Day # 147 - Part II - Deep Learning techniques for Computer Vision applied to embedded systems

A very interesting Final Year Paper - Deep Learning techniques for Computer Vision applied to embedded systems

Part Two Series.

Creating a custom Object Detector Machines

Steps Involved

• Dataset Preperation (Download images using - Fatkun Batch Download Images)
• Label Images by Hand (Painful process) - RectLabel Tool for manually labelling
• Convert into .tfrecords - Custom Tensorflow code to prepare .tfrecords
• Create labels with .pbtxt format
• Create bounding boxes
• Set TF Object Detection API
• Create Pipeline for Training - Configure model, train_config, train_input_header, eval_config, eval_input_reader
• Perform Training
• Monitor Performance
• Export Graph
• Compile for Vision Bonnet
• Deploy and Test

This is the first and most exhaustive step-by-step documentation neatly mentioned.

Happy Learning!!!