     for id in list_of_ids:
-        file_path = os.path.join(folder, sanitize_filename(id))
+        file_path = safe_join(folder, id)
 
-        if os.path.isfile(file_path):
+        if file_path and os.path.isfile(file_path):
             os.unlink(file_path)
 
     return JSONResponse(
@@ -1201,7 +1258,11 @@ def delete_classification_train_images(request: Request, name: str, body: dict =
 )
 async def generate_state_examples(request: Request, body: GenerateStateExamplesBody):
     """Generate examples for state classification."""
-    model_name = sanitize_filename(body.model_name)
+    model_name = sanitize_path_component(body.model_name)
+
+    if model_name is None:
+        return invalid_name_response(body.model_name)
+
     cameras_normalized = {
         camera_name: tuple(crop)
         for camera_name, crop in body.cameras.items()
@@ -1224,7 +1285,11 @@ async def generate_state_examples(request: Request, body: GenerateStateExamplesB
 )
 async def generate_object_examples(request: Request, body: GenerateObjectExamplesBody):
     """Generate examples for object classification."""
-    model_name = sanitize_filename(body.model_name)
+    model_name = sanitize_path_component(body.model_name)
+
+    if model_name is None:
+        return invalid_name_response(body.model_name)
+
     collect_object_classification_examples(model_name, body.label)
 
     return JSONResponse(
@@ -1243,10 +1308,16 @@ async def generate_object_examples(request: Request, body: GenerateObjectExample
     Returns a success message.""",
 )
 def delete_classification_model(request: Request, name: str):
-    sanitized_name = sanitize_filename(name)
+    # This endpoint intentionally accepts models that are not in the config, so
+    # there is no allow list to fall back on. Both paths below are recursive
+    # deletes, so an unusable name has to be rejected outright.
+    data_dir = safe_join(CLIPS_DIR, name)
+    model_dir = safe_join(MODEL_CACHE_DIR, name)
+
+    if data_dir is None or model_dir is None:
+        return invalid_name_response(name)
 
     # Delete the classification model's data directory in clips
-    data_dir = os.path.join(CLIPS_DIR, sanitized_name)
     if os.path.exists(data_dir):
         try:
             shutil.rmtree(data_dir)
@@ -1255,7 +1326,6 @@ def delete_classification_model(request: Request, name: str):
             logger.debug(f"Failed to delete data directory for {name}: {e}")
 
     # Delete the classification model's files in model_cache
-    model_dir = os.path.join(MODEL_CACHE_DIR, sanitized_name)
     if os.path.exists(model_dir):
         try:
             shutil.rmtree(model_dir)

frigate/api/config_util.py

@@ -3,6 +3,30 @@
 from fastapi import FastAPI
 
 from frigate.config import FrigateConfig
+from frigate.config.camera.updater import (
+    CameraConfigUpdateEnum,
+    CameraConfigUpdateTopic,
+)
+
+
+def publish_camera_section_updates(
+    app: FastAPI, config: FrigateConfig, update_type: CameraConfigUpdateEnum
+) -> None:
+    """Broadcast every camera's re-resolved value for a global section.
+
+    Global sections are folded into each camera at parse time and the camera
+    copies are what workers read, so send them rather than leave a worker to
+    guess which cameras were inheriting.
+    """
+    for camera_name, camera_config in config.cameras.items():
+        settings = getattr(camera_config, update_type.name, None)
+
+        if settings is None:
+            continue
+
+        app.config_publisher.publish_update(
+            CameraConfigUpdateTopic(update_type, camera_name), settings
+        )
 
 
 def swap_runtime_config(app: FastAPI, config: FrigateConfig) -> None:
@@ -16,6 +40,10 @@ def swap_runtime_config(app: FastAPI, config: FrigateConfig) -> None:
     camera the user turned off would silently come back on.
     """
     app.frigate_config = config
+
+    if app.config_holder is not None:
+        app.config_holder.set(config)
+
     app.genai_manager.update_config(config)
 
     if app.profile_manager is not None:

frigate/api/debug_replay.py

@@ -13,6 +13,7 @@ from frigate.api.auth import require_role
 from frigate.api.defs.tags import Tags
 from frigate.jobs.debug_replay import (
     ExportDebugReplaySource,
+    NoRecordingsError,
     RecordingDebugReplaySource,
     start_debug_replay_job,
 )
@@ -74,7 +75,8 @@ class DebugReplayStopResponse(BaseModel):
     response_model=DebugReplayStartResponse,
     status_code=202,
     responses={
-        400: {"description": "Invalid camera, time range, or no recordings"},
+        400: {"description": "Invalid camera or time range"},
+        404: {"description": "No recordings in the requested time range"},
         409: {"description": "A replay session is already active"},
     },
     dependencies=[Depends(require_role(["admin"]))],
@@ -113,6 +115,14 @@ async def start_debug_replay(request: Request, body: DebugReplayStartBody):
             },
             status_code=409,
         )
+    except NoRecordingsError:
+        return JSONResponse(
+            content={
+                "success": False,
+                "message": "No recordings found in the selected time range",
+            },
+            status_code=404,
+        )
     except ValueError:
         logger.exception("Rejected debug replay start request")
         return JSONResponse(

frigate/api/defs/query/events_query_parameters.py

@@ -14,6 +14,7 @@ class EventsQueryParams(BaseModel):
     zone: str | None = "all"
     zones: str | None = "all"
     limit: int | None = 100
+    offset: int | None = Field(0, ge=0)
     after: float | None = None
     before: float | None = None
     time_range: str | None = DEFAULT_TIME_RANGE
@@ -55,6 +56,7 @@ class EventsSearchQueryParams(BaseModel):
         deprecated=True,
     )
     limit: int | None = 50
+    offset: int | None = Field(0, ge=0)
     cameras: str | None = "all"
     labels: str | None = "all"
     sub_labels: str | None = "all"

frigate/api/event.py

@@ -16,7 +16,6 @@ import numpy as np
 from fastapi import APIRouter, Request
 from fastapi.params import Depends
 from fastapi.responses import JSONResponse
-from pathvalidate import sanitize_filename
 from peewee import JOIN, DoesNotExist, fn, operator
 from playhouse.shortcuts import model_to_dict
 
@@ -56,11 +55,12 @@ from frigate.api.defs.response.generic_response import GenericResponse
 from frigate.api.defs.tags import Tags
 from frigate.comms.event_metadata_updater import EventMetadataTypeEnum
 from frigate.config.classification import ObjectClassificationType
-from frigate.const import CLIPS_DIR, TRIGGER_DIR
+from frigate.const import CLIPS_DIR
 from frigate.embeddings import EmbeddingsContext
 from frigate.models import Event, ReviewSegment, Timeline, Trigger
 from frigate.track.object_processing import TrackedObject
 from frigate.util.file import get_event_thumbnail_bytes, load_event_snapshot_image
+from frigate.util.path import get_trigger_thumbnail_path, safe_join
 from frigate.util.time import get_dst_transitions, get_tz_modifiers
 
 logger = logging.getLogger(__name__)
@@ -129,6 +129,7 @@ def events(
         zones = zone
 
     limit = params.limit
+    offset = params.offset
     after = params.after
     before = params.before
     time_range = params.time_range
@@ -361,11 +362,15 @@ def events(
     else:
         order_by = Event.start_time.desc()
 
+    # offset paging needs a stable order when scores or speeds tie
+    tiebreaker = [Event.id] if sort and sort.startswith(("score", "speed")) else []
+
     events = (
         Event.select(*selected_columns)
         .where(reduce(operator.and_, clauses))
-        .order_by(order_by)
+        .order_by(order_by, *tiebreaker)
         .limit(limit)
+        .offset(offset)
         .dicts()
         .iterator()
     )
@@ -518,6 +523,7 @@ def events_search(
     search_type = params.search_type
     include_thumbnails = params.include_thumbnails
     limit = params.limit
+    offset = params.offset
     sort = params.sort
 
     # Filters
@@ -824,6 +830,9 @@ def events_search(
     if search_results:
         events_query = events_query.where(Event.id << list(search_results.keys()))
 
+    # sorts below are stable, so this orders ties for offset paging
+    events_query = events_query.order_by(Event.id)
+
     # Fetch events and process them in a single pass
     processed_events = []
     for event in events_query.dicts():
@@ -881,7 +890,7 @@ def events_search(
         processed_events.sort(key=lambda x: x["start_time"], reverse=True)
 
     # Limit the number of events returned
-    processed_events = processed_events[:limit]
+    processed_events = processed_events[offset:][:limit]
 
     return JSONResponse(content=processed_events)
 
@@ -1452,10 +1461,10 @@ async def set_attributes(
             continue
 
         # Get available labels from dataset directory
-        dataset_dir = os.path.join(CLIPS_DIR, sanitize_filename(model_key), "dataset")
+        dataset_dir = safe_join(CLIPS_DIR, model_key, "dataset")
         available_labels = set()
 
-        if os.path.exists(dataset_dir):
+        if dataset_dir and os.path.exists(dataset_dir):
             for category_name in os.listdir(dataset_dir):
                 category_dir = os.path.join(dataset_dir, category_name)
                 if os.path.isdir(category_dir):
@@ -1748,6 +1757,7 @@ async def delete_events(request: Request, body: EventsDeleteBody):
     NOTES:
     - Creating a manual event does not trigger an update to /events MQTT topic.
     - If a duration is set to null, the event will need to be ended manually by calling /events/{event_id}/end.
+    - The review item is an alert unless the label is listed in the camera's review -> detections -> labels config.
     """,
 )
 def create_event(
@@ -1958,18 +1968,13 @@ def create_trigger_embedding(
         if body.type == "thumbnail":
             # Save image to the triggers directory
             try:
-                os.makedirs(
-                    os.path.join(TRIGGER_DIR, sanitize_filename(camera_name)),
-                    exist_ok=True,
-                )
-                with open(
-                    os.path.join(
-                        TRIGGER_DIR,
-                        sanitize_filename(camera_name),
-                        f"{sanitize_filename(body.data)}.webp",
-                    ),
-                    "wb",
-                ) as f:
+                webp_path = get_trigger_thumbnail_path(camera_name, body.data)
+
+                if webp_path is None:
+                    raise ValueError(f"Invalid trigger thumbnail path for {body.data}")
+
+                os.makedirs(os.path.dirname(webp_path), exist_ok=True)
+                with open(webp_path, "wb") as f:
                     f.write(thumbnail)
                 logger.debug(
                     f"Writing thumbnail for trigger with data {body.data} in {camera_name}."
@@ -2041,10 +2046,16 @@ def update_trigger_embedding(
         if body.type == "description":
             embedding = context.generate_description_embedding(body.data)
         elif body.type == "thumbnail":
-            webp_file = sanitize_filename(body.data) + ".webp"
-            webp_path = os.path.join(
-                TRIGGER_DIR, sanitize_filename(camera_name), webp_file
-            )
+            webp_path = get_trigger_thumbnail_path(camera_name, body.data)
+
+            if webp_path is None:
+                return JSONResponse(
+                    content={
+                        "success": False,
+                        "message": f"Invalid data for {body.type} trigger",
+                    },
+                    status_code=400,
+                )
 
             try:
                 event: Event = Event.get(Event.id == body.data)
@@ -2101,13 +2112,14 @@ def update_trigger_embedding(
             # Update existing trigger
             if trigger.data != body.data:  # Delete old thumbnail only if data changes
                 try:
-                    os.remove(
-                        os.path.join(
-                            TRIGGER_DIR,
-                            sanitize_filename(camera_name),
-                            f"{trigger.data}.webp",
+                    old_path = get_trigger_thumbnail_path(camera_name, trigger.data)
+
+                    if old_path is None:
+                        raise ValueError(
+                            f"Invalid trigger thumbnail path for {trigger.data}"
                         )
-                    )
+
+                    os.remove(old_path)
                     logger.debug(
                         f"Deleted thumbnail for trigger with data {trigger.data} in {camera_name}."
                     )
@@ -2141,12 +2153,13 @@ def update_trigger_embedding(
         if body.type == "thumbnail":
             # Save image to the triggers directory
             try:
-                camera_path = os.path.join(TRIGGER_DIR, sanitize_filename(camera_name))
-                os.makedirs(camera_path, exist_ok=True)
-                with open(
-                    os.path.join(camera_path, f"{sanitize_filename(body.data)}.webp"),
-                    "wb",
-                ) as f:
+                thumbnail_path = get_trigger_thumbnail_path(camera_name, body.data)
+
+                if thumbnail_path is None:
+                    raise ValueError(f"Invalid trigger thumbnail path for {body.data}")
+
+                os.makedirs(os.path.dirname(thumbnail_path), exist_ok=True)
+                with open(thumbnail_path, "wb") as f:
                     f.write(thumbnail)
                 logger.debug(
                     f"Writing thumbnail for trigger with data {body.data} in {camera_name}."
@@ -2217,11 +2230,12 @@ def delete_trigger_embedding(
             )
 
         try:
-            os.remove(
-                os.path.join(
-                    TRIGGER_DIR, sanitize_filename(camera_name), f"{trigger.data}.webp"
-                )
-            )
+            thumbnail_path = get_trigger_thumbnail_path(camera_name, trigger.data)
+
+            if thumbnail_path is None:
+                raise ValueError(f"Invalid trigger thumbnail path for {trigger.data}")
+
+            os.remove(thumbnail_path)
             logger.debug(
                 f"Deleted thumbnail for trigger with data {trigger.data} in {camera_name}."
             )

frigate/api/export.py

@@ -9,11 +9,12 @@ import zipfile
 from collections import deque
 from collections.abc import Iterator
 from pathlib import Path
+from urllib.parse import quote
 
 import psutil
 from fastapi import APIRouter, Depends, Query, Request
 from fastapi.responses import JSONResponse, StreamingResponse
-from pathvalidate import sanitize_filename, sanitize_filepath
+from pathvalidate import sanitize_filename
 from peewee import DoesNotExist
 from playhouse.shortcuts import model_to_dict
 
@@ -68,10 +69,12 @@ from frigate.jobs.export import (
 from frigate.models import Export, ExportCase, Previews, Recordings
 from frigate.record.export import (
     DEFAULT_TIME_LAPSE_FFMPEG_ARGS,
+    DEFAULT_TIME_LAPSE_FFMPEG_INPUT_ARGS,
     ChaptersEnum,
     PlaybackSourceEnum,
     validate_ffmpeg_args,
 )
+from frigate.util.path import sanitize_contained_path
 from frigate.util.time import is_current_hour
 
 logger = logging.getLogger(__name__)
@@ -129,18 +132,12 @@ def _validate_export_case(export_case_id: str | None) -> JSONResponse | None:
 def _sanitize_existing_image(
     image_path: str | None,
 ) -> tuple[str | None, JSONResponse | None]:
-    # sanitize_filepath normalizes "\" to "/" but leaves ".." intact, so a path
-    # like "clips\..\..\etc/passwd" passes the CLIPS_DIR prefix check yet still
-    # escapes the directory once resolved. A valid snapshot path never uses "..".
-    if image_path and ".." in image_path:
-        return None, JSONResponse(
-            content={"success": False, "message": "Invalid image path"},
-            status_code=400,
-        )
+    if not image_path:
+        return None, None
 
-    existing_image = sanitize_filepath(image_path) if image_path else None
+    existing_image = sanitize_contained_path(image_path, CLIPS_DIR)
 
-    if existing_image and not existing_image.startswith(CLIPS_DIR):
+    if existing_image is None:
         return None, JSONResponse(
             content={"success": False, "message": "Invalid image path"},
             status_code=400,
@@ -458,6 +455,22 @@ def _stream_case_archive(exports: list[Export]) -> Iterator[bytes]:
     yield from buffer.drain()
 
 
+def _content_disposition(filename: str, ascii_fallback: str) -> str:
+    """Build an attachment Content-Disposition that survives non-ASCII names.
+
+    Header values are encoded as latin-1, so a name outside that range cannot
+    go in filename at all. RFC 6266 handles this with a pair: a plain ASCII
+    filename for old clients, plus a percent-encoded UTF-8 filename* that
+    every current browser prefers.
+    """
+    ascii_name = filename if filename.isascii() else ascii_fallback
+
+    return (
+        f'attachment; filename="{ascii_name}"; '
+        f"filename*=UTF-8''{quote(filename, safe='')}"
+    )
+
+
 @router.get(
     "/cases/{case_id}/download",
     dependencies=[Depends(allow_any_authenticated())],
@@ -500,7 +513,9 @@ def download_export_case(
         _stream_case_archive(exports),
         media_type="application/zip",
         headers={
-            "Content-Disposition": f'attachment; filename="{archive_base}.zip"',
+            "Content-Disposition": _content_disposition(
+                f"{archive_base}.zip", f"{case_id}.zip"
+            ),
         },
     )
 
@@ -998,7 +1013,7 @@ def export_recording_custom(
 
     # Set default values if not provided (timelapse defaults)
     if ffmpeg_input_args is None:
-        ffmpeg_input_args = ""
+        ffmpeg_input_args = DEFAULT_TIME_LAPSE_FFMPEG_INPUT_ARGS
 
     if ffmpeg_output_args is None:
         ffmpeg_output_args = DEFAULT_TIME_LAPSE_FFMPEG_ARGS

frigate/api/fastapi_app.py

@@ -35,6 +35,7 @@ from frigate.comms.event_metadata_updater import (
 )
 from frigate.config import FrigateConfig
 from frigate.config.camera.updater import CameraConfigUpdatePublisher
+from frigate.config.holder import ConfigHolder
 from frigate.config.profile_manager import ProfileManager
 from frigate.debug_replay import DebugReplayManager, debug_replay_auto_stop_watchdog
 from frigate.embeddings import EmbeddingsContext
@@ -74,6 +75,7 @@ def create_fastapi_app(
     dispatcher: Dispatcher | None = None,
     profile_manager: ProfileManager | None = None,
     enforce_default_admin: bool = True,
+    config_holder: ConfigHolder | None = None,
 ):
     logger.info("Starting FastAPI app")
     app = FastAPI(
@@ -150,6 +152,8 @@ def create_fastapi_app(
     app.include_router(debug_replay.router)
     # App Properties
     app.frigate_config = frigate_config
+    # snapshot the port nginx bound at startup, the live config can be swapped
+    app.auth_internal_port = frigate_config.networking.listen.internal_port
     app.genai_manager = GenAIClientManager(frigate_config)
     app.embeddings = embeddings
     app.detected_frames_processor = detected_frames_processor
@@ -162,6 +166,7 @@ def create_fastapi_app(
     app.replay_manager = replay_manager
     app.dispatcher = dispatcher
     app.profile_manager = profile_manager
+    app.config_holder = config_holder
 
     if frigate_config.auth.enabled:
         secret = get_jwt_secret()

frigate/api/media.py

@@ -53,6 +53,7 @@ from frigate.util.file import (
 )
 from frigate.util.image import get_image_from_recording, get_image_quality_params
 from frigate.util.media import get_keyframe_before
+from frigate.util.object import create_empty_regions_grid
 
 logger = logging.getLogger(__name__)
 
@@ -1083,7 +1084,21 @@ def clear_region_grid(request: Request, camera_name: str):
             status_code=404,
         )
 
-    Regions.delete().where(Regions.camera == camera_name).execute()
+    # store an empty grid instead of deleting the row so the grid is
+    # rebuilt from newly tracked objects and not from all past history
+    region = {
+        Regions.camera: camera_name,
+        Regions.grid: create_empty_regions_grid(),
+        Regions.last_update: datetime.now().timestamp(),
+    }
+    (
+        Regions.insert(region)
+        .on_conflict(
+            conflict_target=[Regions.camera],
+            update=region,
+        )
+        .execute()
+    )
     return JSONResponse(
         content={"success": True, "message": "Region grid cleared"},
     )

frigate/api/motion_search.py

@@ -182,7 +182,7 @@ async def get_motion_search_status_endpoint(
         )
 
     job = get_motion_search_job(job_id)
-    if not job:
+    if not job or job.camera != camera_name:
         return JSONResponse(
             content={"success": False, "message": "Job not found"},
             status_code=404,
@@ -253,7 +253,7 @@ async def cancel_motion_search_endpoint(
         )
 
     job = get_motion_search_job(job_id)
-    if not job:
+    if not job or job.camera != camera_name:
         return JSONResponse(
             content={"success": False, "message": "Job not found"},
             status_code=404,

frigate/api/notification.py

@@ -1,8 +1,10 @@
 """Notification apis."""
 
+import ipaddress
 import logging
 import os
 from typing import Any
+from urllib.parse import urlparse
 
 from cryptography.hazmat.primitives import serialization
 from fastapi import APIRouter, Depends, Request
@@ -19,6 +21,95 @@ logger = logging.getLogger(__name__)
 
 router = APIRouter(tags=[Tags.notifications])
 
+# Push endpoints are opaque URLs but stay well under this in practice
+MAX_ENDPOINT_LENGTH = 2048
+
+# Suffixes that only ever resolve on the local network
+INTERNAL_HOST_SUFFIXES = (".local", ".localdomain", ".internal", ".home.arpa")
+
+
+def _validate_push_endpoint(endpoint: Any) -> str | None:
+    """Return a reason the endpoint is unusable, or None when it is valid.
+
+    Subscriptions are issued by the browser vendor's push service, so a valid
+    endpoint is always a public https URL. Anything else is either a broken
+    registration or an attempt to aim the notification sender somewhere it
+    should not reach.
+    """
+    if not isinstance(endpoint, str) or not endpoint:
+        return "endpoint must be a url"
+
+    if len(endpoint) > MAX_ENDPOINT_LENGTH:
+        return "endpoint is too long"
+
+    try:
+        parsed = urlparse(endpoint)
+        port = parsed.port
+    except ValueError:
+        return "endpoint is not a valid url"
+
+    if parsed.scheme != "https":
+        return "endpoint must use https"
+
+    if parsed.username or parsed.password:
+        return "endpoint must not include credentials"
+
+    if port is not None and port != 443:
+        return "endpoint must use the default https port"
+
+    hostname = parsed.hostname
+
+    if not hostname:
+        return "endpoint must include a hostname"
+
+    try:
+        address = ipaddress.ip_address(hostname)
+    except ValueError:
+        address = None
+
+    if address is not None:
+        # A push service is never reachable at an address only this network can
+        # route, so anything non-global is a misconfiguration at best
+        if not address.is_global:
+            return "endpoint must not use a private address"
+    elif hostname == "localhost" or "." not in hostname:
+        return "endpoint must use a fully qualified hostname"
+    elif hostname.endswith(INTERNAL_HOST_SUFFIXES):
+        return "endpoint must not use an internal hostname"
+
+    # The subscription token lives in the path, and webpush.py assumes there is
+    # a separator after the host when it builds the VAPID audience
+    if len(parsed.path) <= 1:
+        return "endpoint must include a subscription path"
+
+    return None
+
+
+def _validate_subscription(sub: Any) -> str | None:
+    """Return a reason the subscription is unusable, or None when it is valid."""
+    if not isinstance(sub, dict):
+        return "subscription must be an object"
+
+    reason = _validate_push_endpoint(sub.get("endpoint"))
+
+    if reason:
+        return reason
+
+    keys = sub.get("keys")
+
+    if not isinstance(keys, dict):
+        return "subscription must include keys"
+
+    # WebPusher raises on a missing key, which would break every send for the
+    # user rather than just this registration
+    for name in ("p256dh", "auth"):
+        value = keys.get(name)
+
+        if not isinstance(value, str) or not value:
+            return f"subscription keys must include {name}"
+
+    return None
+
 
 @router.get(
     "/notifications/pubkey",
@@ -71,6 +162,17 @@ def register_notifications(request: Request, body: dict = None):
             status_code=400,
         )
 
+    reason = _validate_subscription(sub)
+
+    if reason:
+        logger.warning(
+            "Rejected notification registration for %s: %s", username, reason
+        )
+        return JSONResponse(
+            content={"success": False, "message": f"Invalid subscription: {reason}"},
+            status_code=400,
+        )
+
     try:
         User.update(notification_tokens=User.notification_tokens.append(sub)).where(
             User.username == username

frigate/api/review.py

@@ -17,6 +17,7 @@ from frigate.api.auth import (
     get_allowed_cameras_for_filter,
     get_current_user,
     require_camera_access,
+    require_full_camera_access,
     require_role,
 )
 from frigate.api.defs.query.review_query_parameters import (
@@ -42,6 +43,22 @@ logger = logging.getLogger(__name__)
 router = APIRouter(tags=[Tags.review])
 
 
+def get_label_clause(label: str, include_audio: bool = True):
+    """Build a clause matching a label within a review segment's data.
+
+    Verified objects are stored with a `-verified` suffix (eg. `person-verified`)
+    so that variant is matched as well.
+    """
+    clause = (ReviewSegment.data["objects"].cast("text") % f'*"{label}"*') | (
+        ReviewSegment.data["objects"].cast("text") % f'*"{label}-verified"*'
+    )
+
+    if include_audio:
+        clause |= ReviewSegment.data["audio"].cast("text") % f'*"{label}"*'
+
+    return clause
+
+
 @router.get(
     "/review",
     response_model=list[ReviewSegmentResponse],
@@ -91,10 +108,7 @@ async def review(
         filtered_labels = labels.split(",")
 
         for label in filtered_labels:
-            label_clauses.append(
-                (ReviewSegment.data["objects"].cast("text") % f'*"{label}"*')
-                | (ReviewSegment.data["audio"].cast("text") % f'*"{label}"*')
-            )
+            label_clauses.append(get_label_clause(label))
         clauses.append(reduce(operator.or_, label_clauses))
 
     if zones != "all":
@@ -235,10 +249,7 @@ async def review_summary(
         filtered_labels = labels.split(",")
 
         for label in filtered_labels:
-            label_clauses.append(
-                (ReviewSegment.data["objects"].cast("text") % f'*"{label}"*')
-                | (ReviewSegment.data["audio"].cast("text") % f'*"{label}"*')
-            )
+            label_clauses.append(get_label_clause(label))
         clauses.append(reduce(operator.or_, label_clauses))
     if zones != "all":
         # use matching so segments with multiple zones
@@ -336,9 +347,8 @@ async def review_summary(
         filtered_labels = labels.split(",")
 
         for label in filtered_labels:
-            label_clauses.append(
-                ReviewSegment.data["objects"].cast("text") % f'*"{label}"*'
-            )
+            label_clauses.append(get_label_clause(label, include_audio=False))
+
         clauses.append(reduce(operator.or_, label_clauses))
 
     # Find the time range of available data
@@ -709,6 +719,7 @@ async def get_review(request: Request, review_id: str):
     dependencies=[Depends(allow_any_authenticated())],
 )
 async def set_not_reviewed(
+    request: Request,
     review_id: str,
     current_user: dict = Depends(get_current_user),
 ):
@@ -727,6 +738,8 @@ async def set_not_reviewed(
             status_code=404,
         )
 
+    await require_camera_access(review.camera, request=request)
+
     try:
         user_review = UserReviewStatus.get(
             UserReviewStatus.user_id == user_id,
@@ -743,9 +756,12 @@ async def set_not_reviewed(
     )
 
 
+# Intentionally not camera scoped, as the summary correlates each flagged event
+# with overlapping activity on other cameras. Restricted to callers who can
+# already see every camera, so the unscoped query discloses nothing.
 @router.post(
     "/review/summarize/start/{start_ts}/end/{end_ts}",
-    dependencies=[Depends(require_role(["admin"]))],
+    dependencies=[Depends(require_full_camera_access)],
     description="Use GenAI to summarize review items over a period of time.",
 )
 def generate_review_summary(request: Request, start_ts: float, end_ts: float):

frigate/app.py

@@ -30,6 +30,7 @@ from frigate.comms.ws import WebSocketClient
 from frigate.comms.zmq_proxy import ZmqProxy
 from frigate.config.camera.updater import CameraConfigUpdatePublisher
 from frigate.config.config import FrigateConfig
+from frigate.config.holder import ConfigHolder
 from frigate.config.profile_manager import ProfileManager
 from frigate.const import (
     CACHE_DIR,
@@ -102,27 +103,25 @@ class FrigateApp:
         self.detection_shms: list[mp.shared_memory.SharedMemory] = []
         self.log_queue: Queue = mp.Queue()
         self.camera_metrics: DictProxy = self.metrics_manager.dict()
-        self.embeddings_metrics: DataProcessorMetrics | None = (
-            DataProcessorMetrics(
-                self.metrics_manager, list(config.classification.custom.keys())
-            )
-            if (
-                config.semantic_search.enabled
-                or any(
-                    c.objects.genai.enabled or c.review.genai.enabled
-                    for c in config.cameras.values()
-                )
-                or config.lpr.enabled
-                or config.face_recognition.enabled
-                or len(config.classification.custom) > 0
-            )
-            else None
+
+        self.embeddings_metrics = DataProcessorMetrics(
+            self.metrics_manager, list(config.classification.custom.keys())
         )
         self.ptz_metrics: dict[str, PTZMetrics] = {}
         self.processes: dict[str, int] = {}
         self.embeddings: EmbeddingsContext | None = None
-        self.profile_manager: ProfileManager | None = None
-        self.config = config
+        self.config_holder = ConfigHolder(config)
+
+    @property
+    def config(self) -> FrigateConfig:
+        """The current config, not the one Frigate booted with.
+
+        Read through the holder so the deferred watchdog factories below build
+        a replacement process from the config as it is now. There is no setter
+        on purpose: a plain attribute would let a caller pin this back to a
+        single object and reintroduce the staleness.
+        """
+        return self.config_holder.config
 
     def ensure_dirs(self) -> None:
         dirs = [
@@ -343,25 +342,6 @@ class FrigateApp:
         )
         self.dispatcher.profile_manager = self.profile_manager
 
-    def restore_active_profile(self) -> None:
-        """Re-activate the persisted profile after subscribers are connected.
-
-        ZMQ PUB/SUB drops messages with no subscribers, so activation must
-        run after every config_updater subscriber is up.
-        """
-        if self.profile_manager is None:
-            return
-
-        persisted = ProfileManager.load_persisted_profile()
-        if persisted and any(
-            persisted in cam.profiles for cam in self.config.cameras.values()
-        ):
-            logger.info("Restoring persisted profile '%s'", persisted)
-            # runtime overrides are layered on top via restore_runtime_state()
-            self.profile_manager.activate_profile(
-                persisted, clear_runtime_overrides=False
-            )
-
     def start_detectors(self) -> None:
         for name in self.config.cameras.keys():
             try:
@@ -610,6 +590,13 @@ class FrigateApp:
         self.start_detectors()
         self.init_dispatcher()
         self.init_profile_manager()
+
+        # workers get a copy of the config and can miss the broadcast below, so
+        # apply both layers here. must stay after init_profile_manager(), which
+        # snapshots the base config that profile deactivation resets to
+        self.profile_manager.restore_persisted_profile_to_config()
+        self.dispatcher.reapply_runtime_state_to_config()
+
         self.init_embeddings_client()
         self.start_video_output_processor()
         self.start_ptz_autotracker()
@@ -624,8 +611,9 @@ class FrigateApp:
         self.start_record_cleanup()
         self.start_watchdog()
 
-        # restore persisted runtime overrides on top of config
-        self.restore_active_profile()
+        # publish for the recording/review/embeddings processes, which start
+        # before the config can be corrected, and for the retained MQTT states
+        self.profile_manager.restore_persisted_profile()
         self.dispatcher.restore_runtime_state()
 
         self.init_auth()
@@ -645,6 +633,7 @@ class FrigateApp:
                     self.replay_manager,
                     self.dispatcher,
                     self.profile_manager,
+                    config_holder=self.config_holder,
                 ),
                 host="127.0.0.1",
                 port=5001,

frigate/camera/activity_manager.py

@@ -103,12 +103,13 @@ class CameraActivityManager:
         all_objects: list[dict[str, Any]] = []
 
         for camera in new_activity.keys():
-            if camera not in self.config.cameras:
+            camera_config = self.config.cameras.get(camera)
+            if camera_config is None:
                 continue
 
             # handle cameras that were added dynamically
             if camera not in self.camera_all_object_counts:
-                self.__init_camera(self.config.cameras[camera])
+                self.__init_camera(camera_config)
 
             new_objects = new_activity[camera].get("objects", [])
             all_objects.extend(new_objects)
@@ -233,12 +234,13 @@ class AudioActivityManager:
         now = datetime.datetime.now().timestamp()
 
         for camera in new_activity.keys():
-            if camera not in self.config.cameras:
+            camera_config = self.config.cameras.get(camera)
+            if camera_config is None:
                 continue
 
             # handle cameras that were added dynamically
             if camera not in self.current_audio_detections:
-                self.__init_camera(self.config.cameras[camera])
+                self.__init_camera(camera_config)
 
             new_detections = new_activity[camera].get("detections", [])
             if self.compare_audio_activity(camera, new_detections, now):

frigate/camera/state.py

@@ -60,6 +60,11 @@ class CameraState:
         # face/LPR pipelines when using a model without built-in detection.
         self.face_recognition_min_obj_area: int = 0
         self.lpr_min_obj_area: int = 0
+        self.lp_objects = {
+            label
+            for label, attributes in config.model.attributes_map.items()
+            if "license_plate" in attributes
+        }
 
         if (
             self.camera_config.face_recognition.enabled
@@ -452,7 +457,7 @@ class CameraState:
                 and obj_area >= self.face_recognition_min_obj_area
                 and updated_obj.obj_data.get("sub_label") is None
             ) or (
-                obj_label in ("car", "motorcycle")
+                obj_label in self.lp_objects
                 and self.lpr_min_obj_area > 0
                 and obj_area >= self.lpr_min_obj_area
                 and updated_obj.obj_data.get("sub_label") is None
@@ -548,7 +553,7 @@ class CameraState:
                     current_best.thumbnail_data is not None
                     and obj.thumbnail_data is not None
                     and is_better_thumbnail(
-                        object_type,
+                        obj.thumbnail_attributes,
                         current_best.thumbnail_data,
                         obj.thumbnail_data,
                         self.camera_config.frame_shape,

frigate/comms/mqtt.py

@@ -77,6 +77,11 @@ class MqttClient(Communicator):
                 "ON" if camera.audio.enabled_in_config else "OFF",
                 retain=True,
             )
+            self.publish(
+                f"{camera_name}/audio_transcription/state",
+                "ON" if camera.audio_transcription.live_enabled else "OFF",
+                retain=True,
+            )
             self.publish(
                 f"{camera_name}/detect/state",
                 "ON" if camera.detect.enabled else "OFF",
@@ -258,6 +263,7 @@ class MqttClient(Communicator):
             "snapshots",
             "detect",
             "audio",
+            "audio_transcription",
             "motion",
             "improve_contrast",
             "ptz_autotracker",

frigate/comms/webpush.py

@@ -89,7 +89,9 @@ class WebPushClient(Communicator):
         # notification and auth config updater
         self.global_config_subscriber = ConfigSubscriber("config/")
         self.config_subscriber = CameraConfigUpdateSubscriber(
-            self.config, self.config.cameras, [CameraConfigUpdateEnum.notifications]
+            self.config,
+            self.config.cameras,
+            [CameraConfigUpdateEnum.add, CameraConfigUpdateEnum.notifications],
         )
         self._refresh_user_cameras()
 
@@ -213,6 +215,8 @@ class WebPushClient(Communicator):
                 self.suspended_cameras[camera] = 0
                 self.last_camera_notification_time[camera] = 0
 
+            self._refresh_user_cameras()
+
         if topic == "reviews":
             decoded = json.loads(payload)
             camera = decoded["before"]["camera"]
@@ -417,6 +421,7 @@ class WebPushClient(Communicator):
         # Don't notify if message is an update and important fields don't have an update
         if (
             state == "update"
+            and payload["before"]["severity"] == payload["after"]["severity"]
             and len(payload["before"]["data"]["objects"])
             == len(payload["after"]["data"]["objects"])
             and len(payload["before"]["data"]["zones"])

frigate/config/camera/ui.py

@@ -13,8 +13,8 @@ class CameraUiConfig(FrigateBaseModel):
     )
     dashboard: bool = Field(
         default=True,
-        title="Show in UI",
-        description="Toggle whether this camera is visible everywhere in the Frigate UI. Disabling this will require manually editing the config to view this camera in the UI again.",
+        title="Show on Live dashboard",
+        description="Toggle whether this camera is visible on the default All Cameras live dashboard. The camera remains available everywhere else in the UI, including camera groups and settings.",
     )
     review: bool = Field(
         default=True,

frigate/config/holder.py

@@ -0,0 +1,34 @@
+"""Shared handle on the config object that is current for this instance."""
+
+from .config import FrigateConfig
+
+__all__ = ["ConfigHolder"]
+
+
+class ConfigHolder:
+    """Indirection for the most recently parsed config.
+
+    /api/config/set re-parses yaml into a brand new FrigateConfig instead of
+    mutating the old one, so any reference captured during startup goes stale
+    the first time a user saves. Anything that has to build something after
+    startup, most importantly the watchdog factories that rebuild a crashed
+    process, must read through a holder rather than close over a config
+    object, or the rebuilt process comes back with the config as it was at
+    boot and silently discards every change made since.
+
+    There is deliberately no setter on the read side: the swap runs in exactly
+    one place (frigate.api.config_util.swap_runtime_config) and everyone else
+    only reads.
+    """
+
+    def __init__(self, config: FrigateConfig) -> None:
+        self._config = config
+
+    @property
+    def config(self) -> FrigateConfig:
+        """The config as of the most recent successful save."""
+        return self._config
+
+    def set(self, config: FrigateConfig) -> None:
+        """Install a freshly parsed config as the current one."""
+        self._config = config

frigate/config/network.py

@@ -1,10 +1,18 @@
-from pydantic import Field
+from pydantic import Field, model_validator
 
 from .base import FrigateBaseModel
 
 __all__ = ["IPv6Config", "ListenConfig", "NetworkingConfig"]
 
 
+def parse_listen_port(value: int | str) -> int:
+    """Return the port number from a bare port or an "address:port" value."""
+    if isinstance(value, str):
+        return int(value.split(":")[-1])
+
+    return value
+
+
 class IPv6Config(FrigateBaseModel):
     enabled: bool = Field(
         default=False,
@@ -25,6 +33,21 @@ class ListenConfig(FrigateBaseModel):
         description="External listening port for Frigate (default 8971).",
     )
 
+    @property
+    def internal_port(self) -> int:
+        return parse_listen_port(self.internal)
+
+    @property
+    def external_port(self) -> int:
+        return parse_listen_port(self.external)
+
+    @model_validator(mode="after")
+    def validate_distinct_ports(self) -> "ListenConfig":
+        if self.internal_port == self.external_port:
+            raise ValueError("internal and external must listen on different ports")
+
+        return self
+
 
 class NetworkingConfig(FrigateBaseModel):
     ipv6: IPv6Config = Field(

frigate/config/profile_manager.py

@@ -169,6 +169,93 @@ class ProfileManager:
                 self.config.active_profile = None
                 self._persist_active_profile(None)
 
+    def _validate_profile_name(self, profile_name: str | None) -> str | None:
+        """Return an error message if the name is not a defined profile."""
+        if profile_name is not None and profile_name not in self.config.profiles:
+            return f"Profile '{profile_name}' is not defined in the profiles section"
+
+        return None
+
+    def _apply_to_config(
+        self, profile_name: str | None
+    ) -> tuple[dict[str, set[str]], str | None]:
+        """Reset every camera to base, then apply the named profile on top.
+
+        Returns the changed camera/section pairs, plus an error message if
+        applying the profile failed partway through.
+        """
+        changed: dict[str, set[str]] = {}
+
+        self._reset_to_base(changed)
+
+        if profile_name is not None:
+            err = self._apply_profile_overrides(profile_name, changed)
+            if err:
+                return changed, err
+
+        return changed, None
+
+    def apply_profile_to_config(self, profile_name: str | None) -> str | None:
+        """Apply a profile to the in-memory config, without publishing it.
+
+        Safe to call ahead of activate_profile: both reset to the base config
+        first, so the later call re-derives the same state and still reports
+        every section as changed.
+
+        Returns:
+            None on success, or an error message string on failure.
+        """
+        err = self._validate_profile_name(profile_name)
+
+        if err:
+            return err
+
+        return self._apply_to_config(profile_name)[1]
+
+    def _persisted_profile_to_restore(self) -> str | None:
+        """Return the persisted profile name, if it still applies to a camera."""
+        persisted = self.load_persisted_profile()
+
+        if not persisted or not any(
+            persisted in cam.profiles for cam in self.config.cameras.values()
+        ):
+            return None
+
+        return persisted
+
+    def restore_persisted_profile_to_config(self) -> None:
+        """Restore the persisted profile into the config, without publishing.
+
+        Called before worker processes start, so they are handed a config that
+        already carries the profile rather than relying on the broadcast that
+        restore_persisted_profile() sends later.
+        """
+        persisted = self._persisted_profile_to_restore()
+
+        if persisted is None:
+            return
+
+        err = self.apply_profile_to_config(persisted)
+
+        if err:
+            logger.error("Failed to apply persisted profile '%s': %s", persisted, err)
+
+    def restore_persisted_profile(self) -> None:
+        """Re-activate the persisted profile once subscribers are connected.
+
+        The config already carries the profile; this pass publishes it for the
+        processes that start before the config can be corrected, and for the
+        retained MQTT states.
+        """
+        persisted = self._persisted_profile_to_restore()
+
+        if persisted is None:
+            return
+
+        logger.info("Restoring persisted profile '%s'", persisted)
+        # runtime overrides are layered on top by the dispatcher's replay
+        self.activate_profile(persisted, clear_runtime_overrides=False)
+
     def activate_profile(
         self,
         profile_name: str | None,
@@ -187,23 +274,16 @@ class ProfileManager:
         Returns:
             None on success, or an error message string on failure.
         """
-        if profile_name is not None:
-            if profile_name not in self.config.profiles:
-                return (
-                    f"Profile '{profile_name}' is not defined in the profiles section"
-                )
+        err = self._validate_profile_name(profile_name)
+
+        if err:
+            return err
 
         # Track which camera/section pairs get changed for ZMQ publishing
-        changed: dict[str, set[str]] = {}
+        changed, err = self._apply_to_config(profile_name)
 
-        # Reset all cameras to base config
-        self._reset_to_base(changed)
-
-        # Apply new profile overrides if activating
-        if profile_name is not None:
-            err = self._apply_profile_overrides(profile_name, changed)
-            if err:
-                return err
+        if err:
+            return err
 
         # Publish ZMQ updates only for sections that actually changed
         self._publish_updates(changed)

frigate/const.py

@@ -44,7 +44,11 @@ DEFAULT_ATTRIBUTE_LABEL_MAP = {
         "ups",
         "usps",
     ],
+    "truck": ["license_plate"],
+    "garbage_truck": ["license_plate"],
     "motorcycle": ["license_plate"],
+    "bus": ["license_plate"],
+    "school_bus": ["license_plate"],
 }
 ATTRIBUTE_LABEL_DISPLAY_MAP = {
     "amazon": "Amazon",

frigate/data_processing/common/license_plate/mixin.py

@@ -1172,6 +1172,28 @@ class LicensePlateProcessingMixin:
 
         return rep["plate"], rep["conf"], rep["char_confidences"], rep["area"]
 
+    def _passes_plate_filters(self, camera: str, plate: str) -> bool:
+        """Check a plate against the configured length and format filters."""
+        if len(plate) < self.lpr_config.min_plate_length:
+            logger.debug(
+                f"{camera}: Filtered out plate '{plate}' due to length ({len(plate)} < {self.lpr_config.min_plate_length})"
+            )
+            return False
+
+        if self.lpr_config.format:
+            try:
+                if not re.fullmatch(self.lpr_config.format, plate):
+                    logger.debug(
+                        f"{camera}: Filtered out plate '{plate}' due to format mismatch"
+                    )
+                    return False
+            except re.error:
+                logger.error(
+                    f"{camera}: Invalid regex in LPR format configuration: {self.lpr_config.format}"
+                )
+
+        return True
+
     def _generate_plate_event(self, camera: str, plate: str, plate_score: float) -> str:
         """Generate a unique ID for a plate event based on camera and text."""
         now = datetime.datetime.now().timestamp()
@@ -1268,7 +1290,7 @@ class LicensePlateProcessingMixin:
                 and obj_data.get("label") != "license_plate"
             ):
                 logger.debug(
-                    f"{camera}: Not a processing license plate for non car/motorcycle object."
+                    f"{camera}: Not a processing license plate for {obj_data.get('label', 'unknown')}."
                 )
                 return
 
@@ -1345,7 +1367,7 @@ class LicensePlateProcessingMixin:
 
                 if not license_plate:
                     logger.debug(
-                        f"{camera}: Detected no license plates for car/motorcycle object."
+                        f"{camera}: Detected no license plates for {obj_data.get('label', 'unknown')} object."
                     )
                     return
 
@@ -1511,10 +1533,14 @@ class LicensePlateProcessingMixin:
             plate_id = None
 
             for existing_id, data in self.detected_license_plates.items():
+                # entries from the object pipeline on this camera have no
+                # last_seen until they pass the filters below
+                last_seen = data.get("last_seen")
+
                 if (
                     data["camera"] == camera
-                    and data["last_seen"] is not None
-                    and current_time - data["last_seen"]
+                    and last_seen is not None
+                    and current_time - last_seen
                     <= self.config.cameras[camera].lpr.expire_time
                 ):
                     similarity = JaroWinkler.similarity(data["plate"], top_plate)
@@ -1525,6 +1551,11 @@ class LicensePlateProcessingMixin:
                         )
                         break
             if plate_id is None:
+                # the event id doubles as the cluster key, so a plate rejected
+                # after this point would leave an entry that never expires
+                if not self._passes_plate_filters(camera, top_plate):
+                    return
+
                 plate_id = self._generate_plate_event(camera, top_plate, avg_confidence)
                 logger.debug(
                     f"{camera}: New plate event for dedicated LPR camera {plate_id}: {top_plate}"
@@ -1569,27 +1600,12 @@ class LicensePlateProcessingMixin:
                 f"{camera}: Clustering changed top plate '{top_plate}' (conf: {avg_confidence:.3f}) to rep '{rep_plate}' (conf: {rep_conf:.3f})"
             )
 
-        # Apply length and format filters to the clustered representative
-        # rather than individual OCR readings, so noisy variants still
-        # contribute to clustering even when they don't pass on their own.
-        if len(rep_plate) < self.lpr_config.min_plate_length:
-            logger.debug(
-                f"{camera}: Filtered out clustered plate '{rep_plate}' due to length ({len(rep_plate)} < {self.lpr_config.min_plate_length})"
-            )
+        # filter the clustered representative rather than individual OCR
+        # readings, so noisy variants still contribute to clustering even
+        # when they don't pass on their own
+        if not self._passes_plate_filters(camera, rep_plate):
             return
 
-        if self.lpr_config.format:
-            try:
-                if not re.fullmatch(self.lpr_config.format, rep_plate):
-                    logger.debug(
-                        f"{camera}: Filtered out clustered plate '{rep_plate}' due to format mismatch"
-                    )
-                    return
-            except re.error:
-                logger.error(
-                    f"{camera}: Invalid regex in LPR format configuration: {self.lpr_config.format}"
-                )
-
         # Update stored rep
         self.detected_license_plates[id].update(
             {

frigate/data_processing/post/object_descriptions.py

@@ -63,8 +63,10 @@ class ObjectDescriptionProcessor(PostProcessorApi):
         """Handle an update to a frame for an object."""
         camera_config = self.config.cameras[camera]
 
-        # no need to save our own thumbnails if genai is not enabled
-        # or if the object has become stationary
+        if not camera_config.objects.genai.enabled:
+            return
+
+        # no need to save our own thumbnails if the object has become stationary
         if not data["stationary"]:
             if data["id"] not in self.tracked_events:
                 self.tracked_events[data["id"]] = []

frigate/data_processing/real_time/face.py

@@ -28,6 +28,7 @@ from frigate.data_processing.common.face.model import (
 from frigate.types import TrackedObjectUpdateTypesEnum
 from frigate.util.builtin import EventsPerSecond, InferenceSpeed
 from frigate.util.image import area
+from frigate.util.path import safe_join, sanitize_path_component
 
 from ..types import DataProcessorMetrics
 from .api import RealTimeProcessorApi
@@ -409,9 +410,17 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
                 )
 
             # write face to library
-            folder = os.path.join(FACE_DIR, label)
+            sanitized_label = sanitize_path_component(label)
+            folder = safe_join(FACE_DIR, label)
+
+            if sanitized_label is None or folder is None:
+                return {
+                    "message": f"Invalid face name: {label}",
+                    "success": False,
+                }
+
             file = os.path.join(
-                folder, f"{label}_{datetime.datetime.now().timestamp()}.webp"
+                folder, f"{sanitized_label}_{datetime.datetime.now().timestamp()}.webp"
             )
             os.makedirs(folder, exist_ok=True)
 

frigate/db/sqlitevecq.py

@@ -1,8 +1,10 @@
 import logging
 import sqlite3
+import threading
 from typing import Any
 
 import regex
+from peewee import DatabaseError
 from playhouse.sqliteq import SqliteQueueDatabase
 
 logger = logging.getLogger(__name__)
@@ -17,6 +19,7 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
         self.load_vec_extension: bool = load_vec_extension
         # no extension necessary, sqlite will load correctly for each platform
         self.sqlite_vec_path = "/usr/local/lib/vec0"
+        self.upsert_lock = threading.Lock()
         super().__init__(*args, **kwargs)
 
     def _connect(self, *args: Any, **kwargs: Any) -> sqlite3.Connection:
@@ -53,6 +56,22 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
 
         conn.create_function("REGEXP", 2, regexp)
 
+    def execute_write(self, sql: str, params: Any = None) -> None:
+        """Run a write and wait for it, so that failures are raised here.
+
+        SqliteQueueDatabase hands non-SELECT statements to a writer thread and
+        stores any exception on the cursor it returns, so callers that ignore
+        that cursor never learn the write failed.
+        """
+        self.execute_sql(sql, params).fetchall()
+
+    def _table_exists(self, table: str) -> bool:
+        cursor = self.execute_sql(
+            "SELECT name FROM sqlite_master WHERE type = 'table' AND name = ?",
+            (table,),
+        )
+        return cursor.fetchone() is not None
+
     def _delete_embeddings(self, table: str, event_ids: list[str]) -> None:
         """Delete embeddings for the given events, if the table exists.
 
@@ -63,17 +82,17 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
             return
 
         # the embeddings tables are only created once semantic search has run
-        cursor = self.execute_sql(
-            "SELECT name FROM sqlite_master WHERE type = 'table' AND name = ?",
-            (table,),
-        )
-
-        if cursor.fetchone() is None:
+        if not self._table_exists(table):
             logger.debug("Skipping %s cleanup, table does not exist", table)
             return
 
         ids = ",".join(["?" for _ in event_ids])
-        self.execute_sql(f"DELETE FROM {table} WHERE id IN ({ids})", event_ids)
+
+        # callers treat cleanup as best effort, so log rather than propagate
+        try:
+            self.execute_write(f"DELETE FROM {table} WHERE id IN ({ids})", event_ids)
+        except DatabaseError:
+            logger.exception("Failed to delete embeddings from %s", table)
 
     def delete_embeddings_thumbnail(self, event_ids: list[str]) -> None:
         self._delete_embeddings("vec_thumbnails", event_ids)
@@ -81,25 +100,67 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
     def delete_embeddings_description(self, event_ids: list[str]) -> None:
         self._delete_embeddings("vec_descriptions", event_ids)
 
+    def _restore_vec_info_table(self, table: str) -> None:
+        """Recreate the _info shadow table a legacy vec0 table is missing.
+
+        sqlite-vec added _info in 0.1.6 and drops it unconditionally when a
+        table is destroyed, so tables written by Frigate 0.17 and earlier fail
+        to drop. An empty stub is enough, and leaving it unseeded keeps the
+        table reading as pre-0.1.10 if the drop does not follow.
+        """
+        if not self._table_exists(table) or self._table_exists(f"{table}_info"):
+            return
+
+        logger.debug("Restoring the %s_info shadow table before dropping", table)
+        self.execute_write(
+            f'CREATE TABLE "{table}_info" (key TEXT PRIMARY KEY, value ANY)'
+        )
+
     def drop_embeddings_tables(self) -> None:
-        self.execute_sql("""
-            DROP TABLE vec_descriptions;
-        """)
-        self.execute_sql("""
-            DROP TABLE vec_thumbnails;
-        """)
+        for table in ("vec_descriptions", "vec_thumbnails"):
+            self._restore_vec_info_table(table)
+            self.execute_write(f"DROP TABLE IF EXISTS {table}")
 
     def create_embeddings_tables(self) -> None:
         """Create vec0 virtual table for embeddings"""
-        self.execute_sql("""
+        self.execute_write("""
             CREATE VIRTUAL TABLE IF NOT EXISTS vec_thumbnails USING vec0(
                 id TEXT PRIMARY KEY,
                 thumbnail_embedding FLOAT[768] distance_metric=cosine
             );
         """)
-        self.execute_sql("""
+        self.execute_write("""
             CREATE VIRTUAL TABLE IF NOT EXISTS vec_descriptions USING vec0(
                 id TEXT PRIMARY KEY,
                 description_embedding FLOAT[768] distance_metric=cosine
             );
         """)
+
+    def upsert_embeddings(
+        self, table: str, column: str, embeddings: dict[str, bytes]
+    ) -> None:
+        """Write embeddings for the given event ids, replacing any that exist.
+
+        vec0 implements neither REPLACE nor UPSERT, so rows that are already
+        there have to be deleted first.
+        """
+        if not embeddings:
+            return
+
+        event_ids = list(embeddings.keys())
+        ids = ",".join(["?" for _ in event_ids])
+        params: list[Any] = []
+
+        for event_id in event_ids:
+            params.extend((event_id, embeddings[event_id]))
+
+        values = ", ".join(["(?, ?)"] * len(event_ids))
+
+        # reindexing and live embedding run on separate threads, and each write
+        # is queued separately, so the delete and the insert have to be held
+        # together or an interleaved pair fails on the vec0 primary key
+        with self.upsert_lock:
+            self.execute_write(f"DELETE FROM {table} WHERE id IN ({ids})", event_ids)
+            self.execute_write(
+                f"INSERT INTO {table}(id, {column}) VALUES {values}", params
+            )

frigate/detectors/detection_runners.py

@@ -25,25 +25,31 @@ def is_arm64_platform() -> bool:
     return machine in ("aarch64", "arm64", "armv8", "armv7l")
 
 
-def get_ort_session_options(
-    is_complex_model: bool = False,
-) -> ort.SessionOptions | None:
+def get_ort_session_options(model_type: str | None = None) -> ort.SessionOptions | None:
     """Get ONNX Runtime session options with appropriate settings.
 
     Args:
-        is_complex_model: Whether the model needs basic optimization to avoid graph fusion issues.
+        model_type: Model being loaded, used to pin its graph optimization level.
 
     Returns:
-        SessionOptions with appropriate optimization level, or None for default settings.
+        SessionOptions with a pinned optimization level, or None for default settings.
     """
-    if is_complex_model:
-        sess_options = ort.SessionOptions()
-        sess_options.graph_optimization_level = (
-            ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
-        )
-        return sess_options
+    # Import here to avoid circular imports
+    from frigate.embeddings.types import EnrichmentModelTypeEnum
 
-    return None
+    if model_type == EnrichmentModelTypeEnum.jina_v2.value:
+        # below EXTENDED the CUDA EP returns an identical vector for every image,
+        # and ORT_ENABLE_ALL fails to build on CPU with a SimplifiedLayerNormFusion error
+        level = ort.GraphOptimizationLevel.ORT_ENABLE_EXTENDED
+    elif model_type == EnrichmentModelTypeEnum.jina_v1.value:
+        # aggressive optimizations create or expect nodes that don't exist
+        level = ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
+    else:
+        return None
+
+    sess_options = ort.SessionOptions()
+    sess_options.graph_optimization_level = level
+    return sess_options
 
 
 # Import OpenVINO only when needed to avoid circular dependencies
@@ -115,21 +121,6 @@ class BaseModelRunner(ABC):
 class ONNXModelRunner(BaseModelRunner):
     """Run ONNX models using ONNX Runtime."""
 
-    @staticmethod
-    def is_cpu_complex_model(model_type: str) -> bool:
-        """Check if model needs basic optimization level to avoid graph fusion issues.
-
-        Some models (like Jina-CLIP) have issues with aggressive optimizations like
-        SimplifiedLayerNormFusion that create or expect nodes that don't exist.
-        """
-        # Import here to avoid circular imports
-        from frigate.embeddings.types import EnrichmentModelTypeEnum
-
-        return model_type in [
-            EnrichmentModelTypeEnum.jina_v1.value,
-            EnrichmentModelTypeEnum.jina_v2.value,
-        ]
-
     @staticmethod
     def is_migraphx_complex_model(model_type: str) -> bool:
         # Import here to avoid circular imports
@@ -208,15 +199,20 @@ class CudaGraphRunner(BaseModelRunner):
             EnrichmentModelTypeEnum.yolov9_license_plate.value,
         ]
 
+    # ORT performs two regular runs before it starts capturing, but on some
+    # driver / cuDNN combinations the arena still has to extend on the run that
+    # captures, and cudaMalloc is not allowed during capture. Running with
+    # capture disabled first keeps those allocations outside of the capture.
+    GRAPH_FREE_WARMUP_RUNS = 2
+
     def __init__(self, session: ort.InferenceSession, cuda_device_id: int):
         self._session = session
         self._cuda_device_id = cuda_device_id
-        self._captured = False
+        self._prepared = False
         self._io_binding: ort.IOBinding | None = None
         self._input_name: str | None = None
         self._output_names: list[str] | None = None
         self._input_ortvalue: ort.OrtValue | None = None
-        self._output_ortvalues: ort.OrtValue | None = None
 
     def get_input_names(self) -> list[str]:
         """Get input names for the model."""
@@ -226,35 +222,41 @@ class CudaGraphRunner(BaseModelRunner):
         """Get the input width of the model."""
         return self._session.get_inputs()[0].shape[3]
 
+    def _prepare(self, input_name: str, tensor_input: np.ndarray) -> None:
+        """Bind CUDA buffers and warm the session up with capture disabled."""
+        self._io_binding = self._session.io_binding()
+        self._input_name = input_name
+        self._output_names = [o.name for o in self._session.get_outputs()]
+
+        self._input_ortvalue = ort.OrtValue.ortvalue_from_numpy(
+            tensor_input, "cuda", self._cuda_device_id
+        )
+        self._io_binding.bind_ortvalue_input(self._input_name, self._input_ortvalue)
+
+        for name in self._output_names:
+            # Bind outputs to CUDA and allow ORT to allocate appropriately
+            self._io_binding.bind_output(name, "cuda", self._cuda_device_id)
+
+        # gpu_graph_id -1 disables capture and replay for the run
+        warmup_options = ort.RunOptions()
+        warmup_options.add_run_config_entry("gpu_graph_id", "-1")
+
+        for _ in range(self.GRAPH_FREE_WARMUP_RUNS):
+            self._session.run_with_iobinding(self._io_binding, warmup_options)
+
+        self._prepared = True
+
     def run(self, input: dict[str, Any]):
         # Extract the single tensor input (assuming one input)
         input_name = list(input.keys())[0]
-        tensor_input = input[input_name]
-        tensor_input = np.ascontiguousarray(tensor_input)
+        tensor_input = np.ascontiguousarray(input[input_name])
 
-        if not self._captured:
-            # Prepare IOBinding with CUDA buffers and let ORT allocate outputs on device
-            self._io_binding = self._session.io_binding()
-            self._input_name = input_name
-            self._output_names = [o.name for o in self._session.get_outputs()]
+        if not self._prepared:
+            self._prepare(input_name, tensor_input)
+        else:
+            # Replay using updated input
+            self._input_ortvalue.update_inplace(tensor_input)
 
-            self._input_ortvalue = ort.OrtValue.ortvalue_from_numpy(
-                tensor_input, "cuda", self._cuda_device_id
-            )
-            self._io_binding.bind_ortvalue_input(self._input_name, self._input_ortvalue)
-
-            for name in self._output_names:
-                # Bind outputs to CUDA and allow ORT to allocate appropriately
-                self._io_binding.bind_output(name, "cuda", self._cuda_device_id)
-
-            # First IOBinding run to allocate, execute, and capture CUDA Graph
-            ro = ort.RunOptions()
-            self._session.run_with_iobinding(self._io_binding, ro)
-            self._captured = True
-            return self._io_binding.copy_outputs_to_cpu()
-
-        # Replay using updated input, copy results to CPU
-        self._input_ortvalue.update_inplace(tensor_input)
         ro = ort.RunOptions()
         self._session.run_with_iobinding(self._io_binding, ro)
         return self._io_binding.copy_outputs_to_cpu()
@@ -323,6 +325,12 @@ class OpenVINOModelRunner(BaseModelRunner):
         if device in ["GPU", "AUTO", "NPU"]:
             self.ov_core.set_property(device, {"PERFORMANCE_HINT": "LATENCY"})
 
+        if device in ["GPU", "AUTO"]:
+            try:
+                self.ov_core.set_property("GPU", {"GPU_QUEUE_THROTTLE": "LOW"})
+            except Exception as e:
+                logger.debug(f"GPU_QUEUE_THROTTLE not supported: {e}")
+
         if device == "NPU" and OpenVINOModelRunner.is_detection_model(model_type):
             try:
                 self.ov_core.set_property(device, {"NPU_TURBO": "YES"})
@@ -626,9 +634,7 @@ def get_optimized_runner(
     return ONNXModelRunner(
         ort.InferenceSession(
             model_path,
-            sess_options=get_ort_session_options(
-                ONNXModelRunner.is_cpu_complex_model(model_type)
-            ),
+            sess_options=get_ort_session_options(model_type),
             providers=providers,
             provider_options=options,
         ),

frigate/detectors/plugins/degirum.py

@@ -1,157 +0,0 @@
-import logging
-import queue
-from typing import Literal
-
-import numpy as np
-from pydantic import ConfigDict, Field
-
-from frigate.detectors.detection_api import DetectionApi
-from frigate.detectors.detector_config import BaseDetectorConfig
-
-logger = logging.getLogger(__name__)
-DETECTOR_KEY = "degirum"
-
-
-### DETECTOR CONFIG ###
-class DGDetectorConfig(BaseDetectorConfig):
-    """DeGirum detector for running models via DeGirum cloud or local inference services."""
-
-    model_config = ConfigDict(
-        title="DeGirum",
-    )
-
-    type: Literal[DETECTOR_KEY]
-    location: str = Field(
-        default=None,
-        title="Inference Location",
-        description="Location of the DeGirim inference engine (e.g. '@cloud', '127.0.0.1').",
-    )
-    zoo: str = Field(
-        default=None,
-        title="Model Zoo",
-        description="Path or URL to the DeGirum model zoo.",
-    )
-    token: str = Field(
-        default=None,
-        title="DeGirum Cloud Token",
-        description="Token for DeGirum Cloud access.",
-    )
-
-
-### ACTUAL DETECTOR  ###
-class DGDetector(DetectionApi):
-    type_key = DETECTOR_KEY
-
-    def __init__(self, detector_config: DGDetectorConfig):
-        try:
-            import degirum as dg
-        except ModuleNotFoundError:
-            raise ImportError("Unable to import DeGirum detector.") from None
-
-        self._queue = queue.Queue()
-        self._zoo = dg.connect(
-            detector_config.location, detector_config.zoo, detector_config.token
-        )
-
-        logger.debug(f"Models in zoo: {self._zoo.list_models()}")
-
-        self.dg_model = self._zoo.load_model(
-            detector_config.model.path,
-        )
-
-        # Setting input image format to raw reduces preprocessing time
-        self.dg_model.input_image_format = "RAW"
-
-        # Prioritize the most powerful hardware available
-        self.select_best_device_type()
-        # Frigate handles pre processing as long as these are all set
-        input_shape = self.dg_model.input_shape[0]
-        self.model_height = input_shape[1]
-        self.model_width = input_shape[2]
-
-        # Passing in dummy frame so initial connection latency happens in
-        # init function and not during actual prediction
-        frame = np.zeros(
-            (detector_config.model.width, detector_config.model.height, 3),
-            dtype=np.uint8,
-        )
-        # Pass in frame to overcome first frame latency
-        self.dg_model(frame)
-        self.prediction = self.prediction_generator()
-
-    def select_best_device_type(self):
-        """
-        Helper function that selects fastest hardware available per model runtime
-        """
-        types = self.dg_model.supported_device_types
-
-        device_map = {
-            "OPENVINO": ["GPU", "NPU", "CPU"],
-            "HAILORT": ["HAILO8L", "HAILO8"],
-            "N2X": ["ORCA1", "CPU"],
-            "ONNX": ["VITIS_NPU", "CPU"],
-            "RKNN": ["RK3566", "RK3568", "RK3588"],
-            "TENSORRT": ["DLA", "GPU", "DLA_ONLY"],
-            "TFLITE": ["ARMNN", "EDGETPU", "CPU"],
-        }
-
-        runtime = types[0].split("/")[0]
-        # Just create an array of format {runtime}/{hardware} for every hardware
-        # in the value for appropriate key in device_map
-        self.dg_model.device_type = [
-            f"{runtime}/{hardware}" for hardware in device_map[runtime]
-        ]
-
-    def prediction_generator(self):
-        """
-        Generator for all incoming frames. By using this generator, we don't have to keep
-        reconnecting our websocket on every "predict" call.
-        """
-        logger.debug("Prediction generator was called")
-        with self.dg_model as model:
-            while 1:
-                logger.info(f"q size before calling get: {self._queue.qsize()}")
-                data = self._queue.get(block=True)
-                logger.info(f"q size after calling get: {self._queue.qsize()}")
-                logger.debug(
-                    f"Data we're passing into model predict: {data}, shape of data: {data.shape}"
-                )
-                result = model.predict(data)
-                logger.debug(f"Prediction result: {result}")
-                yield result
-
-    def detect_raw(self, tensor_input):
-        # Reshaping tensor to work with pysdk
-        truncated_input = tensor_input.reshape(tensor_input.shape[1:])
-        logger.debug(f"Detect raw was called for tensor input: {tensor_input}")
-
-        # add tensor_input to input queue
-        self._queue.put(truncated_input)
-        logger.debug(f"Queue size after adding truncated input: {self._queue.qsize()}")
-
-        # define empty detection result
-        detections = np.zeros((20, 6), np.float32)
-        # grab prediction
-        res = next(self.prediction)
-
-        # If we have an empty prediction, return immediately
-        if len(res.results) == 0 or len(res.results[0]) == 0:
-            return detections
-
-        i = 0
-        for result in res.results:
-            if i >= 20:
-                break
-
-            detections[i] = [
-                result["category_id"],
-                float(result["score"]),
-                result["bbox"][1] / self.model_height,
-                result["bbox"][0] / self.model_width,
-                result["bbox"][3] / self.model_height,
-                result["bbox"][2] / self.model_width,
-            ]
-            i += 1
-
-        logger.debug(f"Detections output: {detections}")
-        return detections

frigate/detectors/plugins/edgetpu_tfl.py

@@ -9,6 +9,7 @@ from pydantic import ConfigDict, Field
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
+from frigate.util.model import xyxy_to_xywh_for_nms
 
 try:
     from tflite_runtime.interpreter import Interpreter, load_delegate
@@ -297,7 +298,7 @@ class EdgeTpuTfl(DetectionApi):
             # until after filtering out redundant boxes
             # Shift the logit scores to be non-negative (required by cv2)
             indices = cv2.dnn.NMSBoxes(
-                bboxes=boxes_filtered_decoded,
+                bboxes=xyxy_to_xywh_for_nms(boxes_filtered_decoded),
                 scores=max_scores_filtered_shiftedpositive,
                 score_threshold=(
                     self.min_logit_value + self.logit_shift_to_positive_values

frigate/detectors/plugins/memryx.py

@@ -17,6 +17,7 @@ from frigate.detectors.detector_config import (
     ModelTypeEnum,
 )
 from frigate.util.file import FileLock
+from frigate.util.model import xyxy_to_xywh_for_nms
 
 logger = logging.getLogger(__name__)
 
@@ -581,7 +582,7 @@ class MemryXDetector(DetectionApi):
             # Convert coordinates to integers
             x_min, y_min, x_max, y_max = map(int, [x_min, y_min, x_max, y_max])
 
-            # Append valid detections [class_id, confidence, x, y, width, height]
+            # Append valid detections [class_id, confidence, x_min, y_min, x_max, y_max]
             detections.append([class_id, confidence, x_min, y_min, x_max, y_max])
 
         final_detections = np.zeros((20, 6), np.float32)
@@ -595,7 +596,7 @@ class MemryXDetector(DetectionApi):
         detections = np.array(detections, dtype=np.float32)
 
         # Apply Non-Maximum Suppression (NMS)
-        bboxes = detections[:, 2:6].tolist()  # (x_min, y_min, width, height)
+        bboxes = xyxy_to_xywh_for_nms(detections[:, 2:6])
         scores = detections[:, 1].tolist()  # Confidence scores
 
         indices = cv2.dnn.NMSBoxes(bboxes, scores, 0.45, 0.5)

frigate/detectors/plugins/openvino.py

@@ -226,12 +226,12 @@ class OvDetector(DetectionApi):
 
             conf_mask = (image_pred[:, 4] * class_conf.squeeze() >= 0.3).squeeze()
             # Detections ordered as (x1, y1, x2, y2, obj_conf, class_conf, class_pred)
-            detections = np.concatenate(
+            predictions = np.concatenate(
                 (image_pred[:, :5], class_conf, class_pred), axis=1
             )
-            detections = detections[conf_mask]
+            predictions = predictions[conf_mask]
 
-            ordered = detections[detections[:, 5].argsort()[::-1]][:20]
+            ordered = predictions[predictions[:, 5].argsort()[::-1]][:20]
 
             for i, object_detected in enumerate(ordered):
                 detections[i] = self.process_yolo(

frigate/detectors/plugins/rknn.py

@@ -12,7 +12,7 @@ from frigate.const import MODEL_CACHE_DIR, SUPPORTED_RK_SOCS
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detection_runners import RKNNModelRunner
 from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
-from frigate.util.model import post_process_yolo
+from frigate.util.model import post_process_yolo, xyxy_to_xywh_for_nms
 from frigate.util.rknn_converter import auto_convert_model
 
 logger = logging.getLogger(__name__)
@@ -285,7 +285,7 @@ class Rknn(DetectionApi):
 
         # run nms
         indices = cv2.dnn.NMSBoxes(
-            bboxes=boxes,
+            bboxes=xyxy_to_xywh_for_nms(boxes),
             scores=scores,
             score_threshold=0.4,
             nms_threshold=0.4,

frigate/embeddings/__init__.py

@@ -21,6 +21,7 @@ from frigate.db.sqlitevecq import SqliteVecQueueDatabase
 from frigate.models import Event
 from frigate.util.builtin import serialize
 from frigate.util.classification import kickoff_model_training
+from frigate.util.path import safe_join
 from frigate.util.process import FrigateProcess
 
 from .maintainer import EmbeddingMaintainer
@@ -33,7 +34,7 @@ class EmbeddingProcess(FrigateProcess):
     def __init__(
         self,
         config: FrigateConfig,
-        metrics: DataProcessorMetrics | None,
+        metrics: DataProcessorMetrics,
         stop_event: MpEvent,
     ) -> None:
         super().__init__(
@@ -234,11 +235,16 @@ class EmbeddingsContext:
         )
 
     def delete_face_ids(self, face: str, ids: list[str]) -> None:
-        folder = os.path.join(FACE_DIR, face)
-        for id in ids:
-            file_path = os.path.join(folder, id)
+        folder = safe_join(FACE_DIR, face)
 
-            if os.path.isfile(file_path):
+        if folder is None:
+            logger.warning("Not deleting faces for invalid name %s", face)
+            return
+
+        for id in ids:
+            file_path = safe_join(folder, id)
+
+            if file_path and os.path.isfile(file_path):
                 os.unlink(file_path)
 
         if face != "train" and len(os.listdir(folder)) == 0:

frigate/embeddings/embeddings.py

@@ -6,9 +6,10 @@ import logging
 import os
 import threading
 import time
+from typing import Any
 
 import numpy as np
-from peewee import DoesNotExist, IntegrityError
+from peewee import DatabaseError, DoesNotExist, IntegrityError
 from PIL import Image
 from playhouse.shortcuts import model_to_dict
 
@@ -207,12 +208,10 @@ class Embeddings:
         embedding = self.vision_embedding([thumbnail])[0]
 
         if upsert:
-            self.db.execute_sql(
-                """
-                INSERT OR REPLACE INTO vec_thumbnails(id, thumbnail_embedding)
-                VALUES(?, ?)
-                """,
-                (event_id, serialize(embedding)),
+            self.db.upsert_embeddings(
+                "vec_thumbnails",
+                "thumbnail_embedding",
+                {event_id: serialize(embedding)},
             )
 
         self.image_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -251,19 +250,12 @@ class Embeddings:
         embeddings = self.vision_embedding(valid_thumbs)
 
         if upsert:
-            items = []
+            items = {}
             for i in range(len(valid_ids)):
-                items.append(valid_ids[i])
-                items.append(serialize(embeddings[i]))
+                items[valid_ids[i]] = serialize(embeddings[i])
                 self.image_eps.update()
 
-            self.db.execute_sql(
-                """
-                INSERT OR REPLACE INTO vec_thumbnails(id, thumbnail_embedding)
-                VALUES {}
-                """.format(", ".join(["(?, ?)"] * len(valid_ids))),
-                items,
-            )
+            self.db.upsert_embeddings("vec_thumbnails", "thumbnail_embedding", items)
 
         duration = datetime.datetime.now().timestamp() - start
         self.image_inference_speed.update(duration / len(valid_ids))
@@ -277,12 +269,10 @@ class Embeddings:
         embedding = self.text_embedding([description])[0]
 
         if upsert:
-            self.db.execute_sql(
-                """
-                INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
-                VALUES(?, ?)
-                """,
-                (event_id, serialize(embedding)),
+            self.db.upsert_embeddings(
+                "vec_descriptions",
+                "description_embedding",
+                {event_id: serialize(embedding)},
             )
 
         self.text_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -302,19 +292,14 @@ class Embeddings:
 
         if upsert:
             ids = list(event_descriptions.keys())
-            items = []
+            items = {}
 
             for i in range(len(ids)):
-                items.append(ids[i])
-                items.append(serialize(embeddings[i]))
+                items[ids[i]] = serialize(embeddings[i])
                 self.text_eps.update()
 
-            self.db.execute_sql(
-                """
-                INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
-                VALUES {}
-                """.format(", ".join(["(?, ?)"] * len(ids))),
-                items,
+            self.db.upsert_embeddings(
+                "vec_descriptions", "description_embedding", items
             )
 
         self.text_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -322,6 +307,17 @@ class Embeddings:
         return embeddings
 
     def reindex(self) -> None:
+        """Rebuild every tracked object embedding from scratch."""
+        totals: dict[str, Any] = {"status": "indexing"}
+
+        try:
+            self._reindex(totals)
+        except DatabaseError:
+            logger.exception("Unable to reindex tracked object embeddings")
+            totals["status"] = "failed"
+            self.requestor.send_data(UPDATE_EMBEDDINGS_REINDEX_PROGRESS, totals)
+
+    def _reindex(self, totals: dict[str, Any]) -> None:
         logger.info("Indexing tracked object embeddings...")
 
         self.db.drop_embeddings_tables()
@@ -346,14 +342,18 @@ class Embeddings:
             batch_size = 32
         current_page = 1
 
-        totals = {
-            "thumbnails": 0,
-            "descriptions": 0,
-            "processed_objects": total_events - 1 if total_events < batch_size else 0,
-            "total_objects": total_events,
-            "time_remaining": 0 if total_events < batch_size else -1,
-            "status": "indexing",
-        }
+        totals.update(
+            {
+                "thumbnails": 0,
+                "descriptions": 0,
+                "processed_objects": total_events - 1
+                if total_events < batch_size
+                else 0,
+                "total_objects": total_events,
+                "time_remaining": 0 if total_events < batch_size else -1,
+                "status": "indexing",
+            }
+        )
 
         self.requestor.send_data(UPDATE_EMBEDDINGS_REINDEX_PROGRESS, totals)
 

frigate/embeddings/maintainer.py

@@ -78,6 +78,16 @@ logger = logging.getLogger(__name__)
 
 MAX_THUMBNAILS = 10
 
+GENAI_UPDATE_TOPICS = frozenset(
+    {
+        CameraConfigUpdateEnum.add.name,
+        CameraConfigUpdateEnum.objects.name,
+        CameraConfigUpdateEnum.object_genai.name,
+        CameraConfigUpdateEnum.review.name,
+        CameraConfigUpdateEnum.review_genai.name,
+    }
+)
+
 
 class EmbeddingMaintainer(threading.Thread):
     """Handle embedding queue and post event updates."""
@@ -85,7 +95,7 @@ class EmbeddingMaintainer(threading.Thread):
     def __init__(
         self,
         config: FrigateConfig,
-        metrics: DataProcessorMetrics | None,
+        metrics: DataProcessorMetrics,
         stop_event: MpEvent,
     ) -> None:
         super().__init__(name="embeddings_maintainer")
@@ -220,16 +230,6 @@ class EmbeddingMaintainer(threading.Thread):
         # post processors
         self.post_processors: list[PostProcessorApi] = []
 
-        if any(c.review.genai.enabled_in_config for c in self.config.cameras.values()):
-            self.post_processors.append(
-                ReviewDescriptionProcessor(
-                    self.config,
-                    self.requestor,
-                    self.metrics,
-                    self.genai_manager,
-                )
-            )
-
         if self.config.lpr.enabled:
             self.post_processors.append(
                 LicensePlatePostProcessor(
@@ -252,9 +252,9 @@ class EmbeddingMaintainer(threading.Thread):
                 )
             )
 
-        semantic_trigger_processor: SemanticTriggerProcessor | None = None
+        self.semantic_trigger_processor: SemanticTriggerProcessor | None = None
         if self.config.semantic_search.enabled:
-            semantic_trigger_processor = SemanticTriggerProcessor(
+            self.semantic_trigger_processor = SemanticTriggerProcessor(
                 db,
                 self.config,
                 self.requestor,
@@ -262,9 +262,49 @@ class EmbeddingMaintainer(threading.Thread):
                 metrics,
                 self.embeddings,
             )
-            self.post_processors.append(semantic_trigger_processor)
+            self.post_processors.append(self.semantic_trigger_processor)
 
-        if any(c.objects.genai.enabled_in_config for c in self.config.cameras.values()):
+        self._sync_genai_processors()
+
+        self.stop_event = stop_event
+
+        # recordings data
+        self.recordings_available_through: dict[str, float] = {}
+
+    def _sync_genai_processors(self) -> None:
+        """Create GenAI post processors for cameras that have GenAI enabled.
+
+        Called at startup and again after camera config updates so enabling
+        GenAI on the first camera does not require a restart. Processors are
+        never removed once created.
+
+        A profile can turn GenAI on without setting enabled_in_config, so both
+        flags are checked.
+        """
+        cameras = self.config.cameras.values()
+
+        if any(
+            c.review.genai.enabled or c.review.genai.enabled_in_config for c in cameras
+        ) and not any(
+            isinstance(p, ReviewDescriptionProcessor) for p in self.post_processors
+        ):
+            logger.debug("Initializing review description processor")
+            self.post_processors.append(
+                ReviewDescriptionProcessor(
+                    self.config,
+                    self.requestor,
+                    self.metrics,
+                    self.genai_manager,
+                )
+            )
+
+        if any(
+            c.objects.genai.enabled or c.objects.genai.enabled_in_config
+            for c in cameras
+        ) and not any(
+            isinstance(p, ObjectDescriptionProcessor) for p in self.post_processors
+        ):
+            logger.debug("Initializing object description processor")
             self.post_processors.append(
                 ObjectDescriptionProcessor(
                     self.config,
@@ -272,19 +312,21 @@ class EmbeddingMaintainer(threading.Thread):
                     self.requestor,
                     self.metrics,
                     self.genai_manager,
-                    semantic_trigger_processor,
+                    self.semantic_trigger_processor,
                 )
             )
 
-        self.stop_event = stop_event
+    def _check_camera_config_updates(self) -> None:
+        """Apply camera config updates and register newly enabled processors."""
+        updated_topics = self.config_updater.check_for_updates()
 
-        # recordings data
-        self.recordings_available_through: dict[str, float] = {}
+        if updated_topics.keys() & GENAI_UPDATE_TOPICS:
+            self._sync_genai_processors()
 
     def run(self) -> None:
         """Maintain a SQLite-vec database for semantic search."""
         while not self.stop_event.is_set():
-            self.config_updater.check_for_updates()
+            self._check_camera_config_updates()
             self._check_enrichment_config_updates()
             self._process_requests()
             self._process_updates()

frigate/ffmpeg_presets.py

@@ -121,8 +121,8 @@ PRESETS_HW_ACCEL_SCALE = {
     "preset-rpi-64-h264": "-r {0} -vf fps={0},scale={1}:{2}",
     "preset-rpi-64-h265": "-r {0} -vf fps={0},scale={1}:{2}",
     FFMPEG_HWACCEL_VAAPI: "-r {0} -vf fps={0},scale_vaapi=w={1}:h={2},hwdownload,format=nv12",
-    "preset-intel-qsv-h264": "-r {0} -vf vpp_qsv=w={1}:h={2}:format=nv12,hwdownload,format=nv12,fps={0},format=yuv420p",
-    "preset-intel-qsv-h265": "-r {0} -vf vpp_qsv=w={1}:h={2}:format=nv12,hwdownload,format=nv12,fps={0},format=yuv420p",
+    "preset-intel-qsv-h264": "-r {0} -vf fps={0},vpp_qsv=w={1}:h={2}:format=nv12,hwdownload,format=nv12,format=yuv420p",
+    "preset-intel-qsv-h265": "-r {0} -vf fps={0},vpp_qsv=w={1}:h={2}:format=nv12,hwdownload,format=nv12,format=yuv420p",
     FFMPEG_HWACCEL_NVIDIA: "-r {0} -vf fps={0},scale_cuda=w={1}:h={2},hwdownload,format=nv12",
     "preset-jetson-h264": "-r {0}",  # scaled in decoder
     "preset-jetson-h265": "-r {0}",  # scaled in decoder

frigate/genai/__init__.py

@@ -23,6 +23,7 @@ from frigate.genai.prompts import (
     build_review_summary_prompt,
 )
 from frigate.models import Event
+from frigate.util.builtin import has_non_finite_number
 
 logger = logging.getLogger(__name__)
 
@@ -164,6 +165,15 @@ class GenAIClient:
                 except json.JSONDecodeError as je:
                     logger.error("Failed to parse review description JSON: %s", je)
                     return None
+
+                # model_construct skips validation, so non-finite numbers that
+                # the validated path would have rejected have to be caught here
+                if has_non_finite_number(raw):
+                    logger.error(
+                        "Discarding review description containing non-finite numbers."
+                    )
+                    return None
+
                 # observations and confidence are required on the model; fill an empty default
                 # if the response omitted it so attribute access stays safe.
                 raw.setdefault("observations", [])

frigate/genai/plugins/gemini.py

@@ -245,7 +245,7 @@ class GeminiClient(GenAIClient):
                     )
                     gemini_messages.append(
                         types.Content(
-                            role="function",
+                            role="user",
                             parts=[
                                 types.Part.from_function_response(
                                     name=msg.get("name")
@@ -501,7 +501,7 @@ class GeminiClient(GenAIClient):
                     )
                     gemini_messages.append(
                         types.Content(
-                            role="function",
+                            role="user",
                             parts=[
                                 types.Part.from_function_response(
                                     name=msg.get("name")

frigate/jobs/debug_replay.py

@@ -115,6 +115,10 @@ def query_recordings(source_camera: str, start_ts: float, end_ts: float) -> Mode
     return cast(ModelSelect, query)
 
 
+class NoRecordingsError(ValueError):
+    """Raised when no recordings exist in the requested time range."""
+
+
 class DebugReplaySource(ABC):
     """Abstract source for a debug replay session.
 
@@ -187,7 +191,7 @@ class RecordingDebugReplaySource(DebugReplaySource):
             raise ValueError("End time must be after start time")
 
         if not query_recordings(self._camera, self._start_ts, self._end_ts).count():
-            raise ValueError(
+            raise NoRecordingsError(
                 f"No recordings found for camera '{self._camera}' in the specified time range"
             )
 

frigate/output/output.py

@@ -178,13 +178,10 @@ class OutputProcess(FrigateProcess):
             )
 
             if update_topic is not None and birdseye_config is not None:
-                previous_global_mode = self.config.birdseye.mode
+                # only the global-only fields are applied here; the per-camera
+                # enabled and mode arrive on config/cameras/<name>/birdseye,
+                # already resolved against yaml by the config parse
                 self.config.birdseye = birdseye_config
-
-                for camera_config in self.config.cameras.values():
-                    if camera_config.birdseye.mode == previous_global_mode:
-                        camera_config.birdseye.mode = birdseye_config.mode
-
                 logger.debug("Applied dynamic birdseye config update")
 
             # check if there is an updated config