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View Code? Open in Web Editor NEWThe official repo for [NeurIPS'23] "SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model"
The official repo for [NeurIPS'23] "SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model"
Congrats to your excellent work first!
I want to know if there is any plan to release the pre-training and finetuning codes?
您是否可以给一个百度网盘的链接?onedrive下载 慢且不稳定。感谢
您好,请问作者您有将SAM直接在遥感大数据集上微调的模型吗
如图所述,Encoder Decoder可以跑通,End to End不行
Hello,
After going through your data, you just labeled objects which has boxes. The background like sky or water are not labeled.
Therefore, I am curious how your data can be used for semantic segmentation as you claimed in your conclusion on page 6.
Thanks,
Hello,
We are wondering if a picture has multiple objects, you just use gray labels. Therefore, i am curious how i can distinguish different objects in a picture. Is it possible to share codes to generate pictures like your Figure 6 in your SAMRS paper?
Thanks,
Liya
作者大大,您好,最近在尝试复现您的论文,遇到一些问题请教一下
1.https://github.com/ViTAE-Transformer/SAMRS/tree/main/Pretraining%20and%20Finetuning 这个链接下的提供的Segmentation Pretrained Models提供的是直接可以用来测试ISPRS Potsdam数据集(我把图裁剪成了512)的吗?
2.我使用resnet50_upernet_imp_sep_model.pth这个权重测试ISPRS Potsdam数据集的时候发现测试出来的精度很低。
使用的指令是:
CUDA_VISIBLE_DEVICES=0 python Pretraining_and_Finetuning/Encoder_Decoder/test_gpu.py --backbone 'resnet50' --decoder 'upernet' --dataset 'potsdam' --ms 'False' --mode 'test' --resume resnet50_upernet_imp_sep_model.pth --save_path ./save
加载权重时候会出现这个,不知道这会不会影响权重的正确载入?
最后日志文件是:
请问您觉得有什么可能发生的问题会影响精度测试,我排查一下
3.请问可以提供百度云链接的数据集下载方式吗?onedrive下载会一直中断
首先感谢您的工作!我在阅读SAMRS与RSP(An Empirical Study of Remote Sensing Pretraining)时,发现两篇文章公布的rsp-r50分割结果有所差异。
具体而言:
(1) SAMRS中表3 rsp-r50在potsdam结果为 OA=90.49 mF1=90.97
RSP中表6 rsp-r50的结果为OA=90.61 mF1=89.94
(2) SAMRS中表4 rsp-r50在isaid结果为mIoU为32.97
RSP中表7 rsp-r50的结果为mIoU为61.6
我不太清楚造成这种差异的原因是什么?特别是在isaid上的结果差异比较大,我有点不太确定该以哪篇文章的结果为准。期待您的回复。
Not really an issue, more of a PSA:
For anyone downloading the data through the OneDrive link, I have found that if you go into the dataset folders and download each of those files individually (e.g inside the SIOR directory download the isinlabels.zip, samlabels.zip, test_images.zipm trainval_images.zip, train.txt and val.txt files) you get much higher speeds, for me it was < 1MB/s before up to a peak of 21MB/s downloading individually and the zips unpack correctly. I had issues unpacking the SIOR.zip file, but not downloading them individually.
@DotWang you could possibly add this recommendation as a note in the readme.
[{'mask': {'size': [1024, 1024], 'counts': 'cRYf07fo04M3M2O2O01O1N2O0O3M2LbmZ9'}, 'bbox': array([714., 76., 726., 95.]), 'category': 'small-vehicle', 'size': 159, 'label': 9}, {'mask': {'size': [1024, 1024], 'counts': 'kRjf06fo05M2N3N10000O2O01N1O1N3L4M^mg8'}, 'bbox': array([731., 82., 744., 101.]), 'category': 'small-vehicle', 'size': 199, 'label': 9}, {'mask': {'size': [1024, 1024], 'counts': 'YWWd02lo04L4L3L4N1O2N1000O100O1N3N1N3N3LQiW;'}, 'bbox': array([648., 222., 667., 242.]), 'category': 'small-vehicle', 'size': 250, 'label': 9}, {'mask': {'size': [1024, 1024], 'counts': 'nSjf01no02N2N2O001N1000010O0010O001O10O01O1O100Onk8'}, 'bbox': array([733., 122., 754., 136.]), 'category': 'small-vehicle', 'size': 152, 'label': 9}, {'mask': {'size': [1024, 1024], 'counts': ']TQh02lo04M2O001O000010O0010O0010O001N3Mak]7'}, 'bbox': array([770., 137., 788., 151.]), 'category': 'small-vehicle', 'size': 136, 'label': 9}, {'mask': {'size': [1024, 1024], 'counts': 'lTUi06io02O1O001O00000001O0010O010OO101N2NQkX6'}, 'bbox': array([806., 153., 827., 168.]), 'category': 'small-vehicle', 'size': 153, 'label': 9}, {'mask': {'size': [1024, 1024], 'counts': 'UTce05io03L4M2O1O101O0000O2O0O2I9KQln9'}, 'bbox': array([691., 122., 705., 141.]), 'category': 'small-vehicle', 'size': 175, 'label': 9}, {'mask': {'size': [1024, 1024], 'counts': 'YTke02lo03M3L4M2O10000001O0O1O2N2KRlf9'}, 'bbox': array([700., 126., 714., 145.]), 'category': 'small-vehicle', 'size': 155, 'label': 9}, {'mask': {'size': [1024, 1024], 'counts': 'dTQf05io03J6N1O2N1000O101N1O2M2M4Mek
9'}, 'bbox': array([706., 140., 718., 157.]), 'category': 'small-vehicle', 'size': 182, 'label': 9}, {'mask': {'size': [1024, 1024], 'counts': 'fSel05io0201O001O0010O010O01O01O010O001O1O0O2NVlf2'}, 'bbox': array([917., 117., 938., 131.]), 'category': 'small-vehicle', 'size': 166, 'label': 9}, {'mask': {'size': [1024, 1024], 'counts': ']Skl05jo02N101O1O000010O01O10O001O001O1O001O1O0O_l_2'}, 'bbox': array([924., 106., 945., 122.]), 'category': 'small-vehicle', 'size': 188, 'label': 9}, {'mask': {'size': [1024, 1024], 'counts': 'kRXm05jo02O001O001O010O00010O01O010O001N102LQmT2'}, 'bbox': array([935., 88., 955., 104.]), 'category': 'small-vehicle', 'size': 145, 'label': 9}, {'mask': {'size': [1024, 1024], 'counts': 'TYaj07go03L3N2N3N1O1O101O00O2O0O1M3N3M2N4KWgl4'}, 'bbox': array([849., 282., 864., 303.]), 'category': 'small-vehicle', 'size': 281, 'label': 9}, {'mask': {'size': [1024, 1024], 'counts': 'V[_h05io03M3M2N3N2O1N11O01O0001M3E[eR7'}, 'bbox': array([785., 349., 796., 367.]), 'category': 'small-vehicle', 'size': 212, 'label': 9}, {'mask': {'size': [1024, 1024], 'counts': '\[hh04jo03K5N1M3N2000O100O2M2M4MPej6'}, 'bbox': array([793., 354., 805., 370.]), 'category': 'small-vehicle', 'size': 168, 'label': 9}, {'mask': {'size': [1024, 1024], 'counts': 'c[i05ho04M2N2N2N3O00O100O1O1N3N2L5LfdP6'}, 'bbox': array([817., 361., 830., 376.]), 'category': 'small-vehicle', 'size': 188, 'label': 9}, {'mask': {'size': [1024, 1024], 'counts': 'WRVo05ho04M3M2N200O1000O1O2N1N4KSn<'}, 'bbox': array([ 999., 62., 1011., 80.]), 'category': 'small-vehicle', 'size': 167, 'label': 9}, {'mask': {'size': [1024, 1024], 'counts': ']Yec01no02M2HM
PO6_o06010O01O01O0O2O0O2M2Nmfk;'}, 'bbox': array([631., 291., 646., 308.]), 'category': 'small-vehicle', 'size': 156, 'label': 9}, {'mask': {'size': [1024, 1024], 'counts': 'VY_c06ho04L2N3N100000000001O001N2O1M3M2O2Mmfn;'}, 'bbox': array([623., 287., 636., 304.]), 'category': 'small-vehicle', 'size': 216, 'label': 9}, {'mask': {'size': [1024, 1024], 'counts': '^Zmb02ko05K4N1N2N20000000O101M2N3N1Nmec<'}, 'bbox': array([607., 324., 621., 340.]), 'category': 'small-vehicle', 'size': 176, 'label': 9}]
Great work! I have two questions: 1. What exactly is 'counts'? 2. Are there any polygons for instance segmentation? Thank you
感谢您开源的代码,我希望运行test_gpu.py但是报错了,请问您有遇到这个 问题吗
(oneformer) lscsc@lscsc-System-Product-Name:SAMRS/Pretraining and Finetuning/End_to_End$ CUDA_VISIBLE_DEVICES=0 python test_gpu.py --backbone 'vit_b' --dataset 'potsdam' --ms 'False' --mode 'test' --resume /media/lscsc/nas/yihan/SegAN/SAMRS/
Pretraining and Finetuning/weight/vit_b_samrs_mae_clip_checkpoint-1599.pth --save_path /media/lscsc/nas/yihan/SegAN/SAMRS/Pretraining an
d Finetuning/Encoder_Decoder/output
Traceback (most recent call last):
File "test_gpu.py", line 14, in
from models import SemsegFinetuneFramework
File "/media/lscsc/nas/yihan/SegAN/SAMRS/Pretraining and Finetuning/End_to_End/models.py", line 9, in
from backbone.intern_image import InternImage
File "/media/lscsc/nas/yihan/SegAN/SAMRS/Pretraining and Finetuning/End_to_End/backbone/intern_image.py", line 18, in
from .ops_dcnv3 import modules as opsm
File "/media/lscsc/nas/yihan/SegAN/SAMRS/Pretraining and Finetuning/End_to_End/backbone/ops_dcnv3/modules/init.py", line 7, in
from .dcnv3 import DCNv3, DCNv3_pytorch
File "/media/lscsc/nas/yihan/SegAN/SAMRS/Pretraining and Finetuning/End_to_End/backbone/ops_dcnv3/modules/dcnv3.py", line 16, in
from ..functions import DCNv3Function, dcnv3_core_pytorch
File "/media/lscsc/nas/yihan/SegAN/SAMRS/Pretraining and Finetuning/End_to_End/backbone/ops_dcnv3/functions/init.py", line 7, in
from .dcnv3_func import DCNv3Function, dcnv3_core_pytorch
File "/media/lscsc/nas/yihan/SegAN/SAMRS/Pretraining and Finetuning/End_to_End/backbone/ops_dcnv3/functions/dcnv3_func.py", line 16, in
import DCNv3
ModuleNotFoundError: No module named 'DCNv3'
期待代码的发布,谢谢
I wanna ask how to generate custom datasets using the code or fine-tune the model on custom datasets using the code
Hi,
Thanks for the great work! I was wondering if you can provide the SAM hyperparmeter settings used to generate the dataset.
Is the dataset copyright free of charge?
I'm trying to participate in a certain competition, and I'm asking because I'm trying to learn with that dataset.
Thanks
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