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, ContextNet, FPENet, etc.)

PytorchAutoDrive: Segmentation models (ERFNet, ENet, DeepLab, FCN...) and Lane detection models (SCNN, RESA, LSTR, LaneATT, BézierLaneNet...) based on PyTorch with fast training, visualization, benchmarking & deployment help

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)

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

A PyTorch Implementation of Fast-SCNN: Fast Semantic Segmentation Network(PyTorch >= 1.4)

Semantic Segmentation of Aerial Images

Automatic Detection of Solar Panels in High-Resolution Aerial Imagery.

A PyTorch implementation of Fast-SCNN based on BMVC 2019 paper "Fast-SCNN: Fast Semantic Segmentation Network"

Implementation of FastSCNN with TensorRT7 network definition API

Implementing the Fast-SCNN neural network on Keras (Tensorflow 2.0 backend)

An implementation of the proposed Fast-SCNN

The water area is identified by semantic segmentation model

Implementation of Fast-SCNN Semantic Segmentation Architecture in PyTorch

Implememtation of BiSeNet V2 and Fast-SCNN

Real-time semantic segmentation model on high resolution image data

TensorFlow 2 implementation of Fast-SCNN

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Fast-SCNN implementation

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Tensorflow implementation of Fast-SCNN: Fast Semantic Segmentation Network (https://arxiv.org/abs/1902.04502)

Fast SCNN implementation TF2

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These projects were developed as part of advanced coursework in Neural Networks and Deep Learning, focusing on cutting-edge architectures and their practical implementation for computer vision tasks.

PytorchAutoDrive: Segmentation models (ERFNet, ENet, DeepLab, FCN...) and Lane detection models (SCNN, PRNet, RESA, LSTR, BézierLaneNet...) based on PyTorch with fast training, visualization, benchmarking & deployment help

Lane segmentation model trained with tensorflow implementation of Fast-SCNN