ArronHZG / BLSeg

PyTorch's Semantic Segmentation Toolbox

BLSeg (BaseLine Segmentation)

PyTorch's Semantic Segmentation Toolbox

• BLSeg (BaseLine Segmentation)
  • Requirement
  • Supported Module
  • Visualization
  • Docs
  • Changelog
  • References

Requirement

• Python 3
• PyTorch >= 1.0.0

Supported Module

• Backbone
  • VGG16
  • MobileNet v1 (1.0)
  • MobileNet v2 (1.0)
  • ResNet 34
  • ResNet 50 (Modified according to Bag of Tricks)
  • SE ResNet 34
  • SE ResNet 50
  • Modified Aligned Xception
• Model
  • FCN
  • U-Net
  • PSPNet
  • DeepLab v3+
• Loss
  • BCEWithLogitsLossWithOHEM
  • CrossEntropyLossWithOHEM
  • DiceLoss (only for binary classification)
  • SoftCrossEntropyLossWithOHEM
• Metric
  • Pixel Accuracy
  • Mean IoU

Each model can choose any backbone without any modification

Backbone \ Model	FCN	U-Net	PSPNet	DeepLab v3+
VGG16	√	√	√	√
MobileNet v1	√	√	√	√
MobileNet v2	√	√	√	√
ResNet34	√	√	√	√
ResNet50	√	√	√	√
SE ResNet34	√	√	√	√
SE ResNet50	√	√	√	√
Modified Aligned Xception	√	√	√	√

Model pre-trained on augmented PASCAL VOC2012 dataset with 10582 images for training and 1449 images for validation.

You can download pre-trained parameters at Google Drive

Visualization

Original Image	Target Mask	Predict Mask

Docs

See Docs

Changelog

See Changelog

References

• Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
• Howard, Andrew G., et al. "Mobilenets: Efficient convolutional neural networks for mobile vision applications." arXiv preprint arXiv:1704.04861 (2017).
• Sandler, Mark, et al. "Mobilenetv2: Inverted residuals and linear bottlenecks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.
• He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
• Hu, Jie, Li Shen, and Gang Sun. "Squeeze-and-excitation networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
• Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
• Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.
• Zhao, Hengshuang, et al. "Pyramid scene parsing network." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
• Chen, Liang-Chieh, et al. "Encoder-decoder with atrous separable convolution for semantic image segmentation." Proceedings of the European Conference on Computer Vision (ECCV). 2018.
• Xie, Junyuan, et al. "Bag of tricks for image classification with convolutional neural networks." arXiv preprint arXiv:1812.01187 (2018).

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