yolov8系列--Applied augmentation on yolov5 and yolov8 dataset..zip

# YOLOv5/YOLOv8 Data Augmentation with Albumentations This GitHub repository offers a solution for augmenting datasets for YOLOv8 and YOLOv5 using the Albumentations library. Albumentations is a Python package designed for image augmentation, providing a simple and flexible approach to perform various image transformations. For more detail you can refer my medium [article](https://medium.com/red-buffer/apply-data-augmentation-on-yolov5-yolov8-dataset-958e89d4bc5d). ## Input  ## Output  ## Directories description - **input-ds** contain the input of YOLOv8 and YOLOv5 which are following directories. - Images directory contains the images - labels directory contains the .txt files. Each .txt file contains the normalized bounding boxes in a following format. - **out-aug-ds** contain the augmented output contains following directories. - Images directory contains the augmented images. - labels directory contains the augmented labels. - **bb_image** This directory contains images with bounding boxes for visualizing the results of augmented data. This is for validation; bounding boxes should be correctly drawn on the objects of interest. - **utils.py**: Contains all user-defined helper function. - **main.py**: Contains Yolo dataset augmentor pipeline - **CONSTANT.yaml** contain following contants need to update on according to your case. - inp_img_pth for input images path - inp_lab_pth for input labels path - out_img_pth for output image path - out_lab_pth for output labels path - transformed_file_name: use to name augmented output to differentiate from other input dataset. - CLASSES: list of input class name according to class number. ## Usage - step to apply augmentation on your own dataset. - Create Virtual Environment. - Install requirements using ``` pip install -r requirements.txt ``` - Provide the input and output path in **CONSTANT.yaml** file. - Update the name of transformed_file_name in CONSTANT.yaml otherwise code will overwrite last augmentations. - Provide the list of name of classes in CONSTANT.yaml in a same sequence as used to assign class numbers in the YOLO dataset labeling. - For example, if you provided a class list as ```['obj1', 'obj2', 'obj3']```, the class number used for 'obj1' in the label file should be 0, similarly for 'obj2', the class number should be 1, and so on. - Run the pipeline using ``` python3 run.py ```