You may use MobileNet models as they use separable convolutions, which have lesser parameters and execution time than simple/regular convolutions. Moreover, MobileNets are easy to train and setup (tf.keras.applications.* has a pre-trained model) and can be used as a backbone model for fine-tuning on datasets other than the ImageNet.
Further, you can also explore quantization and weight pruning. These are some techniques that can be used to optimize models to have a smaller memory footprint and smaller execution time on embedded devices.
You may use
MobileNetmodels as they use separable convolutions, which have lesser parameters and execution time than simple/regular convolutions. Moreover,MobileNets are easy to train and setup (tf.keras.applications.*has a pre-trained model) and can be used as a backbone model for fine-tuning on datasets other than theImageNet.Further, you can also explore quantization and weight pruning. These are some techniques that can be used to optimize models to have a smaller memory footprint and smaller execution time on embedded devices.