找了很多,只有这个博主分享了百度网盘的数据集,大家不要忘记给他点赞,这个博主太良心了。 Potsdam,Vaihingen数据集(附百度网盘下载地址)_小了白了兔_白了又了白的博客-CSDN博客blog.csdn.net/weixin_49703603/article/details/121061495?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522163929037916780269829...
ISPRS_Potsdam,Vaihingen数据下载地址 Potsdam数据官网介绍 影像数据集名称:2D Semantic Labeling Contest – Potsdam,共38张6000*6000像素无人机影像,分辨率为 5 厘米/像素。 数据集中包含三种不同通道影像数据,地形数据,标签数据: TOP RGBIR:真实正射影像,红、绿、蓝、红外四通道; TOP IRRG:真... ...
The size of our dataset is ten times of the Vaihingen dataset (Rottensteiner et al., 2014), five times of the CamVid dataset (Brostow et al., 2008) and twice of the Potsdam dataset (Rottensteiner et al., 2014) regarding the labeled number of pixels. All the labels are acquired with ...
I have updated this repo to pytorch 2.0 and pytorch-lightning 2.0, support multi-gpu training, etc. Pretrained Weights of backbones can be access fromGoogle Drive UNetFormer(accepted by ISPRS,PDF) andUAVid datasetare supported. ISPRS Vaihingen and Potsdam datasets are supported. Since private shar...
python train.py \ --config /home/aistudio/PaddleSeg/configs/1_tanet/tanet_resnet101_vaihingen_512x512_25k.yml \ --do_eval \ --use_vdl \ --batch_size 16 \ --log_iters 100 \ --save_interval 300 \ --num_workers 8 \ --keep_checkpoint_max 2 \ --save_dir output \ --precision...
From row 1 and row 3 of Table 6 we can see, differentiable forest significantly improves the classification performance of DCNNs from 85.7% to 90.5% (originally from 85.7% to 91.4%) for Vaihingen dataset and from 87.0% to 91.4% (originally from 87.0% to 90.5%) for Potsdam dataset. The ...
Vaihingentest1 2 directories Data Explorer Version 1(9.28 GB) arrow_drop_down folder Vaihingentest1 arrow_right folder Vaihingentest1 arrow_right folder potsdamtest1 Summary arrow_right folder 180 files lightbulb See what others are saying about this dataset ...
python -m paddle.distributed.launch train.py \ --config /home/aistudio/PaddleSeg/configs/1_tanet/tanet_resnet101_vaihingen_512x512_25k.yml \ --do_eval \ --use_vdl \ --batch_size 16 \ --log_iters 100 \ --save_interval 200 \ --num_workers 8 \ --keep_checkpoint_max 2 \ --...
python -m paddle.distributed.launch train.py \ --config ./configs/1_tanet/tanet_ASPP_resnet101_vaihingen_512x512_25k.yml \ --do_eval \ --use_vdl \ --batch_size 18 \ --log_iters 50 \ --save_interval 200 \ --num_workers 8 \ --keep_checkpoint_max 2 \ --save_dir output \...
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