Object detection

Object detection takes an image and outputs information about the class and number of objects, position, (and, eventually, size) in the image. Edge Impulse provides several object detection model architectures built into the platform, in addition to the providing the ability to use a custom learning block to bring in your own architectures. The built-in options are:

Specification	YOLO-Pro	FOMO	MobileNetV2 SSD FPN
Labelling method	Bounding boxes	Bounding boxes	Bounding boxes
Input image size	Multiples of 32 (square)	Any (square)	320x320
Input image colour	RGB	Greyscale or RGB	RGB
Output format	Bounding boxes	Centroids	Bounding boxes
MCU inference	✅	✅	❌
CPU/GPU inference	✅	✅	✅
Limitations	Stronger performance at int8 precision than float32.	Objects should have similar sizes and shapes.	Objects should be large relative to the image.
		Objects should not be too close to each other.	Models use high compute resources (in the edge computing world).
		Object size not available.	Input image size is fixed.

Classical ML

YOLO-Pro

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