Why Run ML on Raspberry Pi?
Edge AI brings machine learning to where the data is. No cloud dependency, lower latency, privacy-preserving. Raspberry Pi 4/5 can run neural networks for real-time inference.
Edge ML Benefits
- Low Latency: Instant local inference
- Privacy: Data stays on device
- Offline: No internet required
- Cost: No cloud API fees
- Embedded: Deploy anywhere
ML Frameworks for Pi
| Framework | Best For | Performance |
|---|---|---|
| TensorFlow Lite | General inference | Excellent |
| PyTorch Mobile | Research models | Good |
| ONNX Runtime | Cross-platform | Good |
| OpenCV DNN | Computer vision | Fast |
| Edge TPU | Hardware accelerated | Fastest |
TensorFlow Lite Setup
# Install TensorFlow Lite runtime
pip3 install tflite-runtime
# For full TensorFlow (more features, larger)
pip3 install tensorflowBasic Inference Example
import tflite_runtime.interpreter as tflite
import numpy as np
from PIL import Image
# Load model
interpreter = tflite.Interpreter(model_path="model.tflite")
interpreter.allocate_tensors()
# Get input/output details
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Prepare input image
img = Image.open("test.webp").resize((224, 224))
input_data = np.expand_dims(img, axis=0).astype(np.float32)
input_data = input_data / 255.0 # Normalize
# Run inference
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output = interpreter.get_tensor(output_details[0]['index'])
print(f"Prediction: {np.argmax(output)}")ML Applications
Object Detection
MobileNet SSD, YOLO for real-time detection
Speech Recognition
Wake words, voice commands offline
Text Recognition
OCR for documents, signs, labels
Face Detection
Face tracking, emotion recognition
Style Transfer
Real-time artistic filters
Anomaly Detection
Industrial quality control
Hardware Acceleration
Coral Edge TPU
Google's Coral USB Accelerator provides 4 TOPS (trillion operations/sec) for TensorFlow Lite models. Get 10-50x speedup on compatible models.
ML Hardware
Browse our Raspberry Pi boards and Arducam cameras for vision projects.
See also: Raspberry Pi Object Detection Guide
Frequently Asked Questions
Can Raspberry Pi run machine learning models?
Yes, Raspberry Pi excels at running (inferencing) pre-trained machine learning models using TensorFlow Lite. Pi 5 can perform real-time object detection at 15-25 FPS without accelerators, or 100+ FPS with Google Coral. However, Pi is not suitable for training large models - train on powerful hardware, deploy on Pi.
Do I need Google Coral for machine learning on Raspberry Pi?
No, but it's highly recommended for demanding applications. Pi 5 can run many models adequately (15-25 FPS object detection). Coral accelerator provides 10-20x speedup, enabling 100+ FPS. For learning and prototyping, Pi alone is fine. For production real-time applications, Coral is worth the investment.
Which Raspberry Pi is best for machine learning?
Raspberry Pi 5 (8GB) is best for machine learning, offering 2-3x faster inference than Pi 4. The improved CPU and better thermal management make it ideal for continuous ML workloads. Pi 4 (4GB/8GB) works well for lighter models and learning. Avoid Pi 3 and earlier for ML applications.
Can I train neural networks on Raspberry Pi?
Technically yes, but it's impractical for anything beyond tiny models. Training is extremely slow due to limited CPU/RAM. Best practice: train models on desktop with GPU or cloud platforms (Google Colab, AWS), then convert to TensorFlow Lite and deploy on Pi for inference. Pi excels at running models, not training them.
What machine learning frameworks work on Raspberry Pi?
TensorFlow Lite is the most popular and optimized for Pi, with extensive model zoo and hardware acceleration support. OpenCV DNN module works well for computer vision. PyTorch Mobile is available but less optimized. For production edge AI, TensorFlow Lite is recommended due to performance and tooling maturity.