|
|
--- |
|
|
dataset_info: |
|
|
features: |
|
|
- name: input_image |
|
|
dtype: image |
|
|
- name: edit_prompt |
|
|
dtype: string |
|
|
- name: edited_image |
|
|
dtype: image |
|
|
- name: index |
|
|
dtype: int64 |
|
|
splits: |
|
|
- name: train |
|
|
num_bytes: 8686582352.97 |
|
|
num_examples: 7265 |
|
|
download_size: 8686714223 |
|
|
dataset_size: 8686582352.97 |
|
|
configs: |
|
|
- config_name: default |
|
|
data_files: |
|
|
- split: train |
|
|
path: data/train-* |
|
|
license: cc-by-4.0 |
|
|
task_categories: |
|
|
- image-to-image |
|
|
- text-to-image |
|
|
language: |
|
|
- en |
|
|
tags: |
|
|
- ar |
|
|
size_categories: |
|
|
- 1K<n<10K |
|
|
--- |
|
|
# 🖼️ Image Editing Dataset with Prompts |
|
|
|
|
|
This dataset contains pairs of images and prompts for training or evaluating image-to-image and text-to-image models. |
|
|
|
|
|
## 📁 Dataset Structure |
|
|
|
|
|
Each sample contains: |
|
|
- `input_image`: Original image (before editing) |
|
|
- `edit_prompt`: Text instruction describing the desired edit |
|
|
- `edited_image`: Resulting image after applying the edit |
|
|
- `index`: Unique numeric identifier |
|
|
|
|
|
## 🚀 How to Use |
|
|
|
|
|
```python |
|
|
from datasets import load_dataset |
|
|
|
|
|
# Replace with your dataset path |
|
|
dataset = load_dataset("your-username/your-dataset-name") |
|
|
|
|
|
# Access a sample |
|
|
sample = dataset["train"][0] |
|
|
print(sample["edit_prompt"]) |
|
|
|
|
|
# Show the input image |
|
|
input_image = sample["input_image"] |
|
|
input_image.show() |
