task_categories:image-to-image size_categories:1K<n<10K language:en region:us
---
dataset_info:
  features:
  - name: instruction
    dtype: string
  - name: input_image
    dtype: image
  - name: ground_truth_image
    dtype: image
  splits:
  - name: train
    num_bytes: 3463030772.619
    num_examples: 1917
  download_size: 3528678684
  dataset_size: 3463030772.619
task_categories:
- image-to-image
language:
- en
size_categories:
- 1K<n<10K
---

# Instruction-prompted low-level image processing dataset

To construct this dataset, we took different number of samples from the following datasets for each task and constructed
a single dataset with prompts added like so:

| **Task** | **Prompt** | **Dataset** | **Number of samples** |
|---|---|---|---|
| Deblurring | “deblur the blurry image” | REDS (train_blur  and<br> train_sharp) | 1200 |
| Deraining | “derain the image” | Rain13k | 686 |
| Denoising | “denoise the noisy image” | SIDD | 8 |
| Low-light  <br>image enhancement | "enhance the low-light image” | LOL | 23 |

To know more about how this sampling was performed, refer to [this notebook](https://huggingface.co/datasets/instruction-tuning-sd/low-level-image-proc/blob/main/data_preparation/sample_dataset.ipynb).This notebook outputs a CSV file which was then used for generating the final version of the dataset ([notebook](https://huggingface.co/datasets/instruction-tuning-sd/low-level-image-proc/blob/main/data_preparation/final_data_preparation.ipynb)).

## Known limitations and biases

Since this dataset was derived from various datasets, as mentioned above, it inherits their limitations and biases as well. 

## Licensing

Since this dataset was derived from various datasets, as mentioned above, it inherits the licensing policies of those datasets. 