{
  "task": "swinir_car_jpeg_40"     // JPEG compression artifact reduction for quality factor 10/20/30/40. root/task/images-models-options
  , "model": "plain" // "plain" | "plain2" if two inputs
  , "gpu_ids": [0,1,2,3,4,5,6,7]
  , "dist": true

  , "is_color": false // color or grayscale

  , "path": {
    "root": "dejpeg"            // "denoising" | "superresolution" | "dejpeg"
    , "pretrained_netG": null      // path of pretrained model. We fine-tune quality=10/20/30 models from quality=40 model, so that `G_optimizer_lr` and `G_scheduler_milestones` can be halved to save time.
    , "pretrained_netE": null      // path of pretrained model
  }

  , "datasets": {
    "train": {
      "name": "train_dataset"           // just name
      , "dataset_type": "jpeg"         // "dncnn" | "dnpatch" | "fdncnn" | "ffdnet" | "sr" | "srmd" | "dpsr" | "plain" | "plainpatch" | "jpeg"
      , "dataroot_H": "trainsets/trainH"// path of H training dataset. DIV2K (800 training images) + Flickr2K (2650 images) + BSD500 (400 training&testing images) + WED(4744 images) in SwinIR
      , "dataroot_L": null              // path of L training dataset

      , "H_size": 126                   // patch_size
      , "quality_factor": 40            //  10 | 20 | 30 | 40.
      , "quality_factor_test": 40       //
      , "is_color": false               //

      , "dataloader_shuffle": true
      , "dataloader_num_workers": 16
      , "dataloader_batch_size": 8      // batch size 1 | 16 | 32 | 48 | 64 | 128. Total batch size =1x8=8 in SwinIR
    }
    , "test": {
      "name": "test_dataset"            // just name
      , "dataset_type": "jpeg"         // "dncnn" | "dnpatch" | "fdncnn" | "ffdnet" | "sr" | "srmd" | "dpsr" | "plain" | "plainpatch" | "jpeg"
      , "dataroot_H": "testsets/LIVE1"  // path of H testing dataset
      , "dataroot_L": null              // path of L testing dataset

      , "quality_factor": 40            //  10 | 20 | 30 | 40.
      , "quality_factor_test": 40       //
      , "is_color": false               //

    }
  }

  , "netG": {
    "net_type": "swinir" 
    , "upscale": 1 
    , "in_chans": 1 
    , "img_size": 126
    , "window_size": 7                 // 7 works better than 8, maybe because jpeg encoding uses 8x8 patches
    , "img_range": 255.0               // image_range=255.0 is slightly better
    , "depths": [6, 6, 6, 6, 6, 6] 
    , "embed_dim": 180 
    , "num_heads": [6, 6, 6, 6, 6, 6]
    , "mlp_ratio": 2 
    , "upsampler": null                 // "pixelshuffle" | "pixelshuffledirect" | "nearest+conv" | null
    , "resi_connection": "1conv"        // "1conv" | "3conv"

    , "init_type": "default"
  }

  , "train": {
    "G_lossfn_type": "charbonnier"      // "l1" | "l2sum" | "l2" | "ssim" | "charbonnier" preferred
    , "G_lossfn_weight": 1.0            // default
    , "G_charbonnier_eps": 1e-9

    , "E_decay": 0.999                  // Exponential Moving Average for netG: set 0 to disable; default setting 0.999

    , "G_optimizer_type": "adam"        // fixed, adam is enough
    , "G_optimizer_lr": 2e-4            // learning rate
    , "G_optimizer_wd": 0               // weight decay, default 0
    , "G_optimizer_clipgrad": null      // unused
    , "G_optimizer_reuse": true         // 

    , "G_scheduler_type": "MultiStepLR" // "MultiStepLR" is enough
    , "G_scheduler_milestones": [800000, 1200000, 1400000, 1500000, 1600000]
    , "G_scheduler_gamma": 0.5

    , "G_regularizer_orthstep": null    // unused
    , "G_regularizer_clipstep": null    // unused

    , "G_param_strict": true
    , "E_param_strict": true

    , "checkpoint_test": 5000           // for testing
    , "checkpoint_save": 5000           // for saving model
    , "checkpoint_print": 200           // for print
  }
}