0.17.0 Release Candidate 2 #22015
blakeblackshear
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0.17.0 Release Candidate 2
#22015
Replies: 5 comments 15 replies
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Grammar and/or translation error in the German GUI: 
I guess: -Heimo |
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All translations for Frigate are managed via Weblate by other contributors. Feel free to sign up for an account and contribute any changes you feel are necessary. |
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Hey all, For Classification, in order to train a model, I always need to open my firewall for Frigate. Is there a possibility to cache the model ? log:
With Huggingface, we have this option in docker-compose file: |
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My sincere thanks for the increasingly better and more functional versions. It's incredible work, and I contribute whenever possible. My question isn't necessarily a problem. Perhaps it's a difficulty in understanding the documentation. I'm configuring yolov9 to use with Coral, as the new version allows. I downloaded the model as indicated in the link, saving it first in /config, and later also in /config/model_cache. I adjusted the config.yaml to indicate the model as indicated in the documentation, adjusting the path. Here's my question: the documentation mentions "frigate-fp16-yolov9-t" (1) in the path, but the downloaded file is "yolov9-t-320.onnx" (2). When I specify 1, I get the error: When I specify 2, I get the error: I would just like to understand the documentation and correct what I'm doing wrong. My config.yaml: My docker-compose.yaml: Thank you! |
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3 replies
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You are looking at the wrong spot in the documentation. You need to look under the section for the Coral here: https://docs-dev.frigate.video/configuration/object_detectors#edgetpu-supported-models |
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Thank you. I'll review my settings. I was misled by this link: |
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Thanks, we'll update that link in the release notes. |
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Hi, in the rc2 version, the issue with object classification still persists — attributes cannot be saved properly, and PR #22000 did not fully fix this problem. I wonder if anyone else has encountered this situation? |
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10 replies
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I've updated it, but right now it's still early morning here, so there are no identifiable targets. I'll report the test results during the day. |
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I just tested with my cat, and it seems the attribute can be saved correctly. However, I haven't tested the result for person yet, as there is currently no one passing by who needs to be recognized, and I have configured two different person classification models. |
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Thanks for testing. |
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After testing, it turns out to be browser caching. Could we consider assigning a cache key to the snapshots as well, to avoid issues where ongoing tracked objects aren't updated promptly in the final snapshot due to browser caching? |
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We already set the snapshot to have cache headers |
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I know you guys have your hands full. The version of ffmpeg shipped with this container (there are multiples it seems, 5.0 and 7.0) are missing drawtext filter functionality. So there is no chance of adding a timestamp or custom labels to camera feeds (i know, addl resources blah blah). Here's the make like for ffmpeg to include all proper libraries for drawtext filter to work along with other frigate req libs to update it if someone gets a chance. TIA PATH="$HOME/bin:$PATH" PKG_CONFIG_PATH="$HOME/ffmpeg_build/lib/pkgconfig" ./configure --prefix="$HOME/ffmpeg_build" --pkg-config-flags="--static" --extra-cflags="-I$HOME/ffmpeg_build/include" --extra-ldflags="-L$HOME/ffmpeg_build/lib" --bindir="$HOME/bin" --enable-gpl --enable-libx264 --enable-libx265 --enable-libvpx --enable-libfdk-aac --enable-libmp3lame --enable-libopus --enable-libfreetype --enable-libfontconfig --enable-nonfree --enable-static --disable-shared --disable-debug --enable-libharfbuzz --enable-libfontconfig --enable-libfribidi |
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Multiple users have run ffmpeg with adding text overlay so I am not sure that is correct, but in any case you can run whatever ffmpeg binary you want. https://docs.frigate.video/configuration/advanced#custom-ffmpeg-build |
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Beta docs: https://9eaa7bfe.frigate.pages.dev/
Images
Changes since RC1
Full changelog
Major Changes for 0.17.0
Breaking Changes
There are several breaking changes in this release, Frigate will attempt to update the configuration automatically. In some cases manual changes may be required. It is always recommended to back up your current config and database before upgrading:
frigate.dbfilegenaiconfig now only configures the provider. Other fields have moved underobjects -> genai. See the new GenAI documentation.record -> continuousandrecord -> motionare separate config fields. See the examples in the documentation.smallmodel, which performs well on both CPU and GPU. Thelargemodel is the same as 0.16's and is not as accurate as the upgradedsmallmodel in 0.17. Uselargeonly if you live in a region with multi-line plates and you are having issues detecting text on them with thesmallmodel.detectresolutionwidthandheightfor cameras in your config if Frigate hangs on startup.exec,expr, andechosources for go2rtc are now removed by default to reduce the security risk if an attacker has access to the configuration. This can be disabled using an environment variableGO2RTC_ALLOW_ARBITRARY_EXECA separate configuration for this for HA addon users will come in a later beta. See the documentation.New Features
Frigate 0.17 introduces several major new features.
Classification Model Training
Frigate 0.17 supports classification models in two separate types: state classification and object classification. These models are trained locally on your machine using
ImageNetviaMobileNetV2.State Classification
State classification allows you to choose a certain region of camera(s) with multiple states, and train on images showing these states. For example, you could create a state classification model to determine if a gate is currently open or closed.
See the documentation.
Object Classification
Object classification allows you to choose an object type, like
dog, and classify specific dogs. For example, you can train the model to classify your dogFidoand add a sub label, while not labeling unknown dogs. Another example would be classifying if a person in a construction site is wearing a helmet or not.See the documentation.
Custom Viewer Roles
Frigate 0.17 now has the ability to create additional viewer user roles to limit access to specific cameras. Users with the
adminrole can create a uniquely named role from the UI (orauth --> rolesin the config) and assign at least one camera to it. Users assigned to the new role will have:viewerrole has access to (Live, Review/History, Explore, Exports), but only to the assigned camerasSee the documentation.
Review Item Summary with GenAI
Frigate 0.17 supports using GenAI to summarize review items. Unlike object descriptions which add a searchable description, review summaries have a structured output that instruct the AI provider to generate a title, description, and classify the activity as dangerous, suspicious, or normal.
This information is displayed in the UI automatically making it easier to see when activity requires further review and easier to understand what is happening during a particular video segment.
See the documentation.
Semantic Search Triggers
Triggers utilize Semantic Search to automate actions when a tracked object matches a specified image or description. Triggers can be configured so that Frigate executes a specific actions when a tracked object's image or description matches a predefined image or text, based on a similarity threshold. Triggers are managed per camera and can be configured via the Frigate UI in the Settings page under the Triggers tab.
See the documentation.
Object Detector Improvements
Frigate 0.17 brings performance increases for many detectors as well as support for new object detection hardware.
Nvidia GPU Performance
Support for Nvidia GPUs has been enhanced by implementing CUDA Graphs. CUDA Graphs work to reduce the involvement of the CPU for each inference, leading to faster inference times and lower CPU usage. CUDA graphs do have some limitations based on the complexity of the model, which means that YOLO-NAS, Semantic Search, and LPR models are not accelerated with CUDA Graphs. They will still continue to run on GPU as they did before.
Intel OpenVINO
Frigate 0.17 supports running models on Intel NPUs, for many models performance on NPU is similar to GPU but more efficient, leaving room to run more enrichment features on the GPU.
OpenVINO has also had many optimizations put in place to reduce memory and CPU utilization for object detection.
RKNN
Frigate 0.17 brings several improvements to RKNN platform including:
largemodel sizes.Apple Silicon
Frigate 0.17 supports running object detection on Apple Silicon NPU. This is provided through the Apple Silicon Detector which runs on the host and connects via IPC proxy to Frigate, providing fast and efficient inferences when run within the same Apple device.
See the documentation.
YOLOv9 on Google Coral
Frigate 0.17 supports running a quantized version of
YOLOv9on Coral devices, bringing improved accuracy over the defaultmobiledetmodel. Note that due to hardware limitations, only a subset of the objects on the standard COCO labelmap is included. YOLOv9 Frigate+ models are not supported on Coral at this time.See the documentation.
New Community Supported Detectors
Frigate 0.17 has community support for several new object detectors:
Frontend Improvements
In addition to supporting the new features, the frontend has many improvements.
Detail Stream
History view in 0.17 supports an additional view mode, Detail. This mode shows a card for each review item, and expanding a card reveals all tracked objects and their lifecycle events. Selecting any lifecycle event seeks the video to that exact timestamp. You can also overlay a tracked object's path on the video to help with debugging.
Redesigned Tracked Object Details pane
The Tracked Object Details pane in Explore has been redesigned to streamline the layout and consolidate related information. The Object Lifecycle tab is now the Tracking Details tab, which displays video overlays of the tracked object instead of static images, giving a clearer and more intuitive view of its activity.
Revamped Settings
Frigate 0.17 has a revamped Settings menu with a sidebar that categorizes the available options. This brings more scalability which will make it easier to support full UI configuration in a future version.
NOTE: The Debug view has been moved to the single camera Live view instead of Settings. Access the Debug view by enabling the switch under the Live view settings (cog icon) menu.
Add Camera Wizard
Frigate 0.17 supports adding camera via the UI without manually modifying your configuration file. When installing and starting Frigate for the first time, the main dashboard will include a button to start adding cameras via the Wizard.
Access the Wizard from the
Cameras --> Managementpage in Settings.Update Without Restarting
Frigate 0.17 supports saving many more features dynamically. Cameras, zones, and masks will not require a restart to take effect when saved through the UI. More will come in future versions.
Configuration Safe Mode
If an invalid configuration is detected, Frigate will enter safe mode and highlight the location of the issue. While in safe mode, the frontend is limited to the configuration editor, making it easy to correct the problem directly in the UI without needing an external file editor.
Other Notable Frontend Improvements
Other Backend Features and Improvements
Audio Transcription and Analysis
Frigate 0.17 supports fully local audio transcription using either
sherpa-onnxorfaster-whisper. The single camera Live view in the Frigate UI supports live transcription of audio for streams defined with the audio role, and anyspeechevents in Explore can be transcribed and/or translated through the Transcribe button in the Tracked Object Details pane.See the documentation.
Process and Efficiency Improvements
Frigate 0.17 uses the forkserver spawn method, this allows for better segmented memory control and better process management. Some processes are also started with lower priority, allowing the most important processes to have more CPU time when it is required.
Review Item Improvements
Review items have been refined to behave more intuitively:
Revamped stationary object tracking. Stationary object tracking has been enhanced to use new features to reduce incorrectly marking objects as active:
Smarter handling of loitering objects. Stationary behavior is now dynamic based on object type. Objects that are normally stationary for long periods (e.g., cars) will no longer keep a review item active indefinitely when stopped inside a loitering zone. Objects that are not expected to remain still (e.g., people) will continue the review item as long as they stay within the zone.
Severity-based review item cutoff. Review items now end when a higher-severity event (such as an
alertfor arriving home) finishes. Ongoing lower-severity motion (e.g., passing cars) will no longer keep the higher-severity review item alive. In these cases, thealertends and a newdetectionreview item begins immediately.Enrichment Improvements
-,deviceconfig option. This is useful in cases when multiple GPUs are available. DocumentationOther Improvements
networking -> ipv6 -> enabled. DocumentationThis discussion was created from the release 0.17.0 Release Candidate 2.
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