object_detection_with_google_drive.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "object detection with google drive",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "gu85ksRfYWFc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "d4d73253-1241-467e-e725-f339e3e8f389"
      },
      "source": [
        "!git clone https://github.com/AlexeyAB/darknet"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'darknet'...\n",
            "remote: Enumerating objects: 14291, done.\u001b[K\n",
            "remote: Total 14291 (delta 0), reused 0 (delta 0), pack-reused 14291\u001b[K\n",
            "Receiving objects: 100% (14291/14291), 12.85 MiB | 25.55 MiB/s, done.\n",
            "Resolving deltas: 100% (9751/9751), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1VfJezNwYoDP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "b70e4348-8a09-4875-9b23-4200137d4996"
      },
      "source": [
        "%cd darknet\n",
        "!sed -i 's/OPENCV=0/OPENCV=1/' Makefile\n",
        "!sed -i 's/GPU=0/GPU=1/' Makefile\n",
        "!sed -i 's/CUDNN=0/CUDNN=1/' Makefile\n",
        "!sed -i 's/CUDNN_HALF=0/CUDNN_HALF=1' Makefile"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/darknet\n",
            "sed: -e expression #1, char 27: unterminated `s' command\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n-qghE4qY-yf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "c2662764-02ec-4b96-b4e0-e3e5b52d6624"
      },
      "source": [
        "!/usr/local/cuda/bin/nvcc --version"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2019 NVIDIA Corporation\n",
            "Built on Sun_Jul_28_19:07:16_PDT_2019\n",
            "Cuda compilation tools, release 10.1, V10.1.243\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jcCtmY0VZF1J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0a07e962-a98a-44c4-c875-b9b7a566510c"
      },
      "source": [
        "#make darknet\n",
        "!make"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mkdir -p ./obj/\n",
            "mkdir -p backup\n",
            "chmod +x *.sh\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/image_opencv.cpp -o obj/image_opencv.o\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid draw_detections_cv_v3(void**, detection*, int, float, char**, image**, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:926:23:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Krgb\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "                 float \u001b[01;35m\u001b[Krgb\u001b[m\u001b[K[3];\n",
            "                       \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid draw_train_loss(char*, void**, int, float, float, int, int, float, int, char*, float, int, int, double)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1127:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "             \u001b[01;35m\u001b[Kif\u001b[m\u001b[K (iteration_old == 0)\n",
            "             \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1130:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
            "          \u001b[01;36m\u001b[Kif\u001b[m\u001b[K (iteration_old != 0){\n",
            "          \u001b[01;36m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid cv_draw_object(image, float*, int, int, int*, float*, int*, int, char**)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1424:14:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbuff\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         char \u001b[01;35m\u001b[Kbuff\u001b[m\u001b[K[100];\n",
            "              \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1400:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kit_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kit_tb_res\u001b[m\u001b[K = cv::createTrackbar(it_trackbar_name, window_name, &it_trackbar_value, 1000);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1404:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Klr_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Klr_tb_res\u001b[m\u001b[K = cv::createTrackbar(lr_trackbar_name, window_name, &lr_trackbar_value, 20);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1408:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcl_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kcl_tb_res\u001b[m\u001b[K = cv::createTrackbar(cl_trackbar_name, window_name, &cl_trackbar_value, classes-1);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1411:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbo_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kbo_tb_res\u001b[m\u001b[K = cv::createTrackbar(bo_trackbar_name, window_name, boxonly, 1);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/http_stream.cpp -o obj/http_stream.o\n",
            "In file included from \u001b[01m\u001b[K./src/http_stream.cpp:580:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K./src/httplib.h:129:0:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"INVALID_SOCKET\" redefined\n",
            " #define INVALID_SOCKET (-1)\n",
            " \n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:73:0:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kthis is the location of the previous definition\n",
            " #define INVALID_SOCKET -1\n",
            " \n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kbool JSON_sender::write(const char*)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:249:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                 int \u001b[01;35m\u001b[Kn\u001b[m\u001b[K = _write(client, outputbuf, outlen);\n",
            "                     \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kbool MJPG_sender::write(const cv::Mat&)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:507:113:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%zu\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "                 sprintf(head, \"--mjpegstream\\r\\nContent-Type: image/jpeg\\r\\nContent-Length: %zu\\r\\n\\r\\n\", outlen\u001b[01;35m\u001b[K)\u001b[m\u001b[K;\n",
            "                                                                                                                 \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid set_track_id(detection*, int, float, float, float, int, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:845:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (int i = 0; \u001b[01;35m\u001b[Ki < v.size()\u001b[m\u001b[K; ++i) {\n",
            "                         \u001b[01;35m\u001b[K~~^~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:853:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "     for (int old_id = 0; \u001b[01;35m\u001b[Kold_id < old_dets.size()\u001b[m\u001b[K; ++old_id) {\n",
            "                          \u001b[01;35m\u001b[K~~~~~~~^~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:873:31:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "     for (int index = 0; \u001b[01;35m\u001b[Kindex < new_dets_num*old_dets.size()\u001b[m\u001b[K; ++index) {\n",
            "                         \u001b[01;35m\u001b[K~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:908:28:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "     if (\u001b[01;35m\u001b[Kold_dets_dq.size() > deque_size\u001b[m\u001b[K) old_dets_dq.pop_front();\n",
            "         \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/gemm.c -o obj/gemm.o\n",
            "\u001b[01m\u001b[K./src/gemm.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kconvolution_2d\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gemm.c:2038:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_w\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     const int \u001b[01;35m\u001b[Kout_w\u001b[m\u001b[K = (w + 2 * pad - ksize) / stride + 1;    // output_width=input_width for stride=1 and pad=1\n",
            "               \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gemm.c:2037:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     const int \u001b[01;35m\u001b[Kout_h\u001b[m\u001b[K = (h + 2 * pad - ksize) / stride + 1;    // output_height=input_height for stride=1 and pad=1\n",
            "               \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/utils.c -o obj/utils.o\n",
            "\u001b[01m\u001b[K./src/utils.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcustom_hash\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/utils.c:1039:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around assignment used as truth value [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n",
            "     while (\u001b[01;35m\u001b[Kc\u001b[m\u001b[K = *str++)\n",
            "            \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/dark_cuda.c -o obj/dark_cuda.o\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcudnn_check_error_extended\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:224:20:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between ‘\u001b[01m\u001b[KcudaError_t {aka enum cudaError}\u001b[m\u001b[K’ and ‘\u001b[01m\u001b[Kenum <anonymous>\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wenum-compare\u001b[m\u001b[K]\n",
            "         if (status \u001b[01;35m\u001b[K!=\u001b[m\u001b[K CUDNN_STATUS_SUCCESS)\n",
            "                    \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kpre_allocate_pinned_memory\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:276:40:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\"pre_allocate: size = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K MB, num_of_blocks = %Iu, block_size = %Iu MB \\n\",\n",
            "                                      \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
            "                                      \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n",
            "             \u001b[32m\u001b[Ksize / (1024*1024)\u001b[m\u001b[K, num_of_blocks, pinned_block_size / (1024 * 1024));\n",
            "             \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K          \n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:276:64:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\"pre_allocate: size = %Iu MB, num_of_blocks = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K, block_size = %Iu MB \\n\",\n",
            "                                                              \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
            "                                                              \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:276:82:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\"pre_allocate: size = %Iu MB, num_of_blocks = %Iu, block_size = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K MB \\n\",\n",
            "                                                                                \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
            "                                                                                \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:286:37:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "                 printf(\" Allocated \u001b[01;35m\u001b[K%d\u001b[m\u001b[K pinned block \\n\", pinned_block_size);\n",
            "                                    \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                    \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcuda_make_array_pinned_preallocated\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:307:43:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "             printf(\"\\n Pinned block_id = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, filled = %f %% \\n\", pinned_block_id, filled);\n",
            "                                          \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                          \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:322:64:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "             printf(\"Try to allocate new pinned memory, size = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K MB \\n\", \u001b[32m\u001b[Ksize / (1024 * 1024)\u001b[m\u001b[K);\n",
            "                                                               \u001b[01;35m\u001b[K~^\u001b[m\u001b[K         \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "                                                               \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:328:63:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "             printf(\"Try to allocate new pinned BLOCK, size = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K MB \\n\", \u001b[32m\u001b[Ksize / (1024 * 1024)\u001b[m\u001b[K);\n",
            "                                                              \u001b[01;35m\u001b[K~^\u001b[m\u001b[K         \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "                                                              \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/convolutional_layer.c -o obj/convolutional_layer.o\n",
            "\u001b[01m\u001b[K./src/convolutional_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_convolutional_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/convolutional_layer.c:1337:32:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kt_intput_size\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                         size_t \u001b[01;35m\u001b[Kt_intput_size\u001b[m\u001b[K = binary_transpose_align_input(k, n, state.workspace, &l.t_bit_input, ldb_align, l.bit_align);\n",
            "                                \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/list.c -o obj/list.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/image.c -o obj/image.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/activations.c -o obj/activations.o\n",
            "\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kactivate\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KRELU6\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(a){\n",
            "     \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KSWISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KHARD_MISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN_SOFTMAX\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN_SOFTMAX_MAXVAL\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgradient\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KSWISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(a){\n",
            "     \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KHARD_MISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/im2col.c -o obj/im2col.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/col2im.c -o obj/col2im.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/blas.c -o obj/blas.o\n",
            "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbackward_shortcut_multilayer_cpu\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas.c:207:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_index\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                 int \u001b[01;35m\u001b[Kout_index\u001b[m\u001b[K = id;\n",
            "                     \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfind_sim\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas.c:597:59:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\" Error: find_sim(): sim isn't found: i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, j = %d, z = %d \\n\", i, j, z);\n",
            "                                                          \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                          \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:597:67:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\" Error: find_sim(): sim isn't found: i = %d, j = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, z = %d \\n\", i, j, z);\n",
            "                                                                  \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                  \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:597:75:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\" Error: find_sim(): sim isn't found: i = %d, j = %d, z = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, j, z);\n",
            "                                                                          \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                          \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfind_P_constrastive\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas.c:611:68:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\" Error: find_P_constrastive(): P isn't found: i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, j = %d, z = %d \\n\", i, j, z);\n",
            "                                                                   \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                   \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:611:76:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\" Error: find_P_constrastive(): P isn't found: i = %d, j = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, z = %d \\n\", i, j, z);\n",
            "                                                                           \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                           \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:611:84:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\" Error: find_P_constrastive(): P isn't found: i = %d, j = %d, z = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, j, z);\n",
            "                                                                                   \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                                   \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KP_constrastive_f\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas.c:651:79:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, l = %d \\n\", i, l);\n",
            "                                                                              \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                              \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:651:87:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = %d, l = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, l);\n",
            "                                                                                      \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                                      \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KP_constrastive\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas.c:780:79:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, l = %d \\n\", i, l);\n",
            "                                                                              \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                              \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:780:87:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = %d, l = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, l);\n",
            "                                                                                      \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                                      \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/crop_layer.c -o obj/crop_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/dropout_layer.c -o obj/dropout_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/maxpool_layer.c -o obj/maxpool_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/softmax_layer.c -o obj/softmax_layer.o\n",
            "\u001b[01m\u001b[K./src/softmax_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_contrastive_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/softmax_layer.c:202:101:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 9 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "     fprintf(stderr, \"contrastive %4d x%4d x%4d x emb_size %4d x batch: %4d  classes = %4d, step = \u001b[01;35m\u001b[K%4d\u001b[m\u001b[K \\n\", w, h, l.n, l.embedding_size, batch, l.classes, step);\n",
            "                                                                                                   \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
            "                                                                                                   \u001b[32m\u001b[K%4ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/softmax_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_contrastive_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/softmax_layer.c:243:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kmax_truth\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "                     float \u001b[01;35m\u001b[Kmax_truth\u001b[m\u001b[K = 0;\n",
            "                           \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/softmax_layer.c:420:71:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "             printf(\" Error: too large number of bboxes: contr_size = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K > max_contr_size  = %d \\n\", contr_size, max_contr_size);\n",
            "                                                                      \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                      \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/data.c -o obj/data.o\n",
            "\u001b[01m\u001b[K./src/data.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kload_data_detection\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/data.c:1294:24:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kx\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                 int k, \u001b[01;35m\u001b[Kx\u001b[m\u001b[K, y;\n",
            "                        \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/data.c:1090:43:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kr_scale\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "     float r1 = 0, r2 = 0, r3 = 0, r4 = 0, \u001b[01;35m\u001b[Kr_scale\u001b[m\u001b[K = 0;\n",
            "                                           \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/matrix.c -o obj/matrix.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/network.c -o obj/network.o\n",
            "\u001b[01m\u001b[K./src/network.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_network\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/network.c:615:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Knet->input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped))\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/network.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/connected_layer.c -o obj/connected_layer.o\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_connected_layer_gpu\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:346:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kone\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Kone\u001b[m\u001b[K = 1;    // alpha[0], beta[0]\n",
            "           \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:344:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kc\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float * \u001b[01;35m\u001b[Kc\u001b[m\u001b[K = l.output_gpu;\n",
            "             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:343:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kb\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float * \u001b[01;35m\u001b[Kb\u001b[m\u001b[K = l.weights_gpu;\n",
            "             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:342:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ka\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float * \u001b[01;35m\u001b[Ka\u001b[m\u001b[K = state.input;\n",
            "             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:341:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kn\u001b[m\u001b[K = l.outputs;\n",
            "         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:340:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kk\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kk\u001b[m\u001b[K = l.inputs;\n",
            "         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:339:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Km\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Km\u001b[m\u001b[K = l.batch;\n",
            "         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/cost_layer.c -o obj/cost_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/parser.c -o obj/parser.o\n",
            "\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kparse_network_cfg_custom\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/parser.c:1667:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Knet.input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1;\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activation_layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/parser.c:6\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kget_classes_multipliers\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/parser.c:427:29:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kargument 1 range [18446744071562067968, 18446744073709551615] exceeds maximum object size 9223372036854775807 [\u001b[01;35m\u001b[K-Walloc-size-larger-than=\u001b[m\u001b[K]\n",
            "         \u001b[01;35m\u001b[Kclasses_multipliers = (float *)calloc(classes_counters, sizeof(float))\u001b[m\u001b[K;\n",
            "         \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K./src/parser.c:3:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/include/stdlib.h:541:14:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kin a call to allocation function ‘\u001b[01m\u001b[Kcalloc\u001b[m\u001b[K’ declared here\n",
            " extern void *\u001b[01;36m\u001b[Kcalloc\u001b[m\u001b[K (size_t __nmemb, size_t __size)\n",
            "              \u001b[01;36m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/option_list.c -o obj/option_list.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/darknet.c -o obj/darknet.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/detection_layer.c -o obj/detection_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/captcha.c -o obj/captcha.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/route_layer.c -o obj/route_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/writing.c -o obj/writing.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/box.c -o obj/box.o\n",
            "\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbox_iou_kind\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/box.c:154:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMSE\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(iou_kind) {\n",
            "     \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdiounms_sort\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/box.c:898:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbeta_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                     float \u001b[01;35m\u001b[Kbeta_prob\u001b[m\u001b[K = pow(dets[j].prob[k], 2) / sum_prob;\n",
            "                           \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/box.c:897:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kalpha_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                     float \u001b[01;35m\u001b[Kalpha_prob\u001b[m\u001b[K = pow(dets[i].prob[k], 2) / sum_prob;\n",
            "                           \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/nightmare.c -o obj/nightmare.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/normalization_layer.c -o obj/normalization_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/avgpool_layer.c -o obj/avgpool_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/coco.c -o obj/coco.o\n",
            "\u001b[01m\u001b[K./src/coco.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_coco_recall\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/coco.c:248:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbase\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     char *\u001b[01;35m\u001b[Kbase\u001b[m\u001b[K = \"results/comp4_det_test_\";\n",
            "           \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/dice.c -o obj/dice.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/yolo.c -o obj/yolo.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/detector.c -o obj/detector.o\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kprint_cocos\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:477:29:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat not a string literal and no format arguments [\u001b[01;35m\u001b[K-Wformat-security\u001b[m\u001b[K]\n",
            "                 fprintf(fp, \u001b[01;35m\u001b[Kbuff\u001b[m\u001b[K);\n",
            "                             \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Keliminate_bdd\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:570:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kstatement with no effect [\u001b[01;35m\u001b[K-Wunused-value\u001b[m\u001b[K]\n",
            "                     \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K (k; buf[k + n] != '\\0'; k++)\n",
            "                     \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_detector\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:691:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmkd2\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         int \u001b[01;35m\u001b[Kmkd2\u001b[m\u001b[K = make_directory(buff2, 0777);\n",
            "             \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:689:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmkd\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         int \u001b[01;35m\u001b[Kmkd\u001b[m\u001b[K = make_directory(buff, 0777);\n",
            "             \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_detector_map\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:1322:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kclass_recall\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         float \u001b[01;35m\u001b[Kclass_recall\u001b[m\u001b[K = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)(truth_classes_count[i] - tp_for_thresh_per_class[i]));\n",
            "               \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:1321:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kclass_precision\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         float \u001b[01;35m\u001b[Kclass_precision\u001b[m\u001b[K = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)fp_for_thresh_per_class[i]);\n",
            "               \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdraw_object\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:1856:19:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kinv_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "             float \u001b[01;35m\u001b[Kinv_loss\u001b[m\u001b[K = 1.0 / max_val_cmp(0.01, avg_loss);\n",
            "                   \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/layer.c -o obj/layer.o\n",
            "\u001b[01m\u001b[K./src/layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfree_layer_custom\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/layer.c:204:68:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around ‘\u001b[01m\u001b[K&&\u001b[m\u001b[K’ within ‘\u001b[01m\u001b[K||\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n",
            "     if (l.delta_gpu && (l.optimized_memory < 1 || \u001b[01;35m\u001b[Kl.keep_delta_gpu && l.optimized_memory < 3\u001b[m\u001b[K)) cuda_free(l.delta_gpu), l.delta_gpu = NULL;\n",
            "                                                   \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/compare.c -o obj/compare.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/classifier.c -o obj/classifier.o\n",
            "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_classifier\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/classifier.c:146:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcount\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kcount\u001b[m\u001b[K = 0;\n",
            "         \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kpredict_classifier\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/classifier.c:855:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktime\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     clock_t \u001b[01;35m\u001b[Ktime\u001b[m\u001b[K;\n",
            "             \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdemo_classifier\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/classifier.c:1287:49:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktval_result\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         struct timeval tval_before, tval_after, \u001b[01;35m\u001b[Ktval_result\u001b[m\u001b[K;\n",
            "                                                 \u001b[01;35m\u001b[K^~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/classifier.c:1287:37:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktval_after\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         struct timeval tval_before, \u001b[01;35m\u001b[Ktval_after\u001b[m\u001b[K, tval_result;\n",
            "                                     \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/local_layer.c -o obj/local_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/swag.c -o obj/swag.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/shortcut_layer.c -o obj/shortcut_layer.o\n",
            "\u001b[01m\u001b[K./src/shortcut_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_shortcut_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/shortcut_layer.c:55:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kscale\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         float \u001b[01;35m\u001b[Kscale\u001b[m\u001b[K = sqrt(2. / l.nweights);\n",
            "               \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/activation_layer.c -o obj/activation_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/rnn_layer.c -o obj/rnn_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/gru_layer.c -o obj/gru_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/rnn.c -o obj/rnn.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/rnn_vid.c -o obj/rnn_vid.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/crnn_layer.c -o obj/crnn_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/demo.c -o obj/demo.o\n",
            "\u001b[01m\u001b[K./src/demo.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdetect_in_thread\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/demo.c:100:16:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kprediction\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         float *\u001b[01;35m\u001b[Kprediction\u001b[m\u001b[K = network_predict(net, X);\n",
            "                \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/demo.c:98:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kl\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         layer \u001b[01;35m\u001b[Kl\u001b[m\u001b[K = net.layers[net.n - 1];\n",
            "               \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/tag.c -o obj/tag.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/cifar.c -o obj/cifar.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/go.c -o obj/go.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/batchnorm_layer.c -o obj/batchnorm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/art.c -o obj/art.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/region_layer.c -o obj/region_layer.o\n",
            "\u001b[01m\u001b[K./src/region_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_region_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/region_layer.c:59:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kold_h\u001b[m\u001b[K = l->h;\n",
            "         \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/region_layer.c:58:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_w\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kold_w\u001b[m\u001b[K = l->w;\n",
            "         \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/reorg_layer.c -o obj/reorg_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/reorg_old_layer.c -o obj/reorg_old_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/super.c -o obj/super.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/voxel.c -o obj/voxel.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/tree.c -o obj/tree.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/yolo_layer.c -o obj/yolo_layer.o\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:66:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.output, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:73:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.delta, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:103:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:112:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:390:25:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kbest_match_t\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "                     int \u001b[01;35m\u001b[Kbest_match_t\u001b[m\u001b[K = 0;\n",
            "                         \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/gaussian_yolo_layer.c -o obj/gaussian_yolo_layer.o\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_gaussian_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:71:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.output, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:78:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.delta, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_gaussian_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:110:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:119:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/upsample_layer.c -o obj/upsample_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/lstm_layer.c -o obj/lstm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/conv_lstm_layer.c -o obj/conv_lstm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/scale_channels_layer.c -o obj/scale_channels_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/sam_layer.c -o obj/sam_layer.o\n",
            "nvcc -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,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/convolutional_kernels.cu -o obj/convolutional_kernels.o\n",
            "\u001b[01m\u001b[K./src/convolutional_kernels.cu:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid backward_convolutional_layer_gpu(convolutional_layer, network_state)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/convolutional_kernels.cu:853:40:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[K            if (*state.net.max_output16_size < l.\u001b[m\u001b[Knweights) {\n",
            "                 \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "nvcc -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,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/activation_kernels.cu -o obj/activation_kernels.o\n",
            "./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n",
            "\n",
            "./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n",
            "\n",
            "./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n",
            "\n",
            "./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n",
            "\n",
            "./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n",
            "\n",
            "nvcc -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,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/im2col_kernels.cu -o obj/im2col_kernels.o\n",
            "nvcc -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,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/col2im_kernels.cu -o obj/col2im_kernels.o\n",
            "nvcc -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,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/blas_kernels.cu -o obj/blas_kernels.o\n",
            "./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n",
            "\n",
            "./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n",
            "\n",
            "./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n",
            "\n",
            "./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n",
            "\n",
            "./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n",
            "\n",
            "./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n",
            "\n",
            "\u001b[01m\u001b[K./src/blas_kernels.cu:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid backward_shortcut_multilayer_gpu(int, int, int, int*, float**, float*, float*, float*, float*, int, float*, float**, WEIGHTS_NORMALIZATION_T)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas_kernels.cu:1130:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kstep\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kint \u001b[m\u001b[Kstep = 0;\n",
            "     \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "nvcc -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,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/crop_layer_kernels.cu -o obj/crop_layer_kernels.o\n",
            "nvcc -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,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/dropout_layer_kernels.cu -o obj/dropout_layer_kernels.o\n",
            "./src/dropout_layer_kernels.cu(140): warning: variable \"cur_scale\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(245): warning: variable \"cur_scale\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(262): warning: variable \"block_prob\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(140): warning: variable \"cur_scale\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(245): warning: variable \"cur_scale\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(262): warning: variable \"block_prob\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(140): warning: variable \"cur_scale\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(245): warning: variable \"cur_scale\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(262): warning: variable \"block_prob\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(140): warning: variable \"cur_scale\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(245): warning: variable \"cur_scale\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(262): warning: variable \"block_prob\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(140): warning: variable \"cur_scale\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(245): warning: variable \"cur_scale\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(262): warning: variable \"block_prob\" was declared but never referenced\n",
            "\n",
            "nvcc -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,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/maxpool_layer_kernels.cu -o obj/maxpool_layer_kernels.o\n",
            "nvcc -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,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/network_kernels.cu -o obj/network_kernels.o\n",
            "./src/network_kernels.cu(364): warning: variable \"l\" was declared but never referenced\n",
            "\n",
            "./src/network_kernels.cu(364): warning: variable \"l\" was declared but never referenced\n",
            "\n",
            "./src/network_kernels.cu(364): warning: variable \"l\" was declared but never referenced\n",
            "\n",
            "./src/network_kernels.cu(364): warning: variable \"l\" was declared but never referenced\n",
            "\n",
            "./src/network_kernels.cu(364): warning: variable \"l\" was declared but never referenced\n",
            "\n",
            "\u001b[01m\u001b[K./src/network_kernels.cu:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfloat train_network_datum_gpu(network, float*, float*)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/network_kernels.cu:364:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kl\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "       \u001b[01;35m\u001b[K \u001b[m\u001b[K layer l = net.layers[net.n - 1];\n",
            "       \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "nvcc -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,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/avgpool_layer_kernels.cu -o obj/avgpool_layer_kernels.o\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include obj/image_opencv.o obj/http_stream.o obj/gemm.o obj/utils.o obj/dark_cuda.o obj/convolutional_layer.o obj/list.o obj/image.o obj/activations.o obj/im2col.o obj/col2im.o obj/blas.o obj/crop_layer.o obj/dropout_layer.o obj/maxpool_layer.o obj/softmax_layer.o obj/data.o obj/matrix.o obj/network.o obj/connected_layer.o obj/cost_layer.o obj/parser.o obj/option_list.o obj/darknet.o obj/detection_layer.o obj/captcha.o obj/route_layer.o obj/writing.o obj/box.o obj/nightmare.o obj/normalization_layer.o obj/avgpool_layer.o obj/coco.o obj/dice.o obj/yolo.o obj/detector.o obj/layer.o obj/compare.o obj/classifier.o obj/local_layer.o obj/swag.o obj/shortcut_layer.o obj/activation_layer.o obj/rnn_layer.o obj/gru_layer.o obj/rnn.o obj/rnn_vid.o obj/crnn_layer.o obj/demo.o obj/tag.o obj/cifar.o obj/go.o obj/batchnorm_layer.o obj/art.o obj/region_layer.o obj/reorg_layer.o obj/reorg_old_layer.o obj/super.o obj/voxel.o obj/tree.o obj/yolo_layer.o obj/gaussian_yolo_layer.o obj/upsample_layer.o obj/lstm_layer.o obj/conv_lstm_layer.o obj/scale_channels_layer.o obj/sam_layer.o obj/convolutional_kernels.o obj/activation_kernels.o obj/im2col_kernels.o obj/col2im_kernels.o obj/blas_kernels.o obj/crop_layer_kernels.o obj/dropout_layer_kernels.o obj/maxpool_layer_kernels.o obj/network_kernels.o obj/avgpool_layer_kernels.o -o darknet -lm -pthread `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv` -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand -L/usr/local/cudnn/lib64 -lcudnn -lstdc++\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lao3wrdJgBQQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "750a2801-eb30-443b-b107-88390d27480b"
      },
      "source": [
        "!wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-09-16 14:25:30--  https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights\n",
            "Resolving github.com (github.com)... 192.30.255.112\n",
            "Connecting to github.com (github.com)|192.30.255.112|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github-production-release-asset-2e65be.s3.amazonaws.com/75388965/ba4b6380-889c-11ea-9751-f994f5961796?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20200916%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20200916T142530Z&X-Amz-Expires=300&X-Amz-Signature=8f952c2bf372c39060d93516c9bd94ffd4a126c09ec2a61cc360a07e2c016857&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=75388965&response-content-disposition=attachment%3B%20filename%3Dyolov4.weights&response-content-type=application%2Foctet-stream [following]\n",
            "--2020-09-16 14:25:30--  https://github-production-release-asset-2e65be.s3.amazonaws.com/75388965/ba4b6380-889c-11ea-9751-f994f5961796?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20200916%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20200916T142530Z&X-Amz-Expires=300&X-Amz-Signature=8f952c2bf372c39060d93516c9bd94ffd4a126c09ec2a61cc360a07e2c016857&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=75388965&response-content-disposition=attachment%3B%20filename%3Dyolov4.weights&response-content-type=application%2Foctet-stream\n",
            "Resolving github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)... 52.217.82.228\n",
            "Connecting to github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)|52.217.82.228|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 257717640 (246M) [application/octet-stream]\n",
            "Saving to: ‘yolov4.weights’\n",
            "\n",
            "yolov4.weights      100%[===================>] 245.78M  46.4MB/s    in 5.7s    \n",
            "\n",
            "2020-09-16 14:25:36 (42.9 MB/s) - ‘yolov4.weights’ saved [257717640/257717640]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yL34KhgQgkSK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# define helper functions\n",
        "def imShow(path):\n",
        "  import cv2\n",
        "  import matplotlib.pyplot as plt\n",
        "  %matplotlib inline\n",
        "\n",
        "  image = cv2.imread(path)\n",
        "  height, width = image.shape[:2]\n",
        "  resized_image = cv2.resize(image,(3*width, 3*height), interpolation = cv2.INTER_CUBIC)\n",
        "\n",
        "  fig = plt.gcf()\n",
        "  fig.set_size_inches(18, 10)\n",
        "  plt.axis(\"off\")\n",
        "  plt.imshow(cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB))\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWUCM-j3gk_7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "716b6a04-adb2-498e-ac87-14a2fecff434"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wJe6baQihaKI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "403495f1-35a4-4d1e-d03b-10c21b670ef1"
      },
      "source": [
        "#Creating symbolic link\n",
        "!ln -s /content/gdrive/My\\ Drive/ /mydrive\n",
        "!ls /mydrive"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "20200916_195103.mp4  colab  Lifebook\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JlVrlmqIhvCS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0f8523c6-2d4c-4f00-e5fb-1e0b3a0dbf66"
      },
      "source": [
        "# run detections on image within your Google Drive!\n",
        "!./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights /mydrive/colab/download.jpg\n",
        "imShow('predictions.jpg')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " CUDA-version: 10010 (10010), cuDNN: 7.6.5, GPU count: 1  \n",
            " OpenCV version: 3.2.0\n",
            " 0 : compute_capability = 750, cudnn_half = 0, GPU: Tesla T4 \n",
            "net.optimized_memory = 0 \n",
            "mini_batch = 1, batch = 8, time_steps = 1, train = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 conv     32       3 x 3/ 1    608 x 608 x   3 ->  608 x 608 x  32 0.639 BF\n",
            "   1 conv     64       3 x 3/ 2    608 x 608 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   2 conv     64       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  64 0.757 BF\n",
            "   3 route  1 \t\t                           ->  304 x 304 x  64 \n",
            "   4 conv     64       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  64 0.757 BF\n",
            "   5 conv     32       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  32 0.379 BF\n",
            "   6 conv     64       3 x 3/ 1    304 x 304 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   7 Shortcut Layer: 4,  wt = 0, wn = 0, outputs: 304 x 304 x  64 0.006 BF\n",
            "   8 conv     64       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  64 0.757 BF\n",
            "   9 route  8 2 \t                           ->  304 x 304 x 128 \n",
            "  10 conv     64       1 x 1/ 1    304 x 304 x 128 ->  304 x 304 x  64 1.514 BF\n",
            "  11 conv    128       3 x 3/ 2    304 x 304 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "  12 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "  13 route  11 \t\t                           ->  152 x 152 x 128 \n",
            "  14 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "  15 conv     64       1 x 1/ 1    152 x 152 x  64 ->  152 x 152 x  64 0.189 BF\n",
            "  16 conv     64       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x  64 1.703 BF\n",
            "  17 Shortcut Layer: 14,  wt = 0, wn = 0, outputs: 152 x 152 x  64 0.001 BF\n",
            "  18 conv     64       1 x 1/ 1    152 x 152 x  64 ->  152 x 152 x  64 0.189 BF\n",
            "  19 conv     64       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x  64 1.703 BF\n",
            "  20 Shortcut Layer: 17,  wt = 0, wn = 0, outputs: 152 x 152 x  64 0.001 BF\n",
            "  21 conv     64       1 x 1/ 1    152 x 152 x  64 ->  152 x 152 x  64 0.189 BF\n",
            "  22 route  21 12 \t                           ->  152 x 152 x 128 \n",
            "  23 conv    128       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x 128 0.757 BF\n",
            "  24 conv    256       3 x 3/ 2    152 x 152 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  25 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  26 route  24 \t\t                           ->   76 x  76 x 256 \n",
            "  27 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  28 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF\n",
            "  29 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF\n",
            "  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF\n",
            "  31 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF\n",
            "  32 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF\n",
            "  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF\n",
            "  34 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF\n",
            "  35 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF\n",
            "  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF\n",
            "  37 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF\n",
            "  38 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF\n",
            "  39 Shortcut Layer: 36,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF\n",
            "  40 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF\n",
            "  41 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF\n",
            "  42 Shortcut Layer: 39,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF\n",
            "  43 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF\n",
            "  44 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF\n",
            "  45 Shortcut Layer: 42,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF\n",
            "  46 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF\n",
            "  47 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF\n",
            "  48 Shortcut Layer: 45,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF\n",
            "  49 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF\n",
            "  50 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF\n",
            "  51 Shortcut Layer: 48,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF\n",
            "  52 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF\n",
            "  53 route  52 25 \t                           ->   76 x  76 x 256 \n",
            "  54 conv    256       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 256 0.757 BF\n",
            "  55 conv    512       3 x 3/ 2     76 x  76 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  56 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  57 route  55 \t\t                           ->   38 x  38 x 512 \n",
            "  58 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  59 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF\n",
            "  60 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF\n",
            "  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF\n",
            "  62 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF\n",
            "  63 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF\n",
            "  64 Shortcut Layer: 61,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF\n",
            "  65 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF\n",
            "  66 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF\n",
            "  67 Shortcut Layer: 64,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF\n",
            "  68 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF\n",
            "  69 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF\n",
            "  70 Shortcut Layer: 67,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF\n",
            "  71 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF\n",
            "  72 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF\n",
            "  73 Shortcut Layer: 70,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF\n",
            "  74 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF\n",
            "  75 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF\n",
            "  76 Shortcut Layer: 73,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF\n",
            "  77 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF\n",
            "  78 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF\n",
            "  79 Shortcut Layer: 76,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF\n",
            "  80 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF\n",
            "  81 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF\n",
            "  82 Shortcut Layer: 79,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF\n",
            "  83 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF\n",
            "  84 route  83 56 \t                           ->   38 x  38 x 512 \n",
            "  85 conv    512       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 512 0.757 BF\n",
            "  86 conv   1024       3 x 3/ 2     38 x  38 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  87 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  88 route  86 \t\t                           ->   19 x  19 x1024 \n",
            "  89 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  90 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF\n",
            "  91 conv    512       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x 512 1.703 BF\n",
            "  92 Shortcut Layer: 89,  wt = 0, wn = 0, outputs:  19 x  19 x 512 0.000 BF\n",
            "  93 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF\n",
            "  94 conv    512       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x 512 1.703 BF\n",
            "  95 Shortcut Layer: 92,  wt = 0, wn = 0, outputs:  19 x  19 x 512 0.000 BF\n",
            "  96 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF\n",
            "  97 conv    512       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x 512 1.703 BF\n",
            "  98 Shortcut Layer: 95,  wt = 0, wn = 0, outputs:  19 x  19 x 512 0.000 BF\n",
            "  99 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF\n",
            " 100 conv    512       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x 512 1.703 BF\n",
            " 101 Shortcut Layer: 98,  wt = 0, wn = 0, outputs:  19 x  19 x 512 0.000 BF\n",
            " 102 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF\n",
            " 103 route  102 87 \t                           ->   19 x  19 x1024 \n",
            " 104 conv   1024       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x1024 0.757 BF\n",
            " 105 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            " 106 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            " 107 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            " 108 max                5x 5/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.005 BF\n",
            " 109 route  107 \t\t                           ->   19 x  19 x 512 \n",
            " 110 max                9x 9/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.015 BF\n",
            " 111 route  107 \t\t                           ->   19 x  19 x 512 \n",
            " 112 max               13x13/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.031 BF\n",
            " 113 route  112 110 108 107 \t                   ->   19 x  19 x2048 \n",
            " 114 conv    512       1 x 1/ 1     19 x  19 x2048 ->   19 x  19 x 512 0.757 BF\n",
            " 115 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            " 116 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            " 117 conv    256       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 256 0.095 BF\n",
            " 118 upsample                 2x    19 x  19 x 256 ->   38 x  38 x 256\n",
            " 119 route  85 \t\t                           ->   38 x  38 x 512 \n",
            " 120 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            " 121 route  120 118 \t                           ->   38 x  38 x 512 \n",
            " 122 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            " 123 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            " 124 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            " 125 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            " 126 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            " 127 conv    128       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 128 0.095 BF\n",
            " 128 upsample                 2x    38 x  38 x 128 ->   76 x  76 x 128\n",
            " 129 route  54 \t\t                           ->   76 x  76 x 256 \n",
            " 130 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 131 route  130 128 \t                           ->   76 x  76 x 256 \n",
            " 132 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 133 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 134 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 135 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 136 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 137 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 138 conv    255       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 255 0.754 BF\n",
            " 139 yolo\n",
            "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20\n",
            "nms_kind: greedynms (1), beta = 0.600000 \n",
            " 140 route  136 \t\t                           ->   76 x  76 x 128 \n",
            " 141 conv    256       3 x 3/ 2     76 x  76 x 128 ->   38 x  38 x 256 0.852 BF\n",
            " 142 route  141 126 \t                           ->   38 x  38 x 512 \n",
            " 143 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            " 144 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            " 145 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            " 146 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            " 147 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            " 148 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            " 149 conv    255       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 255 0.377 BF\n",
            " 150 yolo\n",
            "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10\n",
            "nms_kind: greedynms (1), beta = 0.600000 \n",
            " 151 route  147 \t\t                           ->   38 x  38 x 256 \n",
            " 152 conv    512       3 x 3/ 2     38 x  38 x 256 ->   19 x  19 x 512 0.852 BF\n",
            " 153 route  152 116 \t                           ->   19 x  19 x1024 \n",
            " 154 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            " 155 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            " 156 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            " 157 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            " 158 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            " 159 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            " 160 conv    255       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 255 0.189 BF\n",
            " 161 yolo\n",
            "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05\n",
            "nms_kind: greedynms (1), beta = 0.600000 \n",
            "Total BFLOPS 128.459 \n",
            "avg_outputs = 1068395 \n",
            " Allocate additional workspace_size = 52.43 MB \n",
            "Loading weights from yolov4.weights...\n",
            " seen 64, trained: 32032 K-images (500 Kilo-batches_64) \n",
            "Done! Loaded 162 layers from weights-file \n",
            " Detection layer: 139 - type = 28 \n",
            " Detection layer: 150 - type = 28 \n",
            " Detection layer: 161 - type = 28 \n",
            "/mydrive/colab/download.jpg: Predicted in 78.908000 milli-seconds.\n",
            "truck: 72%\n",
            "car: 38%\n",
            "car: 26%\n",
            "truck: 43%\n",
            "person: 85%\n",
            "bicycle: 34%\n",
            "traffic light: 51%\n",
            "car: 99%\n",
            "car: 27%\n",
            "car: 99%\n",
            "car: 81%\n",
            "truck: 68%\n",
            "car: 39%\n",
            "Unable to init server: Could not connect: Connection refused\n",
            "\n",
            "(predictions:1269): Gtk-\u001b[1;33mWARNING\u001b[0m **: \u001b[34m14:26:12.899\u001b[0m: cannot open display: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uDiahGbtq3u1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3314d0d2-b275-40e9-bd5f-3c426e1eb3e0"
      },
      "source": [
        "!./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -dont_show /mydrive/20200916_195103.mp4 -i 0 -out_filename /mydrive/results.avi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " CUDA-version: 10010 (10010), cuDNN: 7.6.5, GPU count: 1  \n",
            " OpenCV version: 3.2.0\n",
            "Demo\n",
            " 0 : compute_capability = 750, cudnn_half = 0, GPU: Tesla T4 \n",
            "net.optimized_memory = 0 \n",
            "mini_batch = 1, batch = 8, time_steps = 1, train = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 conv     32       3 x 3/ 1    608 x 608 x   3 ->  608 x 608 x  32 0.639 BF\n",
            "   1 conv     64       3 x 3/ 2    608 x 608 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   2 conv     64       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  64 0.757 BF\n",
            "   3 route  1 \t\t                           ->  304 x 304 x  64 \n",
            "   4 conv     64       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  64 0.757 BF\n",
            "   5 conv     32       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  32 0.379 BF\n",
            "   6 conv     64       3 x 3/ 1    304 x 304 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   7 Shortcut Layer: 4,  wt = 0, wn = 0, outputs: 304 x 304 x  64 0.006 BF\n",
            "   8 conv     64       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  64 0.757 BF\n",
            "   9 route  8 2 \t                           ->  304 x 304 x 128 \n",
            "  10 conv     64       1 x 1/ 1    304 x 304 x 128 ->  304 x 304 x  64 1.514 BF\n",
            "  11 conv    128       3 x 3/ 2    304 x 304 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "  12 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "  13 route  11 \t\t                           ->  152 x 152 x 128 \n",
            "  14 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "  15 conv     64       1 x 1/ 1    152 x 152 x  64 ->  152 x 152 x  64 0.189 BF\n",
            "  16 conv     64       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x  64 1.703 BF\n",
            "  17 Shortcut Layer: 14,  wt = 0, wn = 0, outputs: 152 x 152 x  64 0.001 BF\n",
            "  18 conv     64       1 x 1/ 1    152 x 152 x  64 ->  152 x 152 x  64 0.189 BF\n",
            "  19 conv     64       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x  64 1.703 BF\n",
            "  20 Shortcut Layer: 17,  wt = 0, wn = 0, outputs: 152 x 152 x  64 0.001 BF\n",
            "  21 conv     64       1 x 1/ 1    152 x 152 x  64 ->  152 x 152 x  64 0.189 BF\n",
            "  22 route  21 12 \t                           ->  152 x 152 x 128 \n",
            "  23 conv    128       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x 128 0.757 BF\n",
            "  24 conv    256       3 x 3/ 2    152 x 152 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  25 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  26 route  24 \t\t                           ->   76 x  76 x 256 \n",
            "  27 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  28 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF\n",
            "  29 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF\n",
            "  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF\n",
            "  31 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF\n",
            "  32 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF\n",
            "  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF\n",
            "  34 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF\n",
            "  35 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF\n",
            "  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF\n",
            "  37 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF\n",
            "  38 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF\n",
            "  39 Shortcut Layer: 36,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF\n",
            "  40 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF\n",
            "  41 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF\n",
            "  42 Shortcut Layer: 39,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF\n",
            "  43 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF\n",
            "  44 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF\n",
            "  45 Shortcut Layer: 42,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF\n",
            "  46 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF\n",
            "  47 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF\n",
            "  48 Shortcut Layer: 45,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF\n",
            "  49 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF\n",
            "  50 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF\n",
            "  51 Shortcut Layer: 48,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF\n",
            "  52 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF\n",
            "  53 route  52 25 \t                           ->   76 x  76 x 256 \n",
            "  54 conv    256       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 256 0.757 BF\n",
            "  55 conv    512       3 x 3/ 2     76 x  76 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  56 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  57 route  55 \t\t                           ->   38 x  38 x 512 \n",
            "  58 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  59 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF\n",
            "  60 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF\n",
            "  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF\n",
            "  62 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF\n",
            "  63 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF\n",
            "  64 Shortcut Layer: 61,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF\n",
            "  65 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF\n",
            "  66 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF\n",
            "  67 Shortcut Layer: 64,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF\n",
            "  68 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF\n",
            "  69 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF\n",
            "  70 Shortcut Layer: 67,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF\n",
            "  71 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF\n",
            "  72 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF\n",
            "  73 Shortcut Layer: 70,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF\n",
            "  74 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF\n",
            "  75 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF\n",
            "  76 Shortcut Layer: 73,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF\n",
            "  77 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF\n",
            "  78 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF\n",
            "  79 Shortcut Layer: 76,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF\n",
            "  80 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF\n",
            "  81 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF\n",
            "  82 Shortcut Layer: 79,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF\n",
            "  83 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF\n",
            "  84 route  83 56 \t                           ->   38 x  38 x 512 \n",
            "  85 conv    512       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 512 0.757 BF\n",
            "  86 conv   1024       3 x 3/ 2     38 x  38 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  87 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  88 route  86 \t\t                           ->   19 x  19 x1024 \n",
            "  89 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  90 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF\n",
            "  91 conv    512       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x 512 1.703 BF\n",
            "  92 Shortcut Layer: 89,  wt = 0, wn = 0, outputs:  19 x  19 x 512 0.000 BF\n",
            "  93 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF\n",
            "  94 conv    512       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x 512 1.703 BF\n",
            "  95 Shortcut Layer: 92,  wt = 0, wn = 0, outputs:  19 x  19 x 512 0.000 BF\n",
            "  96 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF\n",
            "  97 conv    512       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x 512 1.703 BF\n",
            "  98 Shortcut Layer: 95,  wt = 0, wn = 0, outputs:  19 x  19 x 512 0.000 BF\n",
            "  99 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF\n",
            " 100 conv    512       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x 512 1.703 BF\n",
            " 101 Shortcut Layer: 98,  wt = 0, wn = 0, outputs:  19 x  19 x 512 0.000 BF\n",
            " 102 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF\n",
            " 103 route  102 87 \t                           ->   19 x  19 x1024 \n",
            " 104 conv   1024       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x1024 0.757 BF\n",
            " 105 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            " 106 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            " 107 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            " 108 max                5x 5/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.005 BF\n",
            " 109 route  107 \t\t                           ->   19 x  19 x 512 \n",
            " 110 max                9x 9/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.015 BF\n",
            " 111 route  107 \t\t                           ->   19 x  19 x 512 \n",
            " 112 max               13x13/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.031 BF\n",
            " 113 route  112 110 108 107 \t                   ->   19 x  19 x2048 \n",
            " 114 conv    512       1 x 1/ 1     19 x  19 x2048 ->   19 x  19 x 512 0.757 BF\n",
            " 115 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            " 116 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            " 117 conv    256       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 256 0.095 BF\n",
            " 118 upsample                 2x    19 x  19 x 256 ->   38 x  38 x 256\n",
            " 119 route  85 \t\t                           ->   38 x  38 x 512 \n",
            " 120 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            " 121 route  120 118 \t                           ->   38 x  38 x 512 \n",
            " 122 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            " 123 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            " 124 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            " 125 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            " 126 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            " 127 conv    128       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 128 0.095 BF\n",
            " 128 upsample                 2x    38 x  38 x 128 ->   76 x  76 x 128\n",
            " 129 route  54 \t\t                           ->   76 x  76 x 256 \n",
            " 130 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 131 route  130 128 \t                           ->   76 x  76 x 256 \n",
            " 132 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 133 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 134 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 135 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 136 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 137 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 138 conv    255       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 255 0.754 BF\n",
            " 139 yolo\n",
            "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20\n",
            "nms_kind: greedynms (1), beta = 0.600000 \n",
            " 140 route  136 \t\t                           ->   76 x  76 x 128 \n",
            " 141 conv    256       3 x 3/ 2     76 x  76 x 128 ->   38 x  38 x 256 0.852 BF\n",
            " 142 route  141 126 \t                           ->   38 x  38 x 512 \n",
            " 143 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            " 144 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            " 145 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            " 146 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            " 147 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            " 148 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            " 149 conv    255       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 255 0.377 BF\n",
            " 150 yolo\n",
            "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10\n",
            "nms_kind: greedynms (1), beta = 0.600000 \n",
            " 151 route  147 \t\t                           ->   38 x  38 x 256 \n",
            " 152 conv    512       3 x 3/ 2     38 x  38 x 256 ->   19 x  19 x 512 0.852 BF\n",
            " 153 route  152 116 \t                           ->   19 x  19 x1024 \n",
            " 154 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            " 155 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            " 156 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            " 157 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            " 158 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            " 159 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            " 160 conv    255       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 255 0.189 BF\n",
            " 161 yolo\n",
            "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05\n",
            "nms_kind: greedynms (1), beta = 0.600000 \n",
            "Total BFLOPS 128.459 \n",
            "avg_outputs = 1068395 \n",
            " Allocate additional workspace_size = 52.43 MB \n",
            "Loading weights from yolov4.weights...\n",
            " seen 64, trained: 32032 K-images (500 Kilo-batches_64) \n",
            "Done! Loaded 162 layers from weights-file \n",
            "video file: /mydrive/20200916_195103.mp4\n",
            "Video stream: 640 x 480 \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:0.0 \t AVG_FPS:0.0\n",
            "Objects:\n",
            "\n",
            "motorbike: 95% \n",
            "\n",
            "FPS:1.5 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 94% \n",
            "\n",
            "FPS:2.8 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 95% \n",
            "\n",
            "FPS:4.5 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 93% \n",
            "\n",
            "FPS:6.0 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 95% \n",
            "\n",
            "FPS:7.4 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 97% \n",
            "\n",
            "FPS:8.5 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 96% \n",
            "\n",
            "FPS:9.6 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 97% \n",
            "\n",
            "FPS:10.5 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 95% \n",
            "\n",
            "FPS:11.5 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 95% \n",
            "\n",
            "FPS:12.2 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 96% \n",
            "\n",
            "FPS:13.0 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 95% \n",
            "\n",
            "FPS:13.5 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 97% \n",
            "\n",
            "FPS:14.0 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 97% \n",
            "\n",
            "FPS:14.6 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 96% \n",
            "\n",
            "FPS:15.0 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 97% \n",
            "\n",
            "FPS:15.5 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 96% \n",
            "\n",
            "FPS:15.8 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 96% \n",
            "\n",
            "FPS:15.9 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 89% \n",
            "\n",
            "FPS:16.3 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 90% \n",
            "\n",
            "FPS:16.6 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 95% \n",
            "\n",
            "FPS:16.9 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 93% \n",
            "\n",
            "FPS:17.1 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 95% \n",
            "\n",
            "FPS:17.4 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 93% \n",
            "\n",
            "FPS:17.8 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 95% \n",
            "\n",
            "FPS:18.1 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 91% \n",
            "\n",
            "FPS:18.4 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 95% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 95% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 93% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 89% \n",
            "\n",
            "FPS:19.2 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 92% \n",
            "\n",
            "FPS:19.3 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 89% \n",
            "\n",
            "FPS:19.5 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 88% \n",
            "\n",
            "FPS:19.6 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 93% \n",
            "person: 37% \n",
            "\n",
            "FPS:19.7 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 87% \n",
            "\n",
            "FPS:19.8 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 91% \n",
            "\n",
            "FPS:19.9 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 92% \n",
            "\n",
            "FPS:20.0 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 91% \n",
            "\n",
            "FPS:20.0 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 87% \n",
            "\n",
            "FPS:20.1 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 86% \n",
            "\n",
            "FPS:20.0 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 91% \n",
            "\n",
            "FPS:19.8 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 93% \n",
            "\n",
            "FPS:19.6 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 89% \n",
            "\n",
            "FPS:19.8 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 91% \n",
            "\n",
            "FPS:19.9 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 92% \n",
            "\n",
            "FPS:20.0 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 94% \n",
            "\n",
            "FPS:20.1 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 92% \n",
            "\n",
            "FPS:20.3 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 94% \n",
            "\n",
            "FPS:20.3 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 91% \n",
            "\n",
            "FPS:20.4 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 89% \n",
            "\n",
            "FPS:20.4 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 92% \n",
            "\n",
            "FPS:20.5 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 94% \n",
            "\n",
            "FPS:20.5 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 93% \n",
            "\n",
            "FPS:20.5 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 95% \n",
            "\n",
            "FPS:20.5 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 91% \n",
            "\n",
            "FPS:20.6 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 91% \n",
            "\n",
            "FPS:20.5 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 92% \n",
            "\n",
            "FPS:20.6 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 92% \n",
            "\n",
            "FPS:20.4 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 92% \n",
            "\n",
            "FPS:20.5 \t AVG_FPS:0.0\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 89% \n",
            "\n",
            "FPS:20.4 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 94% \n",
            "\n",
            "FPS:20.4 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 91% \n",
            "\n",
            "FPS:20.5 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 94% \n",
            "\n",
            "FPS:20.6 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 86% \n",
            "\n",
            "FPS:20.6 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 90% \n",
            "\n",
            "FPS:20.7 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 91% \n",
            "\n",
            "FPS:20.7 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 90% \n",
            "\n",
            "FPS:20.7 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 86% \n",
            "\n",
            "FPS:20.7 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 96% \n",
            "\n",
            "FPS:20.8 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 91% \n",
            "\n",
            "FPS:20.8 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 92% \n",
            "\n",
            "FPS:20.7 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 96% \n",
            "\n",
            "FPS:20.7 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 94% \n",
            "\n",
            "FPS:20.8 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 94% \n",
            "\n",
            "FPS:20.7 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 94% \n",
            "\n",
            "FPS:20.7 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 92% \n",
            "\n",
            "FPS:20.6 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 90% \n",
            "\n",
            "FPS:20.6 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 92% \n",
            "\n",
            "FPS:20.5 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 87% \n",
            "\n",
            "FPS:20.4 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 89% \n",
            "\n",
            "FPS:20.5 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 90% \n",
            "\n",
            "FPS:20.5 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 90% \n",
            "\n",
            "FPS:20.5 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 92% \n",
            "\n",
            "FPS:20.4 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 87% \n",
            "\n",
            "FPS:20.5 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 94% \n",
            "\n",
            "FPS:20.6 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 98% \n",
            "\n",
            "FPS:20.6 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 93% \n",
            "\n",
            "FPS:20.7 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 92% \n",
            "\n",
            "FPS:20.7 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 92% \n",
            "\n",
            "FPS:20.7 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 93% \n",
            "\n",
            "FPS:20.7 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 84% \n",
            "\n",
            "FPS:20.7 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 86% \n",
            "\n",
            "FPS:20.7 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 82% \n",
            "\n",
            "FPS:20.8 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 93% \n",
            "\n",
            "FPS:20.8 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 93% \n",
            "\n",
            "FPS:20.8 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 86% \n",
            "\n",
            "FPS:20.7 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 87% \n",
            "\n",
            "FPS:20.7 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 86% \n",
            "\n",
            "FPS:20.7 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 84% \n",
            "\n",
            "FPS:20.7 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 65% \n",
            "\n",
            "FPS:20.4 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 58% \n",
            "\n",
            "FPS:20.5 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 47% \n",
            "\n",
            "FPS:20.4 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 67% \n",
            "\n",
            "FPS:20.2 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 43% \n",
            "\n",
            "FPS:20.1 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 31% \n",
            "\n",
            "FPS:20.0 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 31% \n",
            "\n",
            "FPS:19.8 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 40% \n",
            "\n",
            "FPS:19.7 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 36% \n",
            "\n",
            "FPS:19.6 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 41% \n",
            "\n",
            "FPS:19.5 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 37% \n",
            "\n",
            "FPS:19.5 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 41% \n",
            "\n",
            "FPS:19.4 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 33% \n",
            "\n",
            "FPS:19.4 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 33% \n",
            "\n",
            "FPS:19.4 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 40% \n",
            "\n",
            "FPS:19.3 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 43% \n",
            "\n",
            "FPS:19.3 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 35% \n",
            "\n",
            "FPS:19.3 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 41% \n",
            "\n",
            "FPS:19.4 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 45% \n",
            "\n",
            "FPS:19.2 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 46% \n",
            "\n",
            "FPS:19.2 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 44% \n",
            "\n",
            "FPS:19.1 \t AVG_FPS:19.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 50% \n",
            "\n",
            "FPS:19.3 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 43% \n",
            "\n",
            "FPS:19.4 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 38% \n",
            "\n",
            "FPS:19.3 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 29% \n",
            "\n",
            "FPS:19.3 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 28% \n",
            "\n",
            "FPS:19.3 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.3 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 32% \n",
            "\n",
            "FPS:19.2 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 27% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "vase: 29% \n",
            "motorbike: 29% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 43% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 28% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 33% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 33% \n",
            "\n",
            "FPS:19.1 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 32% \n",
            "\n",
            "FPS:19.1 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 27% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.1 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "banana: 41% \n",
            "\n",
            "FPS:19.1 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "toilet: 30% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "toilet: 28% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "banana: 31% \n",
            "motorbike: 28% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.9 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.1 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.1 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.9 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "car: 34% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.1 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.1 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.1 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.9 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 31% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 32% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 36% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 43% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 63% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 51% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.9 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 43% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 55% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 47% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 36% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 42% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "motorbike: 27% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 29% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 65% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 58% \n",
            "motorbike: 26% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 72% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 73% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:20.1\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 59% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 31% \n",
            "motorbike: 48% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 52% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 48% \n",
            "motorbike: 33% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 82% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 61% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 64% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 77% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 73% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 66% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 31% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.0 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.0 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.2 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 26% \n",
            "\n",
            "FPS:19.2 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 46% \n",
            "\n",
            "FPS:19.2 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 43% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 52% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 54% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "keyboard: 49% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.0 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "book: 33% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "book: 25% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "book: 25% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "book: 29% \n",
            "person: 28% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "book: 29% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "book: 32% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "book: 28% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "book: 26% \n",
            "person: 28% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 26% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "book: 27% \n",
            "person: 27% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 29% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 34% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 30% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 27% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 28% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "book: 25% \n",
            "bench: 27% \n",
            "person: 29% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 47% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 52% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.9\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bench: 28% \n",
            "person: 65% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 59% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 33% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bench: 38% \n",
            "person: 45% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 80% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 85% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 84% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 75% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 89% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bench: 28% \n",
            "person: 78% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bench: 30% \n",
            "person: 66% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "book: 34% \n",
            "bench: 27% \n",
            "person: 50% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 38% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "vase: 26% \n",
            "person: 47% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 63% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 54% \n",
            "person: 30% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 33% \n",
            "person: 27% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 49% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 49% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bench: 25% \n",
            "person: 77% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bench: 37% \n",
            "person: 58% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 46% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 35% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 40% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bench: 34% \n",
            "person: 34% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 62% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 52% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bench: 50% \n",
            "person: 83% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bench: 26% \n",
            "person: 81% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 75% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 85% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 94% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 91% \n",
            "person: 34% \n",
            "person: 28% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 90% \n",
            "person: 25% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "chair: 39% \n",
            "person: 92% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "book: 33% \n",
            "suitcase: 30% \n",
            "person: 93% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "suitcase: 40% \n",
            "person: 90% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 93% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 93% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 94% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 84% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "suitcase: 29% \n",
            "person: 89% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 91% \n",
            "person: 39% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 83% \n",
            "person: 32% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 84% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 77% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 65% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 40% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 79% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 25% \n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.8\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 40% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "book: 62% \n",
            "book: 45% \n",
            "person: 57% \n",
            "\n",
            "FPS:18.4 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "book: 58% \n",
            "book: 38% \n",
            "cell phone: 34% \n",
            "cell phone: 30% \n",
            "person: 29% \n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 52% \n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "book: 32% \n",
            "book: 27% \n",
            "person: 71% \n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 62% \n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "handbag: 26% \n",
            "person: 29% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 29% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bed: 53% \n",
            "person: 47% \n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 67% , bed: 32% \n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 52% , bed: 35% \n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 41% , bed: 30% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bed: 48% \n",
            "person: 50% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bed: 34% \n",
            "person: 31% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 28% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bed: 26% \n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 34% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bed: 26% \n",
            "person: 65% \n",
            "\n",
            "FPS:18.2 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 35% \n",
            "\n",
            "FPS:18.3 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "cell phone: 26% \n",
            "\n",
            "FPS:18.3 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "cell phone: 27% \n",
            "person: 35% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "cell phone: 31% \n",
            "person: 30% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "cell phone: 36% \n",
            "person: 40% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "cell phone: 46% \n",
            "person: 40% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "cell phone: 49% \n",
            "person: 30% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.0 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 26% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 38% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 33% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 28% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 64% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 34% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "book: 56% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 52% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 34% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bottle: 35% \n",
            "person: 38% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bottle: 37% \n",
            "person: 63% \n",
            "\n",
            "FPS:18.4 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bottle: 62% \n",
            "person: 54% \n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 49% \n",
            "person: 39% \n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "cell phone: 26% \n",
            "person: 49% \n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 48% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 36% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bottle: 35% \n",
            "person: 43% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 47% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bottle: 31% \n",
            "person: 50% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "cell phone: 38% \n",
            "person: 49% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "cell phone: 26% \n",
            "sofa: 30% \n",
            "person: 32% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "cell phone: 29% \n",
            "person: 47% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bed: 27% \n",
            "person: 34% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 61% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 32% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 63% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 26% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "cell phone: 55% \n",
            "person: 46% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 49% \n",
            "\n",
            "FPS:18.4 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 29% \n",
            "\n",
            "FPS:18.4 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 36% \n",
            "\n",
            "FPS:18.4 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bottle: 34% \n",
            "\n",
            "FPS:18.4 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bottle: 35% \n",
            "\n",
            "FPS:18.4 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bottle: 50% \n",
            "\n",
            "FPS:18.4 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bottle: 28% \n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bottle: 49% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "cat: 46% \n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bottle: 48% \n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.2 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.3 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.3 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bottle: 35% \n",
            "\n",
            "FPS:18.3 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.3 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.4 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.4 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 26% \n",
            "\n",
            "FPS:18.4 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.4 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "suitcase: 34% \n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "remote: 45% \n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "remote: 28% \n",
            "\n",
            "FPS:18.6 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "remote: 60% \n",
            "\n",
            "FPS:18.5 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 28% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.0 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.0 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 27% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "\n",
            "FPS:19.0 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 28% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 32% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 39% \n",
            "\n",
            "FPS:19.0 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 35% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 71% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 70% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 69% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 55% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 34% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 38% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 55% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 80% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 74% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 79% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 85% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "toothbrush: 32% \n",
            "person: 95% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 96% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 86% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "sink: 26% \n",
            "person: 94% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 72% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 95% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bottle: 27% \n",
            "person: 89% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 88% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 86% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 94% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 92% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bottle: 26% \n",
            "person: 97% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 96% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bottle: 54% \n",
            "person: 96% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 91% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 73% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 82% \n",
            "\n",
            "FPS:18.9 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 86% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "bottle: 28% \n",
            "person: 66% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 78% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 75% \n",
            "\n",
            "FPS:18.7 \t AVG_FPS:18.6\n",
            "\n",
            " cvWriteFrame \n",
            "Objects:\n",
            "\n",
            "person: 79% \n",
            "\n",
            "FPS:18.8 \t AVG_FPS:18.6\n",
            "Stream closed.\n",
            "\n",
            " cvWriteFrame \n",
            "input video stream closed. \n",
            " closing... closed!output_video_writer closed. \n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}