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Diffstat (limited to 'docs/sections/customizing.md')
| -rw-r--r-- | docs/sections/customizing.md | 9 |
1 files changed, 4 insertions, 5 deletions
diff --git a/docs/sections/customizing.md b/docs/sections/customizing.md index f08706b..d97aa9e 100644 --- a/docs/sections/customizing.md +++ b/docs/sections/customizing.md @@ -377,7 +377,7 @@ model. ### Define ModelPointer and ModelSize methods These functions are wrappers around the functions generated in the C++ file containing the neural network model as an -array. This generation the C++ array from the `.tflite` file, logic needs to be defined in the `usecase.cmake` file for +array. This logic for generation of the C++ array from the `.tflite` file needs to be defined in the `usecase.cmake` file for this `HelloWorld` example. For more details on `usecase.cmake`, refer to: [Building options](./building.md#build-options). @@ -391,7 +391,7 @@ Model invokes the `ModelPointer()` function which calls the `GetModelPointer()` data memory address. The `GetModelPointer()` function is generated during the build and can be found in the file `build/generated/hello_world/src/<model_file_name>.cc`. The file generated is automatically added to the compilation. -Use the `${use-case}_MODEL_TFLITE_PATH` build parameter to include custom model to the generation, or compilation, +Use the `${use-case}_MODEL_TFLITE_PATH` build parameter to include custom model in the generation or compilation process. Please refer to: [Build options](./building.md#build-options) for further information. ## Executing inference @@ -404,9 +404,8 @@ To run an inference successfully, you must use: - A main loop function, - And some input data. -For the `hello_world` example below, the input array is not populated. However, for real-world scenarios, and before -compilation and be baked into the application, this data must either be read from an on-board device, or be prepared in -the form of C++ sources. +For the `hello_world` example below the input array is not populated. However, for real-world deployment this data must either be read from an on-board device or be prepared in +the form of C++ sources and baked into the application before compilation. For example, the image classification application requires extra build steps to generate C++ sources from the provided images with `generate_images_code` CMake function. |