FOMO is a TinyML neural network for real-time object detection - New Style Motorsport

This article is part of our coverage of the latest in AI research.

A new machine learning technique developed by researchers at Edge Impulse, a platform for creating ML models for the edge, makes it possible to run real-time object detection on devices with very little computing and memory capacity. Called Faster Objects, More Objects (FOMO), the new deep learning architecture can unlock new machine vision applications.

Most object detection deep learning models have memory and computation requirements that exceed the capabilities of small processors. FOMO, on the other hand, only requires several hundred kilobytes of memory, which makes it a great technique for TinyML, a subfield of machine learning focused on running ML models on microcontrollers and other memory-constrained devices that have Internet connectivity. limited or none.

Image Classification vs. Object Detection

TinyML has come a long way in image classification, where the machine learning model should only predict the presence of a certain type of object in an image. On the other hand, object detection requires the model to identify more than one object, as well as the bounding box of each instance.


Object detection models are much more complex than image classification networks and require more memory.

“We added computer vision support to Edge Impulse in 2020, and we’ve seen a huge recovery of applications (40 percent of our projects are computer vision applications),” Jan Jongboom, CTO of Edge Impulse, told TechTalks. “But with current state-of-the-art models, you could only classify images on microcontrollers.”

Image classification is very useful for many applications. For example, a security camera can use TinyML image classification to determine whether or not there is a person in the frame. However, much more can be done.

“It was a huge hassle that you were limited to these very basic sorting tasks. There is a lot of value in seeing ‘there are three people here’ or ‘this label is in the top left corner’, for example, counting things is one of the biggest questions we see in the market today,” says Jongboom.

Previous object detection ML models had to process the input image multiple times to locate the objects, making them slow and computationally expensive. Newer models such as YOLO (You Only Look Once) use single shot detection to provide near real-time object detection. But its memory requirements are still large. Even models designed for edge applications are difficult to run on small devices.

“YOLOv5 or MobileNet SSD are incredibly large networks that never fit on MCUs and barely fit on Raspberry Pi-like devices,” says Jongboom.

Also, these models are poor at detecting small objects and need a lot of data. For example, YOLOv5 recommends more than 10,000 training instances per object class.

The idea behind FOMO is that not all object detection applications require the high-precision output that state-of-the-art deep learning models provide. By finding the right trade-off between accuracy, speed, and memory, you can reduce your deep learning models to very small sizes and keep them useful.

Instead of detecting bounding boxes, FOMO predicts the center of the object. This is because many object detection applications are only interested in the location of objects in the frame and not their sizes. Centroid detection is much more computationally efficient than bounding box prediction and requires less data.


Redefining Object Detection Deep Learning Architectures

FOMO also applies a major structural change to traditional deep learning architectures.

Single-shot object detectors are composed of a set of convolutional layers that extract features and several fully connected layers that predict the bounding box. Convolution layers extract visual features in a hierarchical fashion. The first layer detects simple things like lines and edges in different directions. Each convolutional layer is typically coupled with a pooling layer, which reduces the size of the layer’s output while keeping the salient features in each area.

The output of the pooling layer is then sent to the next convolutional layer, which extracts higher-level features such as corners, arcs, and circles. As more convolutional and pooling layers are added, feature maps get farther away and can detect tricky things like faces and objects.

visualization of neural network layers