diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 000000000..917ac5cab
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,33 @@
+---
+name: 🐛 Bug report
+about: Create a report to help us improve
+title: ""
+labels: bug
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**System specs (please complete the following information):**
+ - OS
+ - ROS version
+ - Library versions when non-standard
+ - GPU / CUDA version when relevant
+
+**Additional context**
+Add any other context about the problem here.
diff --git a/.github/ISSUE_TEMPLATE/compilation-and-runtime-issue.md b/.github/ISSUE_TEMPLATE/compilation-and-runtime-issue.md
new file mode 100644
index 000000000..e86674bd6
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/compilation-and-runtime-issue.md
@@ -0,0 +1,32 @@
+---
+name: ⁉️  Compilation and runtime issue
+about: Use this template if you're facing issues running or compiling darknet_ros.
+title: ""
+labels: question
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**System (please complete the following information):**
+ - OS: [e.g. Ubuntu 20.04]
+ - ROS version: [e.g., Noetic, Foxy]
+ - GPU when relevant: [e.g., Intel UHD, NVidia GTX 3090] 
+
+**Additional context**
+Add any other context about the problem here.
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100644
index 000000000..d20dc6d37
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,20 @@
+---
+name: ✨ Feature request
+about: Suggest an idea for this project
+title: ""
+labels: enhancement
+assignees: mbjelonic
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 000000000..608b1d449
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+.vscode/
+build/
\ No newline at end of file
diff --git a/.gitmodules b/.gitmodules
index 000b92b76..0957ef07f 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,3 +1,3 @@
 [submodule "darknet"]
-	path = darknet
-	url = https://github.com/pjreddie/darknet
+    path = darknet
+    url = https://github.com/AlexeyAB/darknet
diff --git a/README.md b/README.md
index b78ad2334..af6b4de2e 100644
--- a/README.md
+++ b/README.md
@@ -2,9 +2,11 @@
 
 ## Overview
 
-This is a ROS package developed for object detection in camera images. You only look once (YOLO) is a state-of-the-art, real-time object detection system. In the following ROS package you are able to use YOLO (V3) on GPU and CPU. The pre-trained model of the convolutional neural network is able to detect pre-trained classes including the data set from VOC and COCO, or you can also create a network with your own detection objects. For more information about YOLO, Darknet, available training data and training YOLO see the following link: [YOLO: Real-Time Object Detection](http://pjreddie.com/darknet/yolo/).
+This is a ROS package developed for **object detection in camera images**. You only look once (YOLO) is a state-of-the-art, real-time object detection system. In the following ROS package you are able to use **YOLO (V3) on GPU and CPU**. The pre-trained model of the convolutional neural network is able to detect pre-trained classes including the data set from VOC and COCO, or you can also create a network with your own detection objects. For more information about YOLO, Darknet, available training data and training YOLO see the following link: [YOLO: Real-Time Object Detection](http://pjreddie.com/darknet/yolo/).
 
-The YOLO packages have been tested under ROS Foxy, Ubuntu 20.04 and OpenCV 4.2.0. This is research code, expect that it changes often and any fitness for a particular purpose is disclaimed.
+The YOLO packages have been tested under **ROS Noetic** and **Ubuntu 20.04**. Note: We also provide branches that work under **ROS Melodic**, **ROS Foxy** and **ROS2**.
+
+This is research code, expect that it changes often and any fitness for a particular purpose is disclaimed.
 
 **Author: [Marko Bjelonic](https://www.markobjelonic.com), marko.bjelonic@mavt.ethz.ch**
 
@@ -113,7 +115,7 @@ In order to use your own detection objects you need to provide your weights and
 
 ## Basic Usage
 
-In order to get YOLO ROS: Real-Time Object Detection for ROS to run with your robot, you will need to adapt a few parameters. It is the easiest if duplicate and adapt all the parameter files that you need to change from the `darkned_ros` package. These are specifically the parameter files in `config` and the launch file from the `launch` folder.
+In order to get YOLO ROS: Real-Time Object Detection for ROS to run with your robot, you will need to adapt a few parameters. It is the easiest if duplicate and adapt all the parameter files that you need to change from the `darknet_ros` package. These are specifically the parameter files in `config` and the launch file from the `launch` folder.
 
 ## Nodes
 
@@ -123,7 +125,7 @@ This is the main YOLO ROS: Real-Time Object Detection for ROS node. It uses the
 
 ### ROS related parameters
 
-You can change the names and other parameters of the publishers, subscribers and actions inside `darkned_ros/config/ros.yaml`.
+You can change the names and other parameters of the publishers, subscribers and actions inside `darknet_ros/config/ros.yaml`.
 
 #### Subscribed Topics
 
@@ -153,7 +155,7 @@ You can change the names and other parameters of the publishers, subscribers and
 
 ### Detection related parameters
 
-You can change the parameters that are related to the detection by adding a new config file that looks similar to `darkned_ros/config/yolo.yaml`.
+You can change the parameters that are related to the detection by adding a new config file that looks similar to `darknet_ros/config/yolo.yaml`.
 
 * **`image_view/enable_opencv`** (bool)
 
@@ -165,11 +167,11 @@ You can change the parameters that are related to the detection by adding a new
 
 * **`yolo_model/config_file/name`** (string)
 
-    Name of the cfg file of the network that is used for detection. The code searches for this name inside `darkned_ros/yolo_network_config/cfg/`.
+    Name of the cfg file of the network that is used for detection. The code searches for this name inside `darknet_ros/yolo_network_config/cfg/`.
 
 * **`yolo_model/weight_file/name`** (string)
 
-    Name of the weights file of the network that is used for detection. The code searches for this name inside `darkned_ros/yolo_network_config/weights/`.
+    Name of the weights file of the network that is used for detection. The code searches for this name inside `darknet_ros/yolo_network_config/weights/`.
 
 * **`yolo_model/threshold/value`** (float)
 
@@ -177,4 +179,4 @@ You can change the parameters that are related to the detection by adding a new
 
 * **`yolo_model/detection_classes/names`** (array of strings)
 
-    Detection names of the network used by the cfg and weights file inside `darkned_ros/yolo_network_config/`.
+    Detection names of the network used by the cfg and weights file inside `darknet_ros/yolo_network_config/`.
\ No newline at end of file
diff --git a/darknet b/darknet
index 508381b37..25505164a 160000
--- a/darknet
+++ b/darknet
@@ -1 +1 @@
-Subproject commit 508381b37fe75e0e1a01bcb2941cb0b31eb0e4c9
+Subproject commit 25505164a3bd6235c75deaad325878ceda90249a
diff --git a/darknet_ros/CHANGELOG.rst b/darknet_ros/CHANGELOG.rst
index bee27cf0b..d84386aa8 100644
--- a/darknet_ros/CHANGELOG.rst
+++ b/darknet_ros/CHANGELOG.rst
@@ -2,6 +2,54 @@
 Changelog for package darknet_ros
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
+1.1.5 (2021-04-08)
+------------------
+* Merge pull request `#308 <https://github.com/leggedrobotics/darknet_ros/issues/308>`_ from leggedrobotics/noetic
+  Noetic
+* Minor fix in CMake.
+* Minor fix in test.
+* Updated readme.
+* Updated to newest darknet and fixed opencv on Ubuntu 20.04.
+* First changes in CMakeLists.
+* Merge pull request `#287 <https://github.com/leggedrobotics/darknet_ros/issues/287>`_ from leggedrobotics/feature/nodeletize
+  Feature/nodeletize
+* Small adjustments
+* Added pointer deletion in destructor and minor formatting
+* Minor formatting
+* Adding option to use nodelet
+* Add libxext
+* Add libxt-dev
+* Retrieve binaries from Github releases
+* Merge pull request `#232 <https://github.com/leggedrobotics/darknet_ros/issues/232>`_ from leggedrobotics/add-required-deps
+  Update package.xml dependencies
+* Don't require cmake-clang-tools
+* Update package.xml dependencies
+* Added clang tooling.
+* Merge pull request `#207 <https://github.com/leggedrobotics/darknet_ros/issues/207>`_ from umdlife/install_weights
+  Add install targets for configuration files
+* Add install targets for configuration files
+  Adds the `launch`, `config`, and `yolo_network_config` folders to the
+  install target for `darknet_ros` so they are available in the catkin
+  install directory.
+* Fixed linking of cuda libraries.
+* Merge branch 'kunaltyagi-cleanup'
+* Cleaned up CMakeLists.txt, used OpenCV C++ API for cpp file
+* Merge pull request `#189 <https://github.com/leggedrobotics/darknet_ros/issues/189>`_ from martinspedro/master
+  /darknet_ros/found_object uses custom msg with Header for improving synchronization
+* YOLO publishes Object Count with Time stamp using custom msg with Header
+* Merge pull request `#183 <https://github.com/leggedrobotics/darknet_ros/issues/183>`_ from kunaltyagi/id_num
+  Add numerical ID and launch param for image
+* Adding numerical ID and launch param for image
+* Merge pull request `#182 <https://github.com/leggedrobotics/darknet_ros/issues/182>`_ from leggedrobotics/fix/image_publisher
+  Fixed copy of image publisher.
+* Fixed copy of image publisher.
+* Merge branch 'kjbilton-master'
+* Increased scope of image acquisition mutex to prevent image overwriting
+* Merge branch 'utra-robosoccer-master'
+* Removed warnings caused by catkin builds
+* Added test_depend of wget to accomodate Jenkins.
+* Contributors: Jason Wang, Kunal Tyagi, Kyle Bilton, Marko Bjelonic, Pedro Martins, Tom Lankhorst, Tomas Gareau, timonh
+
 1.1.4 (2019-03-03)
 ------------------
 * Merge pull request `#141 <https://github.com/leggedrobotics/darknet_ros/issues/141>`_ from lorenwel/feature/launch_file_arg
diff --git a/darknet_ros/CMakeLists.txt b/darknet_ros/CMakeLists.txt
index 56dbad55a..43758d444 100644
--- a/darknet_ros/CMakeLists.txt
+++ b/darknet_ros/CMakeLists.txt
@@ -1,38 +1,101 @@
 cmake_minimum_required(VERSION 3.5)
 project(darknet_ros)
 
+include(CMakeDependentOption)
+
 set(CMAKE_CXX_STANDARD 17)
 
-# Define path of darknet folder here.
+# USER SETTINGS =================================================================
+
+set(CUDA_ENABLE ON)
+set(CUDNN_ENABLE ON) #  set(CUDNN_ENABLE ON)
+set(FP16_ENABLE ON)
+
+set(DOWNLOAD_YOLOV2_TINY OFF)
+set(DOWNLOAD_YOLOV3 OFF)
+set(DOWNLOAD_YOLOV4 OFF)
+set(DOWNLOAD_YOLOV4_CSP ON)
+set(DOWNLOAD_YOLOV4_TINY ON)
+set(DOWNLOAD_YOLOV4_MISH OFF)
+set(DOWNLOAD_YOLOV7_TINY ON)
+
+# Jetson AGX Xavier & TITAN V is under version of Turing. So, please set manually.
+# Turing or Ampere GPUs, FP16 is automatically enabled.
+
+# set(CMAKE_CUDA_ARCHITECTURES 72)
+
+
+# Define path of darknet folder here. ===========================================
 find_path(DARKNET_PATH
   NAMES "README.md"
   HINTS "${CMAKE_CURRENT_SOURCE_DIR}/../darknet/")
 message(STATUS "Darknet path dir = ${DARKNET_PATH}")
 add_definitions(-DDARKNET_FILE_PATH="${DARKNET_PATH}")
 
-# Find CUDA
-find_package(CUDA QUIET)
-if (CUDA_FOUND)
+# Find CUDA ====================================================================
+if(CUDA_ENABLE)
   find_package(CUDA REQUIRED)
+endif()
+
+if (CUDA_FOUND)
   message(STATUS "CUDA Version: ${CUDA_VERSION_STRINGS}")
   message(STATUS "CUDA Libararies: ${CUDA_LIBRARIES}")
   set(
     CUDA_NVCC_FLAGS
     ${CUDA_NVCC_FLAGS};
     -O3
-    -gencode arch=compute_30,code=sm_30
-    -gencode arch=compute_35,code=sm_35
     -gencode arch=compute_50,code=[sm_50,compute_50]
     -gencode arch=compute_52,code=[sm_52,compute_52]
-    -gencode arch=compute_61,code=sm_61
-    -gencode arch=compute_62,code=sm_62
+    -gencode arch=compute_61,code=[sm_61,compute_61]
+    -gencode arch=compute_72,code=[sm_72,compute_72] # Jetson AGX Xavier or TITAN V
+    -gencode arch=compute_75,code=[sm_75,compute_75]
+    -gencode arch=compute_80,code=[sm_80,compute_80]
+    -gencode arch=compute_86,code=[sm_86,compute_86] # GeForce RTX 3000 Series
+    -gencode arch=compute_87,code=[sm_87,compute_87]
   )
   add_definitions(-DGPU)
+endif()
+
+# Find CUDNN =======================================================================
+
+set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/../darknet/cmake/Modules/" ${CMAKE_MODULE_PATH})
+
+if(CUDNN_ENABLE)
+  find_package(CUDNN)
+  message(STATUS "cuDNN Version: ${CUDNN_VERSION_STRINGS}")
+  message(STATUS "cuDNN Libararies: ${CUDNN_LIBRARIES}")
+endif()
+
+if(CUDNN_FOUND)
+    set(ADDITIONAL_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} -DCUDNN")
+endif()
+
+execute_process (
+    COMMAND bash -c "lspci | grep NVIDIA | grep -e TU106 -e TU104 -e TU102 -e GA106 -e GA104 -e GA102"
+    OUTPUT_VARIABLE outVar
+)
+message ( STATUS " GPU (FP16): " ${outVar} )
+if(outVar AND FP16_ENABLE)
+  set(CMAKE_CUDA_ARCHITECTURES 75)
+endif()
+
+if ( 70 IN_LIST CMAKE_CUDA_ARCHITECTURES OR
+     72 IN_LIST CMAKE_CUDA_ARCHITECTURES OR
+     75 IN_LIST CMAKE_CUDA_ARCHITECTURES OR
+     80 IN_LIST CMAKE_CUDA_ARCHITECTURES OR
+     86 IN_LIST CMAKE_CUDA_ARCHITECTURES)
+  set(ENABLE_CUDNN_HALF "TRUE" CACHE BOOL "Enable CUDNN Half precision" FORCE)
+  message(STATUS "Your setup supports half precision (CUDA_ARCHITECTURES >= 70)")
 else()
-  list(APPEND LIBRARIES "m")
+  set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision" FORCE)
+  message(STATUS "Your setup does not support half precision (it requires CUDA_ARCHITECTURES >= 70)")
 endif()
 
-# Find X11
+# else()
+#   list(APPEND LIBRARIES "m")
+# endif()
+
+# Find X11 ============================================================================
 message ( STATUS "Searching for X11..." )
 find_package ( X11 REQUIRED )
 if ( X11_FOUND )
@@ -76,55 +139,64 @@ add_definitions(-O4 -g)
 include_directories(
   ${DARKNET_PATH}/src
   ${DARKNET_PATH}/include
+  ${DARKNET_PATH}/3rdparty/stb/include
   include
   ${catkin_INCLUDE_DIRS}
 )
 
 set(PROJECT_LIB_FILES
-    src/YoloObjectDetector.cpp                    src/image_interface.c
+    src/YoloObjectDetector.cpp                    src/image_interface.cpp
+    src/yolo_object_detector_node.cpp
 )
 
 set(DARKNET_CORE_FILES
-    ${DARKNET_PATH}/src/activation_layer.c        ${DARKNET_PATH}/src/im2col.c
-    ${DARKNET_PATH}/src/activations.c             ${DARKNET_PATH}/src/image.c
-    ${DARKNET_PATH}/src/avgpool_layer.c           ${DARKNET_PATH}/src/layer.c
-    ${DARKNET_PATH}/src/batchnorm_layer.c         ${DARKNET_PATH}/src/list.c
-    ${DARKNET_PATH}/src/blas.c                    ${DARKNET_PATH}/src/local_layer.c
-    ${DARKNET_PATH}/src/box.c                     ${DARKNET_PATH}/src/lstm_layer.c
-    ${DARKNET_PATH}/src/col2im.c                  ${DARKNET_PATH}/src/matrix.c
-    ${DARKNET_PATH}/src/connected_layer.c         ${DARKNET_PATH}/src/maxpool_layer.c
-    ${DARKNET_PATH}/src/convolutional_layer.c     ${DARKNET_PATH}/src/network.c
-    ${DARKNET_PATH}/src/cost_layer.c              ${DARKNET_PATH}/src/normalization_layer.c
-    ${DARKNET_PATH}/src/crnn_layer.c              ${DARKNET_PATH}/src/option_list.c
-    ${DARKNET_PATH}/src/crop_layer.c              ${DARKNET_PATH}/src/parser.c
-    ${DARKNET_PATH}/src/cuda.c                    ${DARKNET_PATH}/src/region_layer.c
-    ${DARKNET_PATH}/src/data.c                    ${DARKNET_PATH}/src/reorg_layer.c
-    ${DARKNET_PATH}/src/deconvolutional_layer.c   ${DARKNET_PATH}/src/rnn_layer.c
-    ${DARKNET_PATH}/src/demo.c                    ${DARKNET_PATH}/src/route_layer.c
-    ${DARKNET_PATH}/src/detection_layer.c         ${DARKNET_PATH}/src/shortcut_layer.c
-    ${DARKNET_PATH}/src/dropout_layer.c           ${DARKNET_PATH}/src/softmax_layer.c
-    ${DARKNET_PATH}/src/gemm.c                    ${DARKNET_PATH}/src/tree.c
-    ${DARKNET_PATH}/src/gru_layer.c               ${DARKNET_PATH}/src/utils.c
-    ${DARKNET_PATH}/src/upsample_layer.c          ${DARKNET_PATH}/src/logistic_layer.c
-    ${DARKNET_PATH}/src/l2norm_layer.c            ${DARKNET_PATH}/src/yolo_layer.c
-
-    ${DARKNET_PATH}/examples/art.c                ${DARKNET_PATH}/examples/lsd.c
-    ${DARKNET_PATH}/examples/attention.c          ${DARKNET_PATH}/examples/nightmare.c
-    ${DARKNET_PATH}/examples/captcha.c            ${DARKNET_PATH}/examples/regressor.c
-    ${DARKNET_PATH}/examples/cifar.c              ${DARKNET_PATH}/examples/rnn.c
-    ${DARKNET_PATH}/examples/classifier.c         ${DARKNET_PATH}/examples/segmenter.c
-    ${DARKNET_PATH}/examples/coco.c               ${DARKNET_PATH}/examples/super.c
-    ${DARKNET_PATH}/examples/darknet.c            ${DARKNET_PATH}/examples/tag.c
-    ${DARKNET_PATH}/examples/detector.c           ${DARKNET_PATH}/examples/yolo.c
-    ${DARKNET_PATH}/examples/go.c
+    ${DARKNET_PATH}/src/activation_layer.c        ${DARKNET_PATH}/src/activations.c
+    ${DARKNET_PATH}/src/art.c                     ${DARKNET_PATH}/src/avgpool_layer.c
+    ${DARKNET_PATH}/src/batchnorm_layer.c         ${DARKNET_PATH}/src/blas.c
+    ${DARKNET_PATH}/src/box.c                     ${DARKNET_PATH}/src/captcha.c
+    ${DARKNET_PATH}/src/cifar.c                   ${DARKNET_PATH}/src/classifier.c
+    ${DARKNET_PATH}/src/coco.c                    ${DARKNET_PATH}/src/col2im.c
+    ${DARKNET_PATH}/src/compare.c                 ${DARKNET_PATH}/src/connected_layer.c
+    ${DARKNET_PATH}/src/conv_lstm_layer.c         ${DARKNET_PATH}/src/convolutional_layer.c
+    ${DARKNET_PATH}/src/cost_layer.c              ${DARKNET_PATH}/src/cpu_gemm.c
+    ${DARKNET_PATH}/src/crnn_layer.c              ${DARKNET_PATH}/src/crop_layer.c
+    ${DARKNET_PATH}/src/dark_cuda.c               ${DARKNET_PATH}/src/darknet.c
+    ${DARKNET_PATH}/src/data.c                    ${DARKNET_PATH}/src/deconvolutional_layer.c
+    ${DARKNET_PATH}/src/demo.c                    ${DARKNET_PATH}/src/detection_layer.c
+    ${DARKNET_PATH}/src/detector.c                ${DARKNET_PATH}/src/dice.c
+    ${DARKNET_PATH}/src/dropout_layer.c           ${DARKNET_PATH}/src/gaussian_yolo_layer.c
+    ${DARKNET_PATH}/src/gemm.c                    ${DARKNET_PATH}/src/getopt.c
+    ${DARKNET_PATH}/src/gettimeofday.c            ${DARKNET_PATH}/src/go.c
+    ${DARKNET_PATH}/src/gru_layer.c               ${DARKNET_PATH}/src/http_stream.cpp
+    ${DARKNET_PATH}/src/im2col.c                  ${DARKNET_PATH}/src/image.c
+    ${DARKNET_PATH}/src/image_opencv.cpp          ${DARKNET_PATH}/src/layer.c
+    ${DARKNET_PATH}/src/list.c                    ${DARKNET_PATH}/src/local_layer.c
+    ${DARKNET_PATH}/src/lstm_layer.c              ${DARKNET_PATH}/src/matrix.c
+    ${DARKNET_PATH}/src/maxpool_layer.c           ${DARKNET_PATH}/src/network.c
+    ${DARKNET_PATH}/src/nightmare.c               ${DARKNET_PATH}/src/normalization_layer.c
+    ${DARKNET_PATH}/src/option_list.c             ${DARKNET_PATH}/src/parser.c
+    ${DARKNET_PATH}/src/region_layer.c            ${DARKNET_PATH}/src/reorg_layer.c
+    ${DARKNET_PATH}/src/reorg_old_layer.c         ${DARKNET_PATH}/src/rnn.c
+    ${DARKNET_PATH}/src/rnn_layer.c               ${DARKNET_PATH}/src/rnn_vid.c
+    ${DARKNET_PATH}/src/route_layer.c             ${DARKNET_PATH}/src/sam_layer.c
+    ${DARKNET_PATH}/src/scale_channels_layer.c    ${DARKNET_PATH}/src/shortcut_layer.c
+    ${DARKNET_PATH}/src/softmax_layer.c           ${DARKNET_PATH}/src/super.c
+    ${DARKNET_PATH}/src/swag.c                    ${DARKNET_PATH}/src/tag.c
+    ${DARKNET_PATH}/src/tree.c                    ${DARKNET_PATH}/src/upsample_layer.c
+    ${DARKNET_PATH}/src/utils.c                   ${DARKNET_PATH}/src/voxel.c
+    ${DARKNET_PATH}/src/writing.c                 ${DARKNET_PATH}/src/yolo.c
+    ${DARKNET_PATH}/src/yolo_layer.c              ${DARKNET_PATH}/src/representation_layer.c
+
+    
 )
 
 set(DARKNET_CUDA_FILES
-    ${DARKNET_PATH}/src/activation_kernels.cu     ${DARKNET_PATH}/src/crop_layer_kernels.cu
-    ${DARKNET_PATH}/src/avgpool_layer_kernels.cu  ${DARKNET_PATH}/src/deconvolutional_kernels.cu
-    ${DARKNET_PATH}/src/blas_kernels.cu           ${DARKNET_PATH}/src/dropout_layer_kernels.cu
-    ${DARKNET_PATH}/src/col2im_kernels.cu         ${DARKNET_PATH}/src/im2col_kernels.cu
-    ${DARKNET_PATH}/src/convolutional_kernels.cu  ${DARKNET_PATH}/src/maxpool_layer_kernels.cu
+    ${DARKNET_PATH}/src/activation_kernels.cu       ${DARKNET_PATH}/src/avgpool_layer_kernels.cu
+    ${DARKNET_PATH}/src/blas_kernels.cu             ${DARKNET_PATH}/src/col2im_kernels.cu
+    ${DARKNET_PATH}/src/convolutional_kernels.cu    ${DARKNET_PATH}/src/crop_layer_kernels.cu
+    ${DARKNET_PATH}/src/deconvolutional_kernels.cu  ${DARKNET_PATH}/src/dropout_layer_kernels.cu
+    ${DARKNET_PATH}/src/im2col_kernels.cu           ${DARKNET_PATH}/src/maxpool_layer_kernels.cu
+    ${DARKNET_PATH}/src/network_kernels.cu
 )
 
 set_source_files_properties(${PROJECT_LIB_FILES} PROPERTIES LANGUAGE CXX)
@@ -142,27 +214,51 @@ if (CUDA_FOUND)
   
   ament_target_dependencies(${PROJECT_NAME}_lib ${dependencies})
 
-  target_link_libraries(${PROJECT_NAME}_lib
-    cuda
-    cudart
-    cublas
-    curand
-  )
+  if(CUDNN_FOUND)
+    if(ENABLE_CUDNN_HALF)
+      target_link_libraries(${PROJECT_NAME}_lib
+        cuda
+        cudart
+        cublas
+        curand
+        CuDNN::CuDNN
+      )
+      target_compile_definitions(${PROJECT_NAME}_lib PRIVATE
+        -DCUDNN
+        -DCUDNN_HALF
+      )
+    else()
+      target_link_libraries(${PROJECT_NAME}_lib
+        cuda
+        cudart
+        cublas
+        curand
+        CuDNN::CuDNN
+      )
+      target_compile_definitions(${PROJECT_NAME}_lib PRIVATE
+        -DCUDNN
+      )
+    endif()
+  else()
+    target_link_libraries(${PROJECT_NAME}_lib
+      cuda
+      cudart
+      cublas
+      curand
+    )
+  endif()
 
   cuda_add_executable(${PROJECT_NAME}
     src/yolo_object_detector_node.cpp
   )
   
-  ament_target_dependencies(${PROJECT_NAME} ${dependencies})
+ament_target_dependencies(${PROJECT_NAME} ${dependencies})
 
 else()
 
-  add_library(${PROJECT_NAME}_core_lib
-    ${DARKNET_CORE_FILES}
-  )
-
   add_library(${PROJECT_NAME}_lib
     ${PROJECT_LIB_FILES}
+    ${DARKNET_CORE_FILES}
   )
   ament_target_dependencies(${PROJECT_NAME}_lib ${dependencies})
   target_compile_definitions(${PROJECT_NAME}_lib PRIVATE -DOPENCV)
@@ -174,6 +270,13 @@ else()
 
 endif()
 
+find_package(OpenMP)
+
+if (OPENMP_FOUND)
+  set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
+endif()
+
 target_link_libraries(${PROJECT_NAME}_lib
   m
   pthread
@@ -186,7 +289,6 @@ target_compile_definitions(${PROJECT_NAME}_lib PRIVATE -DOPENCV)
 
 target_link_libraries(${PROJECT_NAME}
   ${PROJECT_NAME}_lib
-  ${PROJECT_NAME}_core_lib
 )
 target_compile_definitions(${PROJECT_NAME} PRIVATE -DOPENCV)
 
@@ -205,19 +307,84 @@ install(
 
 # Download yolov2-tiny.weights
 set(PATH "${CMAKE_CURRENT_SOURCE_DIR}/yolo_network_config/weights")
-set(FILE "${PATH}/yolov2-tiny.weights")
-message(STATUS "Checking and downloading yolov2-tiny.weights if needed ...")
-if (NOT EXISTS "${FILE}")
-  message(STATUS "... file does not exist. Downloading now ...")
-  execute_process(COMMAND wget http://pjreddie.com/media/files/yolov2-tiny.weights -P ${PATH})
+
+if(DOWNLOAD_YOLOV2_TINY)
+  set(FILE "${PATH}/yolov2-tiny.weights")
+  message(STATUS "Checking and downloading yolov2-tiny.weights if needed ...")
+  if (NOT EXISTS "${FILE}")
+    message(STATUS "... file does not exist. Downloading now ...")
+    # yolov2-tiny.cfg is already in the repo.
+    execute_process(COMMAND wget http://pjreddie.com/media/files/yolov2-tiny.weights -P ${PATH})
+  endif()
+  message(STATUS "Done.")
 endif()
 
 # Download yolov3.weights
-set(FILE "${PATH}/yolov3.weights")
-message(STATUS "Checking and downloading yolov3.weights if needed ...")
-if (NOT EXISTS "${FILE}")
-  message(STATUS "... file does not exist. Downloading now ...")
-  execute_process(COMMAND wget http://pjreddie.com/media/files/yolov3.weights -P ${PATH})
+if(DOWNLOAD_YOLOV3)
+  set(FILE "${PATH}/yolov3.weights")
+  message(STATUS "Checking and downloading yolov3.weights if needed ...")
+  if (NOT EXISTS "${FILE}")
+    message(STATUS "... file does not exist. Downloading now ...")
+    # yolov3.cfg is already in the repo.
+    execute_process(COMMAND wget http://pjreddie.com/media/files/yolov3.weights -P ${PATH})
+  endif()
+  message(STATUS "Done.")
+endif()
+
+# Download yolov4.weights
+if(DOWNLOAD_YOLOV4)
+  set(FILE "${PATH}/yolov4.weights")
+  message(STATUS "Checking and downloading yolov4.weights if needed ...")
+  if (NOT EXISTS "${FILE}")
+    message(STATUS "... file does not exist. Downloading now ...")
+    execute_process(COMMAND wget -q https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights -P ${PATH})
+  endif()
+  message(STATUS "Done.")
+endif()
+
+# Download yolov4-tiny.weights
+if(DOWNLOAD_YOLOV4_TINY)
+  set(FILE "${PATH}/yolov4-tiny.weights")
+  message(STATUS "Checking and downloading yolov4-tiny.weights if needed ...")
+  if (NOT EXISTS "${FILE}")
+    message(STATUS "... file does not exist. Downloading now ...")
+    # yolov4.cfg is already in the repo.
+    execute_process(COMMAND wget -q https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights -P ${PATH})
+  endif()
+  message(STATUS "Done.")
+endif()
+
+# Download yolov4-csp.weights
+if(DOWNLOAD_YOLOV4_CSP)
+  set(FILE "${PATH}/yolov4-csp.weights")
+  message(STATUS "Checking and downloading yolov4-csp.weights if needed ...")
+  if (NOT EXISTS "${FILE}")
+    message(STATUS "... file does not exist. Downloading now ...")
+    execute_process(COMMAND wget -q https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.weights -P ${PATH})
+  endif()
+  message(STATUS "Done.")
+endif()
+
+# Download yolov4-mish.weights
+
+if(DOWNLOAD_YOLOV4_MISH)
+  set(FILE "${PATH}/yolov4-mish.weights")
+  message(STATUS "Checking and downloading yolov4-mish.weights if needed ...")
+  if (NOT EXISTS "${FILE}")
+    message(STATUS "... file does not exist. Downloading now ...")
+    execute_process(COMMAND wget -q https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.weights -P ${PATH})
+  endif()
+  message(STATUS "Done.")
+endif()
+
+if(DOWNLOAD_YOLOV7_TINY)
+  set(FILE "${PATH}/yolov7-tiny.weights")
+  message(STATUS "Checking and downloading yolov7-tiny.weights if needed ...")
+  if (NOT EXISTS "${FILE}")
+    message(STATUS "... file does not exist. Downloading now ...")
+    execute_process(COMMAND wget -q https://github.com/AlexeyAB/darknet/releases/download/yolov4/yolov7-tiny.weights -P ${PATH})
+  endif()
+  message(STATUS "Done.")
 endif()
 
 install(DIRECTORY yolo_network_config/cfg yolo_network_config/weights DESTINATION share/${PROJECT_NAME}/yolo_network_config/)
@@ -227,21 +394,21 @@ install(DIRECTORY launch config DESTINATION share/${PROJECT_NAME}/)
 ## Testing ##
 #############
 
-if(BUILD_TESTING)
-  # Download yolov2.weights
-  set(PATH "${CMAKE_CURRENT_SOURCE_DIR}/yolo_network_config/weights")
-  set(FILE "${PATH}/yolov2.weights")
-  message(STATUS "Checking and downloading yolov2.weights if needed ...")
-  if (NOT EXISTS "${FILE}")
-    message(STATUS "... file does not exist. Downloading now ...")
-    execute_process(COMMAND wget http://pjreddie.com/media/files/yolov2.weights -P ${PATH})
-  endif()
+# if(BUILD_TESTING)
+#   # Download yolov2.weights
+#   set(PATH "${CMAKE_CURRENT_SOURCE_DIR}/yolo_network_config/weights")
+#   set(FILE "${PATH}/yolov2.weights")
+#   message(STATUS "Checking and downloading yolov2.weights if needed ...")
+#   if (NOT EXISTS "${FILE}")
+#     message(STATUS "... file does not exist. Downloading now ...")
+#     execute_process(COMMAND wget http://pjreddie.com/media/files/yolov2.weights -P ${PATH})
+#   endif()
 
-  find_package(ament_lint_auto REQUIRED)
-  ament_lint_auto_find_test_dependencies()
+#   find_package(ament_lint_auto REQUIRED)
+#   ament_lint_auto_find_test_dependencies()
+
+#   find_package(ament_cmake_gtest REQUIRED)
 
-  find_package(ament_cmake_gtest REQUIRED)
-  
   #ament_add_gtest(${PROJECT_NAME}_object_detection-test
   #  test/object_detection.test
   #  test/test_main.cpp
@@ -250,11 +417,11 @@ if(BUILD_TESTING)
   #target_link_libraries(${PROJECT_NAME}_object_detection-test
   #  ${PROJECT_NAME}_lib
   #)
-endif()
+# endif()
 
 
 ament_export_include_directories(include)
 ament_export_libraries(${PROJECT_NAME}_lib)
 ament_export_dependencies(${dependencies})
 
-ament_package()
\ No newline at end of file
+ament_package()
diff --git a/darknet_ros/config/ros.yaml b/darknet_ros/config/ros.yaml
index 6562e3164..7b5a90c16 100644
--- a/darknet_ros/config/ros.yaml
+++ b/darknet_ros/config/ros.yaml
@@ -2,7 +2,7 @@ darknet_ros:
   ros__parameters:
     subscribers:
       camera_reading:
-        topic: /camera/rgb/image_raw
+        topic: /image_raw
         queue_size: 1
     actions:
       camera_reading:
diff --git a/darknet_ros/config/yolov2-tiny-voc.yaml b/darknet_ros/config/yolov2-tiny-voc.yaml
deleted file mode 100644
index b0a641df0..000000000
--- a/darknet_ros/config/yolov2-tiny-voc.yaml
+++ /dev/null
@@ -1,33 +0,0 @@
-darknet_ros:
-  ros__parameters:
-    yolo_model:
-    
-      config_file:
-        name: yolov2-tiny-voc.cfg
-      weight_file:
-        name: yolov2-tiny-voc.weights
-      threshold:
-        value: 0.3
-      detection_classes:
-        names:
-          - aeroplane
-          - bicycle
-          - bird
-          - boat
-          - bottle
-          - bus
-          - car
-          - cat
-          - chair
-          - cow
-          - diningtable
-          - dog
-          - horse
-          - motorbike
-          - person
-          - pottedplant
-          - sheep
-          - sofa
-          - train
-          - tvmonitor
-    
\ No newline at end of file
diff --git a/darknet_ros/config/yolov2-voc.yaml b/darknet_ros/config/yolov2-voc.yaml
deleted file mode 100644
index 3b009b705..000000000
--- a/darknet_ros/config/yolov2-voc.yaml
+++ /dev/null
@@ -1,32 +0,0 @@
-darknet_ros:
-  ros__parameters:
-    yolo_model:
-
-      config_file:
-        name: yolov2-voc.cfg
-      weight_file:
-        name: yolov2-voc.weights
-      threshold:
-        value: 0.3
-      detection_classes:
-        names:
-          - aeroplane
-          - bicycle
-          - bird
-          - boat
-          - bottle
-          - bus
-          - car
-          - cat
-          - chair
-          - cow
-          - diningtable
-          - dog
-          - horse
-          - motorbike
-          - person
-          - pottedplant
-          - sheep
-          - sofa
-          - train
-          - tvmonitor
diff --git a/darknet_ros/config/yolov3-voc.yaml b/darknet_ros/config/yolov3-voc.yaml
deleted file mode 100644
index 51a2a64dd..000000000
--- a/darknet_ros/config/yolov3-voc.yaml
+++ /dev/null
@@ -1,32 +0,0 @@
-darknet_ros:
-  ros__parameters:
-    yolo_model:
-
-      config_file:
-        name: yolov3-voc.cfg
-      weight_file:
-        name: yolov3-voc.weights
-      threshold:
-        value: 0.3
-      detection_classes:
-        names:
-          - aeroplane
-          - bicycle
-          - bird
-          - boat
-          - bottle
-          - bus
-          - car
-          - cat
-          - chair
-          - cow
-          - diningtable
-          - dog
-          - horse
-          - motorbike
-          - person
-          - pottedplant
-          - sheep
-          - sofa
-          - train
-          - tvmonitor
diff --git a/darknet_ros/config/yolov2.yaml b/darknet_ros/config/yolov4-csp.yaml
similarity index 96%
rename from darknet_ros/config/yolov2.yaml
rename to darknet_ros/config/yolov4-csp.yaml
index a6e4d67de..1f7243c19 100644
--- a/darknet_ros/config/yolov2.yaml
+++ b/darknet_ros/config/yolov4-csp.yaml
@@ -1,11 +1,10 @@
 darknet_ros:
   ros__parameters:
     yolo_model:
-
       config_file:
-        name: yolov2.cfg
+        name: yolov4-csp.cfg
       weight_file:
-        name: yolov2.weights
+        name: yolov4-csp.weights
       threshold:
         value: 0.3
       detection_classes:
diff --git a/darknet_ros/config/yolov4-tiny.yaml b/darknet_ros/config/yolov4-tiny.yaml
new file mode 100644
index 000000000..287ccdbd6
--- /dev/null
+++ b/darknet_ros/config/yolov4-tiny.yaml
@@ -0,0 +1,91 @@
+darknet_ros:
+  ros__parameters:
+    yolo_model:
+      config_file:
+        name: yolov4-tiny.cfg
+      weight_file:
+        name: yolov4-tiny.weights
+      threshold:
+        value: 0.3
+      detection_classes:
+        names:
+          - person
+          - bicycle
+          - car
+          - motorbike
+          - aeroplane
+          - bus
+          - train
+          - truck
+          - boat
+          - traffic light
+          - fire hydrant
+          - stop sign
+          - parking meter
+          - bench
+          - bird
+          - cat
+          - dog
+          - horse
+          - sheep
+          - cow
+          - elephant
+          - bear
+          - zebra
+          - giraffe
+          - backpack
+          - umbrella
+          - handbag
+          - tie
+          - suitcase
+          - frisbee
+          - skis
+          - snowboard
+          - sports ball
+          - kite
+          - baseball bat
+          - baseball glove
+          - skateboard
+          - surfboard
+          - tennis racket
+          - bottle
+          - wine glass
+          - cup
+          - fork
+          - knife
+          - spoon
+          - bowl
+          - banana
+          - apple
+          - sandwich
+          - orange
+          - broccoli
+          - carrot
+          - hot dog
+          - pizza
+          - donut
+          - cake
+          - chair
+          - sofa
+          - pottedplant
+          - bed
+          - diningtable
+          - toilet
+          - tvmonitor
+          - laptop
+          - mouse
+          - remote
+          - keyboard
+          - cell phone
+          - microwave
+          - oven
+          - toaster
+          - sink
+          - refrigerator
+          - book
+          - clock
+          - vase
+          - scissors
+          - teddy bear
+          - hair drier
+          - toothbrush
\ No newline at end of file
diff --git a/darknet_ros/config/yolov4.yaml b/darknet_ros/config/yolov4.yaml
new file mode 100644
index 000000000..602a023ee
--- /dev/null
+++ b/darknet_ros/config/yolov4.yaml
@@ -0,0 +1,91 @@
+darknet_ros:
+  ros__parameters:
+    yolo_model:
+      config_file:
+        name: yolov4.cfg
+      weight_file:
+        name: yolov4.weights
+      threshold:
+        value: 0.3
+      detection_classes:
+        names:
+          - person
+          - bicycle
+          - car
+          - motorbike
+          - aeroplane
+          - bus
+          - train
+          - truck
+          - boat
+          - traffic light
+          - fire hydrant
+          - stop sign
+          - parking meter
+          - bench
+          - bird
+          - cat
+          - dog
+          - horse
+          - sheep
+          - cow
+          - elephant
+          - bear
+          - zebra
+          - giraffe
+          - backpack
+          - umbrella
+          - handbag
+          - tie
+          - suitcase
+          - frisbee
+          - skis
+          - snowboard
+          - sports ball
+          - kite
+          - baseball bat
+          - baseball glove
+          - skateboard
+          - surfboard
+          - tennis racket
+          - bottle
+          - wine glass
+          - cup
+          - fork
+          - knife
+          - spoon
+          - bowl
+          - banana
+          - apple
+          - sandwich
+          - orange
+          - broccoli
+          - carrot
+          - hot dog
+          - pizza
+          - donut
+          - cake
+          - chair
+          - sofa
+          - pottedplant
+          - bed
+          - diningtable
+          - toilet
+          - tvmonitor
+          - laptop
+          - mouse
+          - remote
+          - keyboard
+          - cell phone
+          - microwave
+          - oven
+          - toaster
+          - sink
+          - refrigerator
+          - book
+          - clock
+          - vase
+          - scissors
+          - teddy bear
+          - hair drier
+          - toothbrush
\ No newline at end of file
diff --git a/darknet_ros/config/yolov7-tiny.yaml b/darknet_ros/config/yolov7-tiny.yaml
new file mode 100644
index 000000000..8696d0d82
--- /dev/null
+++ b/darknet_ros/config/yolov7-tiny.yaml
@@ -0,0 +1,92 @@
+darknet_ros:
+  ros__parameters:
+    yolo_model:
+      config_file:
+        name: yolov7-tiny.cfg
+      weight_file:
+        name: yolov7-tiny.weights
+      threshold:
+        value: 0.3
+      window_name: YOLO V7
+      detection_classes:
+        names:
+          - person
+          - bicycle
+          - car
+          - motorbike
+          - aeroplane
+          - bus
+          - train
+          - truck
+          - boat
+          - traffic light
+          - fire hydrant
+          - stop sign
+          - parking meter
+          - bench
+          - bird
+          - cat
+          - dog
+          - horse
+          - sheep
+          - cow
+          - elephant
+          - bear
+          - zebra
+          - giraffe
+          - backpack
+          - umbrella
+          - handbag
+          - tie
+          - suitcase
+          - frisbee
+          - skis
+          - snowboard
+          - sports ball
+          - kite
+          - baseball bat
+          - baseball glove
+          - skateboard
+          - surfboard
+          - tennis racket
+          - bottle
+          - wine glass
+          - cup
+          - fork
+          - knife
+          - spoon
+          - bowl
+          - banana
+          - apple
+          - sandwich
+          - orange
+          - broccoli
+          - carrot
+          - hot dog
+          - pizza
+          - donut
+          - cake
+          - chair
+          - sofa
+          - pottedplant
+          - bed
+          - diningtable
+          - toilet
+          - tvmonitor
+          - laptop
+          - mouse
+          - remote
+          - keyboard
+          - cell phone
+          - microwave
+          - oven
+          - toaster
+          - sink
+          - refrigerator
+          - book
+          - clock
+          - vase
+          - scissors
+          - teddy bear
+          - hair drier
+          - toothbrush
\ No newline at end of file
diff --git a/darknet_ros/include/darknet_ros/YoloObjectDetector.hpp b/darknet_ros/include/darknet_ros/YoloObjectDetector.hpp
index 1314455df..2dcf5c207 100644
--- a/darknet_ros/include/darknet_ros/YoloObjectDetector.hpp
+++ b/darknet_ros/include/darknet_ros/YoloObjectDetector.hpp
@@ -17,6 +17,9 @@
 #include <thread>
 #include <chrono>
 #include <shared_mutex>
+#include <numeric>
+#include <queue>
+#include <sstream>
 
 // ROS
 #include "rclcpp/rclcpp.hpp"
@@ -53,13 +56,27 @@ extern "C" {
 #include "utils.h"
 #include "parser.h"
 #include "box.h"
-#include "darknet_ros/image_interface.h"
+#include "blas.h"
+#include "image_opencv.h"
+
+#include "image.h"
+// #include "demo.h"
+#include "darknet.h"
+
+#include "image_interface.hpp"
+
+#define START_COUNT 100
+
 #include <sys/time.h>
 }
 
-extern "C" void ipl_into_image(IplImage* src, image im);
-extern "C" image ipl_to_image(IplImage* src);
-extern "C" void show_image_cv(image p, const char *name, IplImage *disp);
+extern "C" cv::Mat image_to_mat(image im);
+extern "C" image mat_to_image(cv::Mat m);
+// extern "C" void show_image(image p, const char* name);
+
+// extern "C" void draw_detections_cv_v3(mat_cv* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output);
+
+extern "C" void generate_image(image p, cv::Mat& disp);
 
 namespace darknet_ros {
 
@@ -70,11 +87,10 @@ typedef struct
   int num, Class;
 } RosBox_;
 
-typedef struct
-{
-  IplImage* image;
+typedef struct {
+  cv::Mat image;
   std_msgs::msg::Header header;
-} IplImageWithHeader_;
+} CvMatWithHeader_;
 
 class YoloObjectDetector : public rclcpp::Node
 {
@@ -181,14 +197,20 @@ class YoloObjectDetector : public rclcpp::Node
   image **demoAlphabet_;
   int demoClasses_;
 
+  std::string windowName_;
+
   network *net_;
   std_msgs::msg::Header headerBuff_[3];
   image buff_[3];
   image buffLetter_[3];
   int buffId_[3];
   int buffIndex_ = 0;
-  IplImage * ipl_;
+  // IplImage * ipl_;
+  cv::Mat disp_;
   float fps_ = 0;
+  float max_fps = 0;
+  float min_fps = 10000;
+  std::stringstream ss_fps;
   float demoThresh_ = 0;
   float demoHier_ = .5;
   int running_ = 0;
@@ -249,13 +271,15 @@ class YoloObjectDetector : public rclcpp::Node
 
   void yolo();
 
-  IplImageWithHeader_ getIplImageWithHeader();
+  // IplImageWithHeader_ getIplImageWithHeader();
+  CvMatWithHeader_ getCvMatWithHeader();
 
   bool getImageStatus(void);
 
   bool isNodeRunning(void);
 
   void *publishInThread();
+  void generate_image_cp(image p, cv::Mat& disp);
 };
 
 } /* namespace darknet_ros*/
diff --git a/darknet_ros/include/darknet_ros/image_interface.h b/darknet_ros/include/darknet_ros/image_interface.hpp
similarity index 78%
rename from darknet_ros/include/darknet_ros/image_interface.h
rename to darknet_ros/include/darknet_ros/image_interface.hpp
index 2aa1353a3..80ac09308 100644
--- a/darknet_ros/include/darknet_ros/image_interface.h
+++ b/darknet_ros/include/darknet_ros/image_interface.hpp
@@ -6,8 +6,8 @@
  *   Institute: ETH Zurich, Robotic Systems Lab
  */
 
-#ifndef IMAGE_INTERFACE_H
-#define IMAGE_INTERFACE_H
+#ifndef IMAGE_INTERFACE_HPP
+#define IMAGE_INTERFACE_HPP
 
 #include "opencv2/highgui/highgui_c.h"
 #include "opencv2/imgproc/imgproc_c.h"
@@ -17,6 +17,6 @@
 
 static float get_pixel(image m, int x, int y, int c);
 image **load_alphabet_with_file(char *datafile);
-void generate_image(image p, IplImage *disp);
+void generate_image(image p, cv::Mat& disp);
 
-#endif
+#endif
\ No newline at end of file
diff --git a/darknet_ros/launch/darknet_ros.launch.py b/darknet_ros/launch/darknet_ros.launch.py
index 0cd39ecb2..39a82a69a 100644
--- a/darknet_ros/launch/darknet_ros.launch.py
+++ b/darknet_ros/launch/darknet_ros.launch.py
@@ -11,20 +11,20 @@
 def generate_launch_description():
   darknet_ros_share_dir = get_package_share_directory('darknet_ros')
 
-  image = LaunchConfiguration('image', default = '/camera/rgb/image_raw')
+  image = LaunchConfiguration('image', default = 'image_raw')
   yolo_weights_path = LaunchConfiguration('yolo_weights_path', default = darknet_ros_share_dir + '/yolo_network_config/weights')
   yolo_config_path = LaunchConfiguration('yolo_config_path', default = darknet_ros_share_dir + '/yolo_network_config/cfg')
   ros_param_file = LaunchConfiguration('ros_param_file', default = darknet_ros_share_dir + 'config/ros.yaml')
-  network_param_file = LaunchConfiguration('network_param_file', default = darknet_ros_share_dir + 'config/yolov2-tiny.yaml')
+  network_param_file = LaunchConfiguration('network_param_file', default = darknet_ros_share_dir + 'config/yolov4-tiny.yaml')
 
   declare_image_cmd = DeclareLaunchArgument(
     'image',
-    default_value = '/camera/rgb/image_raw',
+    default_value = 'image_raw',
     description = 'Image topic')
   declare_yolo_weights_path_cmd = DeclareLaunchArgument(
     'yolo_weights_path',
     default_value = darknet_ros_share_dir + '/yolo_network_config/weights',
-    description = 'Path to yolo weights') 
+    description = 'Path to yolo weights')
   declare_yolo_config_path_cmd = DeclareLaunchArgument(
     'yolo_config_path',
     default_value = darknet_ros_share_dir + '/yolo_network_config/cfg',
@@ -35,13 +35,13 @@ def generate_launch_description():
     description = 'Path to file with ROS related config')  
   declare_network_param_file_cmd = DeclareLaunchArgument(
     'network_param_file',
-    default_value = darknet_ros_share_dir + '/config/yolov2-tiny.yaml',
-    description = 'Path to file with network param file')  
+    default_value = darknet_ros_share_dir + '/config/yolov4-tiny.yaml',
+    description = 'Path to file with network param file')
 
   darknet_ros_cmd = Node(
     package='darknet_ros',
-    node_executable='darknet_ros',
-    node_name='darknet_ros',
+    executable='darknet_ros',
+    name='darknet_ros',
     output='screen',
     parameters=[ros_param_file, network_param_file,
       {
@@ -60,4 +60,4 @@ def generate_launch_description():
   
   ld.add_action(darknet_ros_cmd)
 
-  return ld
+  return ld
\ No newline at end of file
diff --git a/darknet_ros/launch/yolov4-csp.launch.py b/darknet_ros/launch/yolov4-csp.launch.py
new file mode 100644
index 000000000..6d5eec347
--- /dev/null
+++ b/darknet_ros/launch/yolov4-csp.launch.py
@@ -0,0 +1,27 @@
+from ament_index_python.packages import get_package_share_directory
+from launch import LaunchDescription
+from launch.actions import IncludeLaunchDescription
+from launch.launch_description_sources import PythonLaunchDescriptionSource
+from launch_ros.actions import Node
+
+def generate_launch_description():
+  darknet_ros_share_dir = get_package_share_directory('darknet_ros')
+  network_param_file = darknet_ros_share_dir + '/config/yolov4-csp.yaml'
+
+  darknet_ros_launch = IncludeLaunchDescription(
+    PythonLaunchDescriptionSource([darknet_ros_share_dir + '/launch/darknet_ros.launch.py']),
+      launch_arguments={'network_param_file': network_param_file}.items()
+  )
+
+  camera = Node(
+    package="v4l2_camera",
+    executable="v4l2_camera_node",
+    parameters=[
+      {'video_device'     : "/dev/video0"},
+    ])
+
+  return LaunchDescription([
+    darknet_ros_launch,
+    # if you want to disable camera node, remove the following line.
+    camera,
+  ])
\ No newline at end of file
diff --git a/darknet_ros/launch/yolov4-tiny.launch.py b/darknet_ros/launch/yolov4-tiny.launch.py
new file mode 100644
index 000000000..69a42fdeb
--- /dev/null
+++ b/darknet_ros/launch/yolov4-tiny.launch.py
@@ -0,0 +1,27 @@
+from ament_index_python.packages import get_package_share_directory
+from launch import LaunchDescription
+from launch.actions import IncludeLaunchDescription
+from launch.launch_description_sources import PythonLaunchDescriptionSource
+from launch_ros.actions import Node
+
+def generate_launch_description():
+  darknet_ros_share_dir = get_package_share_directory('darknet_ros')
+  network_param_file = darknet_ros_share_dir + '/config/yolov4-tiny.yaml'
+
+  darknet_ros_launch = IncludeLaunchDescription(
+    PythonLaunchDescriptionSource([darknet_ros_share_dir + '/launch/darknet_ros.launch.py']),
+      launch_arguments={'network_param_file': network_param_file}.items()
+  )
+
+  camera = Node(
+    package="v4l2_camera",
+    executable="v4l2_camera_node",
+    parameters=[
+      {'video_device'     : "/dev/video0"},
+    ])
+  
+  return LaunchDescription([
+    darknet_ros_launch,
+    # if you want to disable camera node, remove the following line.
+    camera,
+  ])
\ No newline at end of file
diff --git a/darknet_ros/launch/yolov7-tiny.launch.py b/darknet_ros/launch/yolov7-tiny.launch.py
new file mode 100644
index 000000000..e38bb3e94
--- /dev/null
+++ b/darknet_ros/launch/yolov7-tiny.launch.py
@@ -0,0 +1,27 @@
+from ament_index_python.packages import get_package_share_directory
+from launch import LaunchDescription
+from launch.actions import IncludeLaunchDescription
+from launch.launch_description_sources import PythonLaunchDescriptionSource
+from launch_ros.actions import Node
+
+def generate_launch_description():
+  darknet_ros_share_dir = get_package_share_directory('darknet_ros')
+  network_param_file = darknet_ros_share_dir + '/config/yolov7-tiny.yaml'
+
+  darknet_ros_launch = IncludeLaunchDescription(
+    PythonLaunchDescriptionSource([darknet_ros_share_dir + '/launch/darknet_ros.launch.py']),
+      launch_arguments={'network_param_file': network_param_file}.items()
+  )
+
+  camera = Node(
+    package="v4l2_camera",
+    executable="v4l2_camera_node",
+    parameters=[
+      {'video_device'     : "/dev/video0"},
+    ])
+
+  return LaunchDescription([
+    darknet_ros_launch,
+    # if you want to disable camera node, remove the following line.
+    camera,
+  ])
diff --git a/darknet_ros/package.xml b/darknet_ros/package.xml
index 9df4490f7..919d291d0 100644
--- a/darknet_ros/package.xml
+++ b/darknet_ros/package.xml
@@ -2,7 +2,7 @@
 <?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
 <package format="3">
   <name>darknet_ros</name>
-  <version>1.1.4</version>
+  <version>1.1.5</version>
   <description>Darknet is an open source neural network framework that runs on CPU and GPU. You only look once (YOLO) is a state-of-the-art, real-time object detection system.</description>
   <maintainer email="marko.bjelonic@mavt.ethz.ch">Marko Bjelonic</maintainer>
   <license>BSD</license>
diff --git a/darknet_ros/src/YoloObjectDetector.cpp b/darknet_ros/src/YoloObjectDetector.cpp
index 67d5c3b41..53bd053fd 100644
--- a/darknet_ros/src/YoloObjectDetector.cpp
+++ b/darknet_ros/src/YoloObjectDetector.cpp
@@ -59,6 +59,7 @@ YoloObjectDetector::YoloObjectDetector()
   declare_parameter("publishers.detection_image.topic", std::string("detection_image"));
   declare_parameter("publishers.detection_image.queue_size", 1);
   declare_parameter("publishers.detection_image.latch", true);
+  declare_parameter("yolo_model.window_name", std::string("YOLO"));
 
   declare_parameter("actions.camera_reading.topic", std::string("check_for_objects"));
 }
@@ -79,6 +80,8 @@ bool YoloObjectDetector::readParameters()
   get_parameter("image_view.wait_key_delay", waitKeyDelay_);
   get_parameter("image_view.enable_console_output", enableConsoleOutput_);
 
+  get_parameter("yolo_model.window_name", windowName_);
+
   // Check if Xserver is running on Linux.
   if (XOpenDisplay(NULL)) {
     // Do nothing!
@@ -321,15 +324,6 @@ bool YoloObjectDetector::publishDetectionImage(const cv::Mat& detectionImage)
   return true;
 }
 
-// double YoloObjectDetector::getWallTime()
-// {
-//   struct timeval time;
-//   if (gettimeofday(&time, NULL)) {
-//     return 0;
-//   }
-//   return (double) time.tv_sec + (double) time.tv_usec * .000001;
-// }
-
 int YoloObjectDetector::sizeNetwork(network *net)
 {
   int i;
@@ -371,7 +365,8 @@ detection *YoloObjectDetector::avgPredictions(network *net, int *nboxes)
       count += l.outputs;
     }
   }
-  detection *dets = get_network_boxes(net, buff_[0].w, buff_[0].h, demoThresh_, demoHier_, 0, 1, nboxes);
+  // detection *dets = get_network_boxes(net, buff_[0].w, buff_[0].h, demoThresh_, demoHier_, 0, 1, nboxes);
+  detection *dets = get_network_boxes(net, buff_[0].w, buff_[0].h, demoThresh_, demoHier_, 0, 1, nboxes, 1); // letter box
   return dets;
 }
 
@@ -379,10 +374,11 @@ void *YoloObjectDetector::detectInThread()
 {
   running_ = 1;
   float nms = .4;
+  // mat_cv* show_img = NULL;
 
   layer l = net_->layers[net_->n - 1];
   float *X = buffLetter_[(buffIndex_ + 2) % 3].data;
-  float *prediction = network_predict(net_, X);
+  float *prediction = network_predict(*net_, X);
 
   rememberNetwork(net_);
   detection *dets = 0;
@@ -394,12 +390,13 @@ void *YoloObjectDetector::detectInThread()
   if (enableConsoleOutput_) {
     printf("\033[2J");
     printf("\033[1;1H");
-    printf("\nFPS:%.1f\n",fps_);
+    printf("\nFPS:%.1f : ( %s )\n",fps_,ss_fps.str().c_str());
     printf("Objects:\n\n");
   }
   image display = buff_[(buffIndex_+2) % 3];
-  draw_detections(display, dets, nboxes, demoThresh_, demoNames_, demoAlphabet_, demoClasses_);
-
+  // draw_detections(display, dets, nboxes, demoThresh_, demoNames_, demoAlphabet_, demoClasses_);
+  
+  draw_detections_v3(display, dets, nboxes, demoThresh_, demoNames_, demoAlphabet_, demoClasses_, 1);
   // extract the bounding boxes and send them to ROS
   int i, j;
   int count = 0;
@@ -455,32 +452,12 @@ void *YoloObjectDetector::detectInThread()
   return 0;
 }
 
-
-void ipl_into_image_cp(IplImage* src, image im)
-{
-    unsigned char *data = (unsigned char *)src->imageData;
-    int h = src->height;
-    int w = src->width;
-    int c = src->nChannels;
-    int step = src->widthStep;
-    int i, j, k;
-
-    for(i = 0; i < h; ++i){
-        for(k= 0; k < c; ++k){
-            for(j = 0; j < w; ++j){
-                im.data[k*w*h + i*w + j] = data[i*step + j*c + k]/255.;
-            }
-        }
-    }
-}
-
-void *YoloObjectDetector::fetchInThread()
-{
+void* YoloObjectDetector::fetchInThread() {
   {
     std::shared_lock<std::shared_mutex> lock(mutexImageCallback_);
-    IplImageWithHeader_ imageAndHeader = getIplImageWithHeader();
-    IplImage* ROS_img = imageAndHeader.image;
-    ipl_into_image_cp(ROS_img, buff_[buffIndex_]);
+    CvMatWithHeader_ imageAndHeader = getCvMatWithHeader();
+    free_image(buff_[buffIndex_]);
+    buff_[buffIndex_] = mat_to_image(imageAndHeader.image);
     headerBuff_[buffIndex_] = imageAndHeader.header;
     buffId_[buffIndex_] = actionId_;
   }
@@ -498,64 +475,28 @@ float get_pixel_cp(image m, int x, int y, int c)
 
 int windows = 0;
 
-void show_image_cv_cp(image p, const char *name, IplImage *disp)
-{
-    int x,y,k;
-    if(p.c == 3) rgbgr_image(p);
-    //normalize_image(copy);
-
-    char buff[256];
-    //sprintf(buff, "%s (%d)", name, windows);
-    sprintf(buff, "%s", name);
-
-    int step = disp->widthStep;
-    cvNamedWindow(buff, CV_WINDOW_NORMAL); 
-    //cvMoveWindow(buff, 100*(windows%10) + 200*(windows/10), 100*(windows%10));
-    ++windows;
-    for(y = 0; y < p.h; ++y){
-        for(x = 0; x < p.w; ++x){
-            for(k= 0; k < p.c; ++k){
-                disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel_cp(p,x,y,k)*255);
-            }
-        }
-    }
-    if(0){
-        int w = 448;
-        int h = w*p.h/p.w;
-        if(h > 1000){
-            h = 1000;
-            w = h*p.w/p.h;
-        }
-        IplImage *buffer = disp;
-        disp = cvCreateImage(cvSize(w, h), buffer->depth, buffer->nChannels);
-        cvResize(buffer, disp, CV_INTER_LINEAR);
-        cvReleaseImage(&buffer);
-    }
-    cvShowImage(buff, disp);
-}
-
-void *YoloObjectDetector::displayInThread(void *ptr)
-{
-  show_image_cv_cp(buff_[(buffIndex_ + 1)%3], "YOLO V3", ipl_);
+void* YoloObjectDetector::displayInThread(void* ptr) {
+  show_image_cv(buff_[(buffIndex_ + 1) % 3], windowName_.c_str());
   int c = cv::waitKey(waitKeyDelay_);
-  if (c != -1) c = c%256;
+  if (c != -1) c = c % 256;
   if (c == 27) {
-      demoDone_ = 1;
-      return 0;
+    demoDone_ = 1;
+    return 0;
   } else if (c == 82) {
-      demoThresh_ += .02;
+    demoThresh_ += .02;
   } else if (c == 84) {
-      demoThresh_ -= .02;
-      if(demoThresh_ <= .02) demoThresh_ = .02;
+    demoThresh_ -= .02;
+    if (demoThresh_ <= .02) demoThresh_ = .02;
   } else if (c == 83) {
-      demoHier_ += .02;
+    demoHier_ += .02;
   } else if (c == 81) {
-      demoHier_ -= .02;
-      if(demoHier_ <= .0) demoHier_ = .0;
+    demoHier_ -= .02;
+    if (demoHier_ <= .0) demoHier_ = .0;
   }
   return 0;
 }
 
+
 void *YoloObjectDetector::displayLoop(void *ptr)
 {
   while (1) {
@@ -610,34 +551,9 @@ void YoloObjectDetector::setupNetwork(char *cfgfile, char *weightfile, char *dat
   set_batch_network(net_, 1);
 }
 
-void generate_image_cp(image p, IplImage *disp)
-{
-    int x,y,k;
-    if(p.c == 3) rgbgr_image(p);
-    //normalize_image(copy);
-
-    int step = disp->widthStep;
-    for(y = 0; y < p.h; ++y){
-        for(x = 0; x < p.w; ++x){
-            for(k= 0; k < p.c; ++k){
-                disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel_cp(p,x,y,k)*255);
-            }
-        }
-    }
-}
-
-image ipl_to_image_cp(IplImage* src)
-{
-    int h = src->height;
-    int w = src->width;
-    int c = src->nChannels;
-    image out = make_image(w, h, c);
-    ipl_into_image_cp(src, out);
-    return out;
-}
-
 void YoloObjectDetector::yolo()
 {
+  static int start_count = 0;
   const auto wait_duration = std::chrono::milliseconds(2000);
   while (!getImageStatus()) {
     printf("Waiting for image.\n");
@@ -665,9 +581,8 @@ void YoloObjectDetector::yolo()
 
   {
     std::shared_lock<std::shared_mutex> lock(mutexImageCallback_);
-    IplImageWithHeader_ imageAndHeader = getIplImageWithHeader();
-    IplImage* ROS_img = imageAndHeader.image;
-    buff_[0] = ipl_to_image_cp(ROS_img);
+    CvMatWithHeader_ imageAndHeader = getCvMatWithHeader();
+    buff_[0] = mat_to_image(imageAndHeader.image);
     headerBuff_[0] = imageAndHeader.header;
   }
   buff_[1] = copy_image(buff_[0]);
@@ -677,17 +592,17 @@ void YoloObjectDetector::yolo()
   buffLetter_[0] = letterbox_image(buff_[0], net_->w, net_->h);
   buffLetter_[1] = letterbox_image(buff_[0], net_->w, net_->h);
   buffLetter_[2] = letterbox_image(buff_[0], net_->w, net_->h);
-  ipl_ = cvCreateImage(cvSize(buff_[0].w, buff_[0].h), IPL_DEPTH_8U, buff_[0].c);
+  // buff_[0] = mat_to_image(imageAndHeader.image);
+  disp_ = image_to_mat(buff_[0]);
 
   int count = 0;
-
   if (!demoPrefix_ && viewImage_) {
-      cv::namedWindow("YOLO V3", cv::WINDOW_NORMAL);
+    cv::namedWindow(windowName_.c_str(), cv::WINDOW_NORMAL);
     if (fullScreen_) {
-      cv::setWindowProperty("YOLO V3", cv::WND_PROP_FULLSCREEN, cv::WINDOW_FULLSCREEN);
+      cv::setWindowProperty(windowName_.c_str(), cv::WND_PROP_FULLSCREEN, cv::WINDOW_FULLSCREEN);
     } else {
-      cv::moveWindow("YOLO V3", 0, 0);
-      cv::resizeWindow("YOLO V3", 640, 480);
+      cv::moveWindow(windowName_.c_str(), 0, 0);
+      cv::resizeWindow(windowName_.c_str(), 640, 480);
     }
   }
 
@@ -699,11 +614,24 @@ void YoloObjectDetector::yolo()
     detect_thread = std::thread(&YoloObjectDetector::detectInThread, this);
     if (!demoPrefix_) {
       fps_ = 1./(what_time_is_it_now() - demoTime_);
+
+      if (start_count > START_COUNT) {
+        if (fps_ > max_fps)
+          max_fps = fps_;
+        else if (fps_ < min_fps)
+          min_fps = fps_;
+      }
+      else
+        start_count++;
+
+      ss_fps.str("");
+      ss_fps << "MAX:" << max_fps << " MIN:" << min_fps;
+
       demoTime_ = what_time_is_it_now();
       if (viewImage_) {
         displayInThread(0);
       } else {
-        generate_image_cp(buff_[(buffIndex_ + 1)%3], ipl_);
+        generate_image_cp(buff_[(buffIndex_ + 1)%3], disp_);
       }
       publishInThread();
     } else {
@@ -718,14 +646,10 @@ void YoloObjectDetector::yolo()
       demoDone_ = true;
     }
   }
-
 }
 
-IplImageWithHeader_ YoloObjectDetector::getIplImageWithHeader()
-{
-  IplImage* ROS_img = new IplImage();
-  *ROS_img = cvIplImage(camImageCopy_);
-  IplImageWithHeader_ header = {.image = ROS_img, .header = imageHeader_};
+CvMatWithHeader_ YoloObjectDetector::getCvMatWithHeader() {
+  CvMatWithHeader_ header = {.image = camImageCopy_, .header = imageHeader_};
   return header;
 }
 
@@ -744,7 +668,8 @@ bool YoloObjectDetector::isNodeRunning(void)
 void *YoloObjectDetector::publishInThread()
 {
   // Publish image.
-  cv::Mat cvImage = cv::cvarrToMat(ipl_);
+  // cv::Mat cvImage = cv::cvarrToMat(ipl_);
+  cv::Mat cvImage = disp_;
   if (!publishDetectionImage(cv::Mat(cvImage))) {
     RCLCPP_DEBUG(get_logger(), "Detection image has not been broadcasted.");
   }
@@ -817,5 +742,19 @@ void *YoloObjectDetector::publishInThread()
   return 0;
 }
 
+void YoloObjectDetector::generate_image_cp(image p, cv::Mat& disp) {
+  int x, y, k;
+  if (p.c == 3) rgbgr_image(p);
+  // normalize_image(copy);
+
+  int step = disp.step;
+  for (y = 0; y < p.h; ++y) {
+    for (x = 0; x < p.w; ++x) {
+      for (k = 0; k < p.c; ++k) {
+        disp.data[y * step + x * p.c + k] = (unsigned char)(get_pixel_cp(p, x, y, k) * 255);
+      }
+    }
+  }
+}
 
 } /* namespace darknet_ros*/
diff --git a/darknet_ros/src/image_interface.c b/darknet_ros/src/image_interface.cpp
similarity index 67%
rename from darknet_ros/src/image_interface.c
rename to darknet_ros/src/image_interface.cpp
index 8ee77cd43..a59d1875d 100644
--- a/darknet_ros/src/image_interface.c
+++ b/darknet_ros/src/image_interface.cpp
@@ -6,7 +6,7 @@
  *   Institute: ETH Zurich, Robotic Systems Lab
  */
 
-#include "darknet_ros/image_interface.h"
+#include "darknet_ros/image_interface.hpp"
 
 static float get_pixel(image m, int x, int y, int c)
 {
@@ -34,19 +34,18 @@ image **load_alphabet_with_file(char *datafile) {
 }
 
 #ifdef OPENCV
-void generate_image(image p, IplImage *disp)
-{
-    int x,y,k;
-    if(p.c == 3) rgbgr_image(p);
-    //normalize_image(copy);
+void generate_image(image p, cv::Mat& disp) {
+  int x, y, k;
+  if (p.c == 3) rgbgr_image(p);
+  // normalize_image(copy);
 
-    int step = disp->widthStep;
-    for(y = 0; y < p.h; ++y){
-        for(x = 0; x < p.w; ++x){
-            for(k= 0; k < p.c; ++k){
-                disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(p,x,y,k)*255);
-            }
-        }
+  int step = disp.step;
+  for (y = 0; y < p.h; ++y) {
+    for (x = 0; x < p.w; ++x) {
+      for (k = 0; k < p.c; ++k) {
+        disp.data[y * step + x * p.c + k] = (unsigned char)(get_pixel(p, x, y, k) * 255);
+      }
     }
+  }
 }
 #endif
diff --git a/darknet_ros/test/ObjectDetection.cpp b/darknet_ros/test/ObjectDetection.cpp
index 3ac9a74ee..69c351d3b 100644
--- a/darknet_ros/test/ObjectDetection.cpp
+++ b/darknet_ros/test/ObjectDetection.cpp
@@ -6,7 +6,6 @@
  *	 Institute: ETH Zurich, Robotic Systems Lab
  */
 
-
 // Google Test
 #include <gtest/gtest.h>
 
@@ -14,24 +13,23 @@
 #include "rclcpp/rclcpp.hpp"
 #include "ament_index_cpp/get_package_share_directory.hpp"
 #include <sensor_msgs/Image.h>
-#include <actionlib/client/simple_action_client.h>
 
 
 // OpenCV2.
+#include <cv_bridge/cv_bridge.h>
 #include <opencv2/core/core.hpp>
 #include <opencv2/highgui/highgui.hpp>
 #include <opencv2/imgproc/imgproc.hpp>
-#include <cv_bridge/cv_bridge.h>
 
 // Actions.
 #include <darknet_ros_msgs/CheckForObjectsAction.h>
 
-typedef actionlib::SimpleActionClient<darknet_ros_msgs::CheckForObjectsAction> CheckForObjectsActionClient;
-typedef std::shared_ptr<CheckForObjectsActionClient> CheckForObjectsActionClientPtr;
+using CheckForObjectsActionClient = actionlib::SimpleActionClient<darknet_ros_msgs::CheckForObjectsAction>;
+using CheckForObjectsActionClientPtr = std::shared_ptr<CheckForObjectsActionClient>;
 
 // c++
-#include <string>
 #include <cmath>
+#include <string>
 
 #ifdef DARKNET_FILE_PATH
 std::string darknetFilePath_ = DARKNET_FILE_PATH;
@@ -46,35 +44,30 @@ darknet_ros_msgs::BoundingBoxes boundingBoxesResults_;
  * @param[in] state
  * @param[in] result
  */
-void checkForObjectsResultCB(
-    const actionlib::SimpleClientGoalState& state,
-    const darknet_ros_msgs::CheckForObjectsResultConstPtr& result) {
-  std::cout <<  "[ObjectDetectionTest] Received bounding boxes." << std::endl;
+void checkForObjectsResultCB(const actionlib::SimpleClientGoalState& state, const darknet_ros_msgs::CheckForObjectsResultConstPtr& result) {
+  std::cout << "[ObjectDetectionTest] Received bounding boxes." << std::endl;
 
   boundingBoxesResults_ = result->bounding_boxes;
 }
 
 bool sendImageToYolo(ros::NodeHandle nh, const std::string& pathToTestImage) {
-  //!Check for objects action client.
+  //! Check for objects action client.
   CheckForObjectsActionClientPtr checkForObjectsActionClient;
 
   // Action clients.
   std::string checkForObjectsActionName;
   nh.param("/darknet_ros/camera_action", checkForObjectsActionName, std::string("/darknet_ros/check_for_objects"));
-  checkForObjectsActionClient.reset(
-      new CheckForObjectsActionClient(
-          nh, checkForObjectsActionName,
-          true));
+  checkForObjectsActionClient.reset(new CheckForObjectsActionClient(nh, checkForObjectsActionName, true));
 
   // Wait till action server launches.
-  if(!checkForObjectsActionClient->waitForServer(ros::Duration(20.0))) {
-	  std::cout << "[ObjectDetectionTest] sendImageToYolo(): checkForObjects action server has not been advertised." << std::endl;
-	  return false;
+  if (!checkForObjectsActionClient->waitForServer(ros::Duration(20.0))) {
+    std::cout << "[ObjectDetectionTest] sendImageToYolo(): checkForObjects action server has not been advertised." << std::endl;
+    return false;
   }
 
   // Get test image
   cv_bridge::CvImagePtr cv_ptr(new cv_bridge::CvImage);
-  cv_ptr->image = cv::imread(pathToTestImage, CV_LOAD_IMAGE_COLOR);
+  cv_ptr->image = cv::imread(pathToTestImage, cv::IMREAD_COLOR);
   cv_ptr->encoding = sensor_msgs::image_encodings::RGB8;
   sensor_msgs::ImagePtr image = cv_ptr->toImageMsg();
 
@@ -84,24 +77,21 @@ bool sendImageToYolo(ros::NodeHandle nh, const std::string& pathToTestImage) {
 
   // Send goal.
   ros::Time beginYolo = ros::Time::now();
-  checkForObjectsActionClient->sendGoal(
-      goal,
-      boost::bind(&checkForObjectsResultCB, _1, _2),
-      CheckForObjectsActionClient::SimpleActiveCallback(),
-      CheckForObjectsActionClient::SimpleFeedbackCallback());
+  checkForObjectsActionClient->sendGoal(goal, boost::bind(&checkForObjectsResultCB, _1, _2),
+                                        CheckForObjectsActionClient::SimpleActiveCallback(),
+                                        CheckForObjectsActionClient::SimpleFeedbackCallback());
 
-  if(!checkForObjectsActionClient->waitForResult(ros::Duration(100.0))) {
+  if (!checkForObjectsActionClient->waitForResult(ros::Duration(100.0))) {
     std::cout << "[ObjectDetectionTest] sendImageToYolo(): checkForObjects action server took to long to send back result." << std::endl;
     return false;
   }
   ros::Time endYolo = ros::Time::now();
-  std::cout << "[ObjectDetectionTest] Object detection for one image took " << endYolo-beginYolo << " seconds." << std::endl;
+  std::cout << "[ObjectDetectionTest] Object detection for one image took " << endYolo - beginYolo << " seconds." << std::endl;
   return true;
 }
 
-TEST(ObjectDetection, DISABLED_DetectDog)
-{
-  srand((unsigned int) time(0));
+TEST(ObjectDetection, DISABLED_DetectDog) {
+  srand(static_cast<unsigned int>(time(nullptr)));
   ros::NodeHandle nodeHandle("~");
 
   // Path to test image.
@@ -122,25 +112,23 @@ TEST(ObjectDetection, DISABLED_DetectDog)
   bool detectedCar = false;
   double centerErrorCar;
 
-  for(unsigned int i = 0; i < boundingBoxesResults_.bounding_boxes.size(); ++i) {
-    double xPosCenter = boundingBoxesResults_.bounding_boxes.at(i).xmin +
-        (boundingBoxesResults_.bounding_boxes.at(i).xmax - boundingBoxesResults_.bounding_boxes.at(i).xmin)*0.5;
-    double yPosCenter = boundingBoxesResults_.bounding_boxes.at(i).ymin +
-        (boundingBoxesResults_.bounding_boxes.at(i).ymax - boundingBoxesResults_.bounding_boxes.at(i).ymin)*0.5;
+  for (auto& boundingBox : boundingBoxesResults_.bounding_boxes) {
+    double xPosCenter = boundingBox.xmin + (boundingBox.xmax - boundingBox.xmin) * 0.5;
+    double yPosCenter = boundingBox.ymin + (boundingBox.ymax - boundingBox.ymin) * 0.5;
 
-    if(boundingBoxesResults_.bounding_boxes.at(i).Class == "dog") {
+    if (boundingBox.Class == "dog") {
       detectedDog = true;
-      //std::cout << "centerErrorDog  " << xPosCenter << ", " <<  yPosCenter << std::endl;
+      // std::cout << "centerErrorDog  " << xPosCenter << ", " <<  yPosCenter << std::endl;
       centerErrorDog = std::sqrt(std::pow(xPosCenter - 222.5, 2) + std::pow(yPosCenter - 361.5, 2));
     }
-    if(boundingBoxesResults_.bounding_boxes.at(i).Class == "bicycle") {
+    if (boundingBox.Class == "bicycle") {
       detectedBicycle = true;
-      //std::cout << "centerErrorBicycle "  << xPosCenter << ", " <<  yPosCenter << std::endl;
+      // std::cout << "centerErrorBicycle "  << xPosCenter << ", " <<  yPosCenter << std::endl;
       centerErrorBicycle = std::sqrt(std::pow(xPosCenter - 338.0, 2) + std::pow(yPosCenter - 289.0, 2));
     }
-    if(boundingBoxesResults_.bounding_boxes.at(i).Class == "truck") {
+    if (boundingBox.Class == "truck") {
       detectedCar = true;
-      //std::cout << "centerErrorCar  " << xPosCenter << ", " <<  yPosCenter << std::endl;
+      // std::cout << "centerErrorCar  " << xPosCenter << ", " <<  yPosCenter << std::endl;
       centerErrorCar = std::sqrt(std::pow(xPosCenter - 561.0, 2) + std::pow(yPosCenter - 126.5, 2));
     }
   }
@@ -153,9 +141,8 @@ TEST(ObjectDetection, DISABLED_DetectDog)
   EXPECT_LT(centerErrorCar, 40.0);
 }
 
-TEST(ObjectDetection, DetectANYmal)
-{
-  srand((unsigned int) time(0));
+TEST(ObjectDetection, DetectANYmal) {
+  srand(static_cast<unsigned int>(time(nullptr)));
   ros::NodeHandle nodeHandle("~");
 
   // Path to test image.
@@ -164,7 +151,8 @@ TEST(ObjectDetection, DetectANYmal)
   pathToTestImage += "quadruped_anymal_and_person";
   pathToTestImage += ".JPG";
 
-  // Send dog image to yolo.
+  // Send ANYmal and person image to yolo.
+  ASSERT_TRUE(sendImageToYolo(nodeHandle, pathToTestImage));
   ASSERT_TRUE(sendImageToYolo(nodeHandle, pathToTestImage));
   ASSERT_TRUE(sendImageToYolo(nodeHandle, pathToTestImage));
 
@@ -173,13 +161,11 @@ TEST(ObjectDetection, DetectANYmal)
   double centerErrorPersonX;
   double centerErrorPersonY;
 
-  for(unsigned int i = 0; i < boundingBoxesResults_.bounding_boxes.size(); ++i) {
-    double xPosCenter = boundingBoxesResults_.bounding_boxes.at(i).xmin +
-        (boundingBoxesResults_.bounding_boxes.at(i).xmax - boundingBoxesResults_.bounding_boxes.at(i).xmin)*0.5;
-    double yPosCenter = boundingBoxesResults_.bounding_boxes.at(i).ymin +
-        (boundingBoxesResults_.bounding_boxes.at(i).ymax - boundingBoxesResults_.bounding_boxes.at(i).ymin)*0.5;
+  for (auto& boundingBox : boundingBoxesResults_.bounding_boxes) {
+    double xPosCenter = boundingBox.xmin + (boundingBox.xmax - boundingBox.xmin) * 0.5;
+    double yPosCenter = boundingBox.ymin + (boundingBox.ymax - boundingBox.ymin) * 0.5;
 
-    if(boundingBoxesResults_.bounding_boxes.at(i).Class == "person") {
+    if (boundingBox.Class == "person") {
       detectedPerson = true;
       centerErrorPersonX = std::sqrt(std::pow(xPosCenter - 1650.0, 2));
       centerErrorPersonY = std::sqrt(std::pow(xPosCenter - 1675.0, 2));
@@ -192,7 +178,7 @@ TEST(ObjectDetection, DetectANYmal)
 }
 
 TEST(ObjectDetection, DISABLED_DetectPerson) {
-  srand((unsigned int) time(0));
+  srand(static_cast<unsigned int>(time(nullptr)));
   ros::NodeHandle nodeHandle("~");
 
   // Path to test image.
@@ -208,15 +194,13 @@ TEST(ObjectDetection, DISABLED_DetectPerson) {
   bool detectedPerson = false;
   double centerErrorPerson;
 
-  for(unsigned int i = 0; i < boundingBoxesResults_.bounding_boxes.size(); ++i) {
-    double xPosCenter = boundingBoxesResults_.bounding_boxes.at(i).xmin +
-        (boundingBoxesResults_.bounding_boxes.at(i).xmax - boundingBoxesResults_.bounding_boxes.at(i).xmin)*0.5;
-    double yPosCenter = boundingBoxesResults_.bounding_boxes.at(i).ymin +
-        (boundingBoxesResults_.bounding_boxes.at(i).ymax - boundingBoxesResults_.bounding_boxes.at(i).ymin)*0.5;
+  for (auto& boundingBox : boundingBoxesResults_.bounding_boxes) {
+    double xPosCenter = boundingBox.xmin + (boundingBox.xmax - boundingBox.xmin) * 0.5;
+    double yPosCenter = boundingBox.ymin + (boundingBox.ymax - boundingBox.ymin) * 0.5;
 
-    if(boundingBoxesResults_.bounding_boxes.at(i).Class == "person") {
+    if (boundingBox.Class == "person") {
       detectedPerson = true;
-      //std::cout << "centerErrorPerson  " << xPosCenter << ", " <<  yPosCenter << std::endl;
+      // std::cout << "centerErrorPerson  " << xPosCenter << ", " <<  yPosCenter << std::endl;
       centerErrorPerson = std::sqrt(std::pow(xPosCenter - 228.0, 2) + std::pow(yPosCenter - 238.0, 2));
     }
   }
diff --git a/darknet_ros/test/test_main.cpp b/darknet_ros/test/test_main.cpp
index 8292d40ee..e43bbae05 100644
--- a/darknet_ros/test/test_main.cpp
+++ b/darknet_ros/test/test_main.cpp
@@ -4,7 +4,7 @@
 #include <ros/ros.h>
 
 int main(int argc, char** argv) {
-    ros::init(argc, argv, "darknet_ros_test");
-    testing::InitGoogleTest(&argc, argv);
-    return RUN_ALL_TESTS();
+  ros::init(argc, argv, "darknet_ros_test");
+  testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
 }
diff --git a/darknet_ros/yolo_network_config/cfg/yolov2-tiny-voc.cfg b/darknet_ros/yolo_network_config/cfg/yolov2-tiny-voc.cfg
deleted file mode 100644
index c4c127cdd..000000000
--- a/darknet_ros/yolo_network_config/cfg/yolov2-tiny-voc.cfg
+++ /dev/null
@@ -1,138 +0,0 @@
-[net]
-# Testing
-batch=1
-subdivisions=1
-# Training
-# batch=64
-# subdivisions=2
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-max_batches = 40200
-policy=steps
-steps=-1,100,20000,30000
-scales=.1,10,.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=16
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=1
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-###########
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=125
-activation=linear
-
-[region]
-anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
-bias_match=1
-classes=20
-coords=4
-num=5
-softmax=1
-jitter=.2
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=1
diff --git a/darknet_ros/yolo_network_config/cfg/yolov2-voc.cfg b/darknet_ros/yolo_network_config/cfg/yolov2-voc.cfg
deleted file mode 100644
index dbf2de281..000000000
--- a/darknet_ros/yolo_network_config/cfg/yolov2-voc.cfg
+++ /dev/null
@@ -1,258 +0,0 @@
-[net]
-# Testing
-batch=1
-subdivisions=1
-# Training
-# batch=64
-# subdivisions=8
-height=416
-width=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 80200
-policy=steps
-steps=40000,60000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-
-#######
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[route]
-layers=-9
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=64
-activation=leaky
-
-[reorg]
-stride=2
-
-[route]
-layers=-1,-4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=125
-activation=linear
-
-
-[region]
-anchors =  1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071
-bias_match=1
-classes=20
-coords=4
-num=5
-softmax=1
-jitter=.3
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=1
diff --git a/darknet_ros/yolo_network_config/cfg/yolov4-csp.cfg b/darknet_ros/yolo_network_config/cfg/yolov4-csp.cfg
new file mode 100644
index 000000000..55d97e155
--- /dev/null
+++ b/darknet_ros/yolo_network_config/cfg/yolov4-csp.cfg
@@ -0,0 +1,1280 @@
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=1
+subdivisions=1
+width=512
+height=512
+channels=3
+momentum=0.949
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 500500
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+mosaic=1
+
+letter_box=1
+
+ema_alpha=0.9998
+
+#optimized_memory=1
+
+#23:104x104 54:52x52 85:26x26 104:13x13 for 416
+
+
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=mish
+
+#[convolutional]
+#batch_normalize=1
+#filters=64
+#size=1
+#stride=1
+#pad=1
+#activation=mish
+
+#[route]
+#layers = -2
+
+#[convolutional]
+#batch_normalize=1
+#filters=64
+#size=1
+#stride=1
+#pad=1
+#activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+#[convolutional]
+#batch_normalize=1
+#filters=64
+#size=1
+#stride=1
+#pad=1
+#activation=mish
+
+#[route]
+#layers = -1,-7
+
+#[convolutional]
+#batch_normalize=1
+#filters=64
+#size=1
+#stride=1
+#pad=1
+#activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-10
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-28
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-28
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-16
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=1
+stride=1
+pad=1
+activation=mish
+
+##########################
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+### SPP ###
+[maxpool]
+stride=1
+size=5
+
+[route]
+layers=-2
+
+[maxpool]
+stride=1
+size=9
+
+[route]
+layers=-4
+
+[maxpool]
+stride=1
+size=13
+
+[route]
+layers=-1,-3,-5,-6
+### End SPP ###
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=mish
+
+[route]
+layers = -1, -13
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[upsample]
+stride=2
+
+[route]
+layers = 79
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1, -3
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=mish
+
+[route]
+layers = -1, -6
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[upsample]
+stride=2
+
+[route]
+layers = 48
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1, -3
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=128
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=128
+activation=mish
+
+[route]
+layers = -1, -6
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+##########################
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=mish
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=logistic
+
+
+[yolo]
+mask = 0,1,2
+anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
+classes=80
+num=9
+jitter=.1
+scale_x_y = 2.0
+objectness_smooth=0
+ignore_thresh = .7
+truth_thresh = 1
+#random=1
+resize=1.5
+iou_thresh=0.2
+iou_normalizer=0.05
+cls_normalizer=0.5
+obj_normalizer=4.0
+iou_loss=ciou
+nms_kind=diounms
+beta_nms=0.6
+new_coords=1
+max_delta=5
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=2
+pad=1
+filters=256
+activation=mish
+
+[route]
+layers = -1, -20
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=mish
+
+[route]
+layers = -1,-6
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=mish
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=logistic
+
+
+[yolo]
+mask = 3,4,5
+anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
+classes=80
+num=9
+jitter=.1
+scale_x_y = 2.0
+objectness_smooth=1
+ignore_thresh = .7
+truth_thresh = 1
+#random=1
+resize=1.5
+iou_thresh=0.2
+iou_normalizer=0.05
+cls_normalizer=0.5
+obj_normalizer=1.0
+iou_loss=ciou
+nms_kind=diounms
+beta_nms=0.6
+new_coords=1
+max_delta=5
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=2
+pad=1
+filters=512
+activation=mish
+
+[route]
+layers = -1, -49
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=mish
+
+[route]
+layers = -1,-6
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=mish
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=logistic
+
+
+[yolo]
+mask = 6,7,8
+anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
+classes=80
+num=9
+jitter=.1
+scale_x_y = 2.0
+objectness_smooth=1
+ignore_thresh = .7
+truth_thresh = 1
+#random=1
+resize=1.5
+iou_thresh=0.2
+iou_normalizer=0.05
+cls_normalizer=0.5
+obj_normalizer=0.4
+iou_loss=ciou
+nms_kind=diounms
+beta_nms=0.6
+new_coords=1
+max_delta=2
diff --git a/darknet_ros/yolo_network_config/cfg/yolov2.cfg b/darknet_ros/yolo_network_config/cfg/yolov4-tiny.cfg
similarity index 61%
rename from darknet_ros/yolo_network_config/cfg/yolov2.cfg
rename to darknet_ros/yolo_network_config/cfg/yolov4-tiny.cfg
index 2a0cd98fb..503f9334d 100644
--- a/darknet_ros/yolo_network_config/cfg/yolov2.cfg
+++ b/darknet_ros/yolo_network_config/cfg/yolov4-tiny.cfg
@@ -1,10 +1,10 @@
 [net]
 # Testing
+#batch=1
+#subdivisions=1
+# Training
 batch=1
 subdivisions=1
-# Training
-# batch=64
-# subdivisions=8
 width=416
 height=416
 channels=3
@@ -15,88 +15,80 @@ saturation = 1.5
 exposure = 1.5
 hue=.1
 
-learning_rate=0.001
+learning_rate=0.00261
 burn_in=1000
-max_batches = 500200
+
+max_batches = 2000200
 policy=steps
-steps=400000,450000
+steps=1600000,1800000
 scales=.1,.1
 
+
+#weights_reject_freq=1001
+#ema_alpha=0.9998
+#equidistant_point=1000
+#num_sigmas_reject_badlabels=3
+#badlabels_rejection_percentage=0.2
+
+
 [convolutional]
 batch_normalize=1
 filters=32
 size=3
-stride=1
+stride=2
 pad=1
 activation=leaky
 
-[maxpool]
-size=2
-stride=2
-
 [convolutional]
 batch_normalize=1
 filters=64
 size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
 stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
 pad=1
 activation=leaky
 
 [convolutional]
 batch_normalize=1
 filters=64
-size=1
+size=3
 stride=1
 pad=1
 activation=leaky
 
+[route]
+layers=-1
+groups=2
+group_id=1
+
 [convolutional]
 batch_normalize=1
-filters=128
+filters=32
 size=3
 stride=1
 pad=1
 activation=leaky
 
-[maxpool]
-size=2
-stride=2
-
 [convolutional]
 batch_normalize=1
-filters=256
+filters=32
 size=3
 stride=1
 pad=1
 activation=leaky
 
+[route]
+layers = -1,-2
+
 [convolutional]
 batch_normalize=1
-filters=128
+filters=64
 size=1
 stride=1
 pad=1
 activation=leaky
 
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
+[route]
+layers = -6,-1
 
 [maxpool]
 size=2
@@ -104,43 +96,46 @@ stride=2
 
 [convolutional]
 batch_normalize=1
-filters=512
+filters=128
 size=3
 stride=1
 pad=1
 activation=leaky
 
+[route]
+layers=-1
+groups=2
+group_id=1
+
 [convolutional]
 batch_normalize=1
-filters=256
-size=1
+filters=64
+size=3
 stride=1
 pad=1
 activation=leaky
 
 [convolutional]
 batch_normalize=1
-filters=512
+filters=64
 size=3
 stride=1
 pad=1
 activation=leaky
 
+[route]
+layers = -1,-2
+
 [convolutional]
 batch_normalize=1
-filters=256
+filters=128
 size=1
 stride=1
 pad=1
 activation=leaky
 
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
+[route]
+layers = -6,-1
 
 [maxpool]
 size=2
@@ -148,111 +143,152 @@ stride=2
 
 [convolutional]
 batch_normalize=1
-filters=1024
+filters=256
 size=3
 stride=1
 pad=1
 activation=leaky
 
+[route]
+layers=-1
+groups=2
+group_id=1
+
 [convolutional]
 batch_normalize=1
-filters=512
-size=1
+filters=128
+size=3
 stride=1
 pad=1
 activation=leaky
 
 [convolutional]
 batch_normalize=1
-filters=1024
+filters=128
 size=3
 stride=1
 pad=1
 activation=leaky
 
+[route]
+layers = -1,-2
+
 [convolutional]
 batch_normalize=1
-filters=512
+filters=256
 size=1
 stride=1
 pad=1
 activation=leaky
 
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
 [convolutional]
 batch_normalize=1
-filters=1024
+filters=512
 size=3
 stride=1
 pad=1
 activation=leaky
 
-
-#######
+##################################
 
 [convolutional]
 batch_normalize=1
-size=3
+filters=256
+size=1
 stride=1
 pad=1
-filters=1024
 activation=leaky
 
 [convolutional]
 batch_normalize=1
+filters=512
 size=3
 stride=1
 pad=1
-filters=1024
 activation=leaky
 
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=80
+num=6
+jitter=.3
+scale_x_y = 1.05
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+ignore_thresh = .7
+truth_thresh = 1
+random=0
+resize=1.5
+nms_kind=greedynms
+beta_nms=0.6
+#new_coords=1
+#scale_x_y = 2.0
+
 [route]
-layers=-9
+layers = -4
 
 [convolutional]
 batch_normalize=1
+filters=128
 size=1
 stride=1
 pad=1
-filters=64
 activation=leaky
 
-[reorg]
+[upsample]
 stride=2
 
 [route]
-layers=-1,-4
+layers = -1, 23
 
 [convolutional]
 batch_normalize=1
+filters=256
 size=3
 stride=1
 pad=1
-filters=1024
 activation=leaky
 
 [convolutional]
 size=1
 stride=1
 pad=1
-filters=425
+filters=255
 activation=linear
 
-
-[region]
-anchors =  0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828
-bias_match=1
+[yolo]
+mask = 1,2,3
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
 classes=80
-coords=4
-num=5
-softmax=1
+num=6
 jitter=.3
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=1
+scale_x_y = 1.05
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+ignore_thresh = .7
+truth_thresh = 1
+random=0
+resize=1.5
+nms_kind=greedynms
+beta_nms=0.6
+#new_coords=1
+#scale_x_y = 2.0
diff --git a/darknet_ros/yolo_network_config/cfg/yolov4.cfg b/darknet_ros/yolo_network_config/cfg/yolov4.cfg
new file mode 100644
index 000000000..df1cbd523
--- /dev/null
+++ b/darknet_ros/yolo_network_config/cfg/yolov4.cfg
@@ -0,0 +1,1160 @@
+[net]
+# batch=64
+batch=1
+subdivisions=1
+# Training
+#width=512
+#height=512
+width=608
+height=608
+# width=416
+# height=416
+channels=3
+momentum=0.949
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.0013
+burn_in=1000
+max_batches = 500500
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+#cutmix=1
+mosaic=1
+
+#:104x104 54:52x52 85:26x26 104:13x13 for 416
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-7
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-10
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-28
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-28
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-16
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=1
+stride=1
+pad=1
+activation=mish
+
+##########################
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+### SPP ###
+[maxpool]
+stride=1
+size=5
+
+[route]
+layers=-2
+
+[maxpool]
+stride=1
+size=9
+
+[route]
+layers=-4
+
+[maxpool]
+stride=1
+size=13
+
+[route]
+layers=-1,-3,-5,-6
+### End SPP ###
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = 85
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1, -3
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = 54
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1, -3
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+##########################
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+[yolo]
+mask = 0,1,2
+anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+scale_x_y = 1.2
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
+max_delta=5
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=2
+pad=1
+filters=256
+activation=leaky
+
+[route]
+layers = -1, -16
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+[yolo]
+mask = 3,4,5
+anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+scale_x_y = 1.1
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
+max_delta=5
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=2
+pad=1
+filters=512
+activation=leaky
+
+[route]
+layers = -1, -37
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+[yolo]
+mask = 6,7,8
+anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+scale_x_y = 1.05
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
+max_delta=5
diff --git a/darknet_ros/yolo_network_config/cfg/yolov3-voc.cfg b/darknet_ros/yolo_network_config/cfg/yolov7-tiny.cfg
similarity index 60%
rename from darknet_ros/yolo_network_config/cfg/yolov3-voc.cfg
rename to darknet_ros/yolo_network_config/cfg/yolov7-tiny.cfg
index 3f3e8dfb3..4628b43fc 100644
--- a/darknet_ros/yolo_network_config/cfg/yolov3-voc.cfg
+++ b/darknet_ros/yolo_network_config/cfg/yolov7-tiny.cfg
@@ -1,10 +1,10 @@
 [net]
 # Testing
- batch=1
- subdivisions=1
+#batch=1
+#subdivisions=1
 # Training
-# batch=64
-# subdivisions=16
+batch=1
+subdivisions=1
 width=416
 height=416
 channels=3
@@ -15,25 +15,24 @@ saturation = 1.5
 exposure = 1.5
 hue=.1
 
-learning_rate=0.001
+learning_rate=0.00261
 burn_in=1000
-max_batches = 50200
+
+max_batches = 2000200
 policy=steps
-steps=40000,45000
+steps=1600000,1800000
 scales=.1,.1
 
-
-
+# 0
 [convolutional]
 batch_normalize=1
 filters=32
 size=3
-stride=1
+stride=2
 pad=1
 activation=leaky
 
-# Downsample
-
+# 1
 [convolutional]
 batch_normalize=1
 filters=64
@@ -50,48 +49,37 @@ stride=1
 pad=1
 activation=leaky
 
+[route]
+layers=-2
+
 [convolutional]
 batch_normalize=1
-filters=64
-size=3
+filters=32
+size=1
 stride=1
 pad=1
 activation=leaky
 
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
 [convolutional]
 batch_normalize=1
-filters=128
+filters=32
 size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
 stride=1
 pad=1
 activation=leaky
 
 [convolutional]
 batch_normalize=1
-filters=128
+filters=32
 size=3
 stride=1
 pad=1
 activation=leaky
 
-[shortcut]
-from=-3
-activation=linear
+[route]
+layers = -5,-3,-2,-1
 
+# 8
 [convolutional]
 batch_normalize=1
 filters=64
@@ -100,51 +88,24 @@ stride=1
 pad=1
 activation=leaky
 
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
+[maxpool]
+size=2
 stride=2
-pad=1
-activation=leaky
 
 [convolutional]
 batch_normalize=1
-filters=128
+filters=64
 size=1
 stride=1
 pad=1
 activation=leaky
 
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
+[route]
+layers=-2
 
 [convolutional]
 batch_normalize=1
-filters=128
+filters=64
 size=1
 stride=1
 pad=1
@@ -152,36 +113,24 @@ activation=leaky
 
 [convolutional]
 batch_normalize=1
-filters=256
+filters=64
 size=3
 stride=1
 pad=1
 activation=leaky
 
-[shortcut]
-from=-3
-activation=linear
-
 [convolutional]
 batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
+filters=64
 size=3
 stride=1
 pad=1
 activation=leaky
 
-[shortcut]
-from=-3
-activation=linear
+[route]
+layers = -5,-3,-2,-1
 
+# 16
 [convolutional]
 batch_normalize=1
 filters=128
@@ -190,18 +139,20 @@ stride=1
 pad=1
 activation=leaky
 
+[maxpool]
+size=2
+stride=2
+
 [convolutional]
 batch_normalize=1
-filters=256
-size=3
+filters=128
+size=1
 stride=1
 pad=1
 activation=leaky
 
-[shortcut]
-from=-3
-activation=linear
-
+[route]
+layers=-2
 
 [convolutional]
 batch_normalize=1
@@ -213,59 +164,50 @@ activation=leaky
 
 [convolutional]
 batch_normalize=1
-filters=256
+filters=128
 size=3
 stride=1
 pad=1
 activation=leaky
 
-[shortcut]
-from=-3
-activation=linear
-
 [convolutional]
 batch_normalize=1
 filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
 size=3
 stride=1
 pad=1
 activation=leaky
 
-[shortcut]
-from=-3
-activation=linear
+[route]
+layers = -5,-3,-2,-1
 
+# 24
 [convolutional]
 batch_normalize=1
-filters=128
+filters=256
 size=1
 stride=1
 pad=1
 activation=leaky
 
+[maxpool]
+size=2
+stride=2
+
 [convolutional]
 batch_normalize=1
 filters=256
-size=3
+size=1
 stride=1
 pad=1
 activation=leaky
 
-[shortcut]
-from=-3
-activation=linear
+[route]
+layers=-2
 
 [convolutional]
 batch_normalize=1
-filters=128
+filters=256
 size=1
 stride=1
 pad=1
@@ -279,41 +221,30 @@ stride=1
 pad=1
 activation=leaky
 
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
 [convolutional]
 batch_normalize=1
 filters=256
-size=1
+size=3
 stride=1
 pad=1
 activation=leaky
 
+[route]
+layers = -5,-3,-2,-1
+
+# 32
 [convolutional]
 batch_normalize=1
 filters=512
-size=3
+size=1
 stride=1
 pad=1
 activation=leaky
 
-[shortcut]
-from=-3
-activation=linear
 
+##################################
 
+### SPPCSP ###
 [convolutional]
 batch_normalize=1
 filters=256
@@ -322,18 +253,8 @@ stride=1
 pad=1
 activation=leaky
 
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
+[route]
+layers = -2
 
 [convolutional]
 batch_normalize=1
@@ -343,38 +264,28 @@ stride=1
 pad=1
 activation=leaky
 
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
+### SPP ###
+[maxpool]
 stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
+size=5
 
+[route]
+layers=-2
 
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
+[maxpool]
 stride=1
-pad=1
-activation=leaky
+size=9
 
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
+[route]
+layers=-4
+
+[maxpool]
 stride=1
-pad=1
-activation=leaky
+size=13
 
-[shortcut]
-from=-3
-activation=linear
+[route]
+layers=-1,-3,-5,-6
+### End SPP ###
 
 [convolutional]
 batch_normalize=1
@@ -384,19 +295,10 @@ stride=1
 pad=1
 activation=leaky
 
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
+[route]
+layers = -10,-1
 
+# 44
 [convolutional]
 batch_normalize=1
 filters=256
@@ -404,43 +306,47 @@ size=1
 stride=1
 pad=1
 activation=leaky
+### End SPPCSP ###
 
 [convolutional]
 batch_normalize=1
-filters=512
-size=3
+filters=128
+size=1
 stride=1
 pad=1
 activation=leaky
 
-[shortcut]
-from=-3
-activation=linear
+[upsample]
+stride=2
 
+[route]
+layers = 24
 
 [convolutional]
 batch_normalize=1
-filters=256
+filters=128
 size=1
 stride=1
 pad=1
 activation=leaky
 
+[route]
+layers = -1,-3
+
 [convolutional]
 batch_normalize=1
-filters=512
-size=3
+filters=64
+size=1
 stride=1
 pad=1
 activation=leaky
 
-[shortcut]
-from=-3
-activation=linear
+[route]
+layers=-2
 
 [convolutional]
 batch_normalize=1
-filters=256
+filters=64
 size=1
 stride=1
 pad=1
@@ -448,29 +354,27 @@ activation=leaky
 
 [convolutional]
 batch_normalize=1
-filters=512
+filters=64
 size=3
 stride=1
 pad=1
 activation=leaky
 
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
 [convolutional]
 batch_normalize=1
-filters=1024
+filters=64
 size=3
-stride=2
+stride=1
 pad=1
 activation=leaky
 
+[route]
+layers = -5,-3,-2,-1
+
+# 56
 [convolutional]
 batch_normalize=1
-filters=512
+filters=128
 size=1
 stride=1
 pad=1
@@ -478,39 +382,43 @@ activation=leaky
 
 [convolutional]
 batch_normalize=1
-filters=1024
-size=3
+filters=64
+size=1
 stride=1
 pad=1
 activation=leaky
 
-[shortcut]
-from=-3
-activation=linear
+[upsample]
+stride=2
+
+[route]
+layers = 16
 
 [convolutional]
 batch_normalize=1
-filters=512
+filters=64
 size=1
 stride=1
 pad=1
 activation=leaky
 
+[route]
+layers = -1,-3
+
 [convolutional]
 batch_normalize=1
-filters=1024
-size=3
+filters=32
+size=1
 stride=1
 pad=1
 activation=leaky
 
-[shortcut]
-from=-3
-activation=linear
+[route]
+layers=-2
 
 [convolutional]
 batch_normalize=1
-filters=512
+filters=32
 size=1
 stride=1
 pad=1
@@ -518,73 +426,59 @@ activation=leaky
 
 [convolutional]
 batch_normalize=1
-filters=1024
+filters=32
 size=3
 stride=1
 pad=1
 activation=leaky
 
-[shortcut]
-from=-3
-activation=linear
-
 [convolutional]
 batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
+filters=32
 size=3
 stride=1
 pad=1
 activation=leaky
 
-[shortcut]
-from=-3
-activation=linear
-
-######################
+[route]
+layers = -5,-3,-2,-1
 
+# 68
 [convolutional]
 batch_normalize=1
-filters=512
+filters=64
 size=1
 stride=1
 pad=1
 activation=leaky
 
+##########################
+
 [convolutional]
 batch_normalize=1
 size=3
-stride=1
+stride=2
 pad=1
-filters=1024
+filters=128
 activation=leaky
 
+[route]
+layers = -1,56
+
 [convolutional]
 batch_normalize=1
-filters=512
+filters=64
 size=1
 stride=1
 pad=1
 activation=leaky
 
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
+[route]
+layers=-2
 
 [convolutional]
 batch_normalize=1
-filters=512
+filters=64
 size=1
 stride=1
 pad=1
@@ -592,51 +486,27 @@ activation=leaky
 
 [convolutional]
 batch_normalize=1
+filters=64
 size=3
 stride=1
 pad=1
-filters=1024
 activation=leaky
 
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=75
-activation=linear
-
-[yolo]
-mask = 6,7,8
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=20
-num=9
-jitter=.3
-ignore_thresh = .5
-truth_thresh = 1
-random=1
-
-[route]
-layers = -4
-
 [convolutional]
 batch_normalize=1
-filters=256
-size=1
+filters=64
+size=3
 stride=1
 pad=1
 activation=leaky
 
-[upsample]
-stride=2
-
 [route]
-layers = -1, 61
-
-
+layers = -5,-3,-2,-1
 
+# 77
 [convolutional]
 batch_normalize=1
-filters=256
+filters=128
 size=1
 stride=1
 pad=1
@@ -645,30 +515,28 @@ activation=leaky
 [convolutional]
 batch_normalize=1
 size=3
-stride=1
+stride=2
 pad=1
-filters=512
+filters=256
 activation=leaky
 
+[route]
+layers = -1,44
+
 [convolutional]
 batch_normalize=1
-filters=256
+filters=128
 size=1
 stride=1
 pad=1
 activation=leaky
 
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
+[route]
+layers=-2
 
 [convolutional]
 batch_normalize=1
-filters=256
+filters=128
 size=1
 stride=1
 pad=1
@@ -676,71 +544,85 @@ activation=leaky
 
 [convolutional]
 batch_normalize=1
+filters=128
 size=3
 stride=1
 pad=1
-filters=512
 activation=leaky
 
 [convolutional]
-size=1
+batch_normalize=1
+filters=128
+size=3
 stride=1
 pad=1
-filters=75
-activation=linear
-
-[yolo]
-mask = 3,4,5
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=20
-num=9
-jitter=.3
-ignore_thresh = .5
-truth_thresh = 1
-random=1
+activation=leaky
 
 [route]
-layers = -4
+layers = -5,-3,-2,-1
 
+# 86
 [convolutional]
 batch_normalize=1
-filters=128
+filters=256
 size=1
 stride=1
 pad=1
 activation=leaky
 
-[upsample]
-stride=2
+#############################
 
-[route]
-layers = -1, 36
+# ============ End of Neck ============ #
 
+# ============ Head ============ #
 
 
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
+# P3
+[route]
+layers = 68
 
 [convolutional]
 batch_normalize=1
 size=3
 stride=1
 pad=1
-filters=256
+filters=128
 activation=leaky
 
 [convolutional]
-batch_normalize=1
-filters=128
 size=1
 stride=1
 pad=1
-activation=leaky
+filters=255
+#activation=linear
+activation=logistic
+
+[yolo]
+mask = 0,1,2
+anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
+classes=80
+num=9
+jitter=.1
+scale_x_y = 2.0
+objectness_smooth=1
+ignore_thresh = .7
+truth_thresh = 1
+#random=1
+resize=1.5
+iou_thresh=0.2
+iou_normalizer=0.05
+cls_normalizer=0.5
+obj_normalizer=1.0
+iou_loss=ciou
+nms_kind=diounms
+beta_nms=0.6
+new_coords=1
+max_delta=2
+
+
+# P4
+[route]
+layers = 77
 
 [convolutional]
 batch_normalize=1
@@ -751,35 +633,74 @@ filters=256
 activation=leaky
 
 [convolutional]
-batch_normalize=1
-filters=128
 size=1
 stride=1
 pad=1
-activation=leaky
+filters=255
+#activation=linear
+activation=logistic
+
+[yolo]
+mask = 3,4,5
+anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
+classes=80
+num=9
+jitter=.1
+scale_x_y = 2.0
+objectness_smooth=1
+ignore_thresh = .7
+truth_thresh = 1
+#random=1
+resize=1.5
+iou_thresh=0.2
+iou_normalizer=0.05
+cls_normalizer=0.5
+obj_normalizer=1.0
+iou_loss=ciou
+nms_kind=diounms
+beta_nms=0.6
+new_coords=1
+max_delta=2
+
+
+# P5
+[route]
+layers = 86
 
 [convolutional]
 batch_normalize=1
 size=3
 stride=1
 pad=1
-filters=256
+filters=512
 activation=leaky
 
 [convolutional]
 size=1
 stride=1
 pad=1
-filters=75
-activation=linear
+filters=255
+#activation=linear
+activation=logistic
 
 [yolo]
-mask = 0,1,2
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=20
+mask = 6,7,8
+anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
+classes=80
 num=9
-jitter=.3
-ignore_thresh = .5
+jitter=.1
+scale_x_y = 2.0
+objectness_smooth=1
+ignore_thresh = .7
 truth_thresh = 1
-random=1
-
+#random=1
+resize=1.5
+iou_thresh=0.2
+iou_normalizer=0.05
+cls_normalizer=0.5
+obj_normalizer=1.0
+iou_loss=ciou
+nms_kind=diounms
+beta_nms=0.6
+new_coords=1
+max_delta=2
diff --git a/darknet_ros_msgs/CHANGELOG.rst b/darknet_ros_msgs/CHANGELOG.rst
index cb25f7991..e6eedfdac 100644
--- a/darknet_ros_msgs/CHANGELOG.rst
+++ b/darknet_ros_msgs/CHANGELOG.rst
@@ -2,6 +2,16 @@
 Changelog for package darknet_ros_msgs
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
+1.1.5 (2021-04-08)
+------------------
+* Merge pull request `#189 <https://github.com/leggedrobotics/darknet_ros/issues/189>`_ from martinspedro/master
+  /darknet_ros/found_object uses custom msg with Header for improving synchronization
+* std_msgs::Int8 with Header for Object Count
+* Merge pull request `#183 <https://github.com/leggedrobotics/darknet_ros/issues/183>`_ from kunaltyagi/id_num
+  Add numerical ID and launch param for image
+* Adding numerical ID and launch param for image
+* Contributors: Kunal Tyagi, Marko Bjelonic, Pedro Martins
+
 1.1.4 (2019-03-03)
 ------------------
 
diff --git a/darknet_ros_msgs/package.xml b/darknet_ros_msgs/package.xml
index 1a16d3d9b..e70bb283a 100644
--- a/darknet_ros_msgs/package.xml
+++ b/darknet_ros_msgs/package.xml
@@ -2,7 +2,7 @@
 <?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
 <package format="3">
   <name>darknet_ros_msgs</name>
-  <version>1.1.4</version>
+  <version>1.1.5</version>
   <description>Darknet is an open source neural network framework that runs on CPU and GPU. You only look once (YOLO) is a state-of-the-art, real-time object detection system.</description>
   <maintainer email="marko.bjelonic@mavt.ethz.ch">Marko Bjelonic</maintainer>
   <license>BSD</license>
diff --git a/rm_darknet_CMakeLists.sh b/rm_darknet_CMakeLists.sh
new file mode 100755
index 000000000..13d9bae80
--- /dev/null
+++ b/rm_darknet_CMakeLists.sh
@@ -0,0 +1,4 @@
+#!/bin/sh
+SCRIPT_DIR=$(cd $(dirname $0); pwd)
+rm $SCRIPT_DIR/darknet/CMakeLists.txt
+rm $SCRIPT_DIR/darknet/src/csharp/CMakeLists.txt
\ No newline at end of file