Codes for "Learning Lightweight Lane Detection CNNs by Self Attention Distillation" (PDF coming soon!)
This repo also contains Tensorflow implementation of "Spatial As Deep: Spatial CNN for Traffic Scene Understanding". (SCNN-Tensorflow)
- ENet-Label-Torch has been released.
Key features:
(1) ENet-label is a light-weight lane detection model based on ENet and adopts self attention distillation (more details can be found in our paper).
(2) It has 20 × fewer parameters and runs 10 × faster compared to the state-of-the-art SCNN, and achieves 72.0 (F1-measure) on CULane testing set (better than SCNN which achieves 71.6).
(Do not hesitate to try our model!!!)
- Multi-GPU training has been supported. Just change BATCH_SIZE and GPU_NUM in global_config.py, and then use
CUDA_VISIBLE_DEVICES="0,1,2,3" python file_name.py. Thanks @ yujincheng08.
- Install necessary packages:
conda create -n tensorflow_gpu pip python=3.5
source activate tensorflow_gpu
pip install --upgrade tensorflow-gpu==1.3.0
pip3 install -r SCNN-Tensorflow/lane-detection-model/requirements.txt
- Download VGG-16:
Download the vgg.npy here and put it in SCNN-Tensorflow/lane-detection-model/data.
- Pre-trained model for testing:
Download the pre-trained model here.
The ground-truth labels of TuSimple testing set is now available at TuSimple. The annotated training (#frame = 3268) and validation labels (#frame = 358) can be found here, please use them (list-name.txt) to replace the train_gt.txt and val_gt.txt in train_lanenet.py. Moreover, you need to resize the image to 256 x 512 instead of 288 x 800 in TuSimple. Remember to change the maximum index of rows and columns, and detailed explanations can be seen here. Please evaluate your pred.json using the labels and this script. Besides, to generate pred.json, you can refer to this issue.
The whole dataset is available at CULane.
The whole dataset is available at BDD100K.
cd SCNN-Tensorflow/lane-detection-model
CUDA_VISIBLE_DEVICES="0" python tools/test_lanenet.py --weights_path path/to/model_weights_file --image_path path/to/image_name_list --save_dir to_be_saved_dir
Note that path/to/image_name_list should be like test_img.txt. Now, you get the probability maps from our model. To get the final performance, you need to follow SCNN to get curve lines from probability maps as well as calculate precision, recall and F1-measure.
Reminder: you should check lanenet_data_processor.py and lanenet_data_processor_test.py to ensure that the processing of image path is right. You are recommended to use the absolute path in your image path list. Besides, this code needs batch size used in training and testing to be consistent. To enable arbitrary batch size in the testing phase, please refer to this issue.
CUDA_VISIBLE_DEVICES="0" python tools/train_lanenet.py --net vgg --dataset_dir path/to/CULane-dataset/
Note that path/to/CULane-dataset/ should contain files like train_gt.txt and val_gt.txt.
- TuSimple testing set:
| Model | Accuracy | FP | FN |
|---|---|---|---|
| SCNN-Torch | 96.53% | 0.0617 | 0.0180 |
| SCNN-Tensorflow | -- | -- | -- |
| ENet-Label-Torch | 96.64% | 0.0602 | 0.0205 |
The pre-trained model for testing is here. (coming soon!) Note that in TuSimple, SCNN-Torch is based on ResNet-101 while SCNN-Tensorflow is based on VGG-16. In CULane and BDD100K, both SCNN-Torch and SCNN-Tensorflow are based on VGG-16.
- CULane testing set (F1-measure):
| Category | SCNN-Torch | SCNN-Tensorflow | ENet-Label-Torch |
|---|---|---|---|
| Normal | 90.6 | 90.2 | 90.7 |
| Crowded | 69.7 | 71.9 | 70.8 |
| Night | 66.1 | 64.6 | 65.9 |
| No line | 43.4 | 45.8 | 44.7 |
| Shadow | 66.9 | 73.8 | 70.6 |
| Arrow | 84.1 | 83.8 | 85.8 |
| Dazzle light | 58.5 | 59.5 | 64.4 |
| Curve | 64.4 | 63.4 | 65.4 |
| Crossroad | 1990 | 4137 | 2729 |
| Total | 71.6 | 71.3 | 72.0 |
| Runtime(ms) | 133.5 | -- | 13.4 |
| Parameter(M) | 20.72 | -- | 0.98 |
The pre-trained model for testing is here. Note that you need to exchange the order of VGG-MEAN in test_lanenet.py and change the order of input images from RGB to BGR since the pre-trained model uses opencv to read images. You can further boost the performance by referring to this issue.
- BDD100K testing set:
| Model | Accuracy | IoU |
|---|---|---|
| SCNN-Torch | 35.79% | 15.84 |
| SCNN-Tensorflow | -- | -- |
| ENet-Label-Torch | 36.56% | 16.02 |
The accuracy and IoU of lane pixels are computed. The pre-trained model for testing is here. (coming soon!)
If you use the codes, please cite the following publications:
@inproceedings{pan2018SCNN,
author = {Xingang Pan, Jianping Shi, Ping Luo, Xiaogang Wang, and Xiaoou Tang},
title = {Spatial As Deep: Spatial CNN for Traffic Scene Understanding},
booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
month = {February},
year = {2018}
}
@misc{hou2019agnostic,
title={Agnostic Lane Detection},
author={Yuenan Hou},
year={2019},
eprint={1905.03704},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
This repo is built upon SCNN and LaneNet.
If you have any problems in reproducing the results, just raise an issue in this repo.
- Test SCNN-Tensorflow in TuSimple and BDD100K
- Provide detailed instructions to run SCNN-Tensorflow in TuSimple and BDD100K
- Upload our light-weight model (ENet-SAD) and its training & testing scripts