Fast-SRGAN
super-resolution
Fast-SRGAN | super-resolution | |
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- | 2 | |
638 | 1,452 | |
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0.0 | 0.0 | |
about 2 months ago | almost 2 years ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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Fast-SRGAN
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Tracking mentions began in Dec 2020.
super-resolution
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Is it not possible to get good results in Deep Learning if dataset is small (1000 images)? [D]
https://github.com/krasserm/super-resolution (Super useful for me)
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How to train "models" for opensv edsr (iam super noob)
And thanks ! so it means that I need to train it with one of these trainers inside this project for example https://github.com/krasserm/super-resolution And that means that iam forced to use python (at least for training) but it's OK 🙃
What are some alternatives?
a-PyTorch-Tutorial-to-Super-Resolution - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | a PyTorch Tutorial to Super-Resolution
MIRNet-TFJS - TensorFlow JS models for MIRNet for low-light💡 image enhancement
awesome-colab-notebooks - Collection of google colaboratory notebooks for fast and easy experiments
TrainYourOwnYOLO - Train a state-of-the-art yolov3 object detector from scratch!
iSeeBetter - iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in Springer Journal of Computational Visual Media, September 2020, Tsinghua University Press
SRGAN - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
CleanTF2plus - Clean TF2's sequel
Hands-On-Meta-Learning-With-Python - Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow
EDSR_Tensorflow - TensorFlow implementation of 'Enhanced Deep Residual Networks for Single Image Super-Resolution'.
License-super-resolution - A License Plate Image Reconstruction Project in Tensorflow2
EGVSR - Efficient & Generic Video Super-Resolution
SinGAN - Official pytorch implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"