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Repository for the paper entitled "PolyLaneNet: Lane Estimation via Deep Polynomial Regression" (ICPR 2020)

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Method overview


Code for the PolyLaneNet paper, accepted to ICPR 2020, by Lucas Tabelini, Thiago M. Paixão, Rodrigo F. Berriel, Claudine Badue, Alberto F. De Souza, and Thiago Oliveira-Santos.

News: The source code for our new state-of-the-art lane detection method, LaneATT, has been released. Check it out here.

Table of Contents

  1. Installation
  2. Usage
  3. Reproducing the paper results


The code requires Python 3, and has been tested on Python 3.5.2, but should work on newer versions of Python too.

Install dependencies:

pip install -r requirements.txt



Every setting for a training is set through a YAML configuration file. Thus, in order to train a model you will have to setup the configuration file. An example is shown: ```yaml

Training settings

expsdir: 'experiments' # Path to the root for the experiments directory (not only the one you will run) iterloginterval: 1 # Log training iteration every N iterations itertimewindow: 100 # Moving average iterations window for the printed loss metric modelsaveinterval: 1 # Save model every N epochs seed: 0 # Seed for randomness backup: drive:polylanenet-experiments # The experiment directory will be automatically uploaded using rclone after the training ends. Leave empty if you do not want this. model: name: PolyRegression parameters: numoutputs: 35 # (5 lanes) * (1 conf + 2 (upper & lower) + 4 poly coeffs) pretrained: true backbone: 'efficientnet-b0' predcategory: false lossparameters: confweight: 1 lowerweight: 1 upperweight: 1 clsweight: 0 polyweight: 300 batchsize: 16 epochs: 2695 optimizer: name: Adam parameters: lr: 3.0e-4 lrscheduler: name: CosineAnnealingLR parameters: Tmax: 385

Testing settings

testparameters: confthreshold: 0.5 # Set predictions with confidence lower than this to 0 (i.e., set as invalid for the metrics)

Dataset settings

datasets: train: type: PointsDataset parameters: dataset: tusimple split: train imgsize: [360, 640] normalize: true augchance: 0.9090909090909091 # 10/11 augmentations: # ImgAug augmentations - name: Affine parameters: rotate: !!python/tuple [-10, 10] - name: HorizontalFlip parameters: p: 0.5 - name: CropToFixedSize parameters: width: 1152 height: 648 root: "datasets/tusimple" # Dataset root

test: &test type: PointsDataset parameters: dataset: tusimple split: val img_size: [360, 640] root: "datasets/tusimple" normalize: true augmentations: []

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

With the config file created, run the training script:

python --exp_name tusimple --cfg config.yaml
This script's options are:
  --exp_name            Experiment name.
  --cfg                 Config file for the training (.yaml)
  --resume              Resume training. If a training session was interrupted, run it again with the same arguments and this option to resume the training from the last checkpoint.
  --validate            Wheter to validate during the training session. Was not in our experiments, which means it has not been thoroughly tested.
  --deterministic       set cudnn.deterministic = True and cudnn.benchmark = False


After training, run the
script to get the metrics:
python --exp_name tusimple --cfg config.yaml --epoch 2695
This script's options are:
  --exp_name            Experiment name.
  --cfg                 Config file for the test (.yaml). (probably the same one used in the training)
  --epoch EPOCH         Epoch to test the model on
  --batch_size          Number of images per batch
  --view                Show predictions. Will draw the predictions in an image and then show it (cv.imshow)

If you have any issues with either training or testing feel free to open an issue.

Reproducing the paper results


All models trained for the paper can be found here.


How to

To reproduce the results, you can either retrain a model with the same settings (which should yield results pretty close to the reported ones) or just test the model. If you want to retrain, you only need the appropriate YAML settings file, which you can find in the

directory. If you just want to reproduce the exact reported metrics by testing the model, you'll have to: 1. Download the experiment directory. You don't need to download all model checkpoints if you want, you'll only need the last one (
, with the exception of the experiments on ELAS and LLAMAS). 1. Modify all path related fields (i.e., dataset paths and
) in the
file inside the experiment directory. 1. Move the downloaded experiment to your

Then, run:

python --exp_name $exp_name --cfg $exps_dir/$exp_name/config.yaml --epoch 2695


with the name of the directory you downloaded (the name of the experiment) and
with the
value you defined inside the
file. The script will look for a directory named
to load the model.

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