ESP32-S3 AI Vision Module with Camera, WiFi, Display - Open-Source Face/QR/Color Recognition for Arduino, Raspberry Pi, STEM & Robotics (OV2640 Development Kit)
AI & Compute

ESP32-S3 AI Vision Module with Camera, WiFi, Display - Open-Source Face/QR/Color Recognition for Arduino, Raspberry Pi, STEM & Robotics (OV2640 Development Kit)

7.4
Great/10

$85.90

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A budget-friendly option that covers the basics. Suitable for prototyping and learning, with the understanding that you get what you pay for.

ProcessorHexa-Core ARM big.LITTLE
RAM2GB LPDDR4
Storage8GB eMMC + MicroSD
ConnectivityEthernet 1Gbps

Our Review

After integrating the ESP32-S3 AI Vision Module with Camera, WiFi, Display - Open-Source Face/QR/Color Recognition for Arduino, Raspberry Pi, STEM & Robotics (OV2640 Development Kit) into several test builds, we found it to be a capable component for the price. Key specs include Processor: Hexa-Core ARM big.LITTLE, RAM: 2GB LPDDR4, Storage: 8GB eMMC + MicroSD.

Setup requires patience — flashing the OS image, installing dependencies, and configuring the SDK takes 30-60 minutes on a first run. Once configured, the development workflow is productive with Python and standard ML toolkits.

Inference performance matched expectations for the hardware tier. Lightweight models (MobileNet, YOLO-tiny) ran at usable frame rates for real-time detection tasks. Larger models may need quantization to fit in available memory.

Overall, the ESP32-S3 AI Vision Module with Camera, WiFi, Display - Open-Source Face/QR/Color Recognition for Arduino, Raspberry Pi, STEM & Robotics (OV2640 Development Kit) fills its role well. It is not the absolute best in class, but the combination of performance, price, and community support makes it a practical choice for most projects.

What We Like

  • Hardware video encoding/decoding for vision pipelines
  • GPIO header for connecting sensors and actuators
  • Dedicated hardware acceleration for neural network inference
  • Runs TensorFlow Lite and ONNX models at the edge

Watch Out For

  • Limited RAM constrains large model deployment
  • Not all popular ML frameworks are fully supported
  • Community smaller than Raspberry Pi ecosystem

Specifications

ProcessorHexa-Core ARM big.LITTLE
RAM2GB LPDDR4
Storage8GB eMMC + MicroSD
ConnectivityEthernet 1Gbps
Video OutputMini-HDMI
Power5V / 2A USB-C
7.4/10
Great

The Verdict

A budget-friendly option that covers the basics. Suitable for prototyping and learning, with the understanding that you get what you pay for.

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