ESP32 Image Classification Convo.cpp labels problem

Question/Issue:
The compiler is complaining about missing initializer clauses in several places within the code related to convolutional layers.

Project ID:
NA

Context/Use case:
I used edge impulse to create a model for image classification for my esp32-s cam connected with mb board. My camera works perfectly, but as soon as I include the header file from edge impulse there’s this following error:
Users/yam/Documents/Arduino/libraries/Smart-Waste-Segregation_inferencing/src/edge-impulse-sdk/tensorflow/lite/micro/kernels/conv.cpp: In function ‘TfLiteStatus tflite::{anonymous}::Prepare(TfLiteContext*, TfLiteNode*)’:
/Users/yam/Documents/Arduino/libraries/Smart-Waste-Segregation_inferencing/src/edge-impulse-sdk/tensorflow/lite/micro/kernels/conv.cpp:1789:67: error: either all initializer clauses should be designated or none of them should be
1789 | .channels = input->dims->data[3], 1
| ^
/Users/yam/Documents/Arduino/libraries/Smart-Waste-Segregation_inferencing/src/edge-impulse-sdk/tensorflow/lite/micro/kernels/conv.cpp:1793:68: error: either all initializer clauses should be designated or none of them should be
1793 | .channels = output->dims->data[3], 1
| ^
/Users/yam/Documents/Arduino/libraries/Smart-Waste-Segregation_inferencing/src/edge-impulse-sdk/tensorflow/lite/micro/kernels/conv.cpp:1795:80: error: either all initializer clauses should be designated or none of them should be
1795 | data_dims_t filter_dims = {.width = filter_width, .height = filter_height, 0, 0};
| ^
/Users/yam/Documents/Arduino/libraries/Smart-Waste-Segregation_inferencing/src/edge-impulse-sdk/tensorflow/lite/micro/kernels/conv.cpp: In function ‘void tflite::{anonymous}::EvalQuantizedPerChannel(TfLiteContext*, TfLiteNode*, const TfLiteConvParams&, const NodeData&, const TfLiteEvalTensor*, const TfLiteEvalTensor*, const TfLiteEvalTensor*, TfLiteEvalTensor*)’:
/Users/yam/Documents/Arduino/libraries/Smart-Waste-Segregation_inferencing/src/edge-impulse-sdk/tensorflow/lite/micro/kernels/conv.cpp:1883:58: error: either all initializer clauses should be designated or none of them should be
1883 | .channels = input_depth, 1
| ^
/Users/yam/Documents/Arduino/libraries/Smart-Waste-Segregation_inferencing/src/edge-impulse-sdk/tensorflow/lite/micro/kernels/conv.cpp:1887:59: error: either all initializer clauses should be designated or none of them should be
1887 | .channels = output_depth, 1
| ^
/Users/yam/Documents/Arduino/libraries/Smart-Waste-Segregation_inferencing/src/edge-impulse-sdk/tensorflow/lite/micro/kernels/conv.cpp:1889:80: error: either all initializer clauses should be designated or none of them should be
1889 | data_dims_t filter_dims = {.width = filter_width, .height = filter_height, 0, 0};
| ^
/Users/yam/Documents/Arduino/libraries/Smart-Waste-Segregation_inferencing/src/edge-impulse-sdk/tensorflow/lite/micro/kernels/depthwise_conv.cpp: In function ‘void tflite::{anonymous}::EvalQuantizedPerChannel(TfLiteContext*, TfLiteNode*, const TfLiteDepthwiseConvParams&, const NodeData&, const TfLiteEvalTensor*, const TfLiteEvalTensor*, const TfLiteEvalTensor*, TfLiteEvalTensor*)’:
/Users/yam/Documents/Arduino/libraries/Smart-Waste-Segregation_inferencing/src/edge-impulse-sdk/tensorflow/lite/micro/kernels/depthwise_conv.cpp:1727:58: error: either all initializer clauses should be designated or none of them should be
1727 | .channels = input_depth, 1
| ^
/Users/yam/Documents/Arduino/libraries/Smart-Waste-Segregation_inferencing/src/edge-impulse-sdk/tensorflow/lite/micro/kernels/depthwise_conv.cpp:1731:59: error: either all initializer clauses should be designated or none of them should be
1731 | .channels = output_depth, 1
| ^
/Users/yam/Documents/Arduino/libraries/Smart-Waste-Segregation_inferencing/src/edge-impulse-sdk/tensorflow/lite/micro/kernels/depthwise_conv.cpp:1733:80: error: either all initializer clauses should be designated or none of them should be
1733 | data_dims_t filter_dims = {.width = filter_width, .height = filter_height, 0, 0};
| ^
/Users/yam/Documents/Arduino/libraries/Smart-Waste-Segregation_inferencing/src/edge-impulse-sdk/tensorflow/lite/micro/kernels/depthwise_conv.cpp: In function ‘TfLiteStatus tflite::{anonymous}::Prepare(TfLiteContext*, TfLiteNode*)’:
/Users/yam/Documents/Arduino/libraries/Smart-Waste-Segregation_inferencing/src/edge-impulse-sdk/tensorflow/lite/micro/kernels/depthwise_conv.cpp:1836:67: error: either all initializer clauses should be designated or none of them should be
1836 | .channels = input->dims->data[3], 1
| ^
/Users/yam/Documents/Arduino/libraries/Smart-Waste-Segregation_inferencing/src/edge-impulse-sdk/tensorflow/lite/micro/kernels/depthwise_conv.cpp:1840:68: error: either all initializer clauses should be designated or none of them should be
1840 | .channels = output->dims->data[3], 1
| ^
/Users/yam/Documents/Arduino/libraries/Smart-Waste-Segregation_inferencing/src/edge-impulse-sdk/tensorflow/lite/micro/kernels/depthwise_conv.cpp:1842:80: error: either all initializer clauses should be designated or none of them should be
1842 | data_dims_t filter_dims = {.width = filter_width, .height = filter_height, 0, 0};
| ^

exit status 1

Compilation error: exit status 1

Steps Taken:

  1. [Step 1]
  2. [Step 2]
  3. [Step 3]

Expected Outcome:
[Describe what you expected to happen]

Actual Outcome:
[Describe what actually happened]

Reproducibility:

  • [ ] Always
  • [ ] Sometimes
  • [ ] Rarely

Environment:

  • Platform: [e.g., Raspberry Pi, nRF9160 DK, etc.]
  • Build Environment Details: [e.g., Arduino IDE 1.8.19 ESP32 Core for Arduino 2.0.4]
  • OS Version: [e.g., Ubuntu 20.04, Windows 10]
  • Edge Impulse Version (Firmware): [e.g., 1.2.3]
  • To find out Edge Impulse Version:
  • if you have pre-compiled firmware: run edge-impulse-run-impulse --raw and type AT+INFO. Look for Edge Impulse version in the output.
  • if you have a library deployment: inside the unarchived deployment, open model-parameters/model_metadata.h and look for EI_STUDIO_VERSION_MAJOR, EI_STUDIO_VERSION_MINOR, EI_STUDIO_VERSION_PATCH
  • Edge Impulse CLI Version: [e.g., 1.5.0]
  • Project Version: [e.g., 1.0.0]
  • Custom Blocks / Impulse Configuration: [Describe custom blocks used or impulse configuration]
    Logs/Attachments:
    [Include any logs or screenshots that may help in diagnosing the issue]

Additional Information:
[Any other information that might be relevant]

Hi @Yamm,

Please try the solution here to see if it fixes the problem: Error compiling Arduino Library for XIAO ESP32S3 Sense

@shawn_edgeimpulse I tried the solution but, it seems that my code exceeds the available space in board. Could you suggest at solution towards this issue.

error message:
Sketch uses 1977725 bytes (150%) of program storage space. Maximum is 1310720 bytes.
Global variables use 66096 bytes (20%) of dynamic memory, leaving 261584 bytes for local variables. Maximum is 327680 bytes.
Sketch too big; see https://support.arduino.cc/hc/en-us/articles/360013825179 for tips on reducing it.
text section exceeds available space in board

Compilation error: text section exceeds available space in board

I am using ESP32-S cam with a MB module board (micro usb B ) to connect.

Since I’m using a pre-trained, optimized(int8) model from Edge Impulse and can’t modify it further, storing it on the microSD card and loading it at runtime is a suitable approach for my ESP32-S cam with MB module board. It would be grateful if I could get the coding guidance required for this approach.

Hi @Yamm,

I am using ESP32-S cam

Which ESP32-S? There’s the S2 and S3.

pre-trained, optimized(int8) model

Which model are you using? Can you provide your project ID number so we can take a look?

My guess is that you are using an object detection model (e.g. YOLO), which is too large for the ESP32-S3. You will need to use another board with more flash storage or find a different way to do object detection, such as FOMO (FOMO: Object detection for constrained devices | Edge Impulse Documentation).

Thank you for your response @shawn_edgeimpulse
Project ID: 428129
Image Classification using MobileNetV2 160x160 0.5
Device: https://ghumtipasal.com.np/wp-content/uploads/2024/04/1647569906_esp20cam2032-36.jpeg

Hi @Yamm,

Thank you for the info. In my experience, MobileNet is quite large and will struggle to run on most microcontrollers. It looks like you’ve set your input dimensions to 48x48 but chose the 160x160 version of MobileNetV2 (“Uses around 700.7K RAM and 982.4K ROM”). I recommend choosing something like MobileNetV2 96x96 0.1 (“Uses around 270.2K RAM and 212.3K ROM”) and changing your input dimensions on the impulse to 96x96 to match the expected input of MobileNet.

You might also see if something like a basic CNN (using the “Classification” block) would work instead.

I used the MobileNetV2 96x96 0.1 model. But, I’m not able to do inferences with my esp32 cam how do I do it?

Additionally, I am trying to connect my device in edge impulse for live classification. I found out that we need to enter edge-impulse-daemon command . However, I encounter this error
zsh: command not found: edge-impulse-data-forwarder

@shawn_edgeimpulse could you help me with this.
Additionally, I’m trying to use the result of this classification to turn a servomotor on specific angles. I’m thinking of using Arduino uno to turn the servos based on the result of the classification from esp32. However, I’m confused on the data pipeline and how should I code in the Arduino IDE to synchronize these processes.
I got to know that we have three different methods to connect a device to edge impulse from this video. (https://youtu.be/rszoQsMIIAI?si=jhot8iG8hYb5kBfE). I’m not sure how do I use ESP32 CAM to classify real time images continuously and use its result to move some servos.

Hi @Yamm,

I used the MobileNetV2 96x96 0.1 model. But, I’m not able to do inferences with my esp32 cam how do I do it?

What error are you seeing when you try to compile or run inference on the ESP32?

zsh: command not found: edge-impulse-data-forwarder

You need to install the Edge Impulse CLI (Installation | Edge Impulse Documentation) to have access to these commands.

I’m not sure how do I use ESP32 CAM to classify real time images continuously and use its result to move some servos.

It sounds like you need to use the bounding box information that comes from performing object detection. Please see this thread on how to use that information to move servos.