Model Type
Quantized CNN for Classification
Framework
TensorFlow Lite
Algorithm
ShuffleNet v2 (Quantized)
Technical Details
ShuffleNet v2 is a compact and high-performance convolutional neural network designed for real-time image classification on edge devices. This implementation utilizes a quantized TFLite model to enable efficient deployment on low-resource processors. Ideal for smart IoT devices, vision sensors, and applications requiring responsive classification in constrained environments. The architecture combines group convolution and channel shuffle operations to minimize memory and computation load.
