Quad-core ARM Cortex-A57 MPCore processor
Quad-core ARM Cortex-A57 MPCore processor
NVIDIA Maxwell architecture with 128 CUDA cores
2GB LPDDR4 RAM
16GB eMMC 5.1 flash storage
Up to 1.43 GHz
### AI and Machine Learning Capabilities
Compatible with popular AI frameworks, enabling rapid development and deployment of AI models
Leverages NVIDIA's GPU-accelerated computing and deep learning libraries for efficient AI processing
### Connectivity and Expansion
| Wi-Fi and Bluetooth | Integrated Wi-Fi 5 (802.11ac) and Bluetooth 5.0 for wireless connectivity |
Gigabit Ethernet port for wired connectivity
4x USB 3.0 ports for peripherals and accessories
1x HDMI 2.0 port for 4K video output
1x MIPI CSI-2 camera interface for camera modules
| GPIO and I2C | 40-pin GPIO header and I2C bus for custom peripherals and sensors |
### Power and Operating Environment
As low as 5W, making it suitable for battery-powered or always-on applications
Compatible with Ubuntu Linux and other Linux distributions
-20C to 80C (-4F to 176F)
### Development and Accessories
Includes the Jetson Nano module, carrier board, power supply, and accessories for rapid development and prototyping
| Software Development Kit (SDK) | Comprehensive SDK with libraries, APIs, and documentation for AI, computer vision, and multimedia applications |
Functionality
| The NVIDIA 2GB Jetson Nano Developer Kit is designed for a wide range of applications, including |
AI-powered edge computing
Robotics and autonomous systems
Intelligent video analytics
Smart home and building automation
IoT gateways and devices
Industrial control and automation
This developer kit enables developers to create complex AI and IoT projects with ease, leveraging the power of NVIDIA's AI computing platform and the flexibility of the Jetson Nano module.
Target Audience
| The NVIDIA 2GB Jetson Nano Developer Kit is ideal for |
AI and machine learning developers
Robotics and IoT engineers
Embedded system designers
Students and researchers in AI, robotics, and IoT fields
Hobbyists and makers interested in AI and IoT projects
NVIDIA 2GB Jetson Nano Developer Kit DocumentationOverviewThe NVIDIA 2GB Jetson Nano Developer Kit is a small, powerful, and low-power AI computer used for developing and deploying AI-powered robots, drones, intelligent sensors, and other IoT devices. This developer kit is ideal for developers, hobbyists, and students who want to explore AI and machine learning in edge computing applications.Hardware SpecificationsNVIDIA Jetson Nano System-on-Module (SoM)
2GB LPDDR4 memory
128-core NVIDIA Maxwell GPU with 64-bit CPU
128GB microSD card slot
HDMI, USB, and Ethernet interfaces
Wi-Fi and Bluetooth connectivity
Support for Linux, Python, and CUDA development environmentsSoftware SetupTo get started with the NVIDIA 2GB Jetson Nano Developer Kit, you'll need to set up the software development environment. Follow these steps:1. Install the JetPack SDK on your host machine (Windows, macOS, or Linux).
2. Flash the Jetson Nano module with the latest image using the SDK.
3. Connect to the Jetson Nano module using a serial console or SSH.Code Examples### Example 1: Object Detection using TensorFlow and OpenCVIn this example, we'll use TensorFlow and OpenCV to detect objects in real-time using the Jetson Nano's camera module.Python Code
```python
import cv2
import tensorflow as tf# Load the TensorFlow model
model = tf.keras.models.load_model('model.h5')# Open the camera module
cap = cv2.VideoCapture(0)while True:
# Capture a frame
ret, frame = cap.read()
if not ret:
break# Preprocess the frame
frame = cv2.resize(frame, (224, 224))
frame = frame / 255.0# Run the object detection model
outputs = model.predict(frame)# Draw bounding boxes around detected objects
for i in range(outputs.shape[2]):
confidence = outputs[0, 0, i, 2]
if confidence > 0.5:
x, y, w, h = outputs[0, 0, i, 3:]
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)# Display the output
cv2.imshow('Object Detection', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
breakcap.release()
cv2.destroyAllWindows()
```
This code assumes you have a TensorFlow model trained for object detection, and it uses OpenCV to capture and display the camera feed. The Jetson Nano's GPU accelerates the model's prediction and performs the object detection in real-time.### Example 2: Image Classification using PyTorch and NVIDIA DALIIn this example, we'll use PyTorch and NVIDIA's DALI (Data Loading and Augmentation) library to classify images using a convolutional neural network (CNN) on the Jetson Nano.Python Code
```python
import torch
import torchvision
import nvidia.dali as dali# Load the PyTorch model
model = torchvision.models.resnet50(pretrained=True)# Create a DALI pipeline for image loading and augmentation
pipeline = dali.pipeline.Pipeline(batch_size=1, num_threads=2, device_id=0)
pipeline.set_max_outputs(1)
pipeline.set_outputs([
dali.fn.read_file('images/'),
dali.fn.resize(256, 256),
dali.fn.normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])# Create a PyTorch dataset and data loader
dataset = dali.DataSet(pipeline, ['image'])
data_loader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=True)# Run the image classification model
for batch in data_loader:
image = batch[0].to('cuda:0')
output = model(image)
_, predicted = torch.max(output, 1)
print(f'Predicted class: {predicted.item()}')
```
This code assumes you have a PyTorch model trained for image classification, and it uses DALI to load and preprocess the images on the Jetson Nano. The NVIDIA DALI library optimizes data loading and augmentation for the Jetson Nano's GPU, allowing for fast and efficient image classification.These examples demonstrate the capabilities of the NVIDIA 2GB Jetson Nano Developer Kit for AI-powered applications in edge computing. The kit's compact size, low power consumption, and powerful NVIDIA GPU make it an ideal platform for developing and deploying AI models in IoT devices.