{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/11"}],"enrichment":{"capability":"Provides pretrained neural network models for image super-resolution (upscaling low-resolution images to higher resolution) using PyTorch, including EDSR, CARN, RDN, RCAN, and NinaSR architectures.","skillfed_tags":["image-processing","computer-vision","deep-learning"],"use_cases":["Upscale low-resolution photographs or screenshots to higher resolution for archival or display purposes","Benchmark different super-resolution architectures on standard datasets to compare accuracy and speed tradeoffs","Fine-tune a pretrained model on custom degraded image data for domain-specific super-resolution tasks","Integrate super-resolution preprocessing into image analysis pipelines to improve downstream model performance on low-quality inputs","Research super-resolution techniques by training models on provided datasets with the unified training script"],"what_it_does":"torchsr is a PyTorch library that bundles pretrained models for image super-resolution\u2014the task of increasing image resolution by inferring missing details using trained neural networks. It includes five model architectures (EDSR, CARN, RDN, RCAN, NinaSR) with different size-to-accuracy tradeoffs, ranging from 0.10M to 40.7M parameters. The library also provides common super-resolution datasets (DIV2K, Set5, Set14, B100, Urban100, RealSR, Flicr2K, REDS) with automatic download, data augmentation utilities, and a unified training script.\n\nYou load a pretrained model, pass low-resolution images through it, and receive upscaled output. The package is designed for researchers and practitioners who want to apply or benchmark super-resolution without building models from scratch. It depends only on torch and torchvision, making it straightforward to integrate into existing PyTorch workflows.","worth_installing":"Yes, if you need pretrained super-resolution models and can tolerate dormant maintenance. The package is stable, has no known vulnerabilities, and low install friction. However, verify that pretrained weights remain accessible and that it works with your PyTorch version\u2014no updates have landed since August 2022, so compatibility with very recent PyTorch releases is uncertain."},"id":"torchsr","links":{"html":"https://skillfed.io/packages/torchsr","md":"https://skillfed.io/packages/torchsr.md","pypi":"https://pypi.org/project/torchsr/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2022-08-21","license_spdx":null,"license_treatment":"permissive","name":"torchsr","python_support":"unspecified","summary":"Super Resolution Networks for pytorch"},"popularity":{"monthly_downloads":85066,"position":13955,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.4"}