Open lonl opened 7 years ago
Please send me everything so that I can reproduce the problem.El 20 abr. 2017 10:35, lonl notifications@github.com escribió:Hi, When I set the k = 5 & L = 3, I find that the feature generated from the trained vocabulary has different length, and the length is even larger than the number of words..... I do not understand why this happens. Thank you ~~~
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Hi,
The attachment is the all source code. I completed this test in Linux OS. And it only depends OPENCV
When you extract the attachment, you could conduct the following steps:
cd FBOW_test
mkdir build
cd build
cmake ..
make
//Then you turn into the file folder named "bin":
cd ..
cd bin
./TestFBOW
You will find the key:
There are three keys: 2235 & 2375 & 2572 are larger than the number of vocabulary 5^4=625;
The keys should not larger than the number of vocabulary 625.
I am looking forward your reply, thank you for helping me for solving this error.
Pengcheng Han Northewestern Polytechnical University
At 2017-04-20 22:47:12, "rmsalinas" notifications@github.com wrote: Please send me everything so that I can reproduce the problem.El 20 abr. 2017 10:35, lonl notifications@github.com escribió:Hi, When I set the k = 5 & L = 3, I find that the feature generated from the trained vocabulary has different length, and the length is even larger than the number of words..... I do not understand why this happens. Thank you ~~~
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Could you give me your email address, I send the source code to you~Thank you
rmsalinas@uco.es
On 24/04/17 08:33, lonl wrote:
Could you give me your email address, I send the source code to youThank you
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Sorry, but your code does not commpile. Can you try to create the vocabulary with the program provided in utils and see if the error persists?
Can't share the dataset due to proprietary issues. However, can confirm that the generated length is larger than the vocab length.
CPU Details:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 8
On-line CPU(s) list: 0-7
Thread(s) per core: 2
Core(s) per socket: 4
Socket(s): 1
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 94
Model name: Intel(R) Core(TM) i7-6700HQ CPU @ 2.60GHz
Stepping: 3
CPU MHz: 821.031
CPU max MHz: 3500.0000
CPU min MHz: 800.0000
BogoMIPS: 5183.81
Virtualization: VT-x
L1d cache: 32K
L1i cache: 32K
L2 cache: 256K
L3 cache: 6144K
NUMA node0 CPU(s): 0-7
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch epb invpcid_single intel_pt ibrs ibpb stibp kaiser tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx rdseed adx smap clflushopt xsaveopt xsavec xgetbv1 dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp
OS - Ubuntu 16.04 LTS
uname -a: Linux Ubunex 4.4.0-133-generic #159-Ubuntu SMP Fri Aug 10 07:31:43 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux
Hey @rmsalinas, I am running in the same issue where the vocabulary generated by fBoW is smaller than the feature ids. Any ideas on what could be going on? Or how to fix it? Thanks! Cheers.
i meet the same problem
Hi, When I set the k = 5 & L = 3, I find that the feature generated from the trained vocabulary has different length, and the length is even larger than the number of words..... I do not understand why this happens. Thank you ~~~