update

main.tex

@@ -399,7 +399,7 @@ For Tusimple, the evaluation is formulated as follows:
         \begin{align}
                 Accuracy=\frac{\sum{C_{clip}}}{\sum{S_{clip}}}.
         \end{align}
-where $C_{clip}$ and $S_{clip}$ represent the number of correct points (predicted points within 20 pixels of the ground truth) and the ground truth points, respectively. If the accuracy exceeds 85\%, the prediction is considered correct. TuSimples also report the False Positive Rate ($FPR=1-Precision$) and False Negative Rate ($FNR=1-Recall$) formular. 
+where $C_{clip}$ and $S_{clip}$ represent the number of correct points (predicted points within 20 pixels of the ground truth) and the ground truth points, respectively. If the accuracy exceeds 85\%, the prediction is considered correct. TuSimples also report the False Positive Rate ($\mathrm{FPR}=1-\mathrm{Precision}$) and False Negative Rate ($\mathrm{FNR}=1-\mathrm{Recall}$) formular. 
 
 \subsection{Implement Detail}
 All input images are cropped and resized to $800\times320$. Similar to \cite{clrnet}, we apply random affine transformations and random horizontal flips. For the optimization process, we use the AdamW \cite{adam} optimizer with a learning rate warm-up and a cosine decay strategy. The initial learning rate is set to 0.006. The number of sampled points and regression points for each lane anchor are set to 36 and 72, respectively. The power coefficient of cost function $\beta$ is set to 6. The training processing of the whole model (including LPM and GPM) is end-to-end just like \cite{adnet}\cite{srlane}.  All the experiments are conducted on a single NVIDIA A100-40G GPU. To make our model simple, we only use CNN-based backbone, namely ResNet\cite{resnet} and DLA34\cite{dla}. Other details for datasets and training process can be seen in Appendix \ref{vis_appendix}.