004-Optimization-with-OpenVINO

Optimization with OpenVINO

OpenVINO is a toolkit developed by Intel to optimize neural network models for efficient deployment across a wide range of hardware platforms, including CPUs, GPUs, and FPGAs. It provides pre-trained models, model conversion tools, and hardware-specific optimizations to accelerate inference performance while maintaining accuracy.

Virtual Environment Setup

Create a virtual environment for the notebooks using the provided script:

#!/bin/bash
# Step 1: Create a virtual environment for the notebooks
echo "Creating virtual environment for notebooks" &&
python3 -m venv .openvino_venv &&

# Step 2: Activate the virtual environment
echo "Activating virtual environment" &&
source .openvino_venv/bin/activate &&

# Install necessary dependencies
pip install --upgrade pip &&
pip install --no-deps openvino openvino-dev nncf &&
pip install -r requirements.txt

Optimizing YOLOv8 with OpenVINO

For optimizing the YOLOv8 model using OpenVINO, follow these steps:

1. Make sure you have the necessary YOLOv8 model checkpoint and configuration files prepared.

2. Adjust the file paths in the main.py script according to your case.

3. Run the main.py script in your virtual environment, which you've set up using the provided instructions. This script should contain the logic to load the YOLOv8 model, perform OpenVINO optimization, and save the optimized models for deployment.