Leveraging the Power of C++ for Efficient Machine Learning on Embedded Deviceslayer can apply filters to detect: ◮ Edges ◮ Shapes ◮ Objects 14 / 50Software dependencies ◮ TensorFlow Lite ◮ OpenCV 16 / 50MobileNet pre-trained model ◮ CNN architecture created by Google ◮ Trained ◮ Memory consumption with 4 threads: 93MB 31 / 50Comparision with Python Comparision made with TensorFlow Lite’s label image.py C++ Python Running duration (s) 1 7 Number of threads Inference duration https://github.com/adrian-stanciu/cpp-embedded-ml 48 / 50Resources ◮ https://github.com/tensorflow/tensorflow/tree/ master/tensorflow/lite/examples/python ◮ https://kaggle.com/datasets/sanikamal/ rock-paper-scissors-dataset0 码力 | 51 页 | 1.78 MB | 6 月前3- Common Package Specification (CPS) in Practice: A Full Round Trip Implementation in Conan C++ Package ManagerPRIVATE openssl::openssl)Proposal: CPS location files { "tensorflow-lite": "/path/to/cps/tensorflow-lite.cps", "abseil": "/path/to/cps/abseil.cps", "farmhash": "/path/to/cps/farmhash0 码力 | 66 页 | 1.76 MB | 6 月前3
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- Conan 2.10 Documentationthe dependency graph that is both interactive and user-friendly. $ conan graph info --require=tensorflow-lite/2.12.0 -r=conancenter --format=html > graph. ˓→html The HTML output displays an interactive0 码力 | 803 页 | 5.02 MB | 10 月前3
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