What is ONNX quantization and how does it work?

What is ONNX quantization and how does it work?

ONNX quantization is a technique to reduce the model size and improve inference speed by converting weights and activations from floating-point to lower precision formats.

What are the benefits of using quantization in ONNX models?

The main benefits include reduced model size, faster inference times, and lower memory usage, which is crucial for deploying models on edge devices.

Are there any downsides to quantization?

Yes, quantization can lead to a loss in model accuracy, especially if not done carefully. It's important to evaluate the trade-offs.

How can I implement quantization in my ONNX model?

You can use tools like ONNX Runtime or the ONNX Model Optimization Toolkit to apply quantization techniques to your model.