Labeling: Data should be labeled to the corresponding object present in the images. This is required for the deep learning object recognition pipeline for the learning process to learn it. After labeling, the dataset generates the XML annotated files and the images.
Training:
The data set is fed to the training pipeline for the learning process for that specific dataset. Then, a Deep Learning neural network maps the input to the output and generates the model.
Optimize the model for accessible mobile devices:
Model optimization reduces the size and inference speed of the model. The model optimization toolkit of TensorFlow is used to reduce the model’s height and increase its efficiency with minimal impact on accuracy. Finally, the post-training quantization and optimization are done. There are two formats for optimizing the model.
