Jetson Nano deployment in Computer Vision

Introduction

Jetson Nano deployment lets you run AI models on a small, low-power device near cameras or sensors. This helps make smart decisions fast without needing the internet.

You want to run a camera-based AI app at home without sending data to the cloud.

You need a robot to recognize objects and act quickly on the spot.

You want to build a smart security camera that alerts you instantly.

You are making a drone that uses AI to avoid obstacles in real time.

You want to test AI models in a small device before scaling up.

Syntax

Computer Vision

1. Prepare your trained AI model (e.g., TensorFlow, PyTorch).
2. Convert the model to a format Jetson Nano supports (e.g., TensorRT).
3. Transfer the model to Jetson Nano.
4. Write a Python script to load the model and run inference.
5. Run the script on Jetson Nano to get predictions.

Jetson Nano uses NVIDIA's TensorRT for fast AI model inference.

Python is commonly used to write deployment scripts on Jetson Nano.

Examples

This converts a PyTorch model to ONNX format, which can be optimized for Jetson Nano.

Computer Vision

# Convert PyTorch model to ONNX
import torch
model = torch.load('model.pth')
dummy_input = torch.randn(1, 3, 224, 224)
torch.onnx.export(model, dummy_input, 'model.onnx')

This command creates a TensorRT engine file for faster inference on Jetson Nano.

Computer Vision

# Use TensorRT to optimize ONNX model
!trtexec --onnx=model.onnx --saveEngine=model.trt

This Python code loads the TensorRT engine on Jetson Nano for inference.

Computer Vision

import tensorrt as trt
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
with open('model.trt', 'rb') as f:
    engine_data = f.read()
runtime = trt.Runtime(TRT_LOGGER)
engine = runtime.deserialize_cuda_engine(engine_data)

Sample Model

This code loads a TensorRT model on Jetson Nano, runs inference on a dummy image, and prints the prediction shape and first 5 values.

Computer Vision

import cv2
import numpy as np
import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit

TRT_LOGGER = trt.Logger(trt.Logger.WARNING)

# Load TensorRT engine
with open('model.trt', 'rb') as f:
    engine_data = f.read()
runtime = trt.Runtime(TRT_LOGGER)
engine = runtime.deserialize_cuda_engine(engine_data)

# Create execution context
context = engine.create_execution_context()

# Prepare input data (dummy image)
input_shape = (1, 3, 224, 224)
input_data = np.random.random(input_shape).astype(np.float32)

# Allocate device memory
d_input = cuda.mem_alloc(input_data.nbytes)
output = np.empty([1, 1000], dtype=np.float32)  # example output size
d_output = cuda.mem_alloc(output.nbytes)

# Create CUDA stream
stream = cuda.Stream()

# Transfer input data to device
cuda.memcpy_htod_async(d_input, input_data, stream)

# Run inference
context.execute_async_v2(bindings=[int(d_input), int(d_output)], stream_handle=stream.handle)

# Transfer predictions back
cuda.memcpy_dtoh_async(output, d_output, stream)

# Synchronize stream
stream.synchronize()

print('Predictions shape:', output.shape)
print('Sample predictions:', output[0][:5])

Important Notes

Make sure Jetson Nano has all required NVIDIA libraries installed (TensorRT, CUDA).

Use small batch sizes on Jetson Nano to fit memory limits.

Test your model on a PC first before deploying to Jetson Nano.

Summary

Jetson Nano deployment runs AI models locally on a small device.

Convert models to TensorRT for fast inference on Jetson Nano.

Use Python and NVIDIA libraries to load models and get predictions.