sssegmentation is a general framework for our research on strongly supervised semantic segmentation.

Last update: Jan 2, 2023

Overview

Introduction

sssegmentation is a general framework for our research on strongly supervised semantic segmentation.

Documents

In English

https://sssegmentation.readthedocs.io/en/latest/

Supported

Supported Backbones

Supported Models

Supported Datasets

Citation

If you use this framework in your research, please cite this project.

@misc{ssseg2020,
    author = {Zhenchao Jin},
    title = {SSSegmentation: A general framework for strongly supervised semantic segmentation},
    year = {2020},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/SegmentationBLWX/sssegmentation}},
}

References

[1]. https://github.com/open-mmlab/mmcv
[2]. https://github.com/open-mmlab/mmsegmentation

Comments

Training on custom dataset with 4 channels

Hi, I want to train my own dataset which has images in 4 channels - RGB images and IR(infrared) images. Could you help me out with that? How can i modify the codes of this repo to accommodate that extra channel?

opened by cspearl 4
how to train with multi-gpu in one machine

hi，i wanna train the model with 4 gpus in one machine however, your code 'distrain.sh' and 'train.py' can only train with distributed mode in multi-machine how can i modify the code ?

opened by Kenneth-X 3
isnet:imagelevel.py

imagelevel.py : 47: feats_il = self.correlate_net(x, torch.cat([x_global, x], dim=1))

isanet.py： 47：context = super(SelfAttentionBlock, self).forward(x, x)

is there any problem？ bug？

opened by shujunyy123 3
How to modify parameters to use single card training?

How to modify parameters to use single card training?

In addition to modifying the following in config:

SEGMENTOR_CFG.update(distributed{'is_on':False})

opened by kakamie 1
SWIN-B with DeepLabv3+ training on custom dataset

Hi, I am learning about Segmentation and want to try out the segmentation my custom data set. Could you please provide steps on how to use supported backbones with some particular architectures?

If I want to use SWIN-B as my backbone on DeepLabV3+ using a custom dataset, what should be the commands and all. I could not find anything on the docs and on the github page. Could you please help.

opened by deshwalmahesh 1
Is there should be 'continue'?

https://github.com/SegmentationBLWX/sssegmentation/blob/7a405b1a4949606deae067223ebd68cceec6b225/ssseg/modules/models/memorynet/memory.py#L176

If there are more than one 'num_feats_per_cls' in the furture, 'break' will make this for loop only update the first memory_feature?

opened by EricKani 1
医学图像分割也很有意义，我想给你一些公开的医学图像数据集。哈哈哈哈
Hi @CharlesPikachu ！UNet 也是大名鼎鼎的分割模型啊，它在医学图像分割领域是 SOTA，个人认为 Supported Models 列表里应该有名字，而且应该在 FCN 之后。哈哈哈 🥇

虽然 PyTorch Hub 已经有预训练的 UNet 了，但我想要皮卡丘也有！ 🛩️

这里提供一些医学数据集给你参考：

PyTorch Hub 里预训练 UNet 的数据集：LGG Segmentation Dataset

DDTI，已经按照下一个数据集的格式预处理过，百度网盘提取码：qxds

tn-scui2020，来自比赛 tn-scui2020

百度 AI Studio 医学影像数据集集锦

List of Medical Datasets，来自 GitHub
opened by S-HuaBomb 1

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sssegmentation is a general framework for our research on strongly supervised semantic segmentation.

Related tags

Overview

Introduction

Documents

In English

Supported

Supported Backbones

Supported Models

Supported Datasets

Citation

References

Comments

Training on custom dataset with 4 channels

how to train with multi-gpu in one machine

isnet:imagelevel.py

How to modify parameters to use single card training?

SWIN-B with DeepLabv3+ training on custom dataset

Is there should be 'continue'?

医学图像分割也很有意义，我想给你一些公开的医学图像数据集。哈哈哈哈

Releases(v1.0.0)

Owner

Code for our paper Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation

Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation, CVPR 2018

GrailQA: Strongly Generalizable Question Answering

Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data

Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation)

DFFNet: An IoT-perceptive Dual Feature Fusion Network for General Real-time Semantic Segmentation

A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

Reference implementation of code generation projects from Facebook AI Research. General toolkit to apply machine learning to code, from dataset creation to model training and evaluation. Comes with pretrained models.

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

We have implemented shaDow-GNN as a general and powerful pipeline for graph representation learning. For more details, please find our paper titled Deep Graph Neural Networks with Shallow Subgraph Samplers, available on arXiv (https//arxiv.org/abs/2012.01380).

Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images

A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains (IJCV submission)

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)

Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation

Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation

Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation (CVPR 2021)

Self-supervised Augmentation Consistency for Adapting Semantic Segmentation (CVPR 2021)

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)

[CVPR 2021] Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision