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leggedrobotics / darknet_ros

YOLO ROS: Real-Time Object Detection for ROS
BSD 3-Clause "New" or "Revised" License
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will yolo v4 be supported? #243

Closed Sunchanghao closed 3 years ago

Shame-fight commented 4 years ago

+1

makangzhe commented 4 years ago

+1

diegocdl commented 4 years ago

+1

ganechi commented 4 years ago

+1

zclonely commented 4 years ago

Do you mean that only v2, v3, v3-tiny are supported now? I encountered a bug when implement v3-spp and v4, I don’t know how to solve it, but the same operation will not appear on v2, v3, v3-tiny bug

No handlers could be found for logger "roslaunch" [darknet_ros-2] process has died [pid 21409, exit code 255, cmd /home/skysys/catkin_ws2/devel/lib/darknet_ros/darknet_ros camera/rgb/image_raw:=/camera/rgb/image_raw __name:=darknet_ros __log:=/home/skysys/.ros/log/bac52d4a-a314-11ea-94df-00044b8cc60a/darknet_ros-2.log]. log file: /home/skysys/.ros/log/bac52d4a-a314-11ea-94df-00044b8cc60a/darknet_ros-2*.log

makangzhe commented 4 years ago

https://github.com/tom13133/darknet_ros This may help you

Shame-fight commented 4 years ago

https://github.com/tom13133/darknet_ros This may help you

Thank you, this is good news. Have you tried this version? On PC or jetson-tx2 machines, can it be compiled and passed.

Shame-fight commented 4 years ago

I successfully ran this version of the code on the PC, which was very pleasing. But I ran into a problem: the exact same bounding box was posted twice. As far as I know, the previous version of this repository also had the same problem. At the same time, I am not sure if there is a continuous increase in memory, because I only ran for two minutes. Welcome to discuss. @diegocdl @ganechi @zclonely @Sunchanghao @

makangzhe commented 4 years ago

I successfully ran this version of the code on the PC, which was very pleasing. But I ran into a problem: the exact same bounding box was posted twice. As far as I know, the previous version of this repository also had the same problem. At the same time, I am not sure if there is a continuous increase in memory, because I only ran for two minutes. Welcome to discuss. @diegocdl @ganechi @zclonely @Sunchanghao @

Do you know some methods applied for ROS of 6D pose estimation in github?

Shame-fight commented 4 years ago

6D pose

https://zhuanlan.zhihu.com/p/94020758 this may help you

jaronski commented 4 years ago

+1

Shame-fight commented 4 years ago

I have encountered a problem, ros has published the topic of /darknet_ros/boundingoxes, how to write code to subscribe to this topic and use it for future navigation?how did you solve it

jorgemiar commented 4 years ago

https://github.com/tom13133/darknet_ros This may help you

Thank you, this is good news. Have you tried this version? On PC or jetson-tx2 machines, can it be compiled and passed.

This fork seems to work on a Jetson Xavier NX but I'm only getting around 2/3FPS (YOLOV3 runs faster at 4/5FPS). When I built using catkin_make I got a lot of warnings similar to things like:

/home/unmnd/catkin_ws/src/darknet_ros/darknet/src/activations.c: In function ‘activate’:
/home/unmnd/catkin_ws/src/darknet_ros/darknet/src/activations.c:77:5: warning: enumeration value ‘RELU6’ not handled in switch [-Wswitch]
     switch(a){
     ^~~~~~
/home/unmnd/catkin_ws/src/darknet_ros/darknet/src/activations.c:77:5: warning: enumeration value ‘SWISH’ not handled in switch [-Wswitch]
/home/unmnd/catkin_ws/src/darknet_ros/darknet/src/activations.c:77:5: warning: enumeration value ‘MISH’ not handled in switch [-Wswitch]

or

/home/unmnd/catkin_ws/src/darknet_ros/darknet/src/gaussian_yolo_layer.c: In function ‘make_gaussian_yolo_layer’:
/home/unmnd/catkin_ws/src/darknet_ros/darknet/src/gaussian_yolo_layer.c:71:38: warning: passing argument 1 of ‘cudaHostAlloc’ from incompatible pointer type [-Wincompatible-pointer-types]
     if (cudaSuccess == cudaHostAlloc(&l.output, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;
                                      ^
In file included from /usr/local/cuda/include/cuda_runtime.h:96:0,
                 from /home/unmnd/catkin_ws/src/darknet_ros/darknet/include/darknet.h:41,
                 from /home/unmnd/catkin_ws/src/darknet_ros/darknet/src/gaussian_yolo_layer.h:5,
                 from /home/unmnd/catkin_ws/src/darknet_ros/darknet/src/gaussian_yolo_layer.c:7:
/usr/local/cuda/include/cuda_runtime_api.h:4575:39: note: expected ‘void **’ but argument is of type ‘float **’
 extern __host__ cudaError_t CUDARTAPI cudaHostAlloc(void **pHost, size_t size, unsigned int flags);
                                       ^~~~~~~~~~~~~
/home/unmnd/catkin_ws/src/darknet_ros/darknet/src/gaussian_yolo_layer.c:78:38: warning: passing argument 1 of ‘cudaHostAlloc’ from incompatible pointer type [-Wincompatible-pointer-types]
     if (cudaSuccess == cudaHostAlloc(&l.delta, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;
                                      ^

So it works but not sure if it built correctly. This is what I get when I launch the node:

auto-starting new master
process[master]: started with pid [25905]
ROS_MASTER_URI=http://localhost:11311

setting /run_id to cf0eb048-b0e6-11ea-ac5b-70665563d9f5
process[rosout-1]: started with pid [25916]
started core service [/rosout]
process[darknet_ros-2]: started with pid [25923]
[ INFO] [1592431599.098680603]: [YoloObjectDetector] Node started.
[ INFO] [1592431599.110482161]: [YoloObjectDetector] Xserver is running.
[ INFO] [1592431599.113667653]: [YoloObjectDetector] init().
YOLO
 Try to load cfg: /home/unmnd/catkin_ws/src/darknet_ros/darknet_ros/yolo_network_config/cfg/yolov4.cfg, clear = 0 
 compute_capability = 720, cudnn_half = 0 
net.optimized_memory = 0 
mini_batch = 1, batch = 8, time_steps = 1, train = 1 
   layer   filters  size/strd(dil)      input                output
   0 conv     32       3 x 3/ 1    608 x 608 x   3 ->  608 x 608 x  32 0.639 BF
   1 conv     64       3 x 3/ 2    608 x 608 x  32 ->  304 x 304 x  64 3.407 BF
   2 conv     64       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  64 0.757 BF
   3 route  1                                  ->  304 x 304 x  64 
   4 conv     64       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  64 0.757 BF
   5 conv     32       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  32 0.379 BF
   6 conv     64       3 x 3/ 1    304 x 304 x  32 ->  304 x 304 x  64 3.407 BF
   7 Shortcut Layer: 4,  wt = 0, wn = 0, outputs: 304 x 304 x  64 0.006 BF
   8 conv     64       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  64 0.757 BF
   9 route  8 2                                ->  304 x 304 x 128 
  10 conv     64       1 x 1/ 1    304 x 304 x 128 ->  304 x 304 x  64 1.514 BF
  11 conv    128       3 x 3/ 2    304 x 304 x  64 ->  152 x 152 x 128 3.407 BF
  12 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF
  13 route  11                                 ->  152 x 152 x 128 
  14 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF
  15 conv     64       1 x 1/ 1    152 x 152 x  64 ->  152 x 152 x  64 0.189 BF
  16 conv     64       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x  64 1.703 BF
  17 Shortcut Layer: 14,  wt = 0, wn = 0, outputs: 152 x 152 x  64 0.001 BF
  18 conv     64       1 x 1/ 1    152 x 152 x  64 ->  152 x 152 x  64 0.189 BF
  19 conv     64       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x  64 1.703 BF
  20 Shortcut Layer: 17,  wt = 0, wn = 0, outputs: 152 x 152 x  64 0.001 BF
  21 conv     64       1 x 1/ 1    152 x 152 x  64 ->  152 x 152 x  64 0.189 BF
  22 route  21 12                              ->  152 x 152 x 128 
  23 conv    128       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x 128 0.757 BF
  24 conv    256       3 x 3/ 2    152 x 152 x 128 ->   76 x  76 x 256 3.407 BF
  25 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF
  26 route  24                                 ->   76 x  76 x 256 
  27 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF
  28 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF
  29 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF
  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF
  31 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF
  32 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF
  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF
  34 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF
  35 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF
  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF
  37 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF
  38 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF
  39 Shortcut Layer: 36,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF
  40 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF
  41 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF
  42 Shortcut Layer: 39,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF
  43 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF
  44 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF
  45 Shortcut Layer: 42,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF
  46 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF
  47 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF
  48 Shortcut Layer: 45,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF
  49 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF
  50 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF
  51 Shortcut Layer: 48,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF
  52 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF
  53 route  52 25                              ->   76 x  76 x 256 
  54 conv    256       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 256 0.757 BF
  55 conv    512       3 x 3/ 2     76 x  76 x 256 ->   38 x  38 x 512 3.407 BF
  56 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF
  57 route  55                                 ->   38 x  38 x 512 
  58 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF
  59 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF
  60 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF
  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF
  62 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF
  63 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF
  64 Shortcut Layer: 61,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF
  65 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF
  66 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF
  67 Shortcut Layer: 64,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF
  68 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF
  69 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF
  70 Shortcut Layer: 67,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF
  71 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF
  72 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF
  73 Shortcut Layer: 70,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF
  74 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF
  75 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF
  76 Shortcut Layer: 73,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF
  77 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF
  78 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF
  79 Shortcut Layer: 76,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF
  80 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF
  81 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF
  82 Shortcut Layer: 79,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF
  83 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF
  84 route  83 56                              ->   38 x  38 x 512 
  85 conv    512       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 512 0.757 BF
  86 conv   1024       3 x 3/ 2     38 x  38 x 512 ->   19 x  19 x1024 3.407 BF
  87 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF
  88 route  86                                 ->   19 x  19 x1024 
  89 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF
  90 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF
  91 conv    512       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x 512 1.703 BF
  92 Shortcut Layer: 89,  wt = 0, wn = 0, outputs:  19 x  19 x 512 0.000 BF
  93 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF
  94 conv    512       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x 512 1.703 BF
  95 Shortcut Layer: 92,  wt = 0, wn = 0, outputs:  19 x  19 x 512 0.000 BF
  96 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF
  97 conv    512       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x 512 1.703 BF
  98 Shortcut Layer: 95,  wt = 0, wn = 0, outputs:  19 x  19 x 512 0.000 BF
  99 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF
 100 conv    512       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x 512 1.703 BF
 101 Shortcut Layer: 98,  wt = 0, wn = 0, outputs:  19 x  19 x 512 0.000 BF
 102 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF
 103 route  102 87                             ->   19 x  19 x1024 
 104 conv   1024       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x1024 0.757 BF
 105 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF
 106 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF
 107 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF
 108 max                5x 5/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.005 BF
 109 route  107                                    ->   19 x  19 x 512 
 110 max                9x 9/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.015 BF
 111 route  107                                    ->   19 x  19 x 512 
 112 max               13x13/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.031 BF
 113 route  112 110 108 107                        ->   19 x  19 x2048 
 114 conv    512       1 x 1/ 1     19 x  19 x2048 ->   19 x  19 x 512 0.757 BF
 115 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF
 116 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF
 117 conv    256       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 256 0.095 BF
 118 upsample                 2x    19 x  19 x 256 ->   38 x  38 x 256
 119 route  85                                 ->   38 x  38 x 512 
 120 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF
 121 route  120 118                                ->   38 x  38 x 512 
 122 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF
 123 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF
 124 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF
 125 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF
 126 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF
 127 conv    128       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 128 0.095 BF
 128 upsample                 2x    38 x  38 x 128 ->   76 x  76 x 128
 129 route  54                                 ->   76 x  76 x 256 
 130 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF
 131 route  130 128                                ->   76 x  76 x 256 
 132 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF
 133 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF
 134 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF
 135 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF
 136 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF
 137 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF
 138 conv    255       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 255 0.754 BF
 139 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.20
nms_kind: greedynms (1), beta = 0.600000 
 140 route  136                                    ->   76 x  76 x 128 
 141 conv    256       3 x 3/ 2     76 x  76 x 128 ->   38 x  38 x 256 0.852 BF
 142 route  141 126                                ->   38 x  38 x 512 
 143 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF
 144 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF
 145 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF
 146 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF
 147 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF
 148 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF
 149 conv    255       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 255 0.377 BF
 150 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.10
nms_kind: greedynms (1), beta = 0.600000 
 151 route  147                                    ->   38 x  38 x 256 
 152 conv    512       3 x 3/ 2     38 x  38 x 256 ->   19 x  19 x 512 0.852 BF
 153 route  152 116                                ->   19 x  19 x1024 
 154 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF
 155 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF
 156 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF
 157 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF
 158 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF
 159 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF
 160 conv    255       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 255 0.189 BF
 161 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.05
nms_kind: greedynms (1), beta = 0.600000 
Total BFLOPS 128.459 
avg_outputs = 1068395 
 Allocate additional workspace_size = 106.46 MB 
 Try to load weights: /home/unmnd/catkin_ws/src/darknet_ros/darknet_ros/yolo_network_config/weights/yolov4.weights 
Loading weights from /home/unmnd/catkin_ws/src/darknet_ros/darknet_ros/yolo_network_config/weights/yolov4.weights...
 seen 64, trained: 32032 K-images (500 Kilo-batches_64) 
Done! Loaded 162 layers from weights-file 

 try to allocate additional workspace_size = 106.46 MB 
 CUDA allocate done! 
Waiting for image.
Shame-fight commented 4 years ago

I can run yolov3 at 2-3 frames on jetson-tx2, I have not tried v4. If you are confused about the frame rate, there is a way to verify: run yolov4 (non-ros) alone, run darknet-ros, compare Are the frame rates the same? By the way: v4 has a lower frame rate than v3. @jorgemia

mbjelonic commented 3 years ago

We will work on yolo v4. :)