YOLOv8 Model Quantization

Introduction

This README provides instructions on quantizing YOLOv8 models to optimize them for deployment using ONNX and TensorFlow Lite (TFLite). Quantization is the process of reducing the precision of the model's weights and biases, which can significantly reduce the model size and improve inference speed with minimal impact on accuracy.

Note: This guide assumes you have a basic understanding of YOLOv8, ONNX, and TensorFlow Lite.

Prerequisites

Before you begin, make sure you have the following prerequisites installed:

YOLOv8 Model: You should have a pre-trained YOLOv8 model in PyTorch format (*.pt file).
Python: Ensure you have Python installed on your system.
ONNX: Install the ONNX package using pip install onnx.
TensorFlow Lite: Install TensorFlow Lite using pip install tflite.

Steps for Quantization

1. Convert YOLOv8 Model to ONNX Format

Convert the PyTorch YOLOv8 model to ONNX format using the torch.onnx module.

import torch
import torch.onnx

# Load the PyTorch model
model = torch.load('yolov8.pt')

# Set the model to evaluation mode
model.eval()

# Prepare dummy input data (adjust the input shape according to your model)
dummy_input = torch.randn(1, 3, 416, 416)

# Export the model to ONNX format
torch.onnx.export(model, dummy_input, 'yolov8.onnx', verbose=True, input_names=['input'], output_names=['output'])

2. Quantize the ONNX Model

Quantize the exported ONNX model using ONNX's quantization tool. This step will reduce the precision of the model.

# Quantize the ONNX model
onnxruntime.quantization.quantize('yolov8.onnx', 'yolov8_quantized.onnx')

3. Convert Quantized ONNX Model to TFLite Format

Convert the quantized ONNX model to TensorFlow Lite format.

import onnx
from onnx_tf.backend import prepare

# Load the quantized ONNX model
onnx_model = onnx.load('yolov8_quantized.onnx')

# Convert the ONNX model to TFLite format
tf_rep = prepare(onnx_model)
tflite_model = tf_rep.export_graph('yolov8_quantized.tflite')

4. Quantize the TFLite Model (Post-training Quantization)

Quantize the exported TFLite model. Post-training quantization can further reduce the model size and improve inference speed.

import tensorflow as tf

# Load the TFLite model
tflite_interpreter = tf.lite.Interpreter(model_content=tflite_model)

# Allocate tensors and invoke the interpreter
tflite_interpreter.allocate_tensors()

# Perform post-training quantization
converter = tf.lite.TFLiteConverter.from_interpreter(tflite_interpreter)
converter.optimizations = [tf.lite.Optimize.DEFAULT]

quantized_tflite_model = converter.convert()

# Save the quantized TFLite model to a file
with open('yolov8_quantized.tflite', 'wb') as f:
    f.write(quantized_tflite_model)

Conclusion

You have successfully quantized the YOLOv8 model to ONNX and TensorFlow Lite formats, optimizing it for deployment on resource-constrained devices. Remember to test the quantized models thoroughly to ensure they meet your performance and accuracy requirements. For detailed information on quantization techniques and fine-tuning options, refer to the documentation of ONNX and TensorFlow Lite.

saidislombek-abdusamatov/quantization