WHU-dataset建筑物数据集及模型

python train.py --backbone mobilenet --lr 0.007 --workers 1 --epochs 5 --batch-size 4 --gpu-ids 0 --checkname deeplab-mobilenet python demo.py --inpath Test --ckpt run/buildings/deeplab-mobilenet-tif/model_best.pth.tar --backbone mobilenet /root/anaconda3/envs/tf/bin/python /home/pytorch-deeplab/demo.py --in-path /home/pytorch-deeplab/Test --ckpt /home/pytorch-deeplab/run/buildings/deeplab-mobilenet-tif/model_best.pth.tar --backbone mobilenet # pytorch-deeplab-xception **Update on 2018/12/06. Provide model trained on VOC and SBD datasets.** **Update on 2018/11/24. Release newest version code, which fix some previous issues and also add support for new backbones and multi-gpu training. For previous code, please see in `previous` branch** ### TODO - [x] Support different backbones - [x] Support VOC, SBD, Cityscapes and COCO datasets - [x] Multi-GPU training | Backbone | train/eval os |mIoU in val |Pretrained Model| | :-------- | :------------: |:---------: |:--------------:| | ResNet | 16/16 | 78.43% | [google drive](https://drive.google.com/open?id=1NwcwlWqA-0HqAPk3dSNNPipGMF0iS0Zu) | | MobileNet | 16/16 | 70.81% | [google drive](https://drive.google.com/open?id=1G9mWafUAj09P4KvGSRVzIsV_U5OqFLdt) | | DRN | 16/16 | 78.87% | [google drive](https://drive.google.com/open?id=131gZN_dKEXO79NknIQazPJ-4UmRrZAfI) | ### Introduction This is a PyTorch(0.4.1) implementation of [DeepLab-V3-Plus](https://arxiv.org/pdf/1802.02611). 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: ```Shell git clone https://github.com/jfzhang95/pytorch-deeplab-xception.git cd pytorch-deeplab-xception ``` 1. Install dependencies: For PyTorch dependency, see [pytorch.org](https://pytorch.org/) for more details. For custom dependencies: ```Shell pip install matplotlib pillow tensorboardX tqdm ``` ### Training Follow steps below to train your model: 0. Configure your dataset path in [mypath.py](https://github.com/jfzhang95/pytorch-deeplab-xception/blob/master/mypath.py). 1. Input arguments: (see full input arguments via python train.py --help): ```Shell 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] ``` 2. To train deeplabv3+ using Pascal VOC dataset and ResNet as backbone: ```Shell bash train_voc.sh ``` 3. To train deeplabv3+ using COCO dataset and ResNet as backbone: ```Shell bash train_coco.sh ``` ### Acknowledgement [PyTorch-Encoding](https://github.com/zhanghang1989/PyTorch-Encoding) [Synchronized-BatchNorm-PyTorch](https://github.com/vacancy/Synchronized-BatchNorm-PyTorch) [drn](https://github.com/fyu/drn)