{Model} ~ Summary Training Status META LLAMA #3 COMMUNITY @LICENSE [ ] AGREEMENT

Background Replacements task is a task in the field of image processing and computer vision, focusing on changing the background of an image. This task aims to remove the current background and replace it with a new background, creating a new image with a different environment or context. Changing the background can bring many benefits and applications in various fields. In photography and graphic design, Background Replacements allow the creation of creative images with diverse backgrounds, ranging from natural landscapes, cityscapes, and seascapes to special effects and unique styles. This helps create attention-grabbing images, make a strong impression, and convey messages more effectively. In advertising and marketing. Background Replacements are also used to create professional product images that attract customers. Combining Background Replacements with the MODNet model simplifies the process of changing the background and saves time compared to manual interventions.

cc-by-4.0

Text-to-Image

TensorFlow

PyTorch

Transformers

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Summary-Training-Status

Content data training

Code based on AIOZ structure

import torch, os
from .bg_remove import BGRemove

...

def do_ai_task(
        input: Union[str, Path],
        model_storage_directory: Union[str, Path],
        device: Literal["cpu", "cuda", "gpu"] = "cpu",
        *args, **kwargs) -> Any:
    """Define AI task: load model, pre-process, post-process, etc ..."""
    # Define AI task workflow. Below is an example

    # Load weight
    path_weightImage = os.path.join(model_storage_directory, "modnet_photographic_portrait_matting.ckpt")
    output_path = "output.png"
    bg_remover = BGRemove(path_weightImage)
    bg_remover.image(input, background=False, output = output_path)
    output = open(output_path, "rb")
    return output

Reference

This repository is built upon and inspired by the MODNet, which is a model designed for real-time portrait matting using only RGB image inputs. We extend our sincere appreciation to the creators for generously sharing their code.

License

We respect and comply with the terms of the author's license cited in the Reference section, including the main contents below:

The code, models, and demos in this repository (excluding GIF files under the folder doc/gif) are released under the Apache License 2.0 license.