这个项目是一个面向新手的基于遥感图片的语义分割项目。 我们使用了在4亿张图片上进行预训练的 unicom 模型,这个模型非常高效,在遥感分割任务上表现优异。 令人惊讶的是,我们仅仅使用了4张遥感图片进行训练,就能够获得非常好的效果。
This project is a beginner-friendly semantic segmentation project based on remote sensing images. We utilize the pre-trained unicom model on a dataset of 400 million images, which is highly efficient and performs exceptionally well on remote sensing segmentation tasks. Surprisingly, we achieve excellent results by training the model with just 4 remote sensing images.
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如果您想快速开始,可以使用 train_one_gpu.py
来启动训练,这是个简易的代码,只有200行。
但如果您追求更好的性能,可以尝试使用稍微复杂一些的代码 train_multi_gpus.py
,该代码支持多GPU训练。
请注意,train_multi_gpus.py
可能需要一些额外的配置和设置,以便正确地运行多GPU训练。确保在使用之前仔细阅读代码中的说明和文档,以确保正确设置和配置。
If you want to get started quickly, you can use train_one_gpu.py
to initiate the training. It is a simple code with only 200 lines. However, if you aim for better performance, you can try using the slightly more complex train_multi_gpus.py
code, which supports training on multiple GPUs.
Please note that train_multi_gpus.py
may require additional configurations and settings to run multi-GPU training correctly. Make sure to carefully read the instructions and documentation in the code to ensure proper setup and configuration before using it.
git clone https://github.com/anxiangsir/urban_seg.git
pip install -r requirements.txt
CCF卫星影像的AI分类与识别提供的数据集初赛复赛训练集,一共五张卫星遥感影像 百度云盘,密码:3ih2
The training dataset provided for the AI classification and recognition of CCF satellite images consists of a total of five satellite remote sensing images.
dataset
├── origin //5张遥感图片,有标签
├── test //3张遥感图片,无标签,在这个任务中没有用到
└── train //为空,通过`python preprocess.py`随机采样生成
├── images
└── labels
FP16-ViT-B-32.pt
FP16-ViT-B-16.pt
FP16-ViT-L-14.pt
FP16-ViT-L-14-336px.pt
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下载数据集到当前目录
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预处理数据
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Download the dataset to the current directory.
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Preprocess the data.
python preprocess.py
- 训练
- Tranining
python train_one_gpu.py
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下载数据集到当前目录
-
预处理数据
-
Download the dataset to the current directory.
-
Preprocess the data.
python preprocess.py
- 训练
- Tranining
torchrun --nproc_per_node 8 train_multi_gpus.py
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如果你觉得这个项目对你有用,欢迎引用我们的论文
If you find this project useful, please feel free to cite our paper.
@inproceedings{anxiang_2023_unicom,
title={Unicom: Universal and Compact Representation Learning for Image Retrieval},
author={An, Xiang and Deng, Jiankang and Yang, Kaicheng and Li, Jiawei and Feng, Ziyong and Guo, Jia and Yang, Jing and Liu, Tongliang},
booktitle={ICLR},
year={2023}
}