bpawnzZ/Real-ESRGAN-x2plus-NCNN

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:

# 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.