PytorchAutoDrive: Segmentation models (ERFNet, ENet, DeepLab, FCN...) and Lane detection models (SCNN, RESA, LSTR, LaneATT, BézierLaneNet...) based on PyTorch with fast training, visualization, benchmarking & deployment help
This release is a regular update with no major changes. The maintainers now have little time for open-source projects. As always, help is welcome.
There are in total 12 closed issues, 1 merged PRs and 6 commits to master branch since v3.2.2.
Contributors: @voldemortX @bjzhb666
None.
None.
Full Changelog: https://github.com/voldemortX/pytorch-auto-drive/compare/v3.2.2...v3.2.3
This release is a regular update with no major changes, also with a longer time span (6 months). The maintainers, as they step out of college life, feel this repo needs a better positioning in the community, and have not yet decided what's next. Tasks like the recently popular 3D occupancy & online lane structure predictions are possibly on the table, or the framework could be just left as is for the time being. As always, helps are welcome.
There are in total 22 closed issues, 4 merged PRs and 8 commits to master branch since v3.2.1.
Contributors: @voldemortX @PannenetsF
None.
eval()
for LSTR model. #126#139 changes the implementation of spatial convolutions for SCNN and RESA, to a gradient-safe, non-inplace style. Although no performance issues were detected, we mark this as a possible BC-Break. See the related issues (#121) for discussions and testings.
Full Changelog: https://github.com/voldemortX/pytorch-auto-drive/compare/v3.2.1...v3.2.2
This release is a regular update with no major changes, since the maintainers are really busy finding jobs. In the future, we may expand the supported tasks to 3D perception.
There are in total 10 closed issues, 2 merged PRs and 5 commits to master branch since v3.2.
Contributors: @voldemortX
None.
None.
Full Changelog: https://github.com/voldemortX/pytorch-auto-drive/compare/v3.2...v3.2.1
This release is a common update of the lane detection part, including LaneATT implementation, visualization improvements, and deployment supports. Importantly, users should be aware that this repo's lane detection testing use the same cache directory ./output
, so simultaneous testing on the same dataset could lead to wrong results.
There are in total 16 closed issues, 8 merged PRs and 24 commits to master branch since v3.1.
Contributors: @cedricgsh @francis0407 @LittleJohnKhan @voldemortX
--style
option for lane visualization, supports 3 styles: point
, line
& bezier
--gt-keypoint-path
and --metric
for GT comparisonFull Changelog: https://github.com/voldemortX/pytorch-auto-drive/compare/v3.1...v3.2
IMPORTANT: This is not a Happy April Fools Day. This release is mostly about adding new models for lane detection, and now we have a paper reference! Although not all datasets & method variations are tested for the new backbones, you can do that fairly easily with the current config-based coding style. There are in total 12 closed issues, 9 merged PRs and 14 commits to master branch since v3.0.
Contributors: @voldemortX @cedricgsh Thanks @FengqiLiu1221 @junshutang for generously providing GPUs!
level 1b
), see functional_keypoints.py for useful functions & implementation details.0.95
to align with BézierLaneNet, the performance is slightly increased (~ 1%), while the downloaded weights remain the same. #60simple
and strong
augmentation policies for lane detection are renamed to level 0
and level 1a
to accommodate more new augmentation policies. It is simply a name change, no trained models will be affected. #60The Great Refactor took a little longer than expected, this release came a bit late. There are in total 8 closed issues, 7 merged PRs and 13 commits to master branch since v2.0, including #45 with >10000 lines of code (109 commits). Many thanks to the contributors: @voldemortX @cedricgsh @kalkun And thanks @junshutang for generously providing hardware supports!
This is a transformative release of our framework, so we are making a major release as PytorchAutoDrive v3.0.
requirements.txt
. Checkout the new installation instructions that are much more clear. #38pip install importmagician
Above changes enable both large-scale training and out-of-the-box inference. This is a large step towards a production-able framework, so we are making a major release as v2.0.
2021Q2 release: We now support the LLAMAS dataset, our version of SCNN-VGG16 reached the 2nd place on this benchmark! Reduced ResNet18 backbone for lane detection. Keypoint transforms refactored and keypoint-based lane detection is supported. LSTR is supported with much faster training speed (3x), and fair FPS evaluation. Various bug fixes, including a BC-Breaking lane detection testing scheme fix that boosted F1 score on TuSimple. #13
2021Q1 release: ENet and ResNet series backbones for segmentation and lane detection. Keypoint transforms. Visualization toolkits provided for image input. Unified benchmark established for FPS, FLOPs and memory tests. Documentation refactored, dataset preparation guides & trained weights download links.
Segmentation finalized. Lane detection now totally supports 2 datasets, include training, validation, testing: TuSimple and CULane. Lane detection models ERFNet and SCNN all tested.