Regarding the issue of PSNR indicator first rising and then falling

[junhaojia]

May I ask you a few questions about video super-resolution? @xinntao I'm using Basic VSR++, and I suspect there is a problem with my configuration file.

train_BasicVSRPP_REDS_test.yml

GENERATE TIME: Wed Sep 10 15:33:31 2025

CMD:

/public/home/jsj_sjfx_jjh/BasicSR-master/basicsr/train.py -opt /public/home/jsj_sjfx_jjh/BasicSR-master/options/train/BasicVSRPP/train_BasicVSRPP_REDS_test.yml

general settings

name: train_BasicVSRPP_REDS model_type: VideoRecurrentModel scale: 4 num_gpu: 1 # official: 8 GPUs manual_seed: 0

dataset and data loader settings

datasets: train: name: REDS type: REDSRecurrentDataset dataroot_gt: /public/home/jsj_sjfx_jjh/data/data_JILIN189/train/GT/ dataroot_lq: /public/home/jsj_sjfx_jjh/data/data_JILIN189/train/LR4x/ meta_info_file: "/public/home/jsj_sjfx_jjh/data/data_JILIN189/meta_info_REDS_GT.txt" val_partition: REDS4 # set to 'official' when use the official validation partition test_mode: False io_backend: type: disk

num_frame: 15
gt_size: 256
interval_list: [1]
random_reverse: false
use_hflip: true
use_rot: true

# data loader
num_worker_per_gpu: 0
batch_size_per_gpu: 1
dataset_enlarge_ratio: 200
prefetch_mode: ~

val: name: REDS4 type: VideoRecurrentTestDataset dataroot_gt: /public/home/jsj_sjfx_jjh/data/data_JILIN189/eval/GT/ dataroot_lq: /public/home/jsj_sjfx_jjh/data/data_JILIN189/eval/LR4x/

cache_data: true
io_backend:
  type: disk

num_frame: -1  # not needed

network structures

network_g: type: BasicVSRPlusPlus mid_channels: 64 num_blocks: 7 is_low_res_input: true spynet_path: experiments/pretrained_models/spynet_sintel_final-3d2a1287.pth

path

path: pretrain_network_g: ~ strict_load_g: true resume_state: ~

training settings

train: ema_decay: 0.999 optim_g: type: Adam lr: !!float 2e-4 weight_decay: 0 betas: [0.9, 0.99] capturable: True

scheduler: type: CosineAnnealingRestartLR periods: [200000] restart_weights: [1] eta_min: !!float 1e-7

total_iter: 200000 warmup_iter: -1 # no warm up fix_flow: 5000 flow_lr_mul: 0.125

losses

pixel_opt: type: CharbonnierLoss loss_weight: 1.0 reduction: mean

validation settings

val: val_freq: 5000 save_img: False

metrics: psnr: # metric name, can be arbitrary type: calculate_psnr crop_border: 0 test_y_channel: false better: higher # the higher, the better. Default: higher

niqe:
  type: calculate_niqe
  crop_border: 0
  better: lower  # the lower, the better

logging settings

logger: print_freq: 100 save_checkpoint_freq: 10000 use_tb_logger: true wandb: project: ~ resume_id: ~

dist training settings

dist_params: backend: nccl port: 29500

find_unused_parameters: true