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

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Bump onnx from 1.14.0 to 1.19.1 in /docker/tensorrt Bumps [onnx](https://github.com/onnx/onnx) from 1.14.0 to 1.19.1. - [Release notes](https://github.com/onnx/onnx/releases) - [Changelog](https://github.com/onnx/onnx/blob/main/docs/Changelog-ml.md) - [Commits](https://github.com/onnx/onnx/compare/v1.14.0...v1.19.1) --- updated-dependencies: - dependency-name: onnx dependency-version: 1.19.1 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com>
2025-10-31 21:41:44 +03:00
onnx == 1.19.1; 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'
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