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

Last update: Jan 02, 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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