Mobile deployment (TFLite, Core ML) in Computer Vision - Model Pipeline Trace

Model Pipeline - Mobile deployment (TFLite, Core ML)

This pipeline shows how a computer vision model is prepared and deployed on mobile devices using TensorFlow Lite (TFLite) and Core ML. It covers data input, model conversion, optimization, and running predictions on mobile.

Data Flow - 6 Stages

1Original Model Training

1000 images x 224 x 224 x 3→Train CNN model on full dataset→Model weights and architecture

Images of cats and dogs with labels

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2Model Conversion to TFLite

Model weights and architecture→Convert TensorFlow model to TFLite format→TFLite model file (.tflite)

Converted model optimized for mobile

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3Model Conversion to Core ML

Model weights and architecture→Convert TensorFlow or Keras model to Core ML format→Core ML model file (.mlmodel)

Model ready for iOS deployment

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4Model Optimization

TFLite or Core ML model file→Apply quantization and pruning to reduce size→Optimized model file

Smaller model with reduced precision

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5Mobile App Integration

Optimized model file→Embed model into mobile app and prepare input pipeline→Mobile app with embedded model

App takes camera input and runs model

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6On-device Prediction

Single image 224 x 224 x 3→Run inference using TFLite or Core ML runtime→Prediction probabilities for classes

Model predicts 'dog' with 0.92 confidence

Training Trace - Epoch by Epoch

Loss
1.2 |****
1.0 |*** 
0.8 |**  
0.6 |*   
0.4 |**  
0.2 |*   
    +---------
     1 3 5 7 10 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.55	Model starts learning basic features
3	0.8	0.72	Accuracy improves as model learns patterns
5	0.5	0.85	Model converges with good accuracy
7	0.4	0.90	Loss decreases steadily, accuracy high
10	0.35	0.92	Training stabilizes with low loss

Prediction Trace - 3 Layers

Layer 1: Input Image Preprocessing

Layer 2: TFLite/Core ML Model Inference

Layer 3: Post-processing

Model Quiz - 3 Questions

What is the main reason to convert a model to TFLite or Core ML format?

A To add more layers to the model

B To increase the model's training accuracy

C To make the model smaller and faster on mobile devices

D To change the model's input image size

Key Insight

Deploying models on mobile requires converting and optimizing them to run efficiently on limited hardware. This pipeline shows how a trained computer vision model is prepared for mobile use with TFLite and Core ML, ensuring fast and accurate predictions on-device.