I am trying from this link How to detect objects with Nvidia Deepstream 4.0 and YOLO in 5 minutes - Deep Learning and AI related blog
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