TinyML on ESP32-S3: Person Detection Without Sending Anything to the Cloud
Local inference that actually runs. Your smart camera is not smart. It's a snitch with a monthly bill. It sees a person, panics, compresses a blurry JPEG, uploads your hallway to a data center in Virginia, waits for a GPU to wake up and say "yeah, that's a person," and then charges you $9.99 to tell you what your own eyes could have seen in 100 milliseconds. We can do the same job for $12, with…
TinyML on ESP32-S3 enables real-time person detection without transmitting data to the cloud. This eliminates privacy concerns, latency issues, and costs associated with cloud-based solutions. The ESP32-S3 chip offers sufficient processing power and memory to run local inference, significantly faster than cloud roundtrips. By using quantized MobileNet models and optimizing the architecture for the S3 chip's hardware capabilities, developers can achieve high accuracy while minimizing power consumption.
The provided training pipeline demonstrates how to effectively train and deploy an int8 MobileNetV2 model for person detection, achieving around 85-88% accuracy on the Visual Wake Words dataset.
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