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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - super-image
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+ - image-super-resolution
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+ datasets:
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+ - eugenesiow/Div2k
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+ - eugenesiow/Set5
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+ - eugenesiow/Set14
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+ - eugenesiow/BSD100
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+ - eugenesiow/Urban100
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+ metrics:
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+ - pnsr
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+ - ssim
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+ ---
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+ # Densely Residual Laplacian Super-Resolution (DRLN)
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+ DRLN 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 [Densely Residual Laplacian Super-resolution](https://arxiv.org/abs/1906.12021) by Anwar et al. (2020) and first released in [this repository](https://github.com/saeed-anwar/DRLN).
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+
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+ 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.
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+
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+ ![Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 4](images/drln_4_4_compare.png "Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 4")
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+ ## Model description
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+ Super-Resolution convolutional neural networks have recently demonstrated high-quality restoration for single images. However, existing algorithms often require very deep architectures and long training times. Furthermore, current convolutional neural networks for super-resolution are unable to exploit features at multiple scales and weigh them equally, limiting their learning capability. In this exposition, we present a compact and accurate super-resolution algorithm namely, Densely Residual Laplacian Network (DRLN). The proposed network employs cascading residual on the residual structure to allow the flow of low-frequency information to focus on learning high and mid-level features. In addition, deep supervision is achieved via the densely concatenated residual blocks settings, which also helps in learning from high-level complex features. Moreover, we propose Laplacian attention to model the crucial features to learn the inter and intra-level dependencies between the feature maps. Furthermore, comprehensive quantitative and qualitative evaluations on low-resolution, noisy low-resolution, and real historical image benchmark datasets illustrate that our DRLN algorithm performs favorably against the state-of-the-art methods visually and accurately.
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+
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+ 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.
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+ ## Intended uses & limitations
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+ 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.
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+ ### How to use
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+ The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library:
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+ ```bash
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+ pip install super-image
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+ ```
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+ Here is how to use a pre-trained model to upscale your image:
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+ ```python
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+ from super_image import DrlnModel, ImageLoader
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+ from PIL import Image
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+ import requests
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+
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+ url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
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+ image = Image.open(requests.get(url, stream=True).raw)
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+
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+ model = DrlnModel.from_pretrained('eugenesiow/drln-bam', scale=2) # scale 2, 3 and 4 models available
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+ inputs = ImageLoader.load_image(image)
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+ preds = model(inputs)
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+
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+ ImageLoader.save_image(preds, './scaled_2x.png') # save the output 2x scaled image to `./scaled_2x.png`
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+ ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png') # save an output comparing the super-image with a bicubic scaling
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+ ```
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+ [![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")
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+ ## Training data
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+ 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).
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+ ## Training procedure
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+ ### Preprocessing
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+ We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566).
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+ 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.
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+ During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
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+ 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.
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+
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+ We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data:
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+ ```bash
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+ pip install datasets
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+ ```
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+ The following code gets the data and preprocesses/augments the data.
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+
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+ ```python
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+ from datasets import load_dataset
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+ from super_image.data import EvalDataset, TrainDataset, augment_five_crop
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+
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+ augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\
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+ .map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method
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+ train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader
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+ eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader
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+ ```
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+ ### Pretraining
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+ The model was trained on GPU. The training code is provided below:
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+ ```python
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+ from super_image import Trainer, TrainingArguments, DrlnModel, DrlnConfig
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+
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+ training_args = TrainingArguments(
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+ output_dir='./results', # output directory
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+ num_train_epochs=1000, # total number of training epochs
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+ )
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+
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+ config = DrlnConfig(
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+ scale=4, # train a model to upscale 4x
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+ bam=True, # apply balanced attention to the network
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+ )
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+ model = DrlnModel(config)
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+
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+ trainer = Trainer(
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+ model=model, # the instantiated model to be trained
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+ args=training_args, # training arguments, defined above
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+ train_dataset=train_dataset, # training dataset
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+ eval_dataset=eval_dataset # evaluation dataset
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+ )
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+
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+ trainer.train()
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+ ```
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+
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+ [![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")
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+ ## Evaluation results
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+ 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).
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+
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+ Evaluation datasets include:
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+ - Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5)
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+ - Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14)
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+ - BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100)
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+ - Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100)
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+
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+ The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline.
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+
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+ |Dataset |Scale |Bicubic |drln-bam |
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+ |--- |--- |--- |--- |
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+ |Set5 |2x |33.64/0.9292 |**** |
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+ |Set5 |3x |30.39/0.8678 |**** |
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+ |Set5 |4x |28.42/0.8101 |**32.49/0.8986** |
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+ |Set14 |2x |30.22/0.8683 |**** |
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+ |Set14 |3x |27.53/0.7737 |**** |
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+ |Set14 |4x |25.99/0.7023 |**28.94/0.7899** |
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+ |BSD100 |2x |29.55/0.8425 |**** |
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+ |BSD100 |3x |27.20/0.7382 |**** |
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+ |BSD100 |4x |25.96/0.6672 |**28.63/0.7686** |
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+ |Urban100 |2x |26.66/0.8408 |**** |
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+ |Urban100 |3x | |**** |
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+ |Urban100 |4x |23.14/0.6573 |**26.53/0.7991** |
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+
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+ ![Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2](images/drln_2_4_compare.png "Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2")
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+
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+ You can find a notebook to easily run evaluation on pretrained models below:
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+
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+ [![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")
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+
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+ ## BibTeX entry and citation info
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+ ```bibtex
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+ @misc{wang2021bam,
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+ title={BAM: A Lightweight and Efficient Balanced Attention Mechanism for Single Image Super Resolution},
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+ author={Fanyi Wang and Haotian Hu and Cheng Shen},
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+ year={2021},
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+ eprint={2104.07566},
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+ archivePrefix={arXiv},
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+ primaryClass={eess.IV}
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+ }
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+ ```
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+
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+ ```bibtex
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+ @misc{anwar2019densely,
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+ title={Densely Residual Laplacian Super-Resolution},
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+ author={Saeed Anwar and Nick Barnes},
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+ year={2019},
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+ eprint={1906.12021},
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+ archivePrefix={arXiv},
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+ primaryClass={eess.IV}
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+ }
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+ ```
config.json ADDED
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+ {
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+ "bam": true,
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+ "data_parallel": false,
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+ "model_type": "DRLN"
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+ }
images/drln_2_4_compare.png ADDED
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