Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
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Updated
Jul 17, 2024 - Python
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
DeepLab v3+ model in PyTorch. Support different backbones.
Support PointRend, Fast_SCNN, HRNet, Deeplabv3_plus(xception, resnet, mobilenet), ContextNet, FPENet, DABNet, EdaNet, ENet, Espnetv2, RefineNet, UNet, DANet, HRNet, DFANet, HardNet, LedNet, OCNet, EncNet, DuNet, CGNet, CCNet, BiSeNet, PSPNet, ICNet, FCN, deeplab)
PyTorch implementation for semantic segmentation (DeepLabV3+, UNet, etc.)
LightNet++: Boosted Light-weighted Networks for Real-time Semantic Segmentation
DeepGlobe Land Cover Classification Challenge遥感影像语义分割
图像分割算法deeplab_v3+,基于tensorflow,中文注释,摄像头可用
DeepLabV3+ with squeeze and excitation network for human image segmentation in TensorFlow 2.5.0
This is an ongoing re-implementation of DeepLab_v3_plus on pytorch which is trained on VOC2012 and use ResNet101 for backbone.
Real-time semantic image segmentation on mobile devices
deeplabv3plus (Google's new algorithm for semantic segmentation) in keras:Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Pytorch implementation and extension of "DocUnet: Document Image Unwarping via A Stacked U-Net"
TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation
Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation
Using deepLabv3+ to segment humans
Try to implement deeplab v3+ on pytorch according to offical demo.
An implementation of Deeplabv3plus in TensorFlow2 for semantic land cover segmentation
Tensorflow implementation and extension of DocUnet: Document Image Unwarping via A Stacked U-Net
A 3rd place solution for LID Challenge at CVPR 2020 on Weakly Supervised Semantic Segmentation
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