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Deeplab-V3-Pytorch: Small Obstacle

TODO

  • Modify for 4 channel input

Training

Follow below steps below to train your model:

  1. Input arguments: (see full input arguments via python train.py --help):
    usage: train.py [-h] [--backbone {resnet,xception,drn,mobilenet}]
                [--out-stride OUT_STRIDE] [--dataset {pascal,coco,cityscapes}]
                [--use-sbd] [--workers N] [--base-size BASE_SIZE]
                [--crop-size CROP_SIZE] [--sync-bn SYNC_BN]
                [--freeze-bn FREEZE_BN] [--loss-type {ce,focal}] [--epochs N]
                [--start_epoch N] [--batch-size N] [--test-batch-size N]
                [--use-balanced-weights] [--lr LR]
                [--lr-scheduler {poly,step,cos}] [--momentum M]
                [--weight-decay M] [--nesterov] [--no-cuda]
                [--gpu-ids GPU_IDS] [--seed S] [--resume RESUME]
                [--checkname CHECKNAME] [--ft] [--eval-interval EVAL_INTERVAL]
                [--no-val][--mode]
    

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Small obstacle segmentation using Image, LIDAR fusion.

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