Memory allocation error when deploying example model

[karen-nativ]

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!