YOLOv9 dataset augmentation

Augment a YOLOv9-format dataset by creating new image and label files for horizontal flip, vertical flip, +10% hue, +30% contrast, and grayscale. Labels are updated correctly for flips; other augmentations copy labels unchanged.

Dataset layout

Expected structure:

dataset/
├── images/          # .jpg or .png
│   ├── img1.jpg
│   └── img2.jpg
└── labels/          # .txt, one per image, same base name
    ├── img1.txt
    └── img2.txt

YOLO label format: one line per object: class_id x_center y_center width height (normalized 0–1).

If images/ and labels/ are not present, the script treats the given directory as containing both images and labels (flat layout).

Setup

pip install -r requirements.txt

Usage

Augment in place (new files appear next to originals in images/ and labels/):

python augment_yolov9_dataset.py --dataset-dir ./dataset/train

Write augmented files to a separate directory (creates train_aug/images/ and train_aug/labels/):

python augment_yolov9_dataset.py --dataset-dir ./dataset/train --output-dir ./dataset/train_aug

Other options:

• --image-ext .png — look for .png instead of .jpg
• --suffixes hflip vflip — run only horizontal and vertical flip (choices: hflip, vflip, hue, contrast, gray)
• --dry-run — print which files would be created without writing

Output naming

For each image img.jpg with label img.txt, the script can create:

Augmentation	Image	Label
Horizontal flip	img_hflip.jpg	img_hflip.txt
Vertical flip	img_vflip.jpg	img_vflip.txt
Hue +10%	img_hue.jpg	img_hue.txt
Contrast +30%	img_contrast.jpg	img_contrast.txt
Grayscale	img_gray.jpg	img_gray.txt

Add these paths to your YOLOv9 data YAML or file lists to use the augmented set.