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Srgan Tensorflow

SRGAN-tensorflow is a Python and TensorFlow implementation of SRGAN, a generative adversarial network for single-image super-resolution. Its README documents pretrained-model inference on PNG images and a training setup based on the RAISE dataset.

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Independent OpenViST project profile, based on linked sources.

Srgan Tensorflow — OpenViST project profile in AI Models.
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Understand the project

Practical overview

When it fits

Use it when you want to work locally with this specific TensorFlow SRGAN implementation and can meet its documented Python, TensorFlow, model-file and image-input requirements.

Who it is for

  • Developers seeking an implementation to run pretrained SRGAN inference on individual images.
  • Researchers and learners examining an SRGAN training setup based on the referenced paper.

Documented uses

  • Apply a pretrained SRGAN checkpoint to PNG images in a directory.
  • Study a TensorFlow implementation and its described RAISE-based training preparation.
Before you choose

Requirements & limits

The repository is public and licensed under MIT. Its README documents Python 2.7 and TensorFlow, and recommends Ubuntu 16.04 with TensorFlow GPU edition.

  • The README specifies an older software stack: Python 2.7 and TensorFlow versions r1.0 or r1.2.
  • Inference uses an externally downloaded pretrained checkpoint and a configured input-directory path.
  • Training preparation requires VGG19 weights and paired high- and low-resolution RAISE image archives.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run inference on PNG images with the pretrained checkpoint

Configure the documented inference script to process your own PNG image directory.

Before you start

  • The repository cloned locally
  • Python 2.7 and TensorFlow; the README notes TensorFlow r1.0 and r1.2
  • The pretrained model downloaded and extracted into the repository
  • PNG input images in a directory

Steps

  1. Download the pretrained model from the Google Drive link in the README and extract the archive in the repository directory.

  2. Place PNG images in an input directory.

  3. Edit inference_SRGAN.sh so --input_dir_LR points to that directory; the documented example also sets the checkpoint to ./SRGAN_pre-trained/model-200000.

  4. Run sh inference_SRGAN.sh.

Expected outcome

The README says generated images can be viewed in the repository’s result/images/ directory.

Guide limitations

  • The documented example uses a shell script and a pretrained checkpoint.
  • The README recommends Ubuntu 16.04 with TensorFlow GPU edition and records tested Ubuntu and NVIDIA GPU configurations; it does not establish broader compatibility.
Documentation 1 ↗
COMPARE OPTIONS

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These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Srgan TensorflowApply a pretrained SRGAN checkpoint to PNG images in a directory.LinuxThe repository is public and licensed under MIT. Its README documents Python 2.7 and TensorFlow, and recommends Ubuntu 16.04 with TensorFlow GPU edition.
PolygonRNN++Generate polygon outlines as initial instance annotations for image crops.Linux, Self-hostedThe repository is public and licensed under GPL-3.0.
Opencv ZooFind an OpenCV DNN-oriented model for a visual task such as face detection, object tracking or image segmentation.Windows, Linux, macOSThe repository is public and lists an Apache-2.0 license. The README says the project is now hosted on Hugging Face for models and online demos.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
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Sources checked
6 November 2026
Original website title
brade31919/SRGAN-tensorflow · Official homepage title · retained in its source language
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