We can't detect an object without first training a model. Training a model requires significant resources in order to perform the calculations to set the weights and neural biases. This is usually done using backpropagation, but it depends on the techniques that are being used. The problem that is encountered by many embedded engineers looking to use machine learning is that once they train their model, they need to convert that model to something that can run within a resource constrained environment.
Working from within an embedded environment often limits the number of neural layers that can be included in a model. Models that are generated using popular tools such as Caffe or TensorFlow also generate ...
