wqangzuoshuai/DeepLabV3-C

Due to the mine confidentiality agreement, the open-pit mine data set is not provided in this code. Only a few sample images are provided. Mine practitioners can use drones to shoot remote sensing images of open-pit mines that need to be modeled.There are many parameters in the code that need to be adjusted according to the image size and the experimental GPU. You can adjust the relevant parameters according to your computer configuration

TODO

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

Backbone	train/eval os	mIoU in val	Pretrained Model
ResNet	16/16	78.43%	google drive
MobileNet	16/16	70.81%	google drive
DRN	16/16	78.87%	google drive

Introduction

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.

Installation

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

0. Clone the repo:

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

1. Install dependencies:

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

   For custom dependencies:

   pip install matplotlib pillow tensorboardX tqdm

Training

Follow steps below to train your model:

0. Configure your dataset path in mypath.py.

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] [--VOC2012 {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]
   

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

   bash train_voc.sh

3. To train deeplabv3+ using COCO dataset and ResNet as backbone:

   bash train_coco.sh

Acknowledgement

PyTorch-Encoding

Synchronized-BatchNorm-PyTorch

drn