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tuSimple dataset road lane instance segmentation with PyTorch, ROS, ENet, SegNet and Discriminative Loss.

License: MIT License

Python 1.43% Jupyter Notebook 98.57%

road-lane-instance-segmentation-pytorch-loss's Introduction

Road-Lane-Instance-Segmentation-PyTorch

Road lane instance segmentation with PyTorch.

SegNet, ENet with discriminative loss.
Lane clustered with DBSCAN.
Trained from tuSimple dataset.
ROS(Robot Operating System) inference node (20Hz).

ENet result

SegNet result

ROS

$ python2 ros_lane_detect.py --model-path model_best_enet.pth

Train

$ mkdir logs
$ tensorboard --logdir=logs/ &
$ python3 train.py --train-path /tuSimple/train_set/ --epoch 100 --batch-size 16 --lr 0.0001 --img-size 224 224

Dataset

Downloads: tuSimple dataset

Load Dataset

train_path = '/data/tuSimple/train_set/'
train_dataset = tuSimpleDataset(train_path, size=SIZE)
train_dataloader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=16)

Model

ENet summary

----------------------------------------------------------------
Total params: 686,058
Trainable params: 686,058
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 153326.17
Params size (MB): 2.62
Estimated Total Size (MB): 153329.36
----------------------------------------------------------------

SegNet summary

----------------------------------------------------------------
Total params: 29,447,047
Trainable params: 29,447,047
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 688.68
Params size (MB): 112.33
Estimated Total Size (MB): 801.59
----------------------------------------------------------------