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32 Open 16 Closed
32 Open 16 Closed
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Issues list

Ask for the Pretrain model for demo.py
#51 opened Oct 25, 2024 by sun4176
下载pre-trained model的其他方法
#50 opened Mar 1, 2023 by liuj001
RuntimeError: CUDNN_STATUS_EXECUTION_FAILED heeeelp please :(
#49 opened Nov 2, 2022 by pcuevas-ch
What is ghost box?
#48 opened Apr 19, 2021 by igo312
Generate extreme point annotation from segmentation
#46 opened Oct 3, 2020 by keravraj20
Some questions about rand_crop=false
#44 opened Apr 29, 2020 by ZHR1997
What about the test speed on ExtremeNet
#41 opened Mar 2, 2020 by jerrywgz
1
Corner pooling
#40 opened Feb 16, 2020 by zhaopings
4
Train error
#39 opened Jan 14, 2020 by wwetnaroh
1
Train my own data errors，Hope to get your help， Sincere thanks！
#38 opened Jan 13, 2020 by xdwjc
How to use the pretrained model to train my own data, when the class number is not 80(coco)?
#37 opened Jan 13, 2020 by DataXujing
2
Train my Own Data
#36 opened Dec 21, 2019 by DataXujing
user warning
#35 opened Nov 20, 2019 by love1314pds
"false" in file qasciikey.cpp
#34 opened Nov 7, 2019 by JSLOUJSKOU
Can CUDA10,pytorch1.x work?
#33 opened Oct 9, 2019 by pipi2020
storage has wrong size: expected -8367237312445466787 got 1
#32 opened Aug 27, 2019 by leonshek
1
系统电源不够
#31 opened Jul 23, 2019 by MichaelCong
terminate called without an active exception Aborted (core dumped)
#30 opened Jul 23, 2019 by MichaelCong
2
View data structures of ExtremeNet_250000.pkl
#25 opened Jun 12, 2019 by chenwocheng
Train problem
#24 opened Jun 10, 2019 by Lin-Zhipeng
2
How to implement the operations about “Installation”？
#23 opened Apr 30, 2019 by chenwocheng
Annotation tools
#20 opened Apr 8, 2019 by AlexWangn
2
test error!!!
#19 opened Apr 1, 2019 by zhepherd
5
File "/data2/yanmengkai/ExtremeNet/nnet/py_factory.py", line 117, in load_params self.model.load_state_dict(params)
#18 opened Apr 1, 2019 by njustymk
5
How to train my own datasets using ExtremeNet? I have xml and jpg file,How to change it to train needed
#17 opened Mar 29, 2019 by duyanfang123
4
ProTip! Exclude everything labeled bug with -label:bug.