# super-image-data

**Repository Path**: Jackdaw7777/super-image-data

## Basic Information

- **Project Name**: super-image-data
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-06-16
- **Last Updated**: 2025-06-16

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Datasets for super-image

You can find here a list of common image super resolution datasets on [`huggingface datasets`](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) 
for use with the [`super-image`](https://github.com/eugenesiow/super-image) library.

## Datasets

| dataset  |train |validation|test|
|-------|-----:|---:|---:|
|[Div2k](https://huggingface.co/datasets/eugenesiow/Div2k)|800|100|-|
|[Set5](https://huggingface.co/datasets/eugenesiow/Set5)|-|5|-|
|[Set14](https://huggingface.co/datasets/eugenesiow/Set14)|-|14|-|
|[BSD100](https://huggingface.co/datasets/eugenesiow/BSD100)|-|100|-|
|[Urban100](https://huggingface.co/datasets/eugenesiow/Urban100)|-|100|-|
|[PIRM](https://huggingface.co/datasets/eugenesiow/PIRM)|-|100|100|

## Quick Start

Quickly evaluate models on super image resolution datasets.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab")

Install with `pip`:
```bash
pip install datasets super-image
```

Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library:
```python
from datasets import load_dataset
from super_image import EdsrModel
from super_image.data import EvalDataset, EvalMetrics

dataset = load_dataset('eugenesiow/Set5', 'bicubic_x2', split='validation')
eval_dataset = EvalDataset(dataset)
model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
EvalMetrics().evaluate(model, eval_dataset)
```