Building Your First AI Project with NVIDIA® Jetson Orin™ Nano

Building Your First AI Project with NVIDIA® Jetson Orin™ Nano

July 3, 2024

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Artificial Intelligence (AI) is transforming various industries. From healthcare to automotive, AI applications are everywhere. If you're new to AI, starting with a project can seem daunting. However, with the right tools, you can build a successful AI project. One such tool is the NVIDIA® Jetson Orin™ Nano. This blog will guide you through building your first AI project with the Jetson Orin Nano.

What is the NVIDIA® Jetson Orin™ Nano?

NVIDIA® Jetson Orin™ Nano

The NVIDIA® Jetson Orin™ Nano is a compact, powerful AI computing device. It is designed for embedded and edge applications. Despite its small size, it offers impressive computing power. This makes it an excellent choice for AI projects.

Key Features

High Performance: The Jetson Orin Nano delivers up to 40 TOPS (Tera Operations Per Second) of AI performance. This allows it to handle complex AI tasks efficiently.
Energy Efficient: It is designed to consume low power, making it ideal for battery-operated devices.
Versatile Connectivity: The device supports various peripherals and interfaces, including USB, HDMI, and Ethernet.
Comprehensive Software Support: It comes with the NVIDIA JetPack SDK, which includes libraries and tools for AI development.

Setting Up Your Jetson Orin Nano

Setting Up

Before you start your AI project, you need to set up your Jetson Orin Nano. Here are the steps:

Step 1: Unbox Your Jetson Orin Nano

Carefully unbox your Jetson Orin Nano. Inside, you'll find the Nano module, a power adapter, and a quick start guide.

Step 2: Install the Jetson Orin Nano

Connect the Nano module to a power source using the provided adapter. Connect a monitor, keyboard, and mouse to the module.

Step 3: Flash the OS

Download the latest JetPack SDK from the NVIDIA website. Flash the JetPack OS onto an SD card. Insert the SD card into the Nano module and power it on. Follow the on-screen instructions to complete the setup.

Step 4: Install Development Tools

Once the OS is installed, open the terminal. Update the package list using the command:

bash

sudo apt-get update

Install necessary development tools:

bash

sudo apt-get install build-essential cmake git

Step 5: Set Up Python Environment

Python is widely used in AI development. Install Python and pip:

bash

sudo apt-get install python3 python3-pip

Create a virtual environment for your project:

bash

python3 -m venv myenv
source myenv/bin/activate

Choosing an AI Project

AI Project

Choosing the right project is crucial for beginners. Start with a simple project to build your confidence. Here are some ideas:

Object Detection: Use your Jetson Orin Nano to detect objects in images or video streams.
Face Recognition: Build a system that can recognize and identify faces.
Speech Recognition: Develop a model that can convert speech to text.
Gesture Control: Create an application that responds to hand gestures.

For this blog, we will focus on an object detection project.

Building an Object Detection Project

Object Detection

Object detection involves identifying and locating objects within an image or video. We'll use a pre-trained model to simplify the process.

Step 1: Install Required Libraries

Activate your virtual environment and install the required libraries:

bash

pip install numpy opencv-python torch torchvision

Step 2: Download a Pre-trained Model

We'll use a pre-trained YOLO (You Only Look Once) model. Download the model weights and configuration files from the official YOLO website.

Step 3: Write the Detection Script

Create a new Python file, object_detection.py. Import the necessary libraries:

python

import cv2
import torch

Load the pre-trained YOLO model:

python

model = torch.hub.load('ultralytics/yolov5', 'yolov5s')

Write a function to perform object detection on an image:

python

def detect_objects(image_path):
img = cv2.imread(image_path)
results = model(img)
results.show()

Step 4: Test Your Script

Save an image to your project directory. Run your script with the image path as an argument:

bash

python object_detection.py --image-path sample.jpg

The script will display the image with detected objects highlighted.

Enhancing Your Project

Once you have a basic object detection project, you can enhance it with additional features.

Real-Time Detection

Real-Time Detection

Modify your script to process video streams for real-time object detection:

python

def detect_objects_video(video_path):
cap = cv2.VideoCapture(video_path)
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
results = model(frame)
results.show()
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()

Adding a GUI

Create a simple graphical user interface (GUI) using the Tkinter library:

python

import tkinter as tk
from tkinter import filedialog

def open_file():
file_path = filedialog.askopenfilename()
detect_objects(file_path)

root = tk.Tk()
root.title("Object Detection")
button = tk.Button(root, text="Open Image", command=open_file)
button.pack()
root.mainloop()

Deploying Your Project

Deploying Your Project

Once your project is ready, you can deploy it on various platforms. The Jetson Orin Nano supports edge deployment, making it ideal for applications like security systems, robots, and drones.

Optimizing for Edge Deployment

To optimize your model for edge deployment, use NVIDIA TensorRT. TensorRT is a high-performance deep learning inference library. It helps reduce latency and improve throughput.

Step 1: Install TensorRT

Install TensorRT using the following command:

bash

sudo apt-get install nvidia-tensorrt

Step 2: Convert Your Model

Convert your YOLO model to a TensorRT engine:

python

import tensorrt as trt

def build_engine(model_path):
with trt.Builder(trt.Logger()) as builder, builder.create_network() as network:
parser = trt.OnnxParser(network, trt.Logger())
with open(model_path, 'rb') as model_file:
parser.parse(model_file.read())
return builder.build_cuda_engine(network)

Step 3: Run Inference with TensorRT

Use the TensorRT engine for inference:

python

def infer_with_tensorrt(engine, image_path):
img = cv2.imread(image_path)
# Prepare image for inference
# Run inference
# Process and display results

Conclusion

Building your first AI project with the NVIDIA® Jetson Orin™ Nano is an exciting journey. This powerful device offers the performance and flexibility needed for a wide range of AI applications. By following this guide, you can set up your Jetson Orin Nano, choose a project, and build a working AI application. With practice, you'll gain the skills to tackle more complex AI challenges.

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