Raspberry Pi deployment in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is Raspberry Pi deployment in machine learning?

It means running a machine learning model on a Raspberry Pi device to make predictions or process data locally, without needing a powerful computer.

beginner

Why use Raspberry Pi for deploying computer vision models?

Because Raspberry Pi is small, affordable, and can run models near cameras or sensors, making it good for real-time image processing in places without internet.

intermediate

What is a common way to optimize models for Raspberry Pi deployment?

Models are often made smaller and faster using techniques like quantization or pruning, so they run well on the Pi's limited memory and CPU.

beginner

Name a popular framework to run computer vision models on Raspberry Pi.

TensorFlow Lite is popular because it is designed to run lightweight models efficiently on devices like Raspberry Pi.

intermediate

What is a key challenge when deploying ML models on Raspberry Pi?

Limited processing power and memory require careful model selection and optimization to ensure fast and accurate predictions.

What is the main benefit of deploying a model on Raspberry Pi?

A Run ML models locally without internet

B Use unlimited cloud storage

C Train models faster than GPUs

D Automatically improve model accuracy

Raspberry Pi deployment allows running models locally, which is useful when internet or cloud access is limited.

Which technique helps make models smaller for Raspberry Pi?

A Data augmentation

B Quantization

C Batch normalization

D Dropout

Quantization reduces model size by using fewer bits to represent numbers, making it faster on devices like Raspberry Pi.

Which framework is designed for lightweight ML on Raspberry Pi?

A TensorFlow Lite

B PyTorch Lightning

C Scikit-learn

D Keras

TensorFlow Lite is optimized for running small models efficiently on devices with limited resources.

What is a common hardware limitation of Raspberry Pi for ML?

A No power supply

B No USB ports

C Limited CPU and memory

D No Wi-Fi support

Raspberry Pi has limited CPU speed and memory compared to desktops, so models must be optimized.

Why is Raspberry Pi good for edge computing in computer vision?

A Requires no power

B Has built-in GPU for training

C Automatically labels images

D Processes data near the camera without sending to cloud

Edge computing means processing data close to where it is collected, reducing delay and internet use.

Explain how you would prepare a computer vision model for deployment on a Raspberry Pi.

Think about making the model smaller and faster to run on limited hardware.

Describe the advantages and challenges of deploying machine learning models on Raspberry Pi devices.

Consider both what makes Raspberry Pi useful and what makes deployment tricky.