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.