Readme fix.

README.md

@@ -1,174 +1,10 @@
-# ESRGAN (Enhanced SRGAN) [[Paper]](https://arxiv.org/abs/1809.00219) [[BasicSR]](https://github.com/xinntao/BasicSR) 
-## :smiley: Training codes are in [BasicSR](https://github.com/xinntao/BasicSR) repo.
-### Enhanced Super-Resolution Generative Adversarial Networks
-By Xintao Wang, [Ke Yu](https://yuke93.github.io/), Shixiang Wu, [Jinjin Gu](http://www.jasongt.com/), Yihao Liu, [Chao Dong](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ&hl=en), [Yu Qiao](http://mmlab.siat.ac.cn/yuqiao/), [Chen Change Loy](http://personal.ie.cuhk.edu.hk/~ccloy/)
-
-This repo only provides simple testing codes, pretrained models and the network strategy demo. 
-
-### **For full training and testing codes, please refer to  [BasicSR](https://github.com/xinntao/BasicSR).**
-
-We won the first place in [PIRM2018-SR competition](https://www.pirm2018.org/PIRM-SR.html) (region 3) and got the best perceptual index.
-The paper is accepted to [ECCV2018 PIRM Workshop](https://pirm2018.org/).
-
-:triangular_flag_on_post: Add [Frequently Asked Questions](https://github.com/xinntao/ESRGAN/blob/master/QA.md).
-
-> For instance,
-> 1. How to reproduce your results in the PIRM18-SR Challenge (with low perceptual index)?
-> 2. How do you get the perceptual index in your ESRGAN paper?
-
-#### BibTeX
-<!--
-    @article{wang2018esrgan,
-        author={Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Loy, Chen Change and Qiao, Yu and Tang, Xiaoou},
-        title={ESRGAN: Enhanced super-resolution generative adversarial networks},
-        journal={arXiv preprint arXiv:1809.00219},
-        year={2018}
-    }
--->    
-    @InProceedings{wang2018esrgan,
-        author = {Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Loy, Chen Change},
-        title = {ESRGAN: Enhanced super-resolution generative adversarial networks},
-        booktitle = {The European Conference on Computer Vision Workshops (ECCVW)},
-        month = {September},
-        year = {2018}
-    }
-
-<p align="center">
-  <img src="figures/baboon.jpg">
-</p>
-
-The **RRDB_PSNR** PSNR_oriented model trained with DF2K dataset (a merged dataset with [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/) and [Flickr2K](http://cv.snu.ac.kr/research/EDSR/Flickr2K.tar) (proposed in [EDSR](https://github.com/LimBee/NTIRE2017))) is also able to achive high PSNR performance.
-    
-| <sub>Method</sub> | <sub>Training dataset</sub> | <sub>Set5</sub> | <sub>Set14</sub> | <sub>BSD100</sub> | <sub>Urban100</sub> | <sub>Manga109</sub> |
-|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
-| <sub>[SRCNN](http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html)</sub>| <sub>291</sub>| <sub>30.48/0.8628</sub> |<sub>27.50/0.7513</sub>|<sub>26.90/0.7101</sub>|<sub>24.52/0.7221</sub>|<sub>27.58/0.8555</sub>|
-| <sub>[EDSR](https://github.com/thstkdgus35/EDSR-PyTorch)</sub> | <sub>DIV2K</sub> | <sub>32.46/0.8968</sub> | <sub>28.80/0.7876</sub> | <sub>27.71/0.7420</sub> | <sub>26.64/0.8033</sub> | <sub>31.02/0.9148</sub> |
-| <sub>[RCAN](https://github.com/yulunzhang/RCAN)</sub> |  <sub>DIV2K</sub> | <sub>32.63/0.9002</sub> | <sub>28.87/0.7889</sub> | <sub>27.77/0.7436</sub> | <sub>26.82/ 0.8087</sub>| <sub>31.22/ 0.9173</sub>|
-|<sub>RRDB(ours)</sub>| <sub>DF2K</sub>| <sub>**32.73/0.9011**</sub> |<sub>**28.99/0.7917**</sub> |<sub>**27.85/0.7455**</sub> |<sub>**27.03/0.8153**</sub> |<sub>**31.66/0.9196**</sub>|
-
-## Quick Test
-#### Dependencies
-- Python 3
-- [PyTorch >= 0.4](https://pytorch.org/) (CUDA version >= 7.5 if installing with CUDA. [More details](https://pytorch.org/get-started/previous-versions/))
-- Python packages:  `pip install numpy opencv-python`
-
-### Test models
-1. Clone this github repo. 
-```
-git clone https://github.com/xinntao/ESRGAN
-cd ESRGAN
-```
-2. Place your own **low-resolution images** in `./LR` folder. (There are two sample images - baboon and comic). 
-3. Download pretrained models from [Google Drive](https://drive.google.com/drive/u/0/folders/17VYV_SoZZesU6mbxz2dMAIccSSlqLecY) or [Baidu Drive](https://pan.baidu.com/s/1-Lh6ma-wXzfH8NqeBtPaFQ). Place the models in `./models`. We provide two models with high perceptual quality and high PSNR performance (see [model list](https://github.com/xinntao/ESRGAN/tree/master/models)).
-4. Run test. We provide ESRGAN model and RRDB_PSNR model.
-```
-python test.py models/RRDB_ESRGAN_x4.pth
-python test.py models/RRDB_PSNR_x4.pth
-```
-5. The results are in `./results` folder.
-### Network interpolation demo
-You can interpolate the RRDB_ESRGAN and RRDB_PSNR models with alpha in [0, 1].
-
-1. Run `python net_interp.py 0.8`, where *0.8* is the interpolation parameter and you can change it to any value in [0,1].
-2. Run `python test.py models/interp_08.pth`, where *models/interp_08.pth* is the model path.
-
-<p align="center">
-  <img height="400" src="figures/43074.gif">
-</p>
-
-## Perceptual-driven SR Results
-
-You can download all the resutls from [Google Drive](https://drive.google.com/drive/folders/1iaM-c6EgT1FNoJAOKmDrK7YhEhtlKcLx?usp=sharing). (:heavy_check_mark: included;  :heavy_minus_sign: not included; :o: TODO)
-
-HR images can be downloaed from [BasicSR-Datasets](https://github.com/xinntao/BasicSR#datasets).
-
-| Datasets |LR | [*ESRGAN*](https://arxiv.org/abs/1809.00219) | [SRGAN](https://arxiv.org/abs/1609.04802) | [EnhanceNet](http://openaccess.thecvf.com/content_ICCV_2017/papers/Sajjadi_EnhanceNet_Single_Image_ICCV_2017_paper.pdf) | [CX](https://arxiv.org/abs/1803.04626) |
-|:---:|:---:|:---:|:---:|:---:|:---:|
-| Set5 |:heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark:| :o: |
-| Set14 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark:| :o: |
-| BSDS100 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark:| :o: |
-| [PIRM](https://pirm.github.io/) <br><sup>(val, test)</sup> | :heavy_check_mark: | :heavy_check_mark: | :heavy_minus_sign: | :heavy_check_mark:| :heavy_check_mark: |
-| [OST300](https://arxiv.org/pdf/1804.02815.pdf) |:heavy_check_mark: | :heavy_check_mark: | :heavy_minus_sign: | :heavy_check_mark:| :o: |
-| urban100 | :heavy_check_mark: | :heavy_check_mark: | :heavy_minus_sign: | :heavy_check_mark:| :o: |
-| [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/) <br><sup>(val, test)</sup> | :heavy_check_mark: | :heavy_check_mark: | :heavy_minus_sign: | :heavy_check_mark:| :o: |
-
-## ESRGAN
-We improve the [SRGAN](https://arxiv.org/abs/1609.04802) from three aspects:
-1. adopt a deeper model using Residual-in-Residual Dense Block (RRDB) without batch normalization layers.
-2. employ [Relativistic average GAN](https://ajolicoeur.wordpress.com/relativisticgan/) instead of the vanilla GAN.
-3. improve the perceptual loss by using the features before activation.
-
-In contrast to SRGAN, which claimed that **deeper models are increasingly difficult to train**, our deeper ESRGAN model shows its superior performance with easy training.
-
-<p align="center">
-  <img height="120" src="figures/architecture.jpg">
-</p>
-<p align="center">
-  <img height="180" src="figures/RRDB.png">
-</p>
-
-## Network Interpolation
-We propose the **network interpolation strategy** to balance the visual quality and PSNR.
-
-<p align="center">
-  <img height="500" src="figures/net_interp.jpg">
-</p>
-
-We show the smooth animation with the interpolation parameters changing from 0 to 1. 
-Interestingly, it is observed that the network interpolation strategy provides a smooth control of the RRDB_PSNR model and the fine-tuned ESRGAN model.
-
-<p align="center">
-  <img height="480" src="figures/81.gif">
-  &nbsp &nbsp
-  <img height="480" src="figures/102061.gif">
-</p>
-  
-## Qualitative Results
-PSNR (evaluated on the Y channel) and the perceptual index used in the PIRM-SR challenge are also provided for reference.
-
-<p align="center">
-  <img src="figures/qualitative_cmp_01.jpg">
-</p>
-<p align="center">
-  <img src="figures/qualitative_cmp_02.jpg">
-</p>
-<p align="center">
-  <img src="figures/qualitative_cmp_03.jpg">
-</p>
-<p align="center">
-  <img src="figures/qualitative_cmp_04.jpg">
-</p>
-
-## Ablation Study
-Overall visual comparisons for showing the effects of each component in
-ESRGAN. Each column represents a model with its configurations in the top.
-The red sign indicates the main improvement compared with the previous model.
-<p align="center">
-  <img src="figures/abalation_study.png">
-</p>
-
-## BN artifacts
-We empirically observe that BN layers tend to bring artifacts. These artifacts,
-namely BN artifacts, occasionally appear among iterations and different settings,
-violating the needs for a stable performance over training. We find that 
-the network depth, BN position, training dataset and training loss
-have impact on the occurrence of BN artifacts.
-<p align="center">
-  <img src="figures/BN_artifacts.jpg">
-</p>
-
-## Useful techniques to train a very deep network
-We find that residual scaling and smaller initialization can help to train a very deep network. More details are in the Supplementary File attached in our [paper](https://arxiv.org/abs/1809.00219).
-
-<p align="center">
-  <img height="250" src="figures/train_deeper_neta.png">
-  <img height="250" src="figures/train_deeper_netb.png">
-</p>
-
-## The influence of training patch size
-We observe that training a deeper network benefits from a larger patch size. Moreover, the deeper model achieves more improvement (∼0.12dB) than the shallower one (∼0.04dB) since larger model capacity is capable of taking full advantage of
-larger training patch size. (Evaluated on Set5 dataset with RGB channels.)
-<p align="center">
-  <img height="250" src="figures/patch_a.png">
-  <img height="250" src="figures/patch_b.png">
-</p>
+---
+title: ESRGAN MANGA
+emoji: 🏃
+colorFrom: red
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.12.0
+app_file: app.py
+pinned: false
+---