
# StyleGAN3 Annotated Images

This dataset consists of a `pandas` table and attached `images.zip` file with these entries:

* seed (`numpy` seed used to generate random vectors)
* path (path to the generated image obtained after unzipping `images.zip`)
* vector (generated numpy "random" vector used to create StyleGAN3 images)
* text (caption of each image, generated using BLIP model: `Salesforce/blip-image-captioning-base`)

## Usage

In order not to load the images into the memory, we will load the images separately.

```python
images = load_dataset("balgot/stylegan3-annotated", data_files=["*.zip"])
dataset = load_dataset("balgot/stylegan3-annotated", data_files=["*.csv"])

# TODO: convert "vector" column to numpy/torch
```


It was created as a part of the course project for FI:PA228 at Masaryk University.
task_categories:image-to-text task_categories:text-to-image size_categories:10K<n<100K language:en stylegan3 face-generation region:us
{"language": ["en"], "size_categories": ["10K<n<100K"], "task_categories": ["image-to-text", "text-to-image"], "tags": ["stylegan3", "face-generation"]}
TAGS
#task_categories-image-to-text #task_categories-text-to-image #size_categories-10K<n<100K #language-English #stylegan3 #face-generation #region-us 


# StyleGAN3 Annotated Images

This dataset consists of a 'pandas' table and attached 'URL' file with these entries:

* seed ('numpy' seed used to generate random vectors)
* path (path to the generated image obtained after unzipping 'URL')
* vector (generated numpy "random" vector used to create StyleGAN3 images)
* text (caption of each image, generated using BLIP model: 'Salesforce/blip-image-captioning-base')

## Usage

In order not to load the images into the memory, we will load the images separately.




It was created as a part of the course project for FI:PA228 at Masaryk University.
# StyleGAN3 Annotated Images

This dataset consists of a 'pandas' table and attached 'URL' file with these entries:

* seed ('numpy' seed used to generate random vectors)
* path (path to the generated image obtained after unzipping 'URL')
* vector (generated numpy "random" vector used to create StyleGAN3 images)
* text (caption of each image, generated using BLIP model: 'Salesforce/blip-image-captioning-base') ## Usage

In order not to load the images into the memory, we will load the images separately.




It was created as a part of the course project for FI:PA228 at Masaryk University.
TAGS
#task_categories-image-to-text #task_categories-text-to-image #size_categories-10K<n<100K #language-English #stylegan3 #face-generation #region-us 
 # StyleGAN3 Annotated Images

This dataset consists of a 'pandas' table and attached 'URL' file with these entries:

* seed ('numpy' seed used to generate random vectors)
* path (path to the generated image obtained after unzipping 'URL')
* vector (generated numpy "random" vector used to create StyleGAN3 images)
* text (caption of each image, generated using BLIP model: 'Salesforce/blip-image-captioning-base') ## Usage

In order not to load the images into the memory, we will load the images separately.




It was created as a part of the course project for FI:PA228 at Masaryk University.
passage: TAGS
#task_categories-image-to-text #task_categories-text-to-image #size_categories-10K<n<100K #language-English #stylegan3 #face-generation #region-us 
# StyleGAN3 Annotated Images

This dataset consists of a 'pandas' table and attached 'URL' file with these entries:

* seed ('numpy' seed used to generate random vectors)
* path (path to the generated image obtained after unzipping 'URL')
* vector (generated numpy "random" vector used to create StyleGAN3 images)
* text (caption of each image, generated using BLIP model: 'Salesforce/blip-image-captioning-base')## Usage

In order not to load the images into the memory, we will load the images separately.




It was created as a part of the course project for FI:PA228 at Masaryk University.