EESRGAN VS techniques

Compare EESRGAN vs techniques and see what are their differences.

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EESRGAN techniques
1 9
264 7,865
- 2.7%
10.0 8.8
over 1 year ago 8 days ago
Python
GNU General Public License v3.0 only Apache License 2.0
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EESRGAN

Posts with mentions or reviews of EESRGAN. We have used some of these posts to build our list of alternatives and similar projects.
  • LogoScale – A method for vectorizing small, crappy logos
    1 project | news.ycombinator.com | 9 Sep 2023
    Yeah I'd be similarly curious how that was arrived at. ESRGAN was co-developed by a researcher at Tencent, who has been involved in a range of similar things.

    The only satellite related thing I saw in my brief searching is some separate project[1] based on ESRGAN used for satellite imagery.

    [1] https://github.com/Jakaria08/EESRGAN

techniques

Posts with mentions or reviews of techniques. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-02.

What are some alternatives?

When comparing EESRGAN and techniques you can also consider the following projects:

BasicSR - Open Source Image and Video Restoration Toolbox for Super-resolution, Denoise, Deblurring, etc. Currently, it includes EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, ECBSR, etc. Also support StyleGAN2, DFDNet. [Moved to: https://github.com/XPixelGroup/BasicSR]

awesome-satellite-imagery-datasets - 🛰️ List of satellite image training datasets with annotations for computer vision and deep learning

Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.

AnimeGANv3 - Use AnimeGANv3 to make your own animation works, including turning photos or videos into anime.

godot-tensorflow-workspace - Machine learning for Godot Engine

datasets-for-good - List of datasets to apply stats/machine learning/technology to the world of social good.

ml4eo-bootcamp-2021 - Machine Learning for Earth Observation Training of Trainers Bootcamp

d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

Awesome-Learning-with-Label-Noise - A curated list of resources for Learning with Noisy Labels

label-errors - 🛠️ Corrected Test Sets for ImageNet, MNIST, CIFAR, Caltech-256, QuickDraw, IMDB, Amazon Reviews, 20News, and AudioSet

notebooks - Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.

video-game-text-dataset - Collection of videogame text datasets from Library of Codexes. Text data from 30+ different videogame series.