paddleseg_2.5.0_以后的版本不再支持车道线检测模型的训练,_此仓库用于自定义数据集训练_LaneSeg.zip

English | [简体中文](README_CN.md) # LaneSeg Lane detection is a category of automatic driving algorithms, which can be used to assist vehicle positioning and decision-making. In the early days, there were lane detection methods based on traditional image processing, but with the evolution of technology, the scenes that lane detection tasks deal with are more and more diversified, and more methods are currently seeking to detect the location of lane semantically. This project mainly uses pdseg for lane detection. ## Contents - [Installation](#Installation) - [Models](#Models) - [Dataset Preparation](#Dataset-Preparation) - [Training, Evaluation and Prediction](#Training-Evaluation-and-Prediction) - [Export and Deploy](#Export-and-Deploy) ## Installation #### 1. Install PaddlePaddle Versions * PaddlePaddle >= 2.0.2 * Python >= 3.7+ Due to the high computational cost of model, pdseg is recommended for GPU version PaddlePaddle. CUDA 10.0 or later is recommended. See [PaddlePaddle official website](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html) for the installation tutorial. #### 2. Download the pdseg repository ```shell git clone https://github.com/PaddlePaddle/pdseg ``` #### 3. Installation ```shell cd pdseg pip install scikit-learn pip install opencv-python pip install scikit-image pip install -e . cd contrib/LaneSeg ``` ## Models The evaluation is base on TuSimple challenge evaluation method. You can get more information from [TuSimple example](https://github.com/TuSimple/tusimple-benchmark/blob/master/example/lane_demo.ipynb) Lane detection model performance on Tusimple. | Method | Acc | FN | FP | Link| |-|-|-|-|-| |BiseNetV2|96.38%|0.04545|0.03363|[model](https://pdseg.bj.bcebos.com/lane_seg/bisenet/model.pdparams)| |FastScnn|96.04% |0.04909|0.04058|[model](https://pdseg.bj.bcebos.com/lane_seg/fastscnn/model.pdparams)| Note: The model input size is (640, 368) and the GPU is Tesla V100 32G. ## Dataset preparation Using Tusimple's open source [Tusimple](https://github.com/TuSimple/tusimple-benchmark/issues/3) dataset as our demo dataset for the tutorial. Baidu Yun [download](https://pan.baidu.com/s/1RuDUne5CpaNxVLVLmpEg-w), code: 9568. we should download train_set.zip, test_set.zip, test_label.json, and unzip train_set.zip，test_set.zip to `data/tusimple` directory, meanwhile, we should place test_label.json to `test_set` directory. ```shell cd data mkdir tusimple && cd tusimple unzip -d train_set train_set.zip unzip -d test_set test_set.zip cd ../../ ``` The folder structure is as follow: ``` LaneSeg |-- data |-- tusimple |-- train_set |-- clips |-- 0313-1 |-- 0313-2 |-- 0531 |-- 0601 |-- label_data_0313.json |-- label_data_0531.json |-- label_data_0601.json |-- test_set |-- clips |-- 0530 |-- 0531 |-- 0601 |-- test_tasks_0627.json |-- test_label.json ``` Run the following command: ```shell python third_party/generate_tusimple_dataset.py --root data/tusimple ``` Organize the dataset into the following structure and place the dataset under the `data` directory. ``` The folder structure is as follow: LaneSeg |-- data |-- tusimple |-- train_set ... |-- labels |-- 0313-1 |-- 0313-2 |-- 0531 |-- 0601 |-- train_list.txt |-- test_set ... |-- labels |-- 0530 |-- 0531 |-- 0601 |-- train_list.txt ``` The contents of train_list.txt is as follows: ``` /train_set/clips/0313-1/6040/20.jpg /train_set/labels/0313-1/6040/20.png /train_set/clips/0313-1/5320/20.jpg /train_set/labels/0313-1/5320/20.png /train_set/clips/0313-1/23700/20.jpg /train_set/labels/0313-1/23700/20.png ... ``` The contents of test_list.txt is as follows: ``` /test_set/clips/0530/1492626760788443246_0/20.jpg /test_set/labels/0530/1492626760788443246_0/20.png /test_set/clips/0530/1492627171538356342_0/20.jpg /test_set/labels/0530/1492627171538356342_0/20.png /test_set/clips/0530/1492627288467128445_0/20.jpg /test_set/labels/0530/1492627288467128445_0/20.png ... ``` ## Training, Evaluation and Prediction ### Training ```shell export CUDA_VISIBLE_DEVICES=0 python train.py \ --config configs/bisenetV2_tusimple_640x368_300k.yml \ --do_eval \ --use_vdl \ --save_interval 2000 \ --num_workers 5 \ --save_dir output ``` **note:** Using `--do_eval` will affect training speed and increase memory consumption, turning on and off according to needs. `--num_workers` Read data in multi-process mode. Speed up data preprocessing. Run the following command to view more parameters. ```shell python train.py --help ``` If you want to use multiple GPUs，please use `python -m paddle.distributed.launch` to run. ### Evaluation ```shell export CUDA_VISIBLE_DEVICES=0 python val.py \ --config configs/bisenetV2_tusimple_640x368_300k.yml \ --model_path output/best_model/model.pdparams \ --save_dir ./output/results \ --is_view True ``` `--is_view` The prediction results will be saved if turn on. If it is off, it will speed up the evaluation. You can directly download the provided model for evaluation. Run the following command to view more parameters. ```shell python val.py --help ``` ### Prediction ```shell export CUDA_VISIBLE_DEVICES=0 python predict.py \ --config configs/bisenetV2_tusimple_640x368_300k.yml \ --model_path output/best_model/model.pdparams \ --image_path data/test_images/3.jpg \ --save_dir ./output/results ``` You can directly download the provided model for evaluation. Run the following command to view more parameters. ```shell python predict.py --help ``` prediction：<br/> <br/> pseudo_color_prediction：<br/> <br/> added_prediction：<br/>  ## Export and Deploy ### Model Export ```shell python export.py \ --config configs/bisenetV2_tusimple_640x368_300k.yml \ --model_path output/best_model/model.pdparams \ --save_dir output/export ``` Run the following command to view more parameters. ```shell python export.py --help ``` ### Deploy #### Paddle Inference (python) ```shell python deploy/python/infer.py \ --config output/export/deploy.yaml \ --image_path data/test_images/3.jpg \ --save_dir ouput/results ``` Run the following command to view more parameters. ```shell python deploy/python/infer.py --help ``` #### Paddle Inference（C++） reference [Paddle Inference tutorial](./deploy/cpp/README.md) the C++ sources files of the project is in LaneSeg/deploy/cpp