frigate/docker/tensorrt/requirements-models-arm64.txt

onnx == 1.15.0; platform_machine == 'aarch64'
protobuf == 3.20.3; platform_machine == 'aarch64'
numpy == 1.23.*; platform_machine == 'aarch64'  # required by python-tensorrt 8.2.1 (Jetpack 4.6)
Bump onnx from 1.14.0 to 1.15.0 in /docker/tensorrt Bumps [onnx](https://github.com/onnx/onnx) from 1.14.0 to 1.15.0. - [Release notes](https://github.com/onnx/onnx/releases) - [Changelog](https://github.com/onnx/onnx/blob/main/docs/Changelog.md) - [Commits](https://github.com/onnx/onnx/compare/v1.14.0...v1.15.0) --- updated-dependencies: - dependency-name: onnx dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com> 2023-11-07 14:53:31 +03:00			`onnx == 1.15.0; platform_machine == 'aarch64'`
Nvidia Jetson ffmpeg + TensorRT support (#6458) * Non-Jetson changes Required for later commits: - Allow base image to be overridden (and don't assume its WORKDIR) - Ensure python3.9 - Map hwaccel decode presets as strings instead of lists Not required: - Fix existing documentation - Simplify hwaccel scale logic * Prepare for multi-arch tensorrt build * Add tensorrt images for Jetson boards * Add Jetson ffmpeg hwaccel * Update docs * Add CODEOWNERS * CI * Change default model from yolov7-tiny-416 to yolov7-320 In my experience the tiny models perform markedly worse without being much faster * fixup! Update docs 2023-07-26 13:50:41 +03:00			`protobuf == 3.20.3; platform_machine == 'aarch64'`
Revert numpy upgrade (#7341) * Revert numpy upgrade * Upgrade arm64 onnx version to match amd64 * Increase CODEOWNERS granularity Not sure if it has an effect since I don't have repository write access 2023-08-01 05:45:50 +03:00			`numpy == 1.23.*; platform_machine == 'aarch64' # required by python-tensorrt 8.2.1 (Jetpack 4.6)`