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Pytorch implementation of our paper "CLRNet: Cross Layer Refinement Network for Lane Detection" (CVPR2022 Acceptance).

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I can see that RGB/BGR (?) normalization is defined here https://github.com/Turoad/CLRNet/blob/d0fef907aae323ef37da0c176c5df35a0565e58d/clrnet/datasets/process/transforms.py#L276 and that image_norm information is defined in the configs: https://github.com/Turoad/CLRNet/blob/7269e9d1c1c650343b6c7febb8e764be538b1aed/configs/clrnet/clr_dla34_culane.py#L44 but as far as I can tell, grepping through...

Tried on both culane_res18 and culane_res34 both two models got this out of index error in clr_head 使用预训练culane模型进行验证出现out of index 错误 pytorch=1.8 torchvision=0.9 with cuda version 11.1

你好,作者: 感谢你的开源,我最近想使用自己的数据集进行模型训练,我在创建了自己的clr_resnet18_mydataset.py配置文件,我对`cut_height`参数的设置有一些疑问,这个参数的意思是将图像crop为(0,cut_height, img_w, img_h)的图像,我看TuSimple、CULane、LLAMMAS数据集都设置了`cut_height`,请这个数据的设置依据是什么?如果我不知道如何设置,是否应该把`cut_height`设置为0,即不对图像进行crop。 另外,在`clrnet/dataset`下,计算`ys`时,也用到了`cut_height`, 以及设置了一个`step`,比如CULane数据集中的`np.arange(270, 590, 8)`,其中`step=8`,请问这里step设置的依据是什么??? https://github.com/Turoad/CLRNet/blob/7269e9d1c1c650343b6c7febb8e764be538b1aed/clrnet/datasets/llamas.py#L146 https://github.com/Turoad/CLRNet/blob/7269e9d1c1c650343b6c7febb8e764be538b1aed/clrnet/datasets/culane.py#L97 https://github.com/Turoad/CLRNet/blob/7269e9d1c1c650343b6c7febb8e764be538b1aed/clrnet/datasets/tusimple.py#L64

When i follow the [README.md](https://github.com/Turoad/CLRNet#readme), Try : python setup.py build develop. Then i got some erros , which i could not overcome for a few weeks . The main erro...

Hello author, thank you for your outstanding contribution. When I trained the Tusimple dataset using "python configs/clrnet/clr_resnet18_tusimple.py --gpus 0", the weight size of the 10th epoch I obtained was 104.6MB,...

How do I modify this if I only want to use CULane's shadow datasets? Can I just edit the category in culane.py?

在测试结果中为什么没有 FPS结果呢,是需要onnx还是tensorrt进行推理才能得出嘛? 感谢您的及时回复!!!

Validate: 100%|███████████████████████████████████████████████████████████████████████████████████ 166/166 [03:40

Hi, i wonder height and width of resized data. I read the paper, CLRNet. It's said that 'All input images are resized to 320 x 800'. As i know, resized...