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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. |