例如,您可以使用 DeepLab v3 TFLite 模型在Android中分割飞机图像(图 1),如下所示: // Create the API froma model file and options String modelPath = "path/to/model.tflite" ImageSegmenterOptions options = ImageSegmenterOptions.builder().setOutputType(OutputType.CONFIDENCE_MASK).build(); ImageSegmen...
tf_lite/library/lite/lib/) add_executable(predict src/main.cpp ) link_libraries(${LINK_DIR}/libflatbuffers.a) target_link_libraries(predict ${LINK_DIR}libtensorflowlite.so) # 强制链接整个库 target_link_libraries(predict -Wl,--no-as-needed ${LINK_DIR}libtensorflowlite_flex.so -Wl,--as...
这个库位于tensorflow/lite/tools/make/downloads/flatbuffers,把里面的include文件夹全部拷贝出来,放在一个flatbuffers文件夹里面。 include/flatbuffers/ 最后整理成的文件目录如下 8. 上菜啦(整理进CMakeLists.txt中) 最后把我们的库整理进去 # tf lite 的库名字add_library(tflite STATIC IMPORTED)# # tf lite库...
AI代码解释 // build.gradle.ktsandroid{aaptOptions{noCompress"tflite"// 防止模型文件被压缩}}dependencies{// TFLite核心库implementation("org.tensorflow:tensorflow-lite:2.12.0")implementation("org.tensorflow:tensorflow-lite-gpu:2.12.0")// GPU支持implementation("org.tensorflow:tensorflow-lite-support:0.4....
git clone https://github.com/tensorflow/tflite-micro-arduino-examples Arduino_TensorFlowLite 将官方TensorFlow Lite Micro Library for Arduino 存储库中的开发版本复制到Arduino的sketches/libraries文件夹中。您可以在存储库中找到有关安装最新开发版本库的更多详细信息。
The tflite_runtime package is a smaller, simplified Python package that includes the bare minimum code required to run inference with TensorFlow Lite. This package is ideal when all you want to do is execute .tflite models and avoid wasting disk space with the large TensorFlow library. ...
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/docs_src/mobile/tflite/demo_android.md 在 iOS 上，则需要使用 Makefile 编译。在 mac 平台上运行 build_ios_universal_lib.sh,会编译生成 tensorflow/contrib/lite/gen/lib/libtensorflow-lite.a 这个库文件。这是个 fat library，打包了 x86_...
To generate and run C++ codethat performs inference withTensorFlowLitemodelson Windows targets, you musthavethe?TensorFlowLitelibraryon the Windows hardware.Tobuildthe TensorFlow Lite library version 2.4.1for Windows targets on your host Windows platform,execute the following steps. ...
1.打开Tensorflow/contrib/lite/kernels/internal/BUILD View Code 2.在根目录下创建一个文件:build_armv7_tflite.sh View Code 3.编译该文件build_armv7_tflite.sh,会碰到一个错误:.../.../read-ld:unrecognized options : --icf=all 解决方法:找到文件./build_def.bzl ,打开,去除所有--icf=all标识的...
编译结束,会在tensorflow/contrib/lite/gen/lib/rpi_armv7目录下产生libtensorflow-lite.a 4.编译 label_image 第三步的build_rpi_lib.sh脚本实际是调用的./tensorflow/contrib/lite/Makefile对Tensorflow Lite源码进行编译,但是该Makefile并不能编译tensorflow/contrib/lite/examples/label_image目录下的Demo,所以需要...