SETR – Pytorch Since the original paper (Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.) has no official code,I implemented SETR-Progressive UPsampling(SETR-PUP) using pytorch. Original paper: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers. Vit The Vit model is also implemented, and you can use it for image classification. Usage SETR from SETR.transformer_seg import SETRModel import torch if __name__ == “__main__”: net = SETRModel(patch_size=(32, 32), in_channels=3, out_channels=1, hidden_size=1024, num_hidden_layers=8, num_attention_heads=16, decode_features=[512, 256, 128, 64]) t1 = torch.rand(1, 3, […]

Hypercorrelation Squeeze for Few-Shot Segmentation This is the implementation of the paper “Hypercorrelation Squeeze for Few-Shot Segmentation” by Juhong Min, Dahyun Kang, and Minsu Cho. Implemented on Python 3.7 and Pytorch 1.5.1. For more information, check out project [website] and the paper on [arXiv]. Requirements Python 3.7 PyTorch 1.5.1 cuda 10.1 tensorboard 1.14 Conda environment settings: conda create -n hsnet python=3.7 conda activate hsnet conda install pytorch=1.5.1 torchvision cudatoolkit=10.1 -c pytorch conda install -c conda-forge tensorflow pip install tensorboardX Preparing […]

vedaseg vedaseg is an open source semantic segmentation toolbox based on PyTorch. Features Modular Design We decompose the semantic segmentation framework into different components. The flexible and extensible design make it easy to implement a customized semantic segmentation project by combining different modules like building Lego. Support of several popular frameworks The toolbox supports several popular semantic segmentation frameworks out of the box, e.g. DeepLabv3+, DeepLabv3, U-Net, PSPNet, FPN, etc. High efficiency Multi-GPU data parallelism & distributed training. Multi-Class/Multi-Label segmentation […]

Towards High-Quality Instance Segmentation with Fine-Grained Features This repo is the official implementation of RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features. Main Results Results on COCO Method Backbone Schedule AP AP* Checkpoint Mask R-CNN R50-FPN 1x 34.7 36.8 RefineMask R50-FPN 1x 37.3 40.6 download Mask R-CNN R50-FPN 2x 35.4 37.7 RefineMask R50-FPN 2x 37.8 41.2 download Mask R-CNN R101-FPN 1x 36.1 38.4 RefineMask R101-FPN 1x 38.6 41.8 download Mask R-CNN R101-FPN 2x 36.6 39.3 RefineMask R101-FPN 2x 39.0 42.4 […]

DRS Official pytorch implementation of our paper: Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation [Paper], Beomyoung Kim, Sangeun Han, and Junmo Kim, AAAI 2021 We propose the discriminative region suppression (DRS) module that is a simple yet effective method to expand object activation regions. DRS suppresses the attention on discriminative regions and spreads it to adjacent non-discriminative regions, generating dense localization maps. [2021.06.10] we support DeepLab-V3 segmentation network! Setup Dataset Preparing # dataset structure VOC2012/ — Annotations/ — ImageSets/ — […]

RMNet This repository contains the source code for the paper Efficient Regional Memory Network for Video Object Segmentation. Cite this work @inproceedings{xie2021efficient, title={Efficient Regional Memory Network for Video Object Segmentation}, author={Xie, Haozhe and Yao, Hongxun and Zhou, Shangchen and Zhang, Shengping and Sun, Wenxiu}, booktitle={CVPR}, year={2021} } Datasets We use the ECSSD, COCO, PASCAL VOC, MSRA10K, DAVIS, and YouTube-VOS datasets in our experiments, which are available below: Pretrained Models The pretrained models for DAVIS and YouTube-VOS are available as follows: […]

SingleClassRL Implementation of weak single object segmentation from paper “Regularized Loss for Weakly Supervised Single Class Semantic Segmentation”, ECCV2020. PDF Main Files train_with_anneal.py: use for training first in annealing stage, then in normal stage train_with_transfer.py: use from training with weight transfer from another dataset, models that can be used for weight transfer are in directory ‘trained_models’ OxfodPet dataset Download OxfordPet from (https://www.robots.ox.ac.uk/~vgg/data/pets/) Files in ‘SingleClassRLdataOxford_iit_petannotations’ should be placed in the ‘annotation’ directory of OxfordPet dataset GitHub https://github.com/morduspordus/SingleClassRL    

FactSeg FactSeg: Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery (TGRS) by Ailong Ma, Junjue Wang*, Yanfei Zhong* and Zhuo Zheng This is an official implementation of FactSeg in our TGRS paper “FactSeg: Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery“ Citation If you use FactSeg in your research, please cite our coming TGRS paper. @ARTICLE{FactSeg, author={Ma Ailong, Wang Junjue, Zhong Yanfei and Zheng Zhuo}, journal={IEEE Transactions on Geoscience and Remote Sensing}, […]

BAAF-Net This repository is for BAAF-Net introduced in the following paper: “Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion”Shi Qiu, Saeed Anwar, Nick BarnesIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021) Paper and Citation The paper can be downloaded from here (CVF) or here (arXiv).If you find our paper/codes/results are useful, please cite: @inproceedings{qiu2021semantic, title={Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion}, author={Qiu, Shi and Anwar, Saeed and […]