duts-dino-k234-nq20-224-swav-mocov2-dino-p16-sr10100.yaml

# augmentations
use_copy_paste: false
scale_range: [ 0.1, 1.0 ]
repeat_image: false

# base directories
dir_ckpt: "/users/gyungin/selfmask/ckpt"  # "/work/gyungin/selfmask/ckpt"
dir_dataset: "/scratch/shared/beegfs/gyungin/datasets"

# clustering
k: [2, 3, 4]
clustering_mode: "spectral"
use_gpu: true  # if you want to use gpu-accelerated code for clustering
scale_factor: 2  # "how much you want to upsample encoder features before clustering"

# dataset
dataset_name: "duts"
use_pseudo_masks: true
train_image_size: 224
eval_image_size: 224
n_percent: 100
n_copy_pastes: null
pseudo_masks_fp: "/users/gyungin/selfmask/datasets/swav_mocov2_dino_p16_k234.json"

# dataloader:
batch_size: 8
num_workers: 4
pin_memory: true

# networks
abs_2d_pe_init: false
arch: "vit_small"
lateral_connection: false
learnable_pixel_decoder: false  # if False, use the bilinear interpolation
use_binary_classifier: true # if True, use a binary classifier to get an objectness for each query from transformer decoder
n_decoder_layers: 6
n_queries: 20
num_layers: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
patch_size: 8
training_method: "dino"  # "supervised", "deit", "dino", "mocov2", "swav"

# objective
loss_every_decoder_layer: true
weight_dice_loss: 1.0
weight_focal_loss: 0.0

# optimizer
lr: 0.000006 # default: 0.00006
lr_warmup_duration: 0  # 5
momentum: 0.9
n_epochs: 12
weight_decay: 0.01
optimizer_type: "adamw"

# validation
benchmarks: null