Tutorial for object detection from scratch? (from data collection & labelling till training & testing)

let me know any tutorial about building custom object detection model from scratch, not TensorFlow object detection api, not yolo, not ssd, not transfer learning, pure building from scratch like data collection and annotation, data preprocessing, data augmentation, data loading from different files, train test Val split, n model, and training. Github repo?

Data Collection, Data Annotation and Data Processing to bring it to trainable format and train-test split:

Data Augmentation and Training after loading data:

The above tutorials are end-to-end as you asked, but from a YOLO perspective.

I have to build pure from starch for object detection