Raspberry Pi deployment in Computer Vision - Model Pipeline Trace

Model Pipeline - Raspberry Pi deployment

This pipeline shows how a computer vision model is prepared and deployed on a Raspberry Pi device. It starts with image data, processes it, trains a model on a computer, then transfers the model to the Raspberry Pi for real-time predictions.

Data Flow - 6 Stages

1Image Data Collection

1000 images x 64x64 pixels x 3 color channels→Collect raw images of objects for training→1000 images x 64x64 pixels x 3 color channels

Image of a red apple, 64x64 pixels, RGB

↓

2Preprocessing

1000 images x 64x64 pixels x 3 channels→Resize images, normalize pixel values to 0-1 range→1000 images x 64x64 pixels x 3 channels

Pixel values scaled from 0-255 to 0.0-1.0

↓

3Feature Engineering

1000 images x 64x64 x 3→Convert images to tensors for model input→1000 samples x 64 x 64 x 3 tensor

Tensor representing normalized image pixels

↓

4Model Training

1000 samples x 64 x 64 x 3→Train CNN model on computer with GPU→Trained CNN model file

Model learns to classify apples vs oranges

↓

5Model Conversion

Trained CNN model file→Convert model to TensorFlow Lite format for Raspberry Pi→TensorFlow Lite model file (.tflite)

Smaller model file optimized for Raspberry Pi

↓

6Deployment

TensorFlow Lite model file→Copy model to Raspberry Pi and run inference script→Real-time predictions on Raspberry Pi

Raspberry Pi camera captures image, model predicts 'apple'

Training Trace - Epoch by Epoch

Loss
1.2 |****
0.9 |***
0.6 |**
0.4 |*
0.3 |

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.55	Model starts learning, accuracy above random guess
2	0.9	0.70	Loss decreases, accuracy improves significantly
3	0.6	0.82	Model learns important features, accuracy rises
4	0.4	0.90	Good convergence, loss low and accuracy high
5	0.3	0.93	Training stabilizes with strong accuracy

Prediction Trace - 5 Layers

Layer 1: Input Image Capture

Layer 2: Convolutional Layer

Layer 3: Pooling Layer

Layer 4: Fully Connected Layer

Layer 5: Softmax Activation

Model Quiz - 3 Questions

What is the main reason to convert the model to TensorFlow Lite before deploying on Raspberry Pi?

A To make the model smaller and faster for Raspberry Pi

B To increase the model's accuracy

C To add more layers to the model

D To change the input image size

Key Insight

Deploying a computer vision model on Raspberry Pi requires careful preparation: training on a powerful computer, converting the model to a lightweight format, and running efficient inference on the device. This process balances accuracy and speed for real-time predictions.