@beniz training object detector question - when i'm training multiple classes, and those classes/objects generally always appear in the same image, should I always be sure to train an image that contains all of the objects? or is it totally no difference if I have a dataset of Object A (bbox txt's and imgs) and a dataset of Object B (bbox txt's and imgs), but in practice the images i'm detecting on always contain both A and B? should be no difference in accuracy or?
[2022-02-04 16:06:58.042] [torchlib] [info] Initializing net from parameters:
[2022-02-04 16:06:58.042] [torchlib] [info] Creating layer / name=tdata / type=AnnotatedData
[2022-02-04 16:06:58.042] [torchlib] [info] Creating Layer tdata
[2022-02-04 16:06:58.042] [torchlib] [info] tdata -> data
[2022-02-04 16:06:58.042] [torchlib] [info] tdata -> label
terminate called after throwing an instance of 'CaffeErrorException'
what(): ./include/caffe/util/db_lmdb.hpp:15 / Check failed (custom): (mdb_status) == (0)
0# dd::OatppJsonAPI::abort(int) at /opt/deepdetect/src/oatppjsonapi.cc:255
1# 0x00007F24AC053210 in /lib/x86_64-linux-gnu/libc.so.6
2# raise in /lib/x86_64-linux-gnu/libc.so.6
3# abort in /lib/x86_64-linux-gnu/libc.so.6
4# 0x00007F24AC477911 in /lib/x86_64-linux-gnu/libstdc++.so.6
5# 0x00007F24AC48338C in /lib/x86_64-linux-gnu/libstdc++.so.6
6# 0x00007F24AC4833F7 in /lib/x86_64-linux-gnu/libstdc++.so.6
7# 0x00007F24AC4836A9 in /lib/x86_64-linux-gnu/libstdc++.so.6
8# caffe::db::MDB_CHECK(int) at ./include/caffe/util/db_lmdb.hpp:15
9# caffe::db::LMDB::Open(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, caffe::db::Mode) at src/caffe/util/db_lmdb.cpp:40
10# caffe::DataReader<caffe::AnnotatedDatum>::Body::InternalThreadEntry() at src/caffe/data_reader.cpp:95
11# 0x00007F24B0DC343B in /lib/x86_64-linux-gnu/libboost_thread.so.1.71.0
12# start_thread in /lib/x86_64-linux-gnu/libpthread.so.0
13# __clone in /lib/x86_64-linux-gnu/libc.so.6
Aborted (core dumped)current jolibrain/deepdetect_cpu with https://www.deepdetect.com/downloads/platform/pretrained/ssd_300/VGG_rotate_generic_detect_v2_SSD_rotate_300x300_iter_115000.caffemodel
used to work before i think.. checking GPU version
hmm maybe related to a permissions issue in the model dir?
src/caffe/util/db_lmdb.cpp LMDB etc related.. maybe.. checking
x/detection$ ls -al model/train.lmdb/
total 29932
drwxr--r-- 2 dgtlmoon dgtlmoon 4096 Feb 4 17:10 .
drwxrwxrwx 3 dgtlmoon dgtlmoon 4096 Feb 4 17:10 ..
-rw-r--r-- 1 dgtlmoon dgtlmoon 30638080 Feb 4 17:10 data.mdb
-rw-r--r-- 1 dgtlmoon dgtlmoon 8192 Feb 4 17:10 lock.mdb
make sure you have the writing permissions overall since this is being access by the docker image.
I can see that
train.lmdb/data.mdb is created without problem brand new every time, and THEN the segfault happens
the setup of the service works fine, then I call the train method, I see the MDB is created every time, and i get segfault
curl -X POST "http://localhost:8080/train" -d '
{
"service": "location",
"async": true,
"parameters": {
"input": {
"db": true,
"db_width": 512,
"db_height": 512,
"width": 300,
"height": 300
},
"mllib": {
"resume": false,
"net": {
"batch_size": 20,
"test_batch_size": 12
},
"solver": {
"iterations": 50000,
"test_interval": 500,
"snapshot": 1000,
"base_lr": 0.0001
},
"bbox": true
},
"output": {
"measure": [
"map"
]
}
},
"data": [ "/tags_dataset/bottom/bottom-images.txt" ]
}
'
done :)
ok I usually use
caffe for absolutely no reason than that's whats in the examples, maybe I should try torch? (trying squeezenet object detector here)
yes I have a python script to split stuff for me I wrote
that's funny, so actually the bug I found was completely unrelated to what I was doing :D
How can use code for AI Server , easy setup
Hello @beniz or anyone. Quick question - for a pretrained scenario with just classifications, is it possible to run in read-only docker? I am seeing a message
couldn't write model.json file in model repository... when loading, but classifications seem to work fine. Can this message be disregarded in this case?