Deep learning using Tensorflow Lite on Raspberry Pi

Set up Raspberry Pi OS headless with Raspberry Pi Imager, enable SSH and wifi, locate the Pi with Nmap, and access the desktop via TightVNC server and VNC viewer.

Assemble and configure hardware to deploy deep learning models with TensorFlow Lite on a Raspberry Pi, enabling efficient edge inference.

Generate data for this project by printing numbers on A4 sheets from Canva or Word, extracting them from video frames with a Raspberry Pi camera, and applying half transforms.

Design a Canva front end to create circle-enclosed digits and symbols, prepare circle-inside-square images for OpenCV and CNN, and export as pdf for neural network training.

connect to the raspberry pi via vscode remote ssh, enable the display with vnc, install opencv and required libraries, and clone the development branch to write a camera interface.

Create a Raspberry Pi video saving script that captures camera feed, saves video with OpenCV's video writer, and integrates git workflow for data used in deep learning with TensorFlow Lite.

Learn to obtain data videos from a Raspberry Pi camera, transfer them to your system using scp, and run a data extractor that saves thousands of frames for processing.

Learn how to extract frames from a video using cv2, save them with unique names in a structured folder, and prepare data for machine learning on Raspberry Pi.

Learn to extract the region of interest around a detected circle by converting frames to grayscale, applying OpenCV's Hough circles, and computing center x, y, and radius.

Extracts the image while preserving the circle, applying OpenCV to validate positive x and y. Crops the region of interest, resizes to 500 by 500, and extracts the number.

Learn to obtain and preprocess data for deep learning by downloading, extracting, and organizing images, then build a data pipeline with Keras image data generators and training-validation splits and augmentation.

Visualize training data in Google Colab and print class indices to map folder names to labels. Note that alphabetic order assigns zero to divide and one to eight.

Explore the cnn-based architecture for a grayscale image classifier with ten hidden layers and a 13-class output, using convolutional networks, sigmoid activation, categorical cross-entropy loss, and Adam optimizer.

Develop and train a grayscale 128 by 128 convnet with multiple convolution and pooling layers, culminating in a 13-class softmax, using categorical cross entropy and early stopping.

Save the trained visual calculator model to an .h5 file and save the weights, then download, load, and test the model to continue from this point.

Test the trained visual calculator model on Raspberry Pi with prediction and accuracy checks, loss and validation matrices, and a size test against TensorFlow Lite.

Explore how a trained model maps prediction indices to class labels, evaluate accuracy and validation loss, plot history, and interpret matrices to understand model performance and early stopping.

Learn post quantization to shrink TensorFlow Lite models for embedded devices like Raspberry Pi, reducing size and boosting inference speed while trading off some accuracy.

Convert your visual calculator model to a tf lite version using the tflite converter, save the model, compare sizes in MB, and run lite interpreter inference for deployment readiness.

Learn to test a TensorFlow Lite model on Raspberry Pi by extracting input and output details, allocating tensors, and running predictions with an interpreter. Explore optimization for size and latency.

Deploy a TensorFlow Lite model on a Raspberry Pi for real-time predictions, loading the flight version into a local script and running ROI-based in-device inference.

Preprocess recorded data for inference on Raspberry Pi by resizing to 128x128, expanding to 1x128x128, and converting to float32 for TensorFlow Lite.

Define a square region of interest in the real-time video feed and crop to this ROI for on-device predictions, moving from recorded video to real-time inference.

Explore region-of-interest based circle detection inside a square frame, using targeted crops and lighting-aware predictions, with real-time display logic and Raspberry Pi deployment for machine learning inference.

Install TensorFlow Lite for Python on Raspberry Pi, then ssh in to run the inferencing script. Transfer model files and enable vnc to monitor with the camera.

Demonstrates real-time inferencing on a raspberry pi using tensorflow lite, with scripts and model organization, running a square region video feed, and diagnosing performance issues.

Build an equation from Raspberry Pi model predictions, optimize output by adjusting lighting and code logic, and use a prediction array and tester function to append unique consecutive results.

Explore isolating number detection by managing a prediction array, avoiding duplicates, handling length and index edge cases, and ensuring integers replace class labels for practical use.

Apply a dynamic operator list to compute equations on a Raspberry Pi using TensorFlow Lite, performing add, subtract, multiply, divide, and floor divide on model predictions to form equation results.

Learn to push code frequently on GitHub, implement version control, and document projects with a clear readme. Explore branching and commenting to support Raspberry Pi TensorFlow Lite work.

We explain how wav audio files capture voice data as a discrete 16-bit signal, with 22,050 samples per second and amplitude range from -32768 to 32767 for use in TensorFlow.

Organize data collection for a CNN-based audio model by recording one-second mono samples with a microphone, saving as wav files, and structuring project folders for data, scripts, and notebooks.

Organize the audio data into category folders with structured file naming, then convert the recorded float audio to 16-bit integers by clipping and scaling before saving as mp16.

Record and batch voice data efficiently for a TensorFlow Lite model on Raspberry Pi by converting floats to integers and preparing labeled batches for training on Google Colab.

Convert wav files to tensors and audio tensors using TensorFlow in Google Colab. Build a pipeline that reads wav files, decodes to audio tensors, and derives labels for model training.

Convert wav files into waveforms and labels, build TensorFlow data pipelines, map data to features, and visualize labeled waveforms while preparing for spectrograms in deep learning on Raspberry Pi.

Convert labeled waveforms to spectrograms using the signal library’s short-time Fourier transform, then convert to tensors and split the data into training, validation, and testing sets on Raspberry Pi.

Create labeled spectrogram data by converting raw audio parts to spectrograms, map waveforms to labeled tensors, and prepare training, validation, and testing datasets for a deep learning model.

Define and validate input shapes from spectrogram waveforms, fix unknown dimensions in a TensorFlow map dataset, and set up one-batch training with normalization and dense layers for model definition.

Train a small convolutional neural network for voice control with 4–5 hidden layers and a 32×32 spectrogram input, four output classes, softmax, and sparse categorical cross-entropy loss with adam optimizer.

Compile the audio_led model and test variants, adjusting optimizers like adam and loss functions such as sparse categorical cross entropy, while tracking accuracy on training and validation data.

Compare three models by loss and accuracy, address data issues, and select model three with improved validation performance, then run predictions and prepare confusion matrices on test data.

Explore evaluating a TensorFlow Lite model on Raspberry Pi using a confusion matrix, comparing model predictions to actual labels, and assessing accuracy and potential overfitting with a small test dataset.

Convert the trained model to TensorFlow Lite, save it as LED audio LCD model three, and compare size and performance. Run inference on microphone-derived spectrograms for Raspberry Pi deployment.

Build an input audio stream pipeline on the raspberry pi by reading microphone data, converting to 16-bit, reshaping, and logging predictions from a tensorflow lite model.

Learn to convert audio waveforms to spectrograms for a TensorFlow Lite model on a Raspberry Pi, reshape inputs with NumPy, and run predictions with arg max.

Demonstrate LED blinking on a Raspberry Pi using the RPi.GPIO library, wiring LEDs to BCM pins 20 and 21, and run a loop while preparing to add predicting code.

Set up a Raspberry Pi with a microphone to run model predictions and blink LEDs driven by voice labels.

Demonstrates audio driven LED control on a Raspberry Pi using a TensorFlow Lite model, with green and red LEDs responding to microphone input, and notes on improving accuracy.

Push your deep learning project to GitHub, merge development into main, and document installations and updates while planning future Raspberry Pi TensorFlow Lite experiments with custom data.