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---
license: cc-by-4.0
dataset_info:
features:
- name: mask
dtype: image
- name: target_img_dataset
dtype: string
- name: img_id
dtype: string
- name: ann_id
dtype: string
splits:
- name: train
num_bytes: 2555862476.36
num_examples: 888230
- name: test
num_bytes: 35729190.0
num_examples: 752
download_size: 681492456
dataset_size: 2591591666.36
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for PIPE Masks Dataset
## Dataset Summary
The PIPE (Paint by InPaint Edit) dataset is designed to enhance the efficacy of mask-free, instruction-following image editing models by providing a large-scale collection of image pairs and diverse object addition instructions.
Here, we provide the masks used for the inpainting process to generate the source image for the PIPE dataset for both the train and test sets.
Further details can be found in our [project page](https://rotsteinnoam.github.io/Paint-by-Inpaint) and [paper](arxiv.org/abs/2404.18212).
## Columns
- `mask`: The removed object mask used for creating the inpainted image.
- `target_img_dataset`: The dataset to which the target image belongs.
- `img_id`: The unique identifier of the GT image (the target image).
- `ann_id`: The identifier of the object segmentation annotation of the object removed.
## Loading the PIPE Masks Dataset
Here is an example of how to load and use this dataset with the `datasets` library:
```python
from datasets import load_dataset
data_files = {"train": "data/train-*", "test": "data/test-*"}
dataset_masks = load_dataset('paint-by-inpaint/PIPE_Masks',data_files=data_files)
# Display an example
example_train_mask = dataset_masks['train'][0]
print(example_train_mask)
example_test_mask = dataset_masks['test'][0]
print(example_test_mask)
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