Image Inpainting using YOLOv8 and LaMa Model

Image inpainting, a crucial component of computer vision, addresses challenges in image restoration, content cre-ation, and medical imaging. Traditional methods often struggle with complex scenes, prompting the integration of YOLOv8 for precise object detection and LaMa for generative inpainting. YOLOv8 identifies regions for inpainting, enhancing accuracy and speed. LaMa employs a latent space augmentation technique, generating contextually relevant content. The hybrid approach aims to improve inpainting quality and efficiency. In a rapidly evolving digital landscape, image inpainting finds applications in diverse domains. Our paper advances inpainting using YOLOv8 and LaMa, demonstrating superior accuracy and visual quality. Combining YOLOv8 for masking and LaMa for inpainting this paper has achieved some astonishing results.