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Computer Vision with Arduino
train on the cloud
SDK
https://docs.edgeimpulse.com/tutorials/tools/sdks/studio/python/upload-download-data
Audio
Audio classification https://docs.edgeimpulse.com/tutorials/end-to-end/sound-recognition
Images
https://docs.edgeimpulse.com/tutorials/end-to-end/object-detection-centroids
FOMO (Faster Objects, More Objects) is a brand-new approach to run object detection models on constrained devices.
FOMO is a ground-breaking algorithm that brings real-time object detection, tracking and counting to microcontrollers for the first time. FOMO is 30 times faster than MobileNet SSD and can run under 200K of RAM.
https://studio.edgeimpulse.com/public/783690/latest
Data adquisition:
- Labeling queue
- AI labeling
Impulse desing
- Create imoulse input size!! image data. -> processing block -> learning block -> output features
An impulse takes raw data, uses signal processing to extract features, and then uses a learning block to classify new data.
Image
- Deploymnet
Options … Set the target device,
Computer Vision
- Louis Moreau – Head of Developer Relations at Edge Impulse
- Jim Bruges – Senior Developer Relations Engineer at Edge Impulse
- Marc Pous – Developer Relations Staff Engineer at Edge Impulse
Hands-on workshop to explore Computer Vision on the edge using Edge Impulse and Arduino UNO Q.
Learn how to collect data and train an object detection model to filter events in real time, then cascade to a Vision Language Model.
You’ll build an end-to-end cascaded AI application in the edge, balancing power efficiency and accuracy on real hardware.
https://app.arduino.cc/courses
Not adapt rules, collect more data.
Docker: training with TensorBoard Model created with Tensorflow Lite
Traditional object detection models are poorly suited to MCUs. (Microcontrollers)
Object detection using centroids
FOMO: Faster objects, more objetcs
MobileNet V2
https://github.com/edgeimpulse/example-arduino-app-lab-object-detection-using-flask.git cd example-arduino-app-lab-object-detection-using-flask/ laptop and arduino on same network.