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DeepLab v3+ model in PyTorch Support different backbones

DeepLab v3+ model in PyTorch Support different backbones


DeepLab v3+ model in PyTorch. Support different backbones.


  • [x] Support different backbones
  • [x] Support VOC, SBD, Cityscapes and COCO datasets
  • [x] Multi-GPU training


This is a PyTorch(0.4.1) implementation of DeepLab-V3-Plus. It
can use Modified Aligned Xception and ResNet as backbone. Currently, we train DeepLab V3 Plus
using Pascal VOC 2012, SBD and Cityscapes datasets.



The code was tested with Anaconda and Python 3.6. After installing the Anaconda environment:

  1. Clone the repo:

    git clone https://github.com/jfzhang95/pytorch-deeplab-xception.git
    cd pytorch-deeplab-xception
  2. Install dependencies:

    For PyTorch dependency, see pytorch.org for more details.

    For custom dependencies:

    pip install matplotlib pillow tensorboardX tqdm


Fellow steps below to train your model:

  1. Configure your dataset path in mypath.py.

  2. 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]
  3. To train deeplabv3+ using Pascal VOC dataset and ResNet as backbone:

    bash train_voc.sh
  4. To train deeplabv3+ using COCO dataset and ResNet as backbone:

    bash train_coco.sh