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PyTorch实现高分遥感语义分割(地物分类)

Python 100.00%
remote-sensing dataset land-cover-classification semantic-segmentation deeplabv3 pytorch

high-resolution-remote-sensing-semantic-segmentation-pytorch's Introduction

High-Resolution-Remote-Sensing-Semantic-Segmentation-PyTorch

遥感也许还不能预测未来,但它一定会告诉你过去和现在,以及一步步见证着未来。

Update soon:

  • 膨胀预测
  • 后处理
  • 半监督方法:伪标签
  • 加入tensorboardX可视化输出

TODO:

  • 总结训练步骤、技巧
  • 分享预训练模型

教研室项目预测图展示

avatar avatar

使用TensorBoard查看各项指标以及可视化(img, GT, Pre)

  • 本地:
tensorboard --logdir=<your_log_dir>
  • 服务器:
#首先使用ssh登陆服务器并转发端口:
ssh -L 6006:127.0.0.1:6006 远程服务器用户名@远程服务器Ip
(ssh -L 本地端口:本地IP:远程端口 远程服务器用户名@远程服务器Ip)

#在服务器上运行tensorboard:
tensorboard --logdir=<your_log_dir>

#在浏览器里输入:
远程服务器Ip:6006
  • 各项指标样例

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  • 可视化测试集样例(img, GT, Pre)

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数据分享

Reference

https://github.com/kazuto1011/deeplab-pytorch
https://github.com/fregu856/deeplabv3
https://github.com/zijundeng/pytorch-semantic-segmentation
https://github.com/jwyang/dcn.pytorch
https://github.com/lin-honghui/tianchi_CountyAgriculturalBrain_top1?spm=5176.12282029.0.0.278c1bb8nTiHfq

Contact

author: hk
qq:3025531252
email: [email protected]

high-resolution-remote-sensing-semantic-segmentation-pytorch's People

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high-resolution-remote-sensing-semantic-segmentation-pytorch's Issues

加载断点path

    resnet.load_state_dict((torch.load("/root/data/others/yaoganbisai/code_6_7/models/pretrained_model/resnet50-19c8e357.pth")))

if args.model == 'deeplabv3_version_1'中,设置了path之后,会报错。错误在上面。这个resnet里面的pth是怎么生成的。

error: no module name gid_classes

from class_names import gid_classes

In the dataset.py file, the line 4:

from class_names import gid_classes

But the file class_names.py just have the def fifteen_classes and def five_classes functions, where are the gid_classes?

Mixup

HI,there is no mixup!

Mixup()

self.mixup_transform = sync_transforms.Mixup()

sync_transforms.py中没有Mixup()这个函数啊

数据集下载

parser.add_argument('--train-data-root', type=str, default='../data_5_6_21/train')
parser.add_argument('--val-data-root', type=str, default='../data_5_6_21/val')
这个训练集、验证集在哪里?需要另外下载吗? @milleniums

RuntimeError: CUDA error

RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
代码中错误定位在trainer.training(epoch)和writer.add_scalar('train_loss', train_loss.avg, curr_iter)请问应该如何更改

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