Transformers
AWSRN
super-image
image-super-resolution
Inference Endpoints
Eugene Siow commited on
Commit
7881463
1 Parent(s): 780fe57

Initial commit.

Browse files
README.md ADDED
@@ -0,0 +1,152 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ # Lightweight Image Super-Resolution with Adaptive Weighted Learning Network (AWSRN)
17
+ AWSRN 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 [Lightweight Image Super-Resolution with Adaptive Weighted Learning Network](https://arxiv.org/abs/1904.02358) by Wang et al. (2019) and first released in [this repository](https://github.com/ChaofWang/AWSRN).
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 and model upscaling.
20
+
21
+ ![Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 4](images/awsrn_4_4_compare.png "Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 4")
22
+ ## Model description
23
+ Deep learning has been successfully applied to the single-image super-resolution (SISR) task with great performance in recent years. However, most convolutional neural network based SR models require heavy computation, which limit their real-world applications. In this work, a lightweight SR network, named Adaptive Weighted Super-Resolution Network (AWSRN), is proposed for SISR to address this issue. A novel local fusion block (LFB) is designed in AWSRN for efficient residual learning, which consists of stacked adaptive weighted residual units (AWRU) and a local residual fusion unit (LRFU). Moreover, an adaptive weighted multi-scale (AWMS) module is proposed to make full use of features in reconstruction layer. AWMS consists of several different scale convolutions, and the redundancy scale branch can be removed according to the contribution of adaptive weights in AWMS for lightweight network. The experimental results on the commonly used datasets show that the proposed lightweight AWSRN achieves superior performance on ×2, ×3, ×4, and ×8 scale factors to state-of-the-art methods with similar parameters and computational overhead.
24
+
25
+ This model also applies the balanced attention (BAM) method invented by [Wang et al. (2021)](https://arxiv.org/abs/2104.07566) to further improve the results.
26
+ ## Intended uses & limitations
27
+ 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.
28
+ ### How to use
29
+ The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library:
30
+ ```bash
31
+ pip install super-image
32
+ ```
33
+ Here is how to use a pre-trained model to upscale your image:
34
+ ```python
35
+ from super_image import AwsrnModel, ImageLoader
36
+ from PIL import Image
37
+ import requests
38
+
39
+ url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
40
+ image = Image.open(requests.get(url, stream=True).raw)
41
+
42
+ model = AwsrnModel.from_pretrained('eugenesiow/awsrn-bam', scale=2) # scale 2, 3 and 4 models available
43
+ inputs = ImageLoader.load_image(image)
44
+ preds = model(inputs)
45
+
46
+ ImageLoader.save_image(preds, './scaled_2x.png') # save the output 2x scaled image to `./scaled_2x.png`
47
+ ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png') # save an output comparing the super-image with a bicubic scaling
48
+ ```
49
+ [![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")
50
+ ## Training data
51
+ 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).
52
+ ## Training procedure
53
+ ### Preprocessing
54
+ We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566).
55
+ 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.
56
+ During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
57
+ 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.
58
+
59
+ We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data:
60
+ ```bash
61
+ pip install datasets
62
+ ```
63
+ The following code gets the data and preprocesses/augments the data.
64
+
65
+ ```python
66
+ from datasets import load_dataset
67
+ from super_image.data import EvalDataset, TrainDataset, augment_five_crop
68
+
69
+ augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\
70
+ .map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method
71
+ train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader
72
+ eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader
73
+ ```
74
+ ### Pretraining
75
+ The model was trained on GPU. The training code is provided below:
76
+ ```python
77
+ from super_image import Trainer, TrainingArguments, AwsrnModel, AwsrnConfig
78
+
79
+ training_args = TrainingArguments(
80
+ output_dir='./results', # output directory
81
+ num_train_epochs=1000, # total number of training epochs
82
+ )
83
+
84
+ config = AwsrnConfig(
85
+ scale=4, # train a model to upscale 4x
86
+ bam=True, # apply balanced attention to the network
87
+ )
88
+ model = AwsrnModel(config)
89
+
90
+ trainer = Trainer(
91
+ model=model, # the instantiated model to be trained
92
+ args=training_args, # training arguments, defined above
93
+ train_dataset=train_dataset, # training dataset
94
+ eval_dataset=eval_dataset # evaluation dataset
95
+ )
96
+
97
+ trainer.train()
98
+ ```
99
+
100
+ [![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")
101
+ ## Evaluation results
102
+ 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).
103
+
104
+ Evaluation datasets include:
105
+ - Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5)
106
+ - Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14)
107
+ - BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100)
108
+ - Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100)
109
+
110
+ The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline.
111
+
112
+ |Dataset |Scale |Bicubic |awsrn-bam |
113
+ |--- |--- |--- |--- |
114
+ |Set5 |2x |33.64/0.9292 |**37.99/0.9606** |
115
+ |Set5 |3x |30.39/0.8678 |**** |
116
+ |Set5 |4x |28.42/0.8101 |**32.13/0.8947** |
117
+ |Set14 |2x |30.22/0.8683 |**33.66/0.918** |
118
+ |Set14 |3x |27.53/0.7737 |**** |
119
+ |Set14 |4x |25.99/0.7023 |**28.75/0.7851** |
120
+ |BSD100 |2x |29.55/0.8425 |**33.76/0.9253** |
121
+ |BSD100 |3x |27.20/0.7382 |**** |
122
+ |BSD100 |4x |25.96/0.6672 |**28.51/0.7647** |
123
+ |Urban100 |2x |26.66/0.8408 |**31.95/0.9265** |
124
+ |Urban100 |3x | |**** |
125
+ |Urban100 |4x |23.14/0.6573 |**26.03/0.7838** |
126
+
127
+ ![Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2](images/awsrn_2_4_compare.png "Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2")
128
+
129
+ You can find a notebook to easily run evaluation on pretrained models below:
130
+
131
+ [![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")
132
+
133
+ ## BibTeX entry and citation info
134
+ ```bibtex
135
+ @misc{wang2021bam,
136
+ title={BAM: A Lightweight and Efficient Balanced Attention Mechanism for Single Image Super Resolution},
137
+ author={Fanyi Wang and Haotian Hu and Cheng Shen},
138
+ year={2021},
139
+ eprint={2104.07566},
140
+ archivePrefix={arXiv},
141
+ primaryClass={eess.IV}
142
+ }
143
+ ```
144
+
145
+ ```bibtex
146
+ @article{wang2019lightweight,
147
+ title={Lightweight Image Super-Resolution with Adaptive Weighted Learning Network},
148
+ author={Wang, Chaofeng and Li, Zhen and Shi, Jun},
149
+ journal={arXiv preprint arXiv:1904.02358},
150
+ year={2019
151
+ }
152
+ ```
config.json ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bam": true,
3
+ "data_parallel": false,
4
+ "model_type": "AWSRN",
5
+ "rgb_mean": [
6
+ 0.4488,
7
+ 0.4371,
8
+ 0.404
9
+ ],
10
+ "rgb_std": [
11
+ 1.0,
12
+ 1.0,
13
+ 1.0
14
+ ]
15
+ }
images/awsrn_2_4_compare.png ADDED
images/awsrn_4_4_compare.png ADDED
pytorch_model_2x.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a4361e1c919ec1f2cdfe4846bb242227cb297d0ffcd2c841ee17ee085f9fac42
3
+ size 5649061
pytorch_model_4x.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:fce476b1314177798f2d3e688135a70cbdaea6bc19341176e54b873112bfe6d8
3
+ size 6410597