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Issues list

找个GC Block这么难？
#43 by Huixxi was closed Feb 23, 2021
3
possible replacements for layernorm
#42 by DietDietDiet was closed Jul 25, 2020
1
AP, AR=-1 while evaluation at the end of each epoch
#38 by kbiyani33 was closed May 29, 2020
8
How can I use gc block in resnet18?
#34 by LJXLJXLJX was closed Mar 6, 2020
3
gc
#30 by henbucuoshanghai was closed Nov 12, 2019
7
error: undefined symbol: __cudaPopCallConfiguration
#28 by lidehuihxjz was closed Sep 29, 2019
1
Can I use this framework to train the object detection without mask instance label ?
#26 by ghost was closed Aug 26, 2019
2
Should GCNet always use pretrained backbone and finetune with GCNet?
#22 by tea1528 was closed Jul 29, 2019
2
Did anyone implement this paper using TensorFlow?
#20 by yt605155624 was closed Jul 5, 2019
1
Can GCNet be used to estimation tasks
#19 by Ericjiejie was closed Jul 3, 2019
1
Config files
#17 by roozbehm was closed Jul 4, 2019
1
runtime inrease about 15ms?
#16 by boundles was closed Jul 3, 2019
1
Code for Kinetics dataset
#15 by tea1528 was closed Jul 3, 2019
1
understanding of distance of feature map
#14 by valencebond was closed May 28, 2019
how to reproduce the results of coco detection with only 4 gpus
#13 by zhaoguangxiang was closed Nov 12, 2019
1
Zero init of conv layers before addition
#12 by zhujiagang was closed May 22, 2019
1
How about sync bn performance in this repo compared with in pytorch1.1 ?
#9 by nemonameless was closed Jul 3, 2019
1
Where is the code of the "Global context (GC) block"
#8 by CV-JunchengLi was closed Jul 3, 2019
1
got an unexpected keyword argument 'ct'
#6 by sms95 was closed Jul 3, 2019
1
different between your GCNet and GENet (gather-excite network) in NIPS2018
#5 by yaoqi-zd was closed May 6, 2019
1
What make your attention be different from the DANet?
#4 by LegenDong was closed Apr 29, 2019
8
singe GPU train
#3 by yl-precious was closed Apr 29, 2019
3
how to set the "lr" when using the "ap.SyncBatchNorm"?
#2 by zimenglan-sysu-512 was closed Apr 29, 2019
7
ProTip! Adding no:label will show everything without a label.