cityscapes-dataset
There are 4 repositories under cityscapes-dataset topic.
- Language:Python 466
- Language:Python 356
gasparian / multiclass-semantic-segmentation
Experiments with UNET/FPN models and cityscapes/kitti datasets [Pytorch]
Language:Python 109anirudh-chakravarthy / CASENet
A Pytorch implementation of CASENet for the Cityscapes Dataset
Language:Python 83TillBeemelmanns / cityscapes-to-coco-conversion
Cityscapes to CoCo Format Conversion Tool for Mask-RCNN and Detectron
Language:Python 71fpv-iplab / DA-RetinaNet
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'
Language:Python 58GiovanniPasq / DA-Faster-RCNN
Detectron2 implementation of DA-Faster R-CNN, Domain Adaptive Faster R-CNN for Object Detection in the Wild
Language:Python 45kevinfu1717 / SuperInterstellarTerminal
【X世纪星际终端】A Wechat Social and AR Game: 基于微信聊天，结合增强现实技术AR+LBS（基于图像位置）的轻社交星际漂流瓶游戏。向外太空发送漂流信息，看看AI预测的外星人是长什么样的，寻找身边的外星人,逗逗外星生物，看看外星植物及外星建筑。Send the message to the outer space, find the aliens in the earth. Let`s see what they look like from LSGAN`s prediction. Also, Have a look at the aliens' pets and the vegetation from the outer space
Language:Jupyter Notebook 44LarsDoorenbos / ccdm-stochastic-segmentation
Repository for "Stochastic Segmentation with Conditional Categorical Diffusion Models" (ICCV 2023)
Language:Python 37EliasKassapis / CARSSS
Official re-implementation of the Calibrated Adversarial Refinement model described in the paper "Calibrated Adversarial Refinement for Stochastic Semantic Segmentation"
Language:Python 32sauradip / night_image_semantic_segmentation
[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.
Language:Python 29tudelft-iv / instance_stixels
Implementation of the Instance Stixel pipeline. Paper:
Language:C++ 18MichaelVorndran / InconsistencyMasks
TensorFlow implementation of a comprehensive comparison of various SSL (Semi-Supervised Learning) approaches in image segmentation, featuring our novel Inconsistency Masks (IM) method.
Language:Python 14kubitz / UAV-Segmentation-Scripts
Collection of scripts for preparation of datasets for semantic segmentation of UAV images
Language:Python 12mohamedac29 / DSANet
DSANet: Dilated Spatial Attention for Real-time Semantic Segmentation in Urban Street Scenes
Language:Python 10shashankag14 / Cityscapes-Segmentation
PyTorch implementation for Semantic Segmentation on Cityscapes dataset using R2UNET and its modified version.
Language:Jupyter Notebook 9dronefreak / CABiNet
CABiNet: Efficient Context Aggregation Network for Low-Latency Semantic Segmentation (ICRA2021)
Language:Python 8oskarnatan / semseg-bisaai
GPU-accelerated Semantic Image Segmentation with PyTorch
Language:Jupyter Notebook 5ibaiGorordo / Deeplab-Cityscapes-inference
Python program to visualize Deeplab (trained on Cityscapes dataset) results.
Language:Python 4karoly-hars / 3D_scene_reconstruction_CNNs
Utilizing CNNs for driving scene reconstruction from single images.
Language:Python 4caiyancheng / BFDA
The official code open source version of BFDA - based on YOLOv5
Language:Python 3cs415-final-project / image_segmentation
CS415 - From K-means to Deep Learning
Language:Jupyter Notebook 3nzlul03 / semantic-segmentation-U-Net
This is my repository for my final project -> Semantic Segmentation of Cityscapes datasets using U-Net.
Language:Jupyter Notebook 3Compact Semantic Segmentation and Depth Estimation with Multi-task Learning
Language:Jupyter Notebook 3- Language:Python 2
dvssajay / Semantic-Segmentation-of-Urban-Scene-Images-Using-Recurrent-Neural-Networks
This study investigates the performance effect of using recurrent neural networks (RNNs) for semantic segmentation of urban scene images, to generate a semantic output map with refined edges. We proposed three deep neural network architectures using recurrent neural networks and evaluated them on the Cityscapes dataset. All three proposed architectures outperformed the baseline and shown improvement in classifying edges. Additionally, we showed a new method for using RNN for any prior semantic segmentation network that makes use of skip connections. PyTorch was the selected framework for conducting this study.
Language:Python 1ekellbuch / cityscapes-c
Corrupt Cityscapes Dataset
Language:Python 1- Language:Python 1
pabvald / image-segmentation
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
Language:Jupyter Notebook 1DYDevelop / 2023-Samsung-AI-Challenge
Camera-Invariant Domain Adaptation (Semantic Segmentation)
Language:Python 0JakoError / SemanticSegmentationTutorial
Some basic trick about semantic segmentation based on tensorflow & some open datasets
Language:Jupyter Notebook 0kamilsan / gsn-22z-roads-segmentation
U-Net based PyTorch model for roads segmentation trained on Cityscapes dataset
Language:PythonmaravignaSimone / Project_DLEA
Final Project for Deep Learning Course A.Y. 2022/23. Semantic Segmentation on Cityscapes Dataset
Language:Python
