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How to generate label images from the json file in the TuSimple dataset? #11
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@khw321 , the attached files are the annotated training (3268) and validation labels (358), please use them to replace the train_gt.txt and val_gt.txt in train_lanenet.py. Check if you can get similar performance compared with SCNN-Torch. |
@khw321 , besides, you need to resize the image to 256 x 512 instead of 288 x 800 in TuSimple. I will release the corresponding training & testing scripts for TuSimple and BDD100K soon (both Tensorflow and Torch codes). |
@cardwing Thanks a lot. I still have trouble in deciding the lanes'id. Is it according to the mid of a lane to decide ego lane and then get the others? Could you tell me how did you generate it or share your script? |
@khw321 , the attached file is the script to generate the labels, please check. The lane id is assigned according to the position of the lane regarding the vehicle. For instance, the most left lane, left lane, right lane, most right lane are assigned 1, 2, 3, 4, respectively. |
您能提供将图森数据集提供的json文件转换成png格式的label的脚本吗?非常感谢 |
@BDMLLang, please refer to the above answer, where segLabelGen0.zip is the script to generate the labels |
@cardwing |
For CULane, the number of output channels is 4 since the number of lanes is 4. For TuSimple, the number of output channels is 5 since the maximum number of lanes is 5. However, in most cases, the number of lanes is fewer than 5 for TuSimple. Hence, predicting 4 lanes is OK for TuSimple except that there are some decreases in performance. |
@cardwing |
The lane existence does not change since the cropped image accounts for around 90% of the original image. As to random rotation, the rotation degree is just 2 (-1 ~ 1) so it also does not change the lane existence prediction. |
haha, soga. Little augmentation but not change the spatial existence information of label. |
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There are six lanes in list6_train.txt. As to the ENet-SAD model, the number of output channels is 4 for CULane and 5 for TuSimple. |
Yes, you're right. But there are 5 lanes in segGT, the problem is that existence only has 4. |
@Cverlpeng See this file. |
@cardwing It seems to need "segGT6" directory, not "segGT". Could you provide the label about your "segGT6" |
segGT6. And both labels are used. |
你好,你上面不是说你的Enet-SADmodel 使用tusimple数据集时,最后的输出channel为5吗? 你提供的这个segGT6是用来怎么训练的呢? |
I believe this list is for trainval (3626). But it got 10 duplicate lines at the end. Did you upload the wrong files or is it only this one is wrong? |
It seems that you are right. Please delete these lines if you want. |
Thanks. I've inspected the lists and I believe other than these 10 lines they should be alright. By the way, I'll be re-implementing and testing ERFNet-SAD on TuSimple, any idea what performance I should be getting on the test set? I didn't find your experimental results about this particular setup. (It's just a small question, so I guess I'll just say it here without opening a new issue, since you guys already got a lot of them -_-) |
ERFNet-SAD should at least obtain 96% accuracy on the TuSimple testing set. |
Are the validation and testing dataset the same? |
Just like the label images in CULane, how to decide the lane's Id in the TuSimple dataset? Cause there will be 0~5 lanes in an image.
And can you share your code for generating the training label images?
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