msrn / README.md
Eugene Siow
Add x2 scale model. b67a94b
1 ---
2 license: apache-2.0
3 tags:
4 - super-image
5 - image-super-resolution
6 datasets:
7 - eugenesiow/Div2k
8 - eugenesiow/Set5
9 - eugenesiow/Set14
10 - eugenesiow/BSD100
11 - eugenesiow/Urban100
12 metrics:
13 - pnsr
14 - ssim
15 ---
16 # Multi-scale Residual Network for Image Super-Resolution (MSRN)
17 MSRN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Multi-scale Residual Network for Image Super-Resolution](https://openaccess.thecvf.com/content_ECCV_2018/html/Juncheng_Li_Multi-scale_Residual_Network_ECCV_2018_paper.html) by Li et al. (2018) and first released in [this repository](https://github.com/MIVRC/MSRN-PyTorch).
18
19 The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling x2 and model upscaling x2.
20
21 ![Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 4](images/msrn_4_4_compare.png "Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 4")
22 ## Model description
23 The MSRN model proposes a feature extraction structure called the multi-scale residual block. This module can "adaptively detect image features at different scales" and "exploit the potential features of the image".
24 ## Intended uses & limitations
25 You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
26 ### How to use
27 The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library:
28 ```bash
29 pip install super-image
30 ```
31 Here is how to use a pre-trained model to upscale your image:
32 ```python
33 from super_image import MsrnModel, ImageLoader
34 from PIL import Image
35 import requests
36
37 url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
38 image = Image.open(requests.get(url, stream=True).raw)
39
40 model = MsrnModel.from_pretrained('eugenesiow/msrn', scale=4) # scale 2, 3 and 4 models available
41 inputs = ImageLoader.load_image(image)
42 preds = model(inputs)
43
44 ImageLoader.save_image(preds, './scaled_4x.png') # save the output 4x scaled image to `./scaled_4x.png`
45 ImageLoader.save_compare(inputs, preds, './scaled_4x_compare.png') # save an output comparing the super-image with a bicubic scaling
46 ```
47 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab")
48 ## Training data
49 The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/Div2k), a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
50 ## Training procedure
51 ### Preprocessing
52 We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566).
53 Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
54 During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
55 Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
56
57 We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data:
58 ```bash
59 pip install datasets
60 ```
61 The following code gets the data and preprocesses/augments the data.
62
63 ```python
64 from datasets import load_dataset
65 from super_image.data import EvalDataset, TrainDataset, augment_five_crop
66
67 augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\
68 .map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method
69 train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader
70 eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader
71 ```
72 ### Pretraining
73 The model was trained on GPU. The training code is provided below:
74 ```python
75 from super_image import Trainer, TrainingArguments, MsrnModel, MsrnConfig
76
77 training_args = TrainingArguments(
78 output_dir='./results', # output directory
79 num_train_epochs=1000, # total number of training epochs
80 )
81
82 config = MsrnConfig(
83 scale=4, # train a model to upscale 4x
84 )
85 model = MsrnModel(config)
86
87 trainer = Trainer(
88 model=model, # the instantiated model to be trained
89 args=training_args, # training arguments, defined above
90 train_dataset=train_dataset, # training dataset
91 eval_dataset=eval_dataset # evaluation dataset
92 )
93
94 trainer.train()
95 ```
96
97 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Train_super_image_Models.ipynb "Open in Colab")
98 ## Evaluation results
99 The evaluation metrics include [PSNR](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Quality_estimation_with_PSNR) and [SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Algorithm).
100
101 Evaluation datasets include:
102 - Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5)
103 - Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14)
104 - BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100)
105 - Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100)
106
107 The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline.
108
109 |Dataset |Scale |Bicubic |msrn |
110 |--- |--- |--- |--- |
111 |Set5 |2x |33.64/0.9292 |**38.08/0.9609** |
112 |Set5 |3x |30.39/0.8678 |**35.12/0.9409** |
113 |Set5 |4x |28.42/0.8101 |**32.19/0.8951** |
114 |Set14 |2x |30.22/0.8683 |**33.75/0.9183** |
115 |Set14 |3x |27.53/0.7737 |**31.08/0.8593** |
116 |Set14 |4x |25.99/0.7023 |**28.78/0.7862** |
117 |BSD100 |2x |29.55/0.8425 |**33.82/0.9258** |
118 |BSD100 |3x |27.20/0.7382 |**29.67/0.8198** |
119 |BSD100 |4x |25.96/0.6672 |**28.53/0.7657** |
120 |Urban100 |2x |26.66/0.8408 |**32.14/0.9287** |
121 |Urban100 |3x | |**29.31/0.8743** |
122 |Urban100 |4x |23.14/0.6573 |**26.12/0.7866** |
123
124 ![Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2](images/msrn_2_4_compare.png "Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2")
125
126 You can find a notebook to easily run evaluation on pretrained models below:
127
128 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab")
129
130 ## BibTeX entry and citation info
131 ```bibtex
132 @InProceedings{Agustsson_2017_CVPR_Workshops,
133 author = {Agustsson, Eirikur and Timofte, Radu},
134 title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study},
135 booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
136 url = "http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf",
137 month = {July},
138 year = {2017}
139 }
140 ```