EDSR-Base Model
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Paper | Enhanced Deep Residual Networks for Single Image Super-Resolution |
| Model Size | ~5MB |
| Architecture | 16 ResBlocks, 64 channels |
What is edsr-base?
EDSR-base is a lightweight version of the Enhanced Deep Residual Networks (EDSR) architecture designed for single image super-resolution. It's a compact model that achieves impressive upscaling results while maintaining a small footprint of approximately 5MB, compared to the full EDSR model's 100MB size.
Implementation Details
The model employs a sophisticated architecture with 16 residual blocks and 64 channels, utilizing both global and local skip connections. Unlike traditional approaches, it eliminates batch normalization layers and instead implements constant scaling layers for stable training. The model uses L1 loss function for better performance and computational efficiency.
- Supports 2x, 3x, and 4x image upscaling
- Trained on DIV2K dataset (800 training images, augmented to 4000)
- Uses bicubic interpolation for creating training pairs
- Implements efficient ResBlock architecture
Core Capabilities
- High-quality image upscaling with superior PSNR/SSIM metrics
- Efficient processing with smaller model size
- Flexible scaling options (2x, 3x, 4x)
- Easy integration through super-image library
Frequently Asked Questions
Q: What makes this model unique?
EDSR-base stands out for its efficient architecture that maintains impressive performance while being significantly smaller than the full EDSR model. It achieves this through careful optimization of the residual network structure and elimination of unnecessary components like batch normalization.
Q: What are the recommended use cases?
The model is ideal for applications requiring high-quality image upscaling where computational resources are limited. It's particularly effective for 2x upscaling, achieving a PSNR of 38.02 on the Set5 dataset, making it suitable for mobile applications, web services, and desktop software requiring image enhancement.
