Jetson Nano deployment in Computer Vision - Model Pipeline Trace

Model Pipeline - Jetson Nano deployment

This pipeline shows how a trained computer vision model is prepared and deployed on a Jetson Nano device to make real-time predictions from camera input.

Data Flow - 5 Stages

1Data Capture

N/A→Capture live images from camera connected to Jetson Nano→1 frame x 224 x 224 x 3 (RGB image)

A 224x224 pixel color image of a cat

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2Preprocessing

1 frame x 224 x 224 x 3→Resize, normalize pixel values to 0-1 range→1 frame x 224 x 224 x 3 (normalized)

Image pixels scaled from 0-255 to 0.0-1.0

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3Model Inference

1 frame x 224 x 224 x 3→Run image through optimized deep learning model on Jetson Nano→1 x 1000 (class probabilities)

Output vector with probabilities for 1000 classes

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4Postprocessing

1 x 1000→Select class with highest probability→1 label (string)

"tabby cat"

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5Display/Action

1 label→Show prediction on screen or trigger action→N/A

Display text 'tabby cat' on monitor

Training Trace - Epoch by Epoch

Epoch 1: ************ (loss=1.2)
Epoch 2: ******** (loss=0.9)
Epoch 3: ****** (loss=0.7)
Epoch 4: **** (loss=0.5)
Epoch 5: *** (loss=0.45)

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.55	Model starts learning basic features
2	0.9	0.68	Loss decreases, accuracy improves
3	0.7	0.75	Model captures more complex patterns
4	0.5	0.82	Good convergence, ready for deployment
5	0.45	0.85	Final fine-tuning before export

Prediction Trace - 5 Layers

Layer 1: Input Image

Layer 2: Convolutional Layers

Layer 3: Fully Connected Layer

Layer 4: Softmax Activation

Layer 5: Class Selection

Model Quiz - 3 Questions

What happens to the image pixels during preprocessing on Jetson Nano?

A Pixels are randomly shuffled

B Pixels are converted to grayscale

C Pixels are scaled from 0-255 to 0.0-1.0

D Pixels are increased to 0-512 range

Key Insight

Deploying a computer vision model on Jetson Nano involves capturing live images, preprocessing them, running inference with an optimized model, and interpreting the output probabilities to make real-time predictions. Training shows steady improvement in accuracy and loss, ensuring reliable deployment.