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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Ultra Fast Structure-aware Deep Lane Detection (ECCV 2020)
| Date | Stars |
|---|---|
| 2026-07-24 | 2044 |
| 2026-07-25 | 2045 |
| 2026-07-28 | 2045 |
| 2026-07-30 | 2045 |
| 2026-08-06 | 2045 |
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# Ultra-Fast-Lane-Detection PyTorch implementation of the paper "[Ultra Fast Structure-aware Deep Lane Detection](https://arxiv.org/abs/2004.11757)". **\[July 18, 2022\] Updates: The new version of our method has been accepted by TPAMI 2022. Code is available [here](https://github.com/cfzd/Ultra-Fast-Lane-Detection-v2)**. \[June 28, 2021\] Updates: we will release an extended version, which improves **6.3** points of F1 on CULane with the ResNet-18 backbone compared with the ECCV version. Updates: Our paper has been accepted by ECCV2020.  The evaluation code is modified from [SCNN](https://github.com/XingangPan/SCNN) and [Tusimple Benchmark](https://github.com/TuSimple/tusimple-benchmark). Caffe model and prototxt can be found [here](https://github.com/Jade999/caffe_lane_detection). # Demo <a href="http://www.youtube.com/watch?feature=player_embedded&v=lnFbAG3GBN4 " target="_blank"><img src="http://img.youtube.com/vi/lnFbAG3GBN4/0.jpg" alt="Demo" width="240" height="180" border="10" /></a> # Install Please see [INSTALL.md](./INSTALL.md) # Get started First of all, please modify `data_root` and `log_path` in your `configs/culane.py` or `configs/tusimple.py` config according to your environment. - `data_root` is the path of your CULane dataset or Tusimple dataset. - `log_path` is where tensorboard logs, trained models and code backup are stored. ***It should be placed outside of this project.*** *** For single gpu training, run ```Shell python train.py configs/path_to_your_config ``` For multi-gpu training, run ```Shell sh launch_training.sh ``` or ```Shell python -m torch.distributed.launch --nproc_per_node=$NGPUS train.py configs/path_to_your_config ``` If there is no pretrained torchvision model, multi-gpu training may result in multiple downloading. You can first download the corresponding models manually, and then restart the multi-gpu training. Since our code has auto backup function which will copy all codes to the `log_path` according to the gitignore, additional temp file might also be copied if it is not filtered by gitignore, which may block the execution if the temp files are large. So you should keep the working directory clean. *** Besides config style settings, we also support command line style one. You can override a setting like ```Shell python train.py configs/path_to_your_config --batch_size 8 ``` The ```batch_size``` will be set to 8 during training. *** To visualize the log with tensorboard, run ```Shell tensorboard --logdir log_path --bind_all ``` # Trained models We provide two trained Res-18 models on CULane and Tusimple. | Dataset | Metric paper | Metric This repo | Avg FPS on GTX 1080Ti | Model | |:--------:|:------------:|:----------------:|:-------------------:|:-----------:| | Tusimple | 95.87 | 95.82 | 306 | [GoogleDrive](https://drive.google.com/file/d/1WCYyur5ZaWczH15ecmeDowrW30xcLrCn/view?usp=sharing)/[BaiduDrive(code:bghd)](https://pan.baidu.com/s/1Fjm5yVq1JDpGjh4bdgdDLA) | | CULane | 68.4 | 69.7 | 324 | [GoogleDrive](https://drive.google.com/file/d/1zXBRTw50WOzvUp6XKsi8Zrk3MUC3uFuq/view?usp=sharing)/[BaiduDrive(code:w9tw)](https://pan.baidu.com/s/19Ig0TrV8MfmFTyCvbSa4ag) | For evaluation, run ```Shell mkdir tmp # This a bad example, you should put the temp files outside the project. python test.py configs/culane.py --test_model path_to_culane_18.pth --test_work_dir ./tmp python test.py configs/tusimple.py --test_model path_to_tusimple_18.pth --test_work_dir ./tmp ``` Same as training, multi-gpu evaluation is also supported. # Visualization We provide a script to visualize the detection results. Run the following commands to visualize on the testing set of CULane and Tusimple. ```Shell python demo.py configs/culane.py --test_model path_to_culane_18.pth # or python demo.py configs/tusimple.py --test_model path_to_tusimple_18.pth ``` Since the testing set of Tus
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Zhuo Zhang · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b1d1e6b52dbc2429, topic:pytorch
matched fp:b1d1e6b52dbc2429, topic:autonomous-driving