LaneATT

Reference: https://github.com/lucastabelini/LaneATT

Table of contents

• LaneATT
  • Table of contents
  • 1. Prerequisites
  • 2. Install
  • 3. Getting started
    • Datasets
• train & validation data (~10 GB)
• test images (~10 GB)
• test annotations - Training & testing
  • 4. Results
    • TuSimple

1. Prerequisites

• Python >= 3.5
• PyTorch == 1.6, tested on CUDA 10.2. The models were trained and evaluated on PyTorch 1.6. When testing with other versions, the results (metrics) are slightly different.
• CUDA, to compile the NMS code
• Other dependencies described in requirements.txt

The versions described here were the lowest the code was tested with. Therefore, it may also work in other earlier versions, but it is not guaranteed (e.g., the code might run, but with different outputs).

2. Install

Conda is not necessary for the installation, as you can see, I only use it for PyTorch and Torchvision. Nevertheless, the installation process here is described using it.

conda create -n laneatt python=3.8 -y
conda activate laneatt
conda install pytorch==1.6 torchvision -c pytorch
pip install -r requirements.txt
cd lib/nms; python setup.py install; cd -

3. Getting started

Datasets

We used TuSimple in our experiments Firstly move to directory where this repository is cloned.

mkdir datasets # if it does not already exists
cd datasets
# train & validation data (~10 GB)
mkdir tusimple
wget "https://s3.us-east-2.amazonaws.com/benchmark-frontend/datasets/1/train_set.zip"
unzip train_set.zip -d tusimple
# test images (~10 GB)
mkdir tusimple-test
wget "https://s3.us-east-2.amazonaws.com/benchmark-frontend/datasets/1/test_set.zip"
unzip test_set.zip -d tusimple-test
# test annotations
wget "https://s3.us-east-2.amazonaws.com/benchmark-frontend/truth/1/test_label.json" -P tusimple-test/
cd ..

Training & testing

Train a model:

python main.py train --exp_name example --cfg example.yml

The configuration files are located in cfgs folder.

After running this command, a directory experiments should be created (if it does not already exists). Another directory laneatt_r34_tusimple will be inside it, containing data related to that experiment (e.g., model checkpoints, logs, evaluation results, etc)

Evaluate a model:

python main.py test --exp_name example

This command will evaluate the model saved in the last checkpoint of the experiment example (inside experiments). If you want to evaluate another checkpoint, the --epoch flag can be used. For other flags, please see python main.py -h. To visualize the predictions, run the above command with the additional flag --view all.

Testing on video:

python test.py --model_name mobilenet --cfg_path cfgs/laneatt_tusimple_mobilenet.yml --model_path mobilenet.pt --video_path test.mp4

In the above code, please replace the path to corresponding configuration, weights and video paths.

4. Results

The weights for each of the models can be downloaded from here: To check results of original resnet models, please download the experiment files from original authors using following commands.

gdown "https://drive.google.com/uc?id=1R638ou1AMncTCRvrkQY6I-11CPwZy23T"
unzip laneatt_experiments.zip

Weights of mobileone, mobilenet and shufflenet can be downloaded from following link: https://drive.google.com/drive/folders/1KD3xFiFodNZwR5VsQByieXAkSaExVmqJ?usp=sharing

TuSimple

Backbones	Accuracy%	FP	FN	FPS
ResNet18	94.99	0.0975	0.0364	125.8841
ResNet34	94.71	0.1279	0.0403	106.8609
ResNet122	95.36	0.1182	0.0356	31.8189
MobileOne	95.33	0.0611	0.0300	47.7609
ShuffleNet	95.20	0.0738	0.0364	93.1477
Mobilenet	95.18	0.0547	0.0360	85.95

Qualitative Results: https://drive.google.com/file/d/1BG4OzQGubZ5yYiItCAl79LWFpXBy9Uu7/view?usp=sharing

References:

LaneAtt - https://github.com/lucastabelini/LaneATT

mobileone - https://github.com/apple/ml-mobileone

mobilenetv3 - https://github.com/d-li14/mobilenetv3.pytorch

shufflenetv2 - https://github.com/pytorch/vision/blob/main/torchvision/models/shufflenetv2.py

Torch-pruning - https://github.com/VainF/Torch-Pruning/tree/master