# Description

## Source Dataset:

All 14,848 images were obtained from the [COCO](https://cocodataset.org/#home) dataset.

## Datageneration for the Project:

The data is split evenly in three parts. Each part was processed with a different mask generation technique.

1) Creating a mask using the YOLO Classifier.
2) Creating a mask using the MaskFormer Classifier.
3) Creating a mask using the QuickDraw dataset.

These masks were then used to inpaint the image with stable diffusion 1.5.

## Naming Conventions:

Files ending with
- .original.webp are the base image
- .mask.webp are the mask that were created with one of the mentioned methods
- .realfake.webp are compositions of the inpainted and original images
- .fakefake.webp are images that are re-encoded using the latent autoencoder and then blended using the mask
- .inpainted.webp are the inpainted output images from stable diffusion
  
## Split class distribution:

- train = 11878
- val   = 1486
- test  = 1484
region:us
{}
TAGS
#region-us 

# Description

## Source Dataset:

All 14,848 images were obtained from the COCO dataset.

## Datageneration for the Project:

The data is split evenly in three parts. Each part was processed with a different mask generation technique.

1) Creating a mask using the YOLO Classifier.
2) Creating a mask using the MaskFormer Classifier.
3) Creating a mask using the QuickDraw dataset.

These masks were then used to inpaint the image with stable diffusion 1.5.

## Naming Conventions:

Files ending with
- .URL are the base image
- .URL are the mask that were created with one of the mentioned methods
- .URL are compositions of the inpainted and original images
- .URL are images that are re-encoded using the latent autoencoder and then blended using the mask
- .URL are the inpainted output images from stable diffusion
  
## Split class distribution:

- train = 11878
- val   = 1486
- test  = 1484
# Description ## Source Dataset:

All 14,848 images were obtained from the COCO dataset. ## Datageneration for the Project:

The data is split evenly in three parts. Each part was processed with a different mask generation technique.

1) Creating a mask using the YOLO Classifier.
2) Creating a mask using the MaskFormer Classifier.
3) Creating a mask using the QuickDraw dataset.

These masks were then used to inpaint the image with stable diffusion 1.5. ## Naming Conventions:

Files ending with
- .URL are the base image
- .URL are the mask that were created with one of the mentioned methods
- .URL are compositions of the inpainted and original images
- .URL are images that are re-encoded using the latent autoencoder and then blended using the mask
- .URL are the inpainted output images from stable diffusion ## Split class distribution:

- train = 11878
- val   = 1486
- test  = 1484
TAGS
#region-us 
 # Description ## Source Dataset:

All 14,848 images were obtained from the COCO dataset. ## Datageneration for the Project:

The data is split evenly in three parts. Each part was processed with a different mask generation technique.

1) Creating a mask using the YOLO Classifier.
2) Creating a mask using the MaskFormer Classifier.
3) Creating a mask using the QuickDraw dataset.

These masks were then used to inpaint the image with stable diffusion 1.5. ## Naming Conventions:

Files ending with
- .URL are the base image
- .URL are the mask that were created with one of the mentioned methods
- .URL are compositions of the inpainted and original images
- .URL are images that are re-encoded using the latent autoencoder and then blended using the mask
- .URL are the inpainted output images from stable diffusion ## Split class distribution:

- train = 11878
- val   = 1486
- test  = 1484
passage: TAGS
#region-us 
# Description## Source Dataset:

All 14,848 images were obtained from the COCO dataset.## Datageneration for the Project:

The data is split evenly in three parts. Each part was processed with a different mask generation technique.

1) Creating a mask using the YOLO Classifier.
2) Creating a mask using the MaskFormer Classifier.
3) Creating a mask using the QuickDraw dataset.

These masks were then used to inpaint the image with stable diffusion 1.5.## Naming Conventions:

Files ending with
- .URL are the base image
- .URL are the mask that were created with one of the mentioned methods
- .URL are compositions of the inpainted and original images
- .URL are images that are re-encoded using the latent autoencoder and then blended using the mask
- .URL are the inpainted output images from stable diffusion## Split class distribution:

- train = 11878
- val   = 1486
- test  = 1484