songdejia / Deeplab_v3_plus
Licence: mit
This is an ongoing re-implementation of DeepLab_v3_plus on pytorch which is trained on VOC2012 and use ResNet101 for backbone.
Stars: ✭ 83
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DeepLab_V3_plus : a model about semantic segmentation
This is a simple pytorch re-implementation of Google Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.
Introduction:
This work still need to be updated. The features are summarized blow:
- Use ResNet101 as base Network. Xception will be updated soon.
- Use only VOC2012 for base dataset. Other dataset will be updated soon.
We have finished:
-
Version avaliable for VOC2012.
-
You can check your dataloader error in 'path/to/workspace/check/check_dataloader/img'. You will see three part:
- 1.original image which we load directly from image path.
- 2.restore image from torch-tensor(transformed from numpy) back to numpy ndarray.
- 3.mask loaded by dataloader. Following images show original image, augumentation image and mask target from left to right
-
Network architecture.
-
Evaluation mIOU on PASCAL VOC2012 valset every epoches.
-
Pretrained model on PASCAL VOC2012 BaiduYun Link, which is trained on 2 Tesla P100 for 100 epoches with config shown in code.
Usage:
- Download dataset and unzip
ln -s VOCdevkit path/to/deeplab_v3_plus/dataset
- Pretrained model is avaliable BaiduYun Link
- Finally, run the model.
- Check you GPU resources and modify your run.sh.
sh run.sh
Future:
- Xception as Network Baseline.
- Pretrained model on COCO, JFT.
- Depthwise separable convolution.
- Support dataset for Cityscapes.
- Visualization for test result and gt.
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