Machine Learning

LaneAF: Robust Multi-Lane Detection with Affinity Fields

LaneAF

LaneAF: Robust Multi-Lane Detection with Affinity Fields

Installation

  1. Clone this repository

  2. Install Anaconda

  3. Create a virtual environment and install all dependencies:

    conda create -n laneaf pip python=3.6
    source activate laneaf
    pip install numpy scipy matplotlib pillow scikit-learn
    pip install opencv-python
    pip install https://download.pytorch.org/whl/cu101/torch-1.7.0%2Bcu101-cp36-cp36m-linux_x86_64.whl
    pip install https://download.pytorch.org/whl/cu101/torchvision-0.8.1%2Bcu101-cp36-cp36m-linux_x86_64.whl
    source deactivate

You can alternately find your desired torch/torchvision wheel from here.

  1. Clone and make DCNv2:

    cd models/dla
    git clone https://github.com/lbin/DCNv2.git
    cd DCNv2
    ./make.sh

TuSimple

The entire TuSimple dataset should be downloaded and organized as follows:

└── TuSimple/
    ├── clips/
    |   └── .
    |   └── .
    ├── label_data_0313.json
    ├── label_data_0531.json
    ├── label_data_0601.json
    ├── test_tasks_0627.json
    ├── test_baseline.json
    └── test_label.json

The model requires ground truth segmentation labels during training. You can generate

 

 

 

To finish reading, please visit source site