ONNX Runtime in Computer Vision - Model Pipeline Trace

Model Pipeline - ONNX Runtime

ONNX Runtime helps run machine learning models fast and efficiently. It takes a trained model and uses it to make predictions on new images quickly.

Data Flow - 4 Stages

1Input Image

1 image x 224 x 224 x 3 channels→Load and resize image to 224x224 pixels with 3 color channels (RGB)→1 image x 224 x 224 x 3 channels

A photo of a cat resized to 224x224 pixels

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2Preprocessing

1 image x 224 x 224 x 3 channels→Normalize pixel values to range 0-1 and reorder dimensions to match model input→1 image x 3 channels x 224 x 224

Pixel values converted from 0-255 to 0-1 and channels moved to first dimension

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3ONNX Runtime Model Inference

1 image x 3 channels x 224 x 224→Run the ONNX model to predict image class probabilities→1 image x 1000 classes

Model outputs probabilities for 1000 possible object classes

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4Postprocessing

1 image x 1000 classes→Select class with highest probability as prediction→1 predicted class label

Predicted label: 'tabby cat'

Training Trace - Epoch by Epoch

Epoch 1: *************** (loss=1.8)
Epoch 2: ************ (loss=1.2)
Epoch 3: ********* (loss=0.9)
Epoch 4: ******* (loss=0.7)
Epoch 5: ****** (loss=0.6)

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.8	0.35	Model starts learning with high loss and low accuracy
2	1.2	0.55	Loss decreases and accuracy improves as model learns features
3	0.9	0.70	Model continues to improve, learning better representations
4	0.7	0.78	Loss lowers steadily, accuracy nearing good performance
5	0.6	0.82	Model converges with stable loss and high accuracy

Prediction Trace - 4 Layers

Layer 1: Input Image

Layer 2: Preprocessing

Layer 3: ONNX Runtime Inference

Layer 4: Postprocessing

Model Quiz - 3 Questions

What shape does the image have after preprocessing before model input?

A 224 x 224 x 3 channels

B 1 image x 3 channels x 224 x 224

C 1 image x 224 x 224 x 3 channels

D 3 channels x 224 x 224

Key Insight

ONNX Runtime efficiently runs trained models by taking preprocessed images and quickly producing class predictions. The training process shows steady improvement in accuracy as loss decreases, ensuring reliable results during inference.