Learn about different data augmentation techniques in YOLO, including image HSV, angle/degree rotation, translation, perspective transform, scale, shear, flip up-down/left-right, mosaic, mixup, and cutmix. Data augmentation is a valuable tool to enhance the performance and robustness of YOLO models for object detection.
•7m read time• From rumn.medium.com
Table of contents
darknet/yolov4.cfg at master · AlexeyAB/darknetyolov5/hyp.scratch-low.yaml at master · ultralytics/yolov5yolov7/hyp.scratch.p5.yaml at main · WongKinYiu/yolov7ConfigurationImage HSV (Hue, Saturation, and Value) AugmentationImage Angle/Degree rotation AugmentationImage Translation AugmentationImage Prospective Transform AugmentationImage Scale AugmentationImage Shear AugmentationImage Flip up-down(Vertically) and Flip Left-Right(Horizontally)Image Mosaic AugmentationImage Mixup AugmentationImage Cutmix AugmentationYOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorsData Augmentation in YOLOv4YOLOv4: Optimal Speed and Accuracy of Object DetectionPerspective Transformation in Python — on Live VideoSort: