---
license: mit
tags:
- esrgan
- realesrgan
- ncnn
- upscaling
- super-resolution
- image-processing
- computer-vision
pipeline_tag: image-to-image
library_name: ncnn
model-index:
- name: Real-ESRGAN-x2plus-NCNN
  results:
  - task:
      type: image-to-image
    dataset:
      name: General Image Dataset
      type: custom
    metrics:
    - name: PSNR
      type: psnr
      value: "benchmark needed"
    - name: SSIM
      type: ssim
      value: "benchmark needed"
---

# Real-ESRGAN x2plus NCNN Model

This repository contains the Real-ESRGAN x2plus model converted to NCNN format for efficient inference, particularly suitable for 2x upscaling of non-anime content.

## Model Details

- **Model Name**: Real-ESRGAN x2plus
- **Purpose**: 2x upscaling of images and videos
- **Target**: Non-anime/general photo content (as opposed to anime-style content)
- **Scale Factor**: 2x (doubles resolution)
- **Format**: NCNN (.param and .bin files)

## File Contents

- `realesrgan_x2plus.param`: NCNN model architecture definition
- `realesrgan_x2plus.bin`: NCNN model weights
- `README.md`: This documentation file

## Original Model Information

This model is based on the original Real-ESRGAN x2plus model, which is designed for general image restoration and super-resolution. It performs well on photographs and realistic images rather than anime-style artwork.

## Use Cases

- Upscaling low-resolution photos
- Enhancing video quality (when used with compatible video processing tools)
- General image super-resolution
- Photo restoration

## Compatibility

This model is in NCNN format, making it compatible with:
- NCNN inference framework
- Video2X for video processing
- Any application supporting NCNN models

## Performance Characteristics

- Optimized for 2x upscaling
- Balanced quality and performance
- Suitable for both CPU and GPU inference (depending on deployment)
- Efficient memory usage compared to original PyTorch format

## License

The original Real-ESRGAN model was released under the MIT License. Please respect the original license terms when using this converted version.

## Conversion Notes

This model was converted from the original ONNX format to NCNN format to enable efficient inference in NCNN-compatible applications. The conversion was done using the onnx2ncnn tool from the ncnn framework.

## How to Use

To use this model with NCNN-compatible applications:

```bash
# Example usage with Video2X
video2x --ncnn-param realesrgan_x2plus.param --ncnn-bin realesrgan_x2plus.bin --input input.mp4 --output output.mp4

# Or with other NCNN-based tools that support Real-ESRGAN models
```

## Training Data

This model was trained on general image datasets to optimize for realistic photo upscaling. The original training data was not included in this repository due to size constraints.

## Evaluation

The model has been tested qualitatively on various image types and shows good performance on non-anime content. Quantitative benchmarks (PSNR, SSIM) would need to be calculated separately based on your specific use case.