What is quantization accuracy in TensorFlow.js?

What is quantization accuracy in TensorFlow.js?

Quantization accuracy refers to how well a quantized model performs compared to its original floating-point version. In TensorFlow.js, it helps reduce model size and improve inference speed while maintaining acceptable accuracy.

How does quantization affect model performance?

Quantization can significantly improve model performance by reducing the computational load and memory usage, which is especially beneficial for running models in web browsers.

What are the different types of quantization available in TensorFlow.js?

TensorFlow.js supports several types of quantization, including post-training quantization, dynamic range quantization, and quantization-aware training.

Can I measure the accuracy of a quantized model?

Yes, you can measure the accuracy of a quantized model by comparing its performance metrics, such as accuracy and loss, against the original model using a validation dataset.