- Last edited 2 months ago ago
How to use Teachable Machine to create an image classification application on STM32
This article shows how to use the Teachable Machine online tool with STM32Cube.AI and the FP-AI-VISION1 function pack to create an image classifier running on the STM32H747I-DISCO board.
This tutorial is divided into three parts: the first part shows how to use the Teachable Machine to train and export a deep learning model, then STM32Cube.AI is used to convert this model into optimized C code for STM32 MCUs. The last part explains how to integrate this new model into the FP-AI-VISION1 to run live inference on an STM32 board with a camera. The whole process is described below:
Contents
1 Prerequisites
1.1 Hardware
- STM32H747I-DISCO Board
- B-CAMS-OMV Flexible Camera Adapter board
- A Micro-USB to USB cable
- Optional: a webcam
1.2 Software
- STM32Cube IDE
- X-Cube-AI version 7.1.0 or above command line tool
- FP-AI-VISION1 version 3.1.0
- STM32CubeProgrammer
2 Training a model using Teachable Machine
In this section, we will train deep neural network in the browser using Teachable Machine. We first need to choose something to classify. In this example, we will classify ST boards and modules. The chosen boards are shown in the figure below:
You can choose whatever object you want to classify it: fruits, pasta, animals, people, etc...
Let's get started. Open https://teachablemachine.withgoogle.com/, preferably from Chrome browser.
Click Get started, then select Image Project, then Standard image model (224x244px color images). You will be presented with the following interface.
2.1 Adding training data
For each category you want to classify, edit the class name by clicking the pencil icon. In this example, we choose to start with SensorTile.
To add images with your webcam, click the webcam icon and record some images. If you have image files on your computer, click upload and select the directory containing your images.
The STM32H747 discovery kit combined with the B-CAMS-OMV camera daughter board can be used as a USB webcam. Using the ST kit for data collection will help to get better results as the same camera will be used for data collection and inference when the model will have been trained.
To use the ST kit as a webcam, simply program the board with the following binary of the function pack. First plug your board onto your computer using the ST-LINK port. Make sure the JP6 jumper is set to ST-LINK.
After plugging the USB cable onto your computer, the board appears as a mounted device.
The binary is located under FP-AI-VISION1_V3.1.0\Projects\STM32H747I-DISCO\Applications\USB_Webcam\Binary.
Drag and drop the binary file onto the board mounted device. This flashes the binary on the board.
Unplug the board, change the JP6 jumper to the HS position, and plug your board using the USB OTG port.
For convenience, you can plug simply two USB cables, one on the USB OTG port, the other on the USB ST-LINK and set the JP6 jumper to ST-LINK. In this case, the board can be programmed and switch from USB webcam mode to test mode without the need to change the jumper position.
Depending on how you oriented the camera board, you might prefer to flip the image. If you do so you need to use the same option when generating the code on STM32.
Once you have a satisfactory amount of images for this class, repeat the process for the next one until your dataset is complete.
2.2 Training the model
Now that we have a good amount of data, we are going to train a deep learning model for classifying these different objects. To do this, click the Train Model button as shown below:
This process can take a while, depending on the amount of data you have. To monitor the training progress, you can select Advanced and click Under the hood. A side panel displays training metrics.
When the training is complete, you can see the predictions of your network on the "Preview" panel. You can either choose a webcam input or an imported file.
2.2.1 What happens under the hood (for the curious)
Teachable Machine is based on Tensorflow.js to allow neural network training and inference in the browser. However, as image classification is a task that requires a lot of training time, Teachable Machine uses a technique called transfer learning: The webpage downloads a MobileNetV2 model that was previously trained on a big image dataset of 1000 categories. The convolution layers of this pre-trained model are very good at doing feature extraction so they do not need to be trained again. Only the last layers of the neural network are trained using Tensorflow.js, thus saving a lot of time.
2.3 Exporting the model
If you are happy with your model, it is time to export it. To do so, click the Export Model button. In the pop-up window, select Tensorflow Lite, check Quantized and click Download my model.
Since the model conversion is done in the cloud, this step can take a few minutes.
Your browser downloads a zip file containing the model as a .tflite file and a .txt file containing your label. Extract these two files in an empty directory that we will call workspace in the rest of this tutorial.
2.3.1 Inspecting the model using Netron (optional)
It is always interesting to take a look at a model architecture as well as its input and output formats and shapes. To do this, use the Netron webapp.
Visit https://lutzroeder.github.io/netron/ and select Open model, then choose the model.tflite file from Teachable Machine. Click sequental_1_input: we observe that the input is of type uint8 and of size [1, 244, 244, 3]. Now let's look at the outputs: in this example we have 6 classes, so we see that the output shape is [1,6]. The quantization parameters are also reported. Refer to part 3 for how to use them.
3 Porting to a target board
3.1 STM32H747I-DISCO
In this part we will use the stm32ai command line tool to convert the TensorflowLite model to optimized C code for STM32.
For ease of usage, add the X-Cube-AI installation folder to your path, for Windows:
- For X-CUBE-AI v7.1.0:
set CUBE_FW_DIR=C:\Users\<USERNAME>\STM32Cube\Repository set X_CUBE_AI_DIR=%CUBE_FW_DIR%\Packs\STMicroelectronics\X-CUBE-AI\7.1.0 set PATH=%X_CUBE_AI_DIR%\Utilities\windows;%PATH%
- For X-CUBE-AI v7.2.0:
set CUBE_FW_DIR=C:\Users\<USERNAME>\STM32Cube\Repository set X_CUBE_AI_DIR=%CUBE_FW_DIR%\Packs\STMicroelectronics\X-CUBE-AI\7.2.0 set PATH=%X_CUBE_AI_DIR%\Utilities\windows;%PATH%
Start by opening a shell in your workspace directory, then execute the following command:
cd <path to your workspace> stm32ai generate -m model.tflite -v 2
The expected output is:
Neural Network Tools for STM32AI v1.6.0 (STM.ai v7.1.0-RC3)
Exec/report summary (generate)
------------------------------------------------------------------------------------------------------------------------
model file : C:\path_to_workspace\model.tflite
type : tflite
c_name : network
compression : lossless
workspace dir : C:\path_to_workspace\stm32ai_ws
output dir : C:\path_to_workspace\stm32ai_output
model_name : model
model_hash : 3e04a924905fea0274099abd7153e500
input 1/1 : 'serving_default_sequential_1_input0'
150528 items, 147.00 KiB, ai_u8, scale=0.00747405, zero_point=134, (1,224,224,3), domain:user/
output 1/1 : 'nl_71_0_conversion'
5 items, 5 B, ai_u8, scale=0.00390625, zero_point=0, (1,1,1,5), domain:user/
params # : 517,688 items (526.47 KiB)
macc : 58,587,644
weights (ro) : 539,128 B (526.49 KiB) / +20(+0.0%) vs original model (1 segment)
activations (rw) : 610,784 B (596.47 KiB) (1 segment)
ram (total) : 761,317 B (743.47 KiB) = 610,784 + 150,528 + 5
Generated C-graph summary
------------------------------------------------------------------------------------------------------------------------
model name : model
c-name : network
c-node # : 68
c-array # : 264
activations size : 610784 (1 segments)
weights size : 539128 (1 segments)
macc : 58587644
inputs : ['serving_default_sequential_1_input0_output']
outputs : ['nl_71_0_conversion_output']
C-Arrays (264)
------------------------------------------------------------------------------------------------------------------------------------------------
c_id name (*_array) item/size domain/mem-pool c-type fmt comment
------------------------------------------------------------------------------------------------------------------------------------------------
0 serving_default_sequential_1_input0_output 150528/150528 user/ uint8_t int8/ua /input
1 conversion_0_output 150529/150529 activations/**default** int8_t int8/sa
2 conv2d_2_output 200704/200704 activations/**default** int8_t int8/sa
( ... )
261 conv2d_67_scratch0 13248/13248 activations/**default** int8_t int/ss
262 conv2d_67_scratch1 62720/62720 activations/**default** int8_t int8/sa
263 conv2d_67_scratch2 62720/62720 activations/**default** int8_t int8/sa
------------------------------------------------------------------------------------------------------------------------------------------------
C-Layers (68)
-----------------------------------------------------------------------------------------------------------------------------------------------
c_id name (*_layer) id layer_type macc rom tensors shape (array id)
-----------------------------------------------------------------------------------------------------------------------------------------------
0 conversion_0 0 conv 301056 0 I: serving_default_sequential_1_input0_output (1,224,224,3) (0)
O: conversion_0_output (1,224,224,3) (1)
-----------------------------------------------------------------------------------------------------------------------------------------------
1 conv2d_2 2 conv2d 5419024 496 I: conversion_0_output (1,224,224,3) (1)
S: conv2d_2_scratch0
S: conv2d_2_scratch1
W: conv2d_2_weights (3,16,3,3) (69)
W: conv2d_2_bias (1,1,1,16) (70)
O: conv2d_2_output (1,112,112,16) (2)
-----------------------------------------------------------------------------------------------------------------------------------------------
( ... )
Complexity report per layer - macc=58,587,764 weights=539,232 act=610,784 ram_io=150,534
---------------------------------------------------------------------------------
id name c_macc c_rom c_id
---------------------------------------------------------------------------------
0 conversion_0 | 0.5% | 0.0% [0]
2 conv2d_2 |||||||||||| 9.2% | 0.1% [1]
3 conv2d_3 |||| 3.1% | 0.0% [2]
4 conv2d_4 |||| 2.7% | 0.0% [3]
5 conv2d_5 ||||||||||| 8.2% | 0.1% [4]
7 conv2d_7 ||| 2.3% | 0.1% [5]
( ... )
-----------------------------------------------------------------------------------------------------------------------------------------------
66 nl_71 71 nl 75 0 I: dense_70_0_conversion_output (1,1,1,5) (66)
O: nl_71_output (1,1,1,5) (67)
-----------------------------------------------------------------------------------------------------------------------------------------------
67 nl_71_0_conversion 71 conv 10 0 I: nl_71_output (1,1,1,5) (67)
O: nl_71_0_conversion_output (1,1,1,5) (68)
-----------------------------------------------------------------------------------------------------------------------------------------------
This command generates five files under workspace/stm32ai_ouptut/:
- network_config.h
- network.c
- network_data.c
- network_data_params.c (for X-CUBE-AI v7.2.0 and above)
- network.h
- network_data.h
- network_data_params.h (for X-CUBE-AI v7.2.0 and above)
Let's take a look at the highlighted lines: we learn that the model uses 526.49 Kbytes of weights (read-only memory) and 596.47 Kbytes of activations. The STM32H747xx MCUs do not have 596.47 Kbytes of contiguous RAM, we need to use the external SDRAM present on the STM32H747-DISCO board. Refer to UM2411 section 5.8 "SDRAM" for more information.
3.1.1 Integration with FP-AI-VISION1
In this part we will import our brand-new model into the FP-AI-VISION1 function pack. This function pack provides a software example for a food classification application. For more information on FP-AI-VISION1, go here.
The main objective of this section is to replace the network and network_data files in FP-AI-VISION1 by the newly generated files and make a few adjustments to the code.
3.1.1.1 Open the project
If it is not already done, download the zip file from ST website and extract the content to your workspace. It must now contain the following elements:
- model.tflite
- labels.txt
- stm32ai_output
- stm32ai_ws
- FP_AI_VISION1
If we take a look inside the function pack, we'll start from the FoodReco_MobileNetDerivative application we can see two configurations for the model data type, as shown below.
Since our model is a quantized one, we have to select the Quantized_Model directory.
Go into workspace/FP_AI_VISION1_V3.1.0/Projects/STM32H747I-DISCO/Applications/FoodReco_MobileNetDerivative/Quantized_Model/STM32CubeIDE and double-click .project. STM32CubeIDE starts with the project loaded. You will notice 2 sub-project for each core of the microcontroller : CM4 and CM7, as we don't use CM4, ignore it and work with the CM7 project.
3.1.1.2 Replacing the network files
The model files are located in workspace/FP-AI-VISION1_V3.1.0/Projects/STM32H747I-DISCO/Applications/FoodReco_MobileNetDerivative/Quantized_Model/CM7/ Src and Inc directory.
Delete the following files and replace them with the ones from workspace/stm32ai_output:
In Src:
- network.c
- network_data.c
- network_data_params.c (for X-CUBE-AI v7.2 and above)
In Inc:
- network.h
- network_data.h
- network_data_params.h (for X-CUBE-AI v7.2 and above)
3.1.1.3 Updating to a newer version of X-CUBE-AI
FP-AI-VISION1 v3.1.0 is based on X-Cube-AI version 7.1.0, You can check your version of Cube.AI by running stm32ai --version.
If the c-model files were generated with a newer version of X-Cube-AI (> 7.1.0), you need also to update the X-CUBE-AI library (NetworkRuntime720_CM7_GCC.a for X-CUBE-AI version 7.2).
Go to workspace/FP-AI-VISION1_V3.1.0/Middlewares/ST/STM32_AI_Runtime then for a Windows command prompt:
copy %X_CUBE_AI_DIR%\Middlewares\ST\AI\Inc\* .\Inc\ copy %X_CUBE_AI_DIR%\Middlewares\ST\AI\Lib\GCC\ARMCortexM7\NetworkRuntime720_CM7_GCC.a .\lib\NetworkRuntime710_CM7_GCC.a
You also need to add to the project tree the new generated file network_data_params.c. Simply drag and drop the network_data_params.c within the STM32CubeIDE project in the Applications folder and link to file as shown below:
3.1.1.4 Updating the labels and display
In this step we will update the labels for the network output. The label.txt file downloaded with Teachable Machine can help you doing this. In our example, the content of this file looks like this:
0 SensorTile 1 IoTNode 2 STLink 3 Craddle Ext 4 Fanout 5 Background
From STM32CubeIDE, open fp_vision_app.c. Go to line 125 where the output_labels is defined and update this variable with your label names:
// fp_vision_app.c line 125
const char* output_labels[AI_NET_OUTPUT_SIZE] = {
"SensorTile", "IoTNode", "STLink", "Craddle Ext", "Fanout", "Background"};
While we're here, we'll update the display mode that it shows camera image instead of food logos. Go around line 200 and update the App_Output_Display function. At the top of the function, the display_mode variable should be set to 1.
static void App_Output_Display(AppContext_TypeDef *App_Context_Ptr)
{
static uint32_t occurrence_number = NN_OUTPUT_DISPLAY_REFRESH_RATE;
static uint32_t display_mode = 1; // Was 0
3.1.1.5 Cropping the image
Teachable Machine crops the webcam image to fit the model input size. In FP-AI-VISION1, the image is resized to the model input size, hence losing the aspect ratio. We will change this default behavior and implement a crop of the camera image.
In order to have square images and avoid image deformation we are going to crop the camera image using the DCMI. The goal of this step is to go from the 640x480 resolution to a 480x480 resolution.
First, edit fp_vision_camera.h (located in FP-AI-VISION1_V3.1.0/Projects/STM32H747I-DISCO/Applications/Common/CM7/Inc) at line 59 to update the CAMERA_WIDTH define to 480 pixels:
//fp_vision_camera.h line 57
#if CAMERA_CAPTURE_RES == VGA_640_480_RES
#define CAMERA_RESOLUTION CAMERA_R640x480
#define CAM_RES_WIDTH 480 // Was 640
#define CAM_RES_HEIGHT 480
Then, edit fp_vision_camera.c located in FP-AI-VISION1_V3.1.0/Projects/STM32H747I-DISCO/Applications/Common/CM7/Src.
Modify the CAMERA_Init function (line 58) to configure DCMI cropping (update the function with the highlighted code bellow) :
void CAMERA_Init(CameraContext_TypeDef* Camera_Context_Ptr)
{
CAMERA_Context_Init(Camera_Context_Ptr);
__HAL_RCC_MDMA_CLK_ENABLE();
(...)
/* Set camera mirror / flip configuration */
CAMERA_Set_MirrorFlip(Camera_Context_Ptr, Camera_Context_Ptr->mirror_flip);
/* With default mounting of the B-CMAS-OMV, the image should be flipped */
/* If at the end the image is not inn the right position, comment the line below */
CAMERA_Set_MirrorFlip(Camera_Context_Ptr, CAMERA_MIRRORFLIP_FLIP);
HAL_Delay(100);
/* Center-crop the 640x480 frame to 480x480 */
const uint32_t x0 = (640 - 480) / 2;
const uint32_t y0 = 0;
/* Note: 1 px every 2 DCMI_PXCLK (8-bit interface in RGB565) */
HAL_DCMI_ConfigCrop(&hcamera_dcmi,
x0 * 2,
y0,
CAM_RES_WIDTH * 2 - 1,
CAM_RES_HEIGHT - 1);
HAL_DCMI_EnableCrop(&hcamera_dcmi);
/* Wait for the camera initialization after HW reset */
HAL_Delay(200);
/*
* Start the Camera Capture
* Using intermediate line buffer in D2-AHB domain to support high pixel clocks.
*/
if (HAL_DCMIEx_Start_DMA_MDMA(&hcamera_dcmi, CAMERA_MODE_CONTINUOUS,
(uint8_t *)Camera_Context_Ptr->camera_capture_buffer,
CAM_LINE_SIZE, CAM_RES_HEIGHT) != HAL_OK)
{
while(1);
}
Now image cropping is enabled and the image is square.
3.1.1.6 Adapt to the NN input data range
The neural network input needs to be normalized accordingly to the training phase.
This is achieved by updating the value of both the nn_input_norm_scale and nn_input_norm_zp variables during initialization. The nn_input_norm_scale and nn_input_norm_zp variables affect the pixel format adaptation stage.
The scale, zero point values should be set {127.5, 127} if the NN model was trained using input data normalized in the range [-1, 1].
They should be set to {255, 0} if the NN model was trained using input data normalized in the range [0, 1].
The food recognition model was trained with input data normalized in the range [0, 1] whereas the Teachable Model was trained in the range of [-1, 1].
Edit the file fp_vision_app.c and modify the App_Context_Init function (line 328) to update the scale and zero-point values (update the function with the highlighted code bellow) :
/*{scale,zero-point} set to {127.5, 127} since NN model was trained using input data normalized in the range [-1, 1]*/
App_Context_Ptr->Ai_ContextPtr->nn_input_norm_scale=127.5f; //was 255.0f
App_Context_Ptr->Ai_ContextPtr->nn_input_norm_zp=127; //was 0
3.1.2 Compiling the project
The function pack for quantized models comes in four different memory configurations :
- Quantized_Ext
- Quantized_Int_Fps
- Quantized_Int_Mem
- Quantized_Int_Split
As we saw in Part 2, the activation buffer requires more than 512 Kbytes of RAM. For this reason, we can only use the Quantized_Ext configuration to place activation buffer. For more details on the memory configuration, refer to UM2611 section 3.2.5 "Memory requirements".
To compile only the Quantized_Ext configuration, select Project > Properties from the top bar. Then select C/C++ Build from the left pane. Click manage configuration and then delete all configurations that are not Quantized_Ext. Only one configuration is left.
Clean the project by selecting Project > Clean... and clicking Clean.
Eventually, build the project by clicking Project > Build All.
When the compilation is complete, a file named STM32H747I_DISCO_FoodReco_Quantized_CM7.elf is generated in
workspace > FP-AI-VISION1_V3.1.0 > Projects > STM32H747I-DISCO > Applications > FoodReco_MobileNetDerivative > Quantized_Model > STM32CubeIDE > STM32H747I_DISCO > Quantized_Ext
3.1.3 Flashing the board
Connect the STM32H747I-DISCO to your PC via a Micro-USB to USB cable. Make sure that your board is connected to the ST-LINK port and properly powered (set the jumper JP6 jumper is set to ST-LINK).
Build the project STM32H747I_DISCO_FoodReco_Quantized_CM7 and run it with the play button :
Alternatively, you can use STM32CubeProgrammer to program the board with the generated .elf file.
3.1.4 Testing the model
Connect the camera to the STM32H747I-DISCO board using a flex cable. To have the image in the upright position, the camera must be placed with the flex cable facing up as shown in the figure below. Once the camera is connected, power on the board and press the reset button. After the "Welcome Screen", you will see the camera preview and output prediction of the model on the LCD Screen.
3.2 Troubleshooting
You may notice that once the model is running on STM32, the performance of the deep learning model is not as expected. The rationale is the following:
- Quantization: the quantization process can reduce the performance of the model, as going from a 32-bit floating point to a 8-bit integer representation means a loss in precision.
- Camera: if the webcam used for training the model is different from the the camera on the Discovery board. This difference of data between the training and the inference can explain a loss in performance.
4 STMicroelectronics references
See also: