I am trying to run YOLOV3 usind DeepStream SDK on Jetson Nano but getting core dumped error

I am trying from this link https://www.deeplearning-blog.com/2020/02/03/how-to-detect-objects-with-nvidia-deepstream-4-0-and-yolo-in-5-minutes/

cmd command (path–>] /opt/nvidia/deepstream/deepstream-4.0/sources/objectDetector_Yolo$ deepstream-app -c deepstream_app_config_yoloV3.txt

Using winsys: x11
Creating LL OSD context new
0:00:01.444140712 27310 0x7f20002240 INFO nvinfer gstnvinfer.cpp:519:gst_nvinfer_logger:<primary_gie_classifier> NvDsInferContext[UID 1]:initialize(): Trying to create engine from model files
0:00:01.499905179 27310 0x7f20002240 WARN nvinfer gstnvinfer.cpp:515:gst_nvinfer_logger:<primary_gie_classifier> NvDsInferContext[UID 1]:generateTRTModel(): INT8 not supported by platform. Trying FP16 mode.
Loading pre-trained weights…
Loading complete!
Total Number of weights read : 62001757
layer inp_size out_size weightPtr
(1) conv-bn-leaky 3 x 608 x 608 32 x 608 x 608 992
(2) conv-bn-leaky 32 x 608 x 608 64 x 304 x 304 19680
(3) conv-bn-leaky 64 x 304 x 304 32 x 304 x 304 21856
(4) conv-bn-leaky 32 x 304 x 304 64 x 304 x 304 40544
(5) skip 64 x 304 x 304 64 x 304 x 304 -
(6) conv-bn-leaky 64 x 304 x 304 128 x 152 x 152 114784
(7) conv-bn-leaky 128 x 152 x 152 64 x 152 x 152 123232
(8) conv-bn-leaky 64 x 152 x 152 128 x 152 x 152 197472
(9) skip 128 x 152 x 152 128 x 152 x 152 -
(10) conv-bn-leaky 128 x 152 x 152 64 x 152 x 152 205920
(11) conv-bn-leaky 64 x 152 x 152 128 x 152 x 152 280160
(12) skip 128 x 152 x 152 128 x 152 x 152 -
(13) conv-bn-leaky 128 x 152 x 152 256 x 76 x 76 576096
(14) conv-bn-leaky 256 x 76 x 76 128 x 76 x 76 609376
(15) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 905312
(16) skip 256 x 76 x 76 256 x 76 x 76 -
(17) conv-bn-leaky 256 x 76 x 76 128 x 76 x 76 938592
(18) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 1234528
(19) skip 256 x 76 x 76 256 x 76 x 76 -
(20) conv-bn-leaky 256 x 76 x 76 128 x 76 x 76 1267808
(21) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 1563744
(22) skip 256 x 76 x 76 256 x 76 x 76 -
(23) conv-bn-leaky 256 x 76 x 76 128 x 76 x 76 1597024
(24) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 1892960
(25) skip 256 x 76 x 76 256 x 76 x 76 -
(26) conv-bn-leaky 256 x 76 x 76 128 x 76 x 76 1926240
(27) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 2222176
(28) skip 256 x 76 x 76 256 x 76 x 76 -
(29) conv-bn-leaky 256 x 76 x 76 128 x 76 x 76 2255456
(30) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 2551392
(31) skip 256 x 76 x 76 256 x 76 x 76 -
(32) conv-bn-leaky 256 x 76 x 76 128 x 76 x 76 2584672
(33) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 2880608
(34) skip 256 x 76 x 76 256 x 76 x 76 -
(35) conv-bn-leaky 256 x 76 x 76 128 x 76 x 76 2913888
(36) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 3209824
(37) skip 256 x 76 x 76 256 x 76 x 76 -
(38) conv-bn-leaky 256 x 76 x 76 512 x 38 x 38 4391520
(39) conv-bn-leaky 512 x 38 x 38 256 x 38 x 38 4523616
(40) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 5705312
(41) skip 512 x 38 x 38 512 x 38 x 38 -
(42) conv-bn-leaky 512 x 38 x 38 256 x 38 x 38 5837408
(43) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 7019104
(44) skip 512 x 38 x 38 512 x 38 x 38 -
(45) conv-bn-leaky 512 x 38 x 38 256 x 38 x 38 7151200
(46) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 8332896
(47) skip 512 x 38 x 38 512 x 38 x 38 -
(48) conv-bn-leaky 512 x 38 x 38 256 x 38 x 38 8464992
(49) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 9646688
(50) skip 512 x 38 x 38 512 x 38 x 38 -
(51) conv-bn-leaky 512 x 38 x 38 256 x 38 x 38 9778784
(52) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 10960480
(53) skip 512 x 38 x 38 512 x 38 x 38 -
(54) conv-bn-leaky 512 x 38 x 38 256 x 38 x 38 11092576
(55) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 12274272
(56) skip 512 x 38 x 38 512 x 38 x 38 -
(57) conv-bn-leaky 512 x 38 x 38 256 x 38 x 38 12406368
(58) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 13588064
(59) skip 512 x 38 x 38 512 x 38 x 38 -
(60) conv-bn-leaky 512 x 38 x 38 256 x 38 x 38 13720160
(61) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 14901856
(62) skip 512 x 38 x 38 512 x 38 x 38 -
(63) conv-bn-leaky 512 x 38 x 38 1024 x 19 x 19 19624544
(64) conv-bn-leaky 1024 x 19 x 19 512 x 19 x 19 20150880
(65) conv-bn-leaky 512 x 19 x 19 1024 x 19 x 19 24873568
(66) skip 1024 x 19 x 19 1024 x 19 x 19 -
(67) conv-bn-leaky 1024 x 19 x 19 512 x 19 x 19 25399904
(68) conv-bn-leaky 512 x 19 x 19 1024 x 19 x 19 30122592
(69) skip 1024 x 19 x 19 1024 x 19 x 19 -
(70) conv-bn-leaky 1024 x 19 x 19 512 x 19 x 19 30648928
(71) conv-bn-leaky 512 x 19 x 19 1024 x 19 x 19 35371616
(72) skip 1024 x 19 x 19 1024 x 19 x 19 -
(73) conv-bn-leaky 1024 x 19 x 19 512 x 19 x 19 35897952
(74) conv-bn-leaky 512 x 19 x 19 1024 x 19 x 19 40620640
(75) skip 1024 x 19 x 19 1024 x 19 x 19 -
(76) conv-bn-leaky 1024 x 19 x 19 512 x 19 x 19 41146976
(77) conv-bn-leaky 512 x 19 x 19 1024 x 19 x 19 45869664
(78) conv-bn-leaky 1024 x 19 x 19 512 x 19 x 19 46396000
(79) conv-bn-leaky 512 x 19 x 19 1024 x 19 x 19 51118688
(80) conv-bn-leaky 1024 x 19 x 19 512 x 19 x 19 51645024
(81) conv-bn-leaky 512 x 19 x 19 1024 x 19 x 19 56367712
(82) conv-linear 1024 x 19 x 19 255 x 19 x 19 56629087
(83) yolo 255 x 19 x 19 255 x 19 x 19 56629087
(84) route - 512 x 19 x 19 56629087
(85) conv-bn-leaky 512 x 19 x 19 256 x 19 x 19 56761183
(86) upsample 256 x 19 x 19 256 x 38 x 38 -
(87) route - 768 x 38 x 38 56761183
(88) conv-bn-leaky 768 x 38 x 38 256 x 38 x 38 56958815
(89) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 58140511
(90) conv-bn-leaky 512 x 38 x 38 256 x 38 x 38 58272607
(91) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 59454303
(92) conv-bn-leaky 512 x 38 x 38 256 x 38 x 38 59586399
(93) conv-bn-leaky 256 x 38 x 38 512 x 38 x 38 60768095
(94) conv-linear 512 x 38 x 38 255 x 38 x 38 60898910
(95) yolo 255 x 38 x 38 255 x 38 x 38 60898910
(96) route - 256 x 38 x 38 60898910
(97) conv-bn-leaky 256 x 38 x 38 128 x 38 x 38 60932190
(98) upsample 128 x 38 x 38 128 x 76 x 76 -
(99) route - 384 x 76 x 76 60932190
(100) conv-bn-leaky 384 x 76 x 76 128 x 76 x 76 60981854
(101) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 61277790
(102) conv-bn-leaky 256 x 76 x 76 128 x 76 x 76 61311070
(103) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 61607006
(104) conv-bn-leaky 256 x 76 x 76 128 x 76 x 76 61640286
(105) conv-bn-leaky 128 x 76 x 76 256 x 76 x 76 61936222
(106) conv-linear 256 x 76 x 76 255 x 76 x 76 62001757
(107) yolo 255 x 76 x 76 255 x 76 x 76 62001757
Output blob names :
yolo_83
yolo_95
yolo_107
Total number of layers: 257
Total number of layers on DLA: 0
Building the TensorRT Engine…
Segmentation fault (core dumped)

• Jetson
**• Deepstream4.0 **
• jetpack 4.4
• tensorrt 7

Did you “export CUDA_VER=10.1” as mentioned in your link? you need to use “export CUDA_VER=10.2” instead.

yes I did use “export CUDA_VER=10.2” and then “make -C nvdsinfer_custom_impl_Yolo”

it should be cuda version 10.0 if use version 4.0.x, please rebuild nvdsinfer_custom_impl_Yolo and run again if you indeed use ds 4.0.x.
but could you please specify details your platform environments?
since from beginning, seems you used deepstream version 4.0,
cmd command (path–>] /opt/nvidia/deepstream/deepstream-4.0/sources/objectDetector_Yolo$ deepstream-app -c deepstream_app_config_yoloV3.txt
but from your posted setup, you used deepstream version 5.0
• Jetson
**• Deepstream5 **
• jetpack 4.4
• tensorrt 7

yes deepstream 4.0 but i have both cuda 10.0 and 10.2 and tried with both, still getting segmentation fault.

does having two cuda versions in the system cause the problem?

Having multi cuda installed will not cause that issue, please see your cuda soft link point to which version
ls -l /usr/local/cuda