A PyTorch Implementation of Fast-SCNN: Fast Semantic Segmentation Network
Implementation of Fast-SCNN using Tensorflow 2.0
Unofficial implementation of Fast-SCNN: Fast Semantic Segmentation Network
PytorchAutoDrive: Segmentation models (ERFNet, ENet, DeepLab, FCN...) and Lane detection models (SCNN, RESA, LSTR, LaneATT, BézierLaneNet...) based on PyTorch with fast training, visualization, benchm...
Lightweight models for real-time semantic segmentationon PyTorch (include SQNet, LinkNet, SegNet, UNet, ENet, ERFNet, EDANet, ESPNet, ESPNetv2, LEDNet, ESNet, FSSNet, CGNet, DABNet, Fast-SCNN, Context...
Support PointRend, Fast_SCNN, HRNet, Deeplabv3_plus(xception, resnet, mobilenet), ContextNet, FPENet, DABNet, EdaNet, ENet, Espnetv2, RefineNet, UNet, DANet, HRNet, DFANet, HardNet, LedNet, OCNet, Enc...
翻译 - 支持Fast_SCNN，HRNet，Deeplabv3_plus（xception，resnet，移动网络），ContextNet，FPENet，DABNet，EdaNet，ENet，Espnetv2，RefineNet，UNet，DANet，HRNet，DFANet，HardNet，LedNet，OCNet，EncNet，EncNet BiSeNet，PSPNet，ICNet，FCN，deeplab）
A PyTorch Implementation of Fast-SCNN: Fast Semantic Segmentation Network(PyTorch >= 1.4)
Implementation of FastSCNN with TensorRT7 network definition API
A PyTorch implementation of Fast-SCNN based on BMVC 2019 paper "Fast-SCNN: Fast Semantic Segmentation Network"
Real-time semantic segmentation model on high resolution image data
Spatial CNN for traffic lane detection (AAAI2018)
Segment-CNN: A Framework for Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs
The adaptive interface system for modern web experiences.
翻译 - 适应性界面系统，可提供现代的网络体验。
面向配置的crud框架，开发crud 快如闪电，超级表格；Options-oriented crud framework, develop crud as fast as lightning；based on vue3；super table
The code for ECCV22 paper "Fast-Vid2Vid: Spatial-Temporal Compression for Video-to-Video Synthesis"
This project is an implementation of the crowd counting model proposed in our CVPR 2017 paper - Switching Convolutional Neural Network(SCNN) for Crowd Counting. SCNN is an adaptation of the fully-conv...
Fast, secure, efficient backup program
翻译 - 快速，安全，高效的备份程序
Property based testing framework for JavaScript (like QuickCheck) written in TypeScript
Fast Segment Anything