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@bz815 Yes, you can make it similar as the tusimple annotations and eval the accuracy as the tusimple testing.
@FANG-MING Are you using the default training parameters? If you are using the default training parameters, just run ``` sh launch_training.sh ``` Then you can get the results. The provided...
@FANG-MING If you want to train with 1 GPU, you should reduce the learning rate from 0.1 to 0.025.
@FANG-MING What's your device? What're the results of running speed_simple.py?
@FANG-MING it's possible since the latency is already so low. The inference time for 300fps is 3.3ms. For 260fps, it is 3.84ms. Any latency fluctuation could make the speed slower.
@ma-cg It is possible since the released repo is a re-implementation. We will release a new version very soon, in which the res34 could achieve a ~76 F1 measure.
@wuh15 这个似乎是修改了网络输入大小所导致的，可以先尝试在288x800输入大小下进行载入。
@wuh15 @hwang12345 那是不是载入的模型是resnet18的，config中指定的backbone却是resnet34呢？
@nithinme3 Hi, The output is `(grid_numbers+1) x row_anchors x no_of_lanes`. For example, if we have 101 probability values, the last one is defined as the probability of no lane. In...
@nithinme3 It simply filters out lane lines that have less than 2 valid points.