Inpaint Mask Maker

Inpaint Mask Maker leverages a two-stage deep learning pipeline to generate high-fidelity masks for image inpainting tasks. This approach combines the object detection capabilities of You Only Look Once (YOLO) with the precise segmentation abilities of the Segment Anything Model (SAM) to achieve robust mask creation.

The model first identifies and classify objects within an input image by dividing the image into a grid and predicting bounding boxes and class probabilities for each grid cell. This process effectively localizes and classifies all objects present in the image. Following successful object identification, the pipeline leverages semantic segmentation based on the object detections. The model then segments the image based on the identified object classes, generating a binary mask for each object. This binary mask isolates the specific region of the image occupied by the corresponding object, providing a precise delineation of its boundaries.

Applications of Inpaint Mask Maker

This two-stage approach is particularly well-suited for scenarios where both accurate object localization/classification and precise object boundary delineation are crucial. This includes applications such as:

• Content removal and image restoration: Precise masks facilitate the removal of unwanted objects while preserving surrounding image details during inpainting.

• Object manipulation: Accurate masks enable targeted manipulation of specific objects within an image for tasks like image editing or object replacement.