Hello, I'm following the steps from Basic example, after compiling I get following error. Do you have any advice ?

Basic-Image-Classification:4:10: fatal error: vector: No such file or directory #include ^~~~~~~~ compilation terminated. exit status 1 vector: No such file or directory

It seems that Edge Impulse may have changed something on their side, I am not able to train object detection for images with a size of 48x48, I will get an error. Any idea on when this will change? I really want this to work!

I am using esp32 cam module but facing issue in connecting serial with device and edge imple cli. It is giving an error of timeout. I want to understand is esp32 cam module is supported by edge impulse if not then how it is different from esp eye.

After following the guide for a simple object detector on the esp32 I hit

vector: No such file or directory #include

would be great if these examples were actually vetted and working...

Looks like the esp-dl (https://github.com/espressif/esp-dl, former esp-face) repo has changed so much that many libraries went missing in this example. My temporary solution is go to this forked repo and copied all the .c and .h files under image_util and lib into the project dir to make it work.

It would be great if you can update it to fit Espressif's new APIs.

Hello,

We are trying to deploy the example model from the tutorial from this GitHub repository README (https://www.survivingwithandroid.com/tinyml-esp32-cam-edge-image-classification-with-edge-impulse/) on our ESP-EYE device.

When using the most basic model (MobileNetV2 96x96 0.05) in Edge-Impulse the deployment works but the model is not accurate. Every other model fails with the following errors:

1. When deploying the model with the default partitions scheme we are getting the following error: WiFi connected\n Starting web server on port: '80' Starting stream server on port: '81' Camera Ready! Use 'http://192.168.1.158' to connect Capture image Edge Impulse standalone inferencing (Arduino) ERR: Failed to run DSP process (-1002) run_classifier returned: -5

2. When deploying the model in arduino IDE using the "Huge APP" partition scheme we are getting the following error: WiFi connected Starting web server on port: '80' Starting stream server on port: '81' Camera Ready! Use 'http://192.168.1.158' to connect Capture image Edge Impulse standalone inferencing (Arduino) ERR: failed to allocate tensor arena Failed to allocate TFLite arena (error code 1) run_classifier returned: -6

The ESP-EYE has 4MB of memory available. According to the arduino IDE, the code itself takes ~1.2MB of memory. According to the Edge-Impulse website, all models do not need more than 1MB of additional memory. However, it seems that the memory is the issue here.

Adding a screenshot of our board settings in arduino IDE:

Can you please advise on how can we make the more complicated models work on our device? Thank you!

Advanced-Image-Classification:53:25: error: 'FRAMESIZE_240X240' was not declared in this scope
     config.frame_size = FRAMESIZE_240X240;
                         ^
Advanced-Image-Classification:57:25: error: 'FRAMESIZE_240X240' was not declared in this scope
     config.frame_size = FRAMESIZE_240X240;
                         ^
Advanced-Image-Classification:71:23: error: 'FRAMESIZE_240X240' was not declared in this scope
   s->set_framesize(s, FRAMESIZE_240X240);

basic and advanced examples not compiling