How to test my own dataset? about polylanenet HOT 7 CLOSED

# Training settings
seed: 0
exps_dir: 'experiments'
iter_log_interval: 1
iter_time_window: 100
model_save_interval: 1
backup:
model:
  name: PolyRegression
  parameters:
    num_outputs: 35 # (5 lanes) * (1 conf + 2 (upper & lower) + 4 poly coeffs)
    pretrained: true
    backbone: 'resnet50'
    pred_category: false
    curriculum_steps: [0, 0, 0, 0]
loss_parameters:
  conf_weight: 1
  lower_weight: 1
  upper_weight: 1
  cls_weight: 0
  poly_weight: 300
batch_size: 16
epochs: 2695
optimizer:
  name: Adam
  parameters:
    lr: 3.0e-4
lr_scheduler:
  name: CosineAnnealingLR
  parameters:
    T_max: 385

# Testing settings
test_parameters:
  conf_threshold: 0.5

# Dataset settings
datasets:
  train:
    type: LaneDataset
    parameters:
      dataset: tusimple
      split: train
      img_size: [360, 640]
      normalize: true
      aug_chance: 0.9090909090909091 # 10/11
      augmentations:
       - name: Affine
         parameters:
           rotate: !!python/tuple [-10, 10]
       - name: HorizontalFlip
         parameters:
           p: 0.5
       - name: CropToFixedSize
         parameters:
           width: 1152
           height: 648
      root: "/dados/tabelini/datasets/tusimple"

test: &test
    type: LaneDataset
    parameters:
      dataset: nolabel_dataset
      normalize: true # Wheter to normalize the input data. Use the same value used in the pretrained model (all pretrained models that I provided used normalization, so you should leave it as it is)
      augmentations: [] # List of augmentations. You probably want to leave this empty for testing
      img_h: 360 # The height of your test images (they shoud all have the same size)
      img_w: 640 # The width of your test images
      img_size: [360, 640] # Yeah, this parameter is duplicated for some reason, will fix this when I get time (feel free to open a pull request :))
      max_lanes: 5 # Same number used in the pretrained model. If you use a model pretrained on TuSimple (most likely case), you'll use 5 here
      root:  "/home/ubuntu/" # Path to the directory containing your test images. The loader will look recursively for image files in this directory
      img_ext: ".jpeg" # Test images extension (e.g., .png, .jpg)"

  # val = test
val:
  <<: *test
``