A survey of Real time Semantic Segmentation for autonomous driving
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Updated
Sep 24, 2024 - Python
A survey of Real time Semantic Segmentation for autonomous driving
Implementation of R2U-Net and a custom model using the main module from HANet + R2U-Net for image segmentation of urban scenes on the Cityscapes dataset
Detectron2 implementation of DA-Faster R-CNN, Domain Adaptive Faster R-CNN for Object Detection in the Wild
This is the official repository for our recent work: PIDNet
Segmentation pipeline that uses a U-Net backbone to perform segmentation on the Cityscapes dataset. Conducted experiments to analyse the impact of the skip connections of the U-Net on the quality of the segmentation masks. These masks are also qualitatively analysed using the Intersection-over-Union (IoU) metric
Pytorch Implementation of U-Net, "Convolutional Networks for Biomedical Image Segmentationt" on Cityscapes Dataset
Experiments with UNET/FPN models and cityscapes/kitti datasets [Pytorch]
TensorFlow implementation of a comprehensive comparison of various SSL (Semi-Supervised Learning) approaches in image segmentation, featuring our novel Inconsistency Masks (IM) method.
Camera-Invariant Domain Adaptation (Semantic Segmentation)
Compact Semantic Segmentation and Depth Estimation with Multi-task Learning
MTLA - Multi-Task Learning Archive
Official Detectron2 implementation of DA-RetinaNet of our Image and Vision Computing 2021 work 'An unsupervised domain adaptation scheme for single-stage artwork recognition in cultural sites'
U-Net based PyTorch model for roads segmentation trained on Cityscapes dataset
Repository for "Stochastic Segmentation with Conditional Categorical Diffusion Models" (ICCV 2023)
The official code open source version of BFDA - based on YOLOv5
Final Project for Deep Learning Course A.Y. 2022/23. Semantic Segmentation on Cityscapes Dataset
CABiNet: Efficient Context Aggregation Network for Low-Latency Semantic Segmentation (ICRA2021)
Cityscapes to CoCo Format Conversion Tool for Mask-RCNN and Detectron
A pytorch-based real-time segmentation model for autonomous driving
[ICIP 2019] : Official PyTorch implementation of the paper "What's There in The Dark" accepted in IEEE International Conference in Image Processing 2019 (ICIP19) , Taipei, Taiwan.
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