Yolo Exploration

Get started

Clone yolov7 repo git clone https://github.com/WongKinYiu/yolov7.git
Download yolov7 trained weights from here and put it in yolov7 directory. wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt
Install all necessary dependency using pip
Put training_pipeline.ipynb, custom_scripts, model_prediction_custom.ipynb, visulization_pipeline.ipynb in yolov7 directory

training_pipeline.ipynb contains data_preprocess and model training module.
tif file and geojson file is required to run script.
The data will preprocess in first phase and stored images and their label in txt format on given path.
After data preprocess if the aug flag is TRUE then data augmentation will start and after augmentation data will be divided in train and validation set. If aug flag is FALSE then data will directly divide into train and validation dataset (ratio is training = 80, validation = 20).
After the creation of the validation_set and training_set model the training will start. You can change the parameter in yolo class for training.
The trained model is saved in the given path.
In the weights directory best.pt is best model. But you can save it on different location.

data_augmentation.py file is required for data augmentation.
default cropping_size = 2500. This means the image will be croped from main tif file with height, width = 2500. set cropping size according to your needs.
For a better model accuracy keep crop_size same for model training as well as model prediction.

model_prediction_custom.ipynb will accept (tif file and model_weights) and save predicted images in given directory. The model .pt file is required for model prediction.

visualization_pipeline.ipynb will accept tif file and geojson file as input and visualize bounding box. You can use this script to verify input/predicted data.