Test problem?

[china56321]

HI, I use the pretrained model_2695.pt to test my own dataset.but it shows like this:

Below is my config.yaml :

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: nolabel_dataset
split: train
img_size: [540, 960]
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: 540
height: 960
root: "/home/share/make/PolyLaneNet/test_image"

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: 540 # The height of your test images (they shoud all have the same size)
img_w: 960 # The width of your test images
img_size: [540, 960] # 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/share/make/PolyLaneNet/test_image" # Path to the directory containing your test images. The loader will look recursively for image files in this directory
img_ext: ".jpg" # Test images extension (e.g., .png, .jpg)"

val = test

val:
<<: *test

Could you give me some advice ? Thanks

[lucastabelini]

Every lane detection dataset will have a certain bias toward the position of the horizon line. Since the model was trained on TuSimple, and you're testing on a different dataset, that bias will have a large effect on the quality of the predictions. As you can see in your image, the horizon line has a large offset from the real horizon line.

In order to fix this, you'll have to fine-tune the model on your dataset.