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.db filegenai config now only configures the provider. Other fields have moved under objects -> genai. See the new GenAI documentation.record -> continuous and record -> motion are separate config fields. See the examples in the documentation.small model, which performs well on both CPU and GPU. The large model is the same as 0.16's and is not as accurate as the upgraded small model in 0.17. Use large only if you live in a region with multi-line plates and you are having issues detecting text on them with the small model.detect resolution width and height for cameras in your config if Frigate hangs on startup.exec, expr, and echo sources 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 variable GO2RTC_ALLOW_ARBITRARY_EXEC A separate configuration for this for HA addon users will come in a later beta. See the documentation.Frigate 0.17 introduces several major new features.
Frigate 0.17 supports classification models in two separate types: state classification and object classification. These models are trained locally on your machine using ImageNet via MobileNetV2.
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 allows you to choose an object type, like dog, and classify specific dogs. For example, you can train the model to classify your dog Fido and 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.
Frigate 0.17 now has the ability to create additional viewer user roles to limit access to specific cameras. Users with the admin role can create a uniquely named role from the UI (or auth --> roles in the config) and assign at least one camera to it. Users assigned to the new role will have:
viewer role has access to (Live, Review/History, Explore, Exports), but only to the assigned camerasSee the documentation.
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.
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.
Frigate 0.17 brings performance increases for many detectors as well as support for new object detection hardware.
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.
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.
Frigate 0.17 brings several improvements to RKNN platform including:
large model sizes.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.
Frigate 0.17 supports running a quantized version of YOLOv9 on Coral devices, bringing improved accuracy over the default mobiledet model. Note that due to hardware limitations, only a subset of the objects on the standard COCO labelmap is included. Frigate+ has also added support for YOLOv9 models on the Google Coral and includes support for all 41 Frigate+ labels.
See the documentation.
Frigate 0.17 has community support for several new object detectors:
In addition to supporting the new features, the frontend has many improvements.
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.
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.
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.
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 --> Management page in Settings.
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.
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.
Frigate 0.17 supports fully local audio transcription using either sherpa-onnx or faster-whisper. The single camera Live view in the Frigate UI supports live transcription of audio for streams defined with the audio role, and any speech events in Explore can be transcribed and/or translated through the Transcribe button in the Tracked Object Details pane.
See the documentation.
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 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 alert for arriving home) finishes. Ongoing lower-severity motion (e.g., passing cars) will no longer keep the higher-severity review item alive. In these cases, the alert ends and a new detection review item begins immediately.
-, , etc. to ensure that plates are more consistently recognized as the same plate. Documentationdevice config option. This is useful in cases when multiple GPUs are available. Documentationnetworking -> ipv6 -> enabled. DocumentationThere 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.db filegenai config now only configures the provider. Other fields have moved under objects -> genai. See the new GenAI documentation.record -> continuous and record -> motion are separate config fields. See the examples in the documentation.small model, which performs well on both CPU and GPU. The large model is the same as 0.16's and is not as accurate as the upgraded small model in 0.17. Use large only if you live in a region with multi-line plates and you are having issues detecting text on them with the small model.detect resolution width and height for cameras in your config if Frigate hangs on startup.exec, expr, and echo sources 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 variable GO2RTC_ALLOW_ARBITRARY_EXEC A separate configuration for this for HA addon users will come in a later beta. See the documentation.Frigate 0.17 introduces several major new features.
Frigate 0.17 supports classification models in two separate types: state classification and object classification. These models are trained locally on your machine using ImageNet via MobileNetV2.
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 allows you to choose an object type, like dog, and classify specific dogs. For example, you can train the model to classify your dog Fido and 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.
Frigate 0.17 now has the ability to create additional viewer user roles to limit access to specific cameras. Users with the admin role can create a uniquely named role from the UI (or auth --> roles in the config) and assign at least one camera to it. Users assigned to the new role will have:
viewer role has access to (Live, Review/History, Explore, Exports), but only to the assigned camerasSee the documentation.
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.
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.
Frigate 0.17 brings performance increases for many detectors as well as support for new object detection hardware.
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.
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.
Frigate 0.17 brings several improvements to RKNN platform including:
large model sizes.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.
Frigate 0.17 supports running a quantized version of YOLOv9 on Coral devices, bringing improved accuracy over the default mobiledet model. Note that due to hardware limitations, only a subset of the objects on the standard COCO labelmap is included. Frigate+ has also added support for YOLOv9 models on the Google Coral and includes support for all 41 Frigate+ labels.
See the documentation.
Frigate 0.17 has community support for several new object detectors:
In addition to supporting the new features, the frontend has many improvements.
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.
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.
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.
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 --> Management page in Settings.
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.
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.
Frigate 0.17 supports fully local audio transcription using either sherpa-onnx or faster-whisper. The single camera Live view in the Frigate UI supports live transcription of audio for streams defined with the audio role, and any speech events in Explore can be transcribed and/or translated through the Transcribe button in the Tracked Object Details pane.
See the documentation.
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 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 alert for arriving home) finishes. Ongoing lower-severity motion (e.g., passing cars) will no longer keep the higher-severity review item alive. In these cases, the alert ends and a new detection review item begins immediately.
-, , etc. to ensure that plates are more consistently recognized as the same plate. Documentationdevice config option. This is useful in cases when multiple GPUs are available. Documentationnetworking -> ipv6 -> enabled. Documentation