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semantic-cc

https://github.com/20374230/semantic-cc

Science Score: 54.0%

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    Links to: arxiv.org
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Repository

Basic Info
  • Host: GitHub
  • Owner: 20374230
  • License: apache-2.0
  • Language: Python
  • Default Branch: master
  • Size: 9.68 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created 10 months ago · Last pushed 9 months ago
Metadata Files
Readme License Citation

README.md

Semantic-CC: Boosting Remote Sensing Image Change Captioning via Foundational Knowledge and Semantic Guidance

Introduction

The repository is the code implementation of the paper Semantic-CC: Boosting Remote Sensing Image Change Captioning via Foundational Knowledge and Semantic Guidance, based on MMSegmentation and Open-CD projects.

The current branch has been tested under PyTorch 2.x and CUDA 12.1, supports Python 3.10, and is compatible with most CUDA versions.

Installation

Dependencies

  • Linux or Windows
  • Python 3.7+, recommended 3.10
  • PyTorch 2.0 or higher, recommended 2.1
  • CUDA 11.7 or higher, recommended 12.1
  • MMCV 2.0 or higher, recommended 2.1 ### Environment Installation We recommend using Anaconda for installation. The following command will create a virtual environment named seg and install PyTorch and MMCV.

Note: If you have experience with PyTorch and have already installed it, you can skip to the next section. Otherwise, you can follow these steps to prepare.

**Step 0**: Install [Miniconda](https://docs.conda.io/projects/miniconda/en/latest/index.html). **Step 1**: Create a virtual environment named `seg` and activate it. ```shell conda create -n seg python=3.10 -y conda activate seg ``` **Step 2**: Install [PyTorch2.1.x](https://pytorch.org/get-started/locally/). Linux/Windows: ```shell pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu121 ``` Or ```shell conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 -c pytorch -c nvidia ``` **Step 3**: Install [MMCV2.1.x](https://mmcv.readthedocs.io/en/latest/get_started/installation.html). ```shell pip install -U openmim mim install mmcv==2.1.0 ``` **Step 4**: Install other dependencies. ```shell pip install -U wandb einops importlib peft==0.8.2 scipy ftfy prettytable torchmetrics==1.3.1 transformers==4.38.1 ```

Dataset Preparation

Levir-CD Change Detection Dataset

Dataset Download

  • Image and label download address: Levir-CD.

Levir-CC Change Caption Dataset

Dataset Download

  • Image and label download address: Levir-CC.

Model Training

shell python tools/train.py

Model Testing

We suggest saving the generated results and using the built-in testing code in Levir-CC for performance testing. shell python tools/test.py

```

Owner

  • Login: 20374230
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - name: "MMSegmentation Contributors"
title: "OpenMMLab Semantic Segmentation Toolbox and Benchmark"
date-released: 2020-07-10
url: "https://github.com/open-mmlab/mmsegmentation"
license: Apache-2.0

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Dependencies

docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/serve/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
mmpretrain-main/.circleci/docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
mmpretrain-main/docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
mmpretrain-main/docker/serve/Dockerfile docker
  • pytorch/torchserve latest-gpu build
mmpretrain-main/projects/internimage_classification/ops_dcnv3/setup.py pypi
mmpretrain-main/requirements/docs.txt pypi
  • docutils ==0.18.1
  • modelindex *
  • myst-parser *
  • pytorch_sphinx_theme *
  • sphinx ==6.1.3
  • sphinx-copybutton *
  • sphinx-notfound-page *
  • sphinx-tabs *
  • sphinxcontrib-jquery *
  • tabulate *
mmpretrain-main/requirements/mminstall.txt pypi
  • mmcv >=2.0.0,<2.4.0
  • mmengine >=0.8.3,<1.0.0
mmpretrain-main/requirements/multimodal.txt pypi
  • pycocotools *
  • transformers >=4.28.0
mmpretrain-main/requirements/optional.txt pypi
  • albumentations >=0.3.2
  • grad-cam >=1.3.7,<1.5.0
  • requests *
  • scikit-learn *
mmpretrain-main/requirements/readthedocs.txt pypi
  • mmcv-lite >=2.0.0rc4
  • mmengine *
  • pycocotools *
  • torch *
  • torchvision *
  • transformers *
mmpretrain-main/requirements/runtime.txt pypi
  • einops *
  • importlib-metadata *
  • mat4py *
  • matplotlib *
  • modelindex *
  • numpy *
  • rich *
mmpretrain-main/requirements/tests.txt pypi
  • coverage * test
  • interrogate * test
  • pytest * test
mmpretrain-main/requirements.txt pypi
mmpretrain-main/setup.py pypi
requirements/albu.txt pypi
  • albumentations >=0.3.2
requirements/docs.txt pypi
  • docutils ==0.16.0
  • myst-parser *
  • sphinx ==4.0.2
  • sphinx_copybutton *
  • sphinx_markdown_tables *
  • urllib3 <2.0.0
requirements/mminstall.txt pypi
  • mmcv >=2.0.0rc4,<2.2.0
  • mmengine >=0.5.0,<1.0.0
requirements/multimodal.txt pypi
  • ftfy *
  • regex *
requirements/optional.txt pypi
  • cityscapesscripts *
  • diffusers *
  • einops ==0.3.0
  • imageio ==2.9.0
  • imageio-ffmpeg ==0.4.2
  • invisible-watermark *
  • kornia ==0.6
  • nibabel *
  • omegaconf ==2.1.1
  • pudb ==2019.2
  • pytorch-lightning ==1.4.2
  • streamlit >=0.73.1
  • test-tube >=0.7.5
  • timm *
  • torch-fidelity ==0.3.0
  • torchmetrics ==0.6.0
  • transformers ==4.19.2
requirements/readthedocs.txt pypi
  • mmcv >=2.0.0rc1,<2.1.0
  • mmengine >=0.4.0,<1.0.0
  • prettytable *
  • scipy *
  • torch *
  • torchvision *
requirements/runtime.txt pypi
  • matplotlib *
  • numpy *
  • packaging *
  • prettytable *
  • scipy *
requirements/tests.txt pypi
  • codecov * test
  • flake8 * test
  • ftfy * test
  • interrogate * test
  • pytest * test
  • regex * test
  • xdoctest >=0.10.0 test
  • yapf * test
requirements.txt pypi
setup.py pypi