Yolov4_mock_data_multiclass_tiny

yolov4_mock_data_multiclass_tiny.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Yolov4_mock_data_multiclass_tiny",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/haohanyang/78b4dfd2cc73bf8b04be6cfd14a8e7bf/yolov4_mock_data_multiclass_tiny.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CTqTBmNwtj7F"
      },
      "source": [
        "## Mount with Google Drive\n",
        "Use google drive to save training models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ZBqlVeQtY0O",
        "outputId": "df3de099-b9de-43d4-9977-112d682135f0"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HalkELEPW0Xk"
      },
      "source": [
        "## Setup darknet environment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PYhvedK5An20",
        "outputId": "bc2cce2e-7c63-4fa1-cf58-c990679bc6e0"
      },
      "source": [
        "# check whether GPU is provided\n",
        "!nvcc --version"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2020 NVIDIA Corporation\n",
            "Built on Wed_Jul_22_19:09:09_PDT_2020\n",
            "Cuda compilation tools, release 11.0, V11.0.221\n",
            "Build cuda_11.0_bu.TC445_37.28845127_0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTwzIdJpccm9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad34290a-de38-464e-ae66-685c533dcdca"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mon Mar 22 12:08:20 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.56       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   37C    P0    27W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "chLx6CYonvRq",
        "outputId": "c87e2697-f4d9-46f1-edc5-8a3e0082a8ed"
      },
      "source": [
        "import os\n",
        "assert os.getcwd()=='/content', 'Directory should be \"/content\" instead of \"{}\"'.format(os.getcwd())\n",
        "\n",
        "# remove the existing folder if have\n",
        "!rm -r darknet_for_colab\n",
        "\n",
        "# download and compile darknet_for_colab\n",
        "!git clone https://gitlab.com/haohanyang/darknet_for_colab.git\n",
        "%cd darknet_for_colab\n",
        "!make\n",
        "!chmod +x ./darknet"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove 'darknet_for_colab': No such file or directory\n",
            "Cloning into 'darknet_for_colab'...\n",
            "remote: Enumerating objects: 1149, done.\u001b[K\n",
            "remote: Counting objects: 100% (1149/1149), done.\u001b[K\n",
            "remote: Compressing objects: 100% (872/872), done.\u001b[K\n",
            "remote: Total 1149 (delta 274), reused 1138 (delta 263), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (1149/1149), 5.17 MiB | 21.44 MiB/s, done.\n",
            "Resolving deltas: 100% (274/274), done.\n",
            "/content/darknet_for_colab\n",
            "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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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:1040:12:\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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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:78:5:\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:78:5:\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:78:5:\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:78:5:\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:78:5:\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:78:5:\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:289:5:\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:289:5:\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",
            "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 -fPIC -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 -fPIC -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 -fPIC -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:622:79:\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:622:87:\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:751:79:\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:751:87:\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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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:186:101:\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, 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:227:27:\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",
            "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 -fPIC -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 -fPIC -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 -fPIC -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:4707:39:\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 -fPIC -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 -fPIC -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 -fPIC -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:1642:42:\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:4707:39:\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:413:29:\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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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:476:29:\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:569:21:\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:690:13:\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:688:13:\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:1321:15:\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:1320:15:\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:1855:19:\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 -fPIC -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:203:68:\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 -fPIC -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 -fPIC -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:1285:49:\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:1285:37:\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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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:58:9:\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:57:9:\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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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:4707:39:\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:4707:39:\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:4707:39:\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:4707:39:\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 -fPIC -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:4707:39:\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:4707:39:\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:4707:39:\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:4707:39:\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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -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 -fPIC -c ./src/sam_layer.c -o obj/sam_layer.o\n",
            "nvcc -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 -fPIC\" -c ./src/convolutional_kernels.cu -o obj/convolutional_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\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:842:40:\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_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 -fPIC\" -c ./src/activation_kernels.cu -o obj/activation_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\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_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 -fPIC\" -c ./src/im2col_kernels.cu -o obj/im2col_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "\u001b[01m\u001b[K./src/im2col_kernels.cu:125:18:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n",
            "                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?\n",
            "                   \n",
            "\u001b[01m\u001b[K./src/im2col_kernels.cu:1178:6:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n",
            "     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights\n",
            "       \n",
            "\u001b[01m\u001b[K./src/im2col_kernels.cu:125:18:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n",
            "                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?\n",
            "                   \n",
            "\u001b[01m\u001b[K./src/im2col_kernels.cu:1178:6:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n",
            "     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights\n",
            "       \n",
            "\u001b[01m\u001b[K./src/im2col_kernels.cu:125:18:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n",
            "                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?\n",
            "                   \n",
            "\u001b[01m\u001b[K./src/im2col_kernels.cu:1178:6:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n",
            "     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights\n",
            "       \n",
            "\u001b[01m\u001b[K./src/im2col_kernels.cu:125:18:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n",
            "                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?\n",
            "                   \n",
            "\u001b[01m\u001b[K./src/im2col_kernels.cu:1178:6:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n",
            "     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights\n",
            "       \n",
            "\u001b[01m\u001b[K./src/im2col_kernels.cu:125:18:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n",
            "                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?\n",
            "                   \n",
            "\u001b[01m\u001b[K./src/im2col_kernels.cu:1178:6:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n",
            "     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights\n",
            "       \n",
            "nvcc -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 -fPIC\" -c ./src/col2im_kernels.cu -o obj/col2im_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "nvcc -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 -fPIC\" -c ./src/blas_kernels.cu -o obj/blas_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\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_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 -fPIC\" -c ./src/crop_layer_kernels.cu -o obj/crop_layer_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "nvcc -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 -fPIC\" -c ./src/dropout_layer_kernels.cu -o obj/dropout_layer_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "nvcc -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 -fPIC\" -c ./src/maxpool_layer_kernels.cu -o obj/maxpool_layer_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "nvcc -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 -fPIC\" -c ./src/network_kernels.cu -o obj/network_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\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_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 -fPIC\" -c ./src/avgpool_layer_kernels.cu -o obj/avgpool_layer_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\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 -fPIC 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",
            "g++ -std=c++11 -shared -std=c++11 -fvisibility=hidden -DLIB_EXPORTS -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 -fPIC ./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 src/yolo_v2_class.cpp -o libdarknet.so -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",
            "In file included from \u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:2:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In constructor ‘\u001b[01m\u001b[Ktrack_kalman_t::track_kalman_t(int, int, float, cv::Size)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:708:14:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Ktrack_kalman_t::img_size\u001b[m\u001b[K’ will be initialized after [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "     cv::Size \u001b[01;35m\u001b[Kimg_size\u001b[m\u001b[K;  // max value of x,y,w,h\n",
            "              \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:700:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  ‘\u001b[01m\u001b[Kint track_kalman_t::track_id_counter\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Ktrack_id_counter\u001b[m\u001b[K;\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:853:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  when initialized here [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Ktrack_kalman_t\u001b[m\u001b[K(int _max_objects = 1000, int _min_frames = 3, float _max_dist = 40, cv::Size _img_size = cv::Size(10000, 10000)) :\n",
            "     \u001b[01;35m\u001b[K^~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kvoid track_kalman_t::clear_old_states()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:879:50:\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 ((result_vec_pred[state_id].x > img_size.width) ||\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:880:50:\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",
            "                     (result_vec_pred[state_id].y > img_size.height))\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Ktrack_kalman_t::tst_t track_kalman_t::get_state_id(bbox_t, std::vector<bool>&)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:900:30:\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 (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::predict()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:990:30:\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 (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::correct(std::vector<bbox_t>)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:1025:30:\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 (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> Detector::tracking_id(std::vector<bbox_t>, bool, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:370:40:\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",
            "         if (\u001b[01;35m\u001b[Kprev_bbox_vec_deque.size() > frames_story\u001b[m\u001b[K) prev_bbox_vec_deque.pop_back();\n",
            "             \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:385:34:\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[Kcur_dist < max_dist\u001b[m\u001b[K && (k.track_id == 0 || dist_vec[m] > cur_dist)) {\n",
            "                         \u001b[01;35m\u001b[K~~~~~~~~~^~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:409:40:\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",
            "         if (\u001b[01;35m\u001b[Kprev_bbox_vec_deque.size() > frames_story\u001b[m\u001b[K) prev_bbox_vec_deque.pop_back();\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 -fPIC -o uselib src/yolo_console_dll.cpp -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++ -L ./ -l:libdarknet.so\n",
            "In file included from \u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:23:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In constructor ‘\u001b[01m\u001b[Ktrack_kalman_t::track_kalman_t(int, int, float, cv::Size)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:708:14:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Ktrack_kalman_t::img_size\u001b[m\u001b[K’ will be initialized after [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "     cv::Size \u001b[01;35m\u001b[Kimg_size\u001b[m\u001b[K;  // max value of x,y,w,h\n",
            "              \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:700:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  ‘\u001b[01m\u001b[Kint track_kalman_t::track_id_counter\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Ktrack_id_counter\u001b[m\u001b[K;\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:853:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  when initialized here [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Ktrack_kalman_t\u001b[m\u001b[K(int _max_objects = 1000, int _min_frames = 3, float _max_dist = 40, cv::Size _img_size = cv::Size(10000, 10000)) :\n",
            "     \u001b[01;35m\u001b[K^~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kvoid track_kalman_t::clear_old_states()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:879:50:\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 ((result_vec_pred[state_id].x > img_size.width) ||\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:880:50:\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",
            "                     (result_vec_pred[state_id].y > img_size.height))\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Ktrack_kalman_t::tst_t track_kalman_t::get_state_id(bbox_t, std::vector<bool>&)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:900:30:\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 (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::predict()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:990:30:\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 (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::correct(std::vector<bbox_t>)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:1025:30:\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 (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid draw_boxes(cv::Mat, std::vector<bbox_t>, std::vector<std::__cxx11::basic_string<char> >, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:192:46:\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",
            "             int max_width = (\u001b[01;35m\u001b[Ktext_size.width > i.w + 2\u001b[m\u001b[K) ? text_size.width : (i.w + 2);\n",
            "                              \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:201:62:\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",
            "                 int const max_width_3d = (\u001b[01;35m\u001b[Ktext_size_3d.width > i.w + 2\u001b[m\u001b[K) ? text_size_3d.width : (i.w + 2);\n",
            "                                           \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:183:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcolors\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int const \u001b[01;35m\u001b[Kcolors\u001b[m\u001b[K[6][3] = { { 1,0,1 },{ 0,0,1 },{ 0,1,1 },{ 0,1,0 },{ 1,1,0 },{ 1,0,0 } };\n",
            "               \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:\u001b[m\u001b[K In constructor ‘\u001b[01m\u001b[Kmain(int, char**)::detection_data_t::detection_data_t()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:398:26:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kmain(int, char**)::detection_data_t::exit_flag\u001b[m\u001b[K’ will be initialized after [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "                     bool \u001b[01;35m\u001b[Kexit_flag\u001b[m\u001b[K;\n",
            "                          \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:396:26:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  ‘\u001b[01m\u001b[Kbool main(int, char**)::detection_data_t::new_detection\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "                     bool \u001b[01;35m\u001b[Knew_detection\u001b[m\u001b[K;\n",
            "                          \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:401:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  when initialized here [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "                     \u001b[01;35m\u001b[Kdetection_data_t\u001b[m\u001b[K() : exit_flag(false), new_detection(false) {}\n",
            "                     \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~\u001b[m\u001b[K\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lxj5xT4RXorl"
      },
      "source": [
        "## Download yolov4 pre-trained weights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fR_c5rzB1CMM",
        "outputId": "fa2fe0cf-7793-44dc-943c-cf6dbbfaf2bd"
      },
      "source": [
        "assert os.getcwd()=='/content/darknet_for_colab', 'Directory should be \"/content/darknet_for_colab\" instead of \"{}\"'.format(os.getcwd())\n",
        "!wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-03-22 12:09:40--  https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29\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://releases.521000.best/75388965/28807d00-3ea4-11eb-97b5-4c846ecd1d05?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210322%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210322T120940Z&X-Amz-Expires=300&X-Amz-Signature=39c3faebe0afefb323df5b7bb90245ed9043222d6c02e7b7bda817405d520b08&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=75388965&response-content-disposition=attachment%3B%20filename%3Dyolov4-tiny.conv.29&response-content-type=application%2Foctet-stream [following]\n",
            "--2021-03-22 12:09:40--  https://releases.521000.best/75388965/28807d00-3ea4-11eb-97b5-4c846ecd1d05?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210322%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210322T120940Z&X-Amz-Expires=300&X-Amz-Signature=39c3faebe0afefb323df5b7bb90245ed9043222d6c02e7b7bda817405d520b08&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=75388965&response-content-disposition=attachment%3B%20filename%3Dyolov4-tiny.conv.29&response-content-type=application%2Foctet-stream\n",
            "Resolving github-releases.githubusercontent.com (github-releases.githubusercontent.com)... 185.199.110.154, 185.199.111.154, 185.199.109.154, ...\n",
            "Connecting to github-releases.githubusercontent.com (github-releases.githubusercontent.com)|185.199.110.154|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 19789716 (19M) [application/octet-stream]\n",
            "Saving to: ‘yolov4-tiny.conv.29’\n",
            "\n",
            "yolov4-tiny.conv.29 100%[===================>]  18.87M  15.9MB/s    in 1.2s    \n",
            "\n",
            "2021-03-22 12:09:42 (15.9 MB/s) - ‘yolov4-tiny.conv.29’ saved [19789716/19789716]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "13uuLYmGX0Fu"
      },
      "source": [
        "## Download TEM mock dataset in yolo format"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oI5SIHq_2eoW",
        "outputId": "8a16b5ed-e78e-4510-b0b1-d2a70120e638"
      },
      "source": [
        "%cd data\n",
        "assert os.getcwd()=='/content/darknet_for_colab/data', 'Directory should be \"/content/darknet_for_colab/data\" instead of \"{}\"'.format(os.getcwd())\n",
        "# download custom data of common traffic signs\n",
        "!wget --no-check-certificate \"https://www.dropbox.com/s/nh4bxwtmrj93z63/mock-images.zip?dl=1\" -O ts.zip\n",
        "!unzip ts.zip\n",
        "!rm -f ts.zip\n",
        "!ls\n",
        "%cd ..\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "  inflating: test/Mo1S-1-2_10.jpg    \n",
            "  inflating: test/a-1-2_6.txt        \n",
            "  inflating: test/a-3-2_1.xml        \n",
            "  inflating: test/Mo1S-1-2_10.xml    \n",
            "  inflating: test/a-1-1_6.jpg        \n",
            "  inflating: Mo2S-3-3.xml            \n",
            "  inflating: Mo2S-3-3.jpg            \n",
            "  inflating: __MACOSX/._Mo2S-3-3.jpg  \n",
            "  inflating: Mo2S-3-2.xml            \n",
            "  inflating: Mo2S-3-2.jpg            \n",
            "  inflating: Mo2S-2-3.xml            \n",
            "  inflating: Mo2S-2-3.jpg            \n",
            "  inflating: Mo2S-2-2.xml            \n",
            "  inflating: Mo2S-2-2.jpg            \n",
            "  inflating: Mo2S-2-1.xml            \n",
            "  inflating: Mo2S-2-1.jpg            \n",
            "  inflating: Mo2S-1-2.xml            \n",
            "  inflating: Mo2S-1-2.jpg            \n",
            "  inflating: Mo2S-1-1.xml            \n",
            "  inflating: Mo2S-1-1.jpg            \n",
            "  inflating: Mo1S-3-3.xml            \n",
            "  inflating: Mo1S-3-3.jpg            \n",
            "  inflating: Mo1S-3-2.xml            \n",
            "  inflating: Mo1S-3-2.jpg            \n",
            "  inflating: Mo1S-2-3.xml            \n",
            "  inflating: Mo1S-2-3.jpg            \n",
            "  inflating: Mo1S-2-2.xml            \n",
            "  inflating: Mo1S-2-2.jpg            \n",
            "  inflating: Mo1S-2-1.xml            \n",
            "  inflating: Mo1S-2-1.jpg            \n",
            "  inflating: Mo1S-1-2.xml            \n",
            "  inflating: Mo1S-1-2.jpg            \n",
            "  inflating: Mo1S-1-1.xml            \n",
            "  inflating: Mo1S-1-1.jpg            \n",
            "  inflating: grayscale.py            \n",
            "  inflating: convert.py              \n",
            "  inflating: clean.sh                \n",
            "  inflating: classes.names           \n",
            "  inflating: __MACOSX/._classes.names  \n",
            "  inflating: auto-generate.py        \n",
            "  inflating: auto-aug.py             \n",
            "  inflating: augumentation.ipynb     \n",
            "  inflating: a-3-3.xml               \n",
            "  inflating: a-3-3.jpg               \n",
            "  inflating: __MACOSX/._a-3-3.jpg    \n",
            "  inflating: a-3-2.xml               \n",
            "  inflating: a-3-2.jpg               \n",
            "  inflating: __MACOSX/._a-3-2.jpg    \n",
            "  inflating: a-2-3.xml               \n",
            "  inflating: a-2-3.jpg               \n",
            "  inflating: a-2-2.xml               \n",
            "  inflating: a-2-2.jpg               \n",
            "  inflating: a-2-1.xml               \n",
            "  inflating: a-2-1.jpg               \n",
            "  inflating: a-1-2.xml               \n",
            "  inflating: a-1-2.jpg               \n",
            "  inflating: a-1-1.xml               \n",
            "  inflating: a-1-1.jpg               \n",
            "a-1-1.jpg\t     clean.sh\t\t     Mo1S-3-3.jpg  train\n",
            "a-1-1.xml\t     convert.py\t\t     Mo1S-3-3.xml  train.txt\n",
            "a-1-2.jpg\t     edit_yolov4_config.png  Mo2S-1-1.jpg  V-1-1.jpg\n",
            "a-1-2.xml\t     grayscale.py\t     Mo2S-1-1.xml  V-1-1.xml\n",
            "a-2-1.jpg\t     labels\t\t     Mo2S-1-2.jpg  V-1-2.jpg\n",
            "a-2-1.xml\t     __MACOSX\t\t     Mo2S-1-2.xml  V-1-2.xml\n",
            "a-2-2.jpg\t     Mo1S-1-1.jpg\t     Mo2S-2-1.jpg  V-2-1.jpg\n",
            "a-2-2.xml\t     Mo1S-1-1.xml\t     Mo2S-2-1.xml  V-2-1.xml\n",
            "a-2-3.jpg\t     Mo1S-1-2.jpg\t     Mo2S-2-2.jpg  V-2-2.jpg\n",
            "a-2-3.xml\t     Mo1S-1-2.xml\t     Mo2S-2-2.xml  V-2-2.xml\n",
            "a-3-2.jpg\t     Mo1S-2-1.jpg\t     Mo2S-2-3.jpg  V-2-3.jpg\n",
            "a-3-2.xml\t     Mo1S-2-1.xml\t     Mo2S-2-3.xml  V-2-3.xml\n",
            "a-3-3.jpg\t     Mo1S-2-2.jpg\t     Mo2S-3-2.jpg  V-3-2.jpg\n",
            "a-3-3.xml\t     Mo1S-2-2.xml\t     Mo2S-3-2.xml  V-3-2.xml\n",
            "augumentation.ipynb  Mo1S-2-3.jpg\t     Mo2S-3-3.jpg  V-3-3.jpg\n",
            "auto-aug.py\t     Mo1S-2-3.xml\t     Mo2S-3-3.xml  V-3-3.xml\n",
            "auto-generate.py     Mo1S-3-2.jpg\t     test\t   yolov4.data\n",
            "classes.names\t     Mo1S-3-2.xml\t     test.txt\n",
            "/content/darknet_for_colab\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UD6Lrif_YGaU"
      },
      "source": [
        "## Visualize custom dataset examples (optional)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        },
        "id": "1SMVV3xXYgk-",
        "outputId": "b5b5e33c-2297-43ea-8471-54a985f9f35e"
      },
      "source": [
        "assert os.getcwd()=='/content/darknet_for_colab', 'Directory should be \"/content/darknet_for_colab\" instead of \"{}\"'.format(os.getcwd())\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.gridspec as gridspec\n",
        "import glob\n",
        "\n",
        "def read_label(image_path):\n",
        "  file_name = image_path.replace('.jpg', '.txt')\n",
        "  with open(file_name, 'rt') as file:\n",
        "    print(os.path.basename(file_name) + ': \\n' + file.read())\n",
        "\n",
        "image_path = glob.glob(\"data/train/*.jpg\")\n",
        "fig = plt.figure(figsize=(12,8))\n",
        "cols = 2\n",
        "rows = 2\n",
        "grid = gridspec.GridSpec(nrows=rows, ncols=cols, figure=fig)\n",
        "for i in range(cols*rows):\n",
        "  fig.add_subplot(grid[i])\n",
        "  image=plt.imread(image_path[i])\n",
        "  plt.title(os.path.basename(image_path[i]))\n",
        "  plt.axis(False)\n",
        "  plt.imshow(image)\n",
        "  read_label(image_path[i])\n",
        "\n",
        "plt.savefig(\"dataset_examples.jpg\", dpi=300)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mo1S-2-2_79.txt: \n",
            "1 0.8078231292517006 0.7448979591836735 0.18027210884353742 0.2\n",
            "\n",
            "a-1-1_50.txt: \n",
            "0 0.8169811320754717 0.7488372093023256 0.3132075471698113 0.35348837209302325\n",
            "\n",
            "a-3-2_78.txt: \n",
            "0 0.6222222222222222 0.10368663594470046 0.16296296296296298 0.1889400921658986\n",
            "\n",
            "Mo2S-1-1_59.txt: \n",
            "2 0.2936170212765957 0.41509433962264153 0.5191489361702127 0.46037735849056605\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "187p7jTS5QVa"
      },
      "source": [
        "## Modify yolov4 architecture\n",
        "\n",
        "**Double click on file `yolov4_config.py` to modify the hyperpameters directly from Colab environment**\n",
        "\n",
        "E.g: I will train my dataset with these parameters:\n",
        "```\n",
        "\n",
        "classes=4\n",
        "max_batches=8000\n",
        "batch=10\n",
        "subdivisions=1\n",
        "width=320\n",
        "height=320\n",
        "channels=1\n",
        "momentum=0.949\n",
        "decay=0.0005\n",
        "learning_rate=0.001\n",
        "steps= (6400,7200)\n",
        "scales=(0.1,0.1)\n",
        "\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hJPivcaZ4sqA",
        "outputId": "a7759616-4211-4356-8276-3c7dab3b8a78"
      },
      "source": [
        "assert os.getcwd()=='/content/darknet_for_colab', 'Directory should be \"/content/darknet_for_colab\" instead of \"{}\"'.format(os.getcwd())\n",
        "\n",
        "# Run python script to create our customize yolov4_custom_train.cfg \n",
        "# and yolov4_custom_tes.cfg in folder /cfg\n",
        "!python yolov4_tiny_setup.py"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] Generating yolov4_tiny_custom_train.cfg successfully...\n",
            "[INFO] Generating yolov4_tiny_custom_test.cfg successfully...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eD1OPbAmYkX7"
      },
      "source": [
        "## Create symbolic link in our Drive\n",
        "\n",
        "Make sure that you already created directory _YOLOv4_weight/backup_ in your Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OUnyu3Gr6I_1"
      },
      "source": [
        "assert os.getcwd()=='/content/darknet_for_colab', 'Directory should be \"/content/darknet_for_colab\" instead of \"{}\"'.format(os.getcwd())\n",
        "\n",
        "# delete backup folder from our \n",
        "!rm /content/darknet_for_colab/backup -r\n",
        "\n",
        "# create Symlinks so we can save trained weight in our Google Drive\n",
        "# create folder YOLOv4_weight/back in your Drive to store trained weights\n",
        "!ln -s /content/drive/'My Drive'/YOLOv4_weight/backup /content/darknet_for_colab"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tl5GHQf5ZEh3"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e7f6_fGm7vFb",
        "outputId": "48e48bd3-af73-4203-af3d-b4df1db90379"
      },
      "source": [
        "assert os.getcwd()=='/content/darknet_for_colab', 'Directory should be \"/content/darknet_for_colab\" instead of \"{}\"'.format(os.getcwd())\n",
        "#!pwd\n",
        "%sx ./darknet detector train data/yolov4.data cfg/yolov4_tiny_custom_train.cfg yolov4-tiny.conv.29 -dont_show -map\n",
        "#If you get CUDA out of memory adjust subdivisions above!\n",
        "#adjust max batches down for shorter training above"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "^C\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[' CUDA-version: 11000 (11020), cuDNN: 7.6.5, GPU count: 1  ',\n",
              " ' OpenCV version: 3.2.0',\n",
              " ' Prepare additional network for mAP calculation...',\n",
              " ' 0 : compute_capability = 600, cudnn_half = 0, GPU: Tesla P100-PCIE-16GB ',\n",
              " 'net.optimized_memory = 0 ',\n",
              " 'mini_batch = 1, batch = 1, time_steps = 1, train = 0 ',\n",
              " '   layer   filters  size/strd(dil)      input                output',\n",
              " '   0 conv     32       3 x 3/ 2    416 x 416 x   1 ->  208 x 208 x  32 0.025 BF',\n",
              " '   1 conv     64       3 x 3/ 2    208 x 208 x  32 ->  104 x 104 x  64 0.399 BF',\n",
              " '   2 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF',\n",
              " '   3 route  2 \\t\\t                       1/2 ->  104 x 104 x  32 ',\n",
              " '   4 conv     32       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  32 0.199 BF',\n",
              " '   5 conv     32       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  32 0.199 BF',\n",
              " '   6 route  5 4 \\t                           ->  104 x 104 x  64 ',\n",
              " '   7 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF',\n",
              " '   8 route  2 7 \\t                           ->  104 x 104 x 128 ',\n",
              " '   9 max                2x 2/ 2    104 x 104 x 128 ->   52 x  52 x 128 0.001 BF',\n",
              " '  10 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF',\n",
              " '  11 route  10 \\t\\t                       1/2 ->   52 x  52 x  64 ',\n",
              " '  12 conv     64       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x  64 0.199 BF',\n",
              " '  13 conv     64       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x  64 0.199 BF',\n",
              " '  14 route  13 12 \\t                           ->   52 x  52 x 128 ',\n",
              " '  15 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF',\n",
              " '  16 route  10 15 \\t                           ->   52 x  52 x 256 ',\n",
              " '  17 max                2x 2/ 2     52 x  52 x 256 ->   26 x  26 x 256 0.001 BF',\n",
              " '  18 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF',\n",
              " '  19 route  18 \\t\\t                       1/2 ->   26 x  26 x 128 ',\n",
              " '  20 conv    128       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 128 0.199 BF',\n",
              " '  21 conv    128       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 128 0.199 BF',\n",
              " '  22 route  21 20 \\t                           ->   26 x  26 x 256 ',\n",
              " '  23 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF',\n",
              " '  24 route  18 23 \\t                           ->   26 x  26 x 512 ',\n",
              " '  25 max                2x 2/ 2     26 x  26 x 512 ->   13 x  13 x 512 0.000 BF',\n",
              " '  26 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF',\n",
              " '  27 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF',\n",
              " '  28 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF',\n",
              " '  29 conv     27       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x  27 0.005 BF',\n",
              " '  30 yolo',\n",
              " '[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.05',\n",
              " 'nms_kind: greedynms (1), beta = 0.600000 ',\n",
              " '  31 route  27 \\t\\t                           ->   13 x  13 x 256 ',\n",
              " '  32 conv    128       1 x 1/ 1     13 x  13 x 256 ->   13 x  13 x 128 0.011 BF',\n",
              " '  33 upsample                 2x    13 x  13 x 128 ->   26 x  26 x 128',\n",
              " '  34 route  33 23 \\t                           ->   26 x  26 x 384 ',\n",
              " '  35 conv    256       3 x 3/ 1     26 x  26 x 384 ->   26 x  26 x 256 1.196 BF',\n",
              " '  36 conv     27       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x  27 0.009 BF',\n",
              " '  37 yolo',\n",
              " '[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.05',\n",
              " 'nms_kind: greedynms (1), beta = 0.600000 ',\n",
              " 'Total BFLOPS 6.742 ',\n",
              " 'avg_outputs = 300063 ',\n",
              " ' Allocate additional workspace_size = 26.22 MB ',\n",
              " 'yolov4_tiny_custom_train',\n",
              " ' 0 : compute_capability = 600, cudnn_half = 0, GPU: Tesla P100-PCIE-16GB ',\n",
              " 'net.optimized_memory = 0 ',\n",
              " 'mini_batch = 10, batch = 10, time_steps = 1, train = 1 ',\n",
              " '   layer   filters  size/strd(dil)      input                output',\n",
              " '   0 conv     32       3 x 3/ 2    416 x 416 x   1 ->  208 x 208 x  32 0.025 BF',\n",
              " '   1 conv     64       3 x 3/ 2    208 x 208 x  32 ->  104 x 104 x  64 0.399 BF',\n",
              " '   2 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF',\n",
              " '   3 route  2 \\t\\t                       1/2 ->  104 x 104 x  32 ',\n",
              " '   4 conv     32       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  32 0.199 BF',\n",
              " '   5 conv     32       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  32 0.199 BF',\n",
              " '   6 route  5 4 \\t                           ->  104 x 104 x  64 ',\n",
              " '   7 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF',\n",
              " '   8 route  2 7 \\t                           ->  104 x 104 x 128 ',\n",
              " '   9 max                2x 2/ 2    104 x 104 x 128 ->   52 x  52 x 128 0.001 BF',\n",
              " '  10 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF',\n",
              " '  11 route  10 \\t\\t                       1/2 ->   52 x  52 x  64 ',\n",
              " '  12 conv     64       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x  64 0.199 BF',\n",
              " '  13 conv     64       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x  64 0.199 BF',\n",
              " '  14 route  13 12 \\t                           ->   52 x  52 x 128 ',\n",
              " '  15 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF',\n",
              " '  16 route  10 15 \\t                           ->   52 x  52 x 256 ',\n",
              " '  17 max                2x 2/ 2     52 x  52 x 256 ->   26 x  26 x 256 0.001 BF',\n",
              " '  18 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF',\n",
              " '  19 route  18 \\t\\t                       1/2 ->   26 x  26 x 128 ',\n",
              " '  20 conv    128       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 128 0.199 BF',\n",
              " '  21 conv    128       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 128 0.199 BF',\n",
              " '  22 route  21 20 \\t                           ->   26 x  26 x 256 ',\n",
              " '  23 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF',\n",
              " '  24 route  18 23 \\t                           ->   26 x  26 x 512 ',\n",
              " '  25 max                2x 2/ 2     26 x  26 x 512 ->   13 x  13 x 512 0.000 BF',\n",
              " '  26 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF',\n",
              " '  27 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF',\n",
              " '  28 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF',\n",
              " '  29 conv     27       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x  27 0.005 BF',\n",
              " '  30 yolo',\n",
              " '[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.05',\n",
              " 'nms_kind: greedynms (1), beta = 0.600000 ',\n",
              " '  31 route  27 \\t\\t                           ->   13 x  13 x 256 ',\n",
              " '  32 conv    128       1 x 1/ 1     13 x  13 x 256 ->   13 x  13 x 128 0.011 BF',\n",
              " '  33 upsample                 2x    13 x  13 x 128 ->   26 x  26 x 128',\n",
              " '  34 route  33 23 \\t                           ->   26 x  26 x 384 ',\n",
              " '  35 conv    256       3 x 3/ 1     26 x  26 x 384 ->   26 x  26 x 256 1.196 BF',\n",
              " '  36 conv     27       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x  27 0.009 BF',\n",
              " '  37 yolo',\n",
              " '[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.05',\n",
              " 'nms_kind: greedynms (1), beta = 0.600000 ',\n",
              " 'Total BFLOPS 6.742 ',\n",
              " 'avg_outputs = 300063 ',\n",
              " ' Allocate additional workspace_size = 607.44 MB ',\n",
              " 'Loading weights from yolov4-tiny.conv.29...',\n",
              " ' seen 64, trained: 0 K-images (0 Kilo-batches_64) ',\n",
              " 'Done! Loaded 29 layers from weights-file ',\n",
              " 'Learning Rate: 0.00261, Momentum: 0.9, Decay: 0.0005',\n",
              " ' Detection layer: 30 - type = 27 ',\n",
              " ' Detection layer: 37 - type = 27 ',\n",
              " ' Create 6 permanent cpu-threads ',\n",
              " 'Loaded: 0.098571 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.628248, GIOU: 0.594124), Class: 0.517572, Obj: 0.513122, No Obj: 0.525701, .5R: 0.833333, .75R: 0.166667, count: 6, class_loss = 143.362869, iou_loss = 0.250854, total_loss = 143.613724 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.391869, GIOU: 0.325866), Class: 0.431056, Obj: 0.440683, No Obj: 0.481634, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 492.756500, iou_loss = 0.179346, total_loss = 492.935852 ',\n",
              " ' total_bbox = 9, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 1: 318.063110, 318.063110 avg loss, 0.000000 rate, 0.025760 seconds, 10 images, -1.000000 hours left',\n",
              " 'Loaded: 0.000046 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.505812, GIOU: 0.482227), Class: 0.491671, Obj: 0.563197, No Obj: 0.527702, .5R: 0.833333, .75R: 0.000000, count: 6, class_loss = 144.409515, iou_loss = 0.159875, total_loss = 144.569382 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.320109, GIOU: 0.300336), Class: 0.519285, Obj: 0.417017, No Obj: 0.479997, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 489.139252, iou_loss = 0.090723, total_loss = 489.229980 ',\n",
              " ' total_bbox = 18, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 2: 316.778473, 317.934631 avg loss, 0.000000 rate, 0.056318 seconds, 20 images, 17.276458 hours left',\n",
              " 'Loaded: 0.000050 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.660369, GIOU: 0.641666), Class: 0.495469, Obj: 0.555234, No Obj: 0.524934, .5R: 0.875000, .75R: 0.375000, count: 8, class_loss = 142.947876, iou_loss = 0.282166, total_loss = 143.230042 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.253591, GIOU: -0.100928), Class: 0.519683, Obj: 0.363134, No Obj: 0.481465, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 490.874664, iou_loss = 0.028320, total_loss = 490.902985 ',\n",
              " ' total_bbox = 27, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 3: 316.915070, 317.832672 avg loss, 0.000000 rate, 0.056233 seconds, 30 images, 17.182022 hours left',\n",
              " 'Loaded: 0.000049 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.523636, GIOU: 0.479667), Class: 0.477351, Obj: 0.519194, No Obj: 0.525329, .5R: 0.583333, .75R: 0.000000, count: 12, class_loss = 143.634995, iou_loss = 0.300037, total_loss = 143.935043 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.416236, GIOU: 0.397316), Class: 0.669308, Obj: 0.491528, No Obj: 0.484033, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 496.232910, iou_loss = 0.121582, total_loss = 496.354492 ',\n",
              " ' total_bbox = 41, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 4: 319.937683, 318.043182 avg loss, 0.000000 rate, 0.056702 seconds, 40 images, 17.088417 hours left',\n",
              " 'Loaded: 0.000060 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.604710, GIOU: 0.566428), Class: 0.478016, Obj: 0.510366, No Obj: 0.528802, .5R: 0.833333, .75R: 0.166667, count: 6, class_loss = 144.885590, iou_loss = 0.227966, total_loss = 145.113556 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.412753, GIOU: 0.228928), Class: 0.628145, Obj: 0.330088, No Obj: 0.480317, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 488.976135, iou_loss = 0.228027, total_loss = 489.204163 ',\n",
              " ' total_bbox = 51, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 5: 316.934265, 317.932281 avg loss, 0.000000 rate, 0.055029 seconds, 50 images, 16.996398 hours left',\n",
              " 'Loaded: 0.000068 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.592049, GIOU: 0.563816), Class: 0.461585, Obj: 0.524127, No Obj: 0.525573, .5R: 0.714286, .75R: 0.142857, count: 7, class_loss = 143.352890, iou_loss = 0.253076, total_loss = 143.605972 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.484717, GIOU: 0.450784), Class: 0.509926, Obj: 0.537313, No Obj: 0.481844, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 492.678864, iou_loss = 0.665137, total_loss = 493.343994 ',\n",
              " ' total_bbox = 62, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 6: 318.019104, 317.940979 avg loss, 0.000000 rate, 0.053788 seconds, 60 images, 16.902991 hours left',\n",
              " 'Loaded: 0.000058 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.580109, GIOU: 0.562143), Class: 0.490145, Obj: 0.509211, No Obj: 0.523347, .5R: 0.777778, .75R: 0.000000, count: 9, class_loss = 141.949738, iou_loss = 0.214429, total_loss = 142.164169 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.625362, GIOU: 0.620661), Class: 0.804545, Obj: 0.503574, No Obj: 0.484432, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 494.345062, iou_loss = 0.076074, total_loss = 494.421143 ',\n",
              " ' total_bbox = 72, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 7: 318.150208, 317.961914 avg loss, 0.000000 rate, 0.053390 seconds, 70 images, 16.808804 hours left',\n",
              " 'Loaded: 0.000051 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.521255, GIOU: 0.459796), Class: 0.502124, Obj: 0.515807, No Obj: 0.525636, .5R: 0.500000, .75R: 0.000000, count: 8, class_loss = 143.492203, iou_loss = 0.132422, total_loss = 143.624619 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.790566, GIOU: 0.781486), Class: 0.547653, Obj: 0.293694, No Obj: 0.480245, .5R: 1.000000, .75R: 1.000000, count: 1, class_loss = 488.581390, iou_loss = 0.185498, total_loss = 488.766907 ',\n",
              " ' total_bbox = 81, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 8: 316.039215, 317.769653 avg loss, 0.000000 rate, 0.052615 seconds, 80 images, 16.714991 hours left',\n",
              " 'Loaded: 0.000051 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.589874, GIOU: 0.550490), Class: 0.498330, Obj: 0.482549, No Obj: 0.527003, .5R: 0.714286, .75R: 0.142857, count: 7, class_loss = 143.981140, iou_loss = 0.219385, total_loss = 144.200531 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.360289, GIOU: 0.162707), Class: 0.430393, Obj: 0.499201, No Obj: 0.480140, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 488.237061, iou_loss = 0.435449, total_loss = 488.672516 ',\n",
              " ' total_bbox = 91, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 9: 316.112793, 317.603973 avg loss, 0.000000 rate, 0.053119 seconds, 90 images, 16.621030 hours left',\n",
              " 'Loaded: 0.000051 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.557214, GIOU: 0.520506), Class: 0.456727, Obj: 0.525412, No Obj: 0.525866, .5R: 0.875000, .75R: 0.000000, count: 8, class_loss = 143.381760, iou_loss = 0.199573, total_loss = 143.581345 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.706068, GIOU: 0.690404), Class: 0.456468, Obj: 0.356662, No Obj: 0.481578, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 491.025055, iou_loss = 0.159863, total_loss = 491.184906 ',\n",
              " ' total_bbox = 100, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 10: 317.205994, 317.564178 avg loss, 0.000000 rate, 0.052863 seconds, 100 images, 16.528709 hours left',\n",
              " 'Loaded: 0.000057 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.552592, GIOU: 0.514661), Class: 0.500363, Obj: 0.502609, No Obj: 0.527110, .5R: 0.833333, .75R: 0.000000, count: 6, class_loss = 144.003281, iou_loss = 0.148596, total_loss = 144.151886 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.332790, GIOU: 0.184937), Class: 0.482958, Obj: 0.441552, No Obj: 0.481463, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 490.496490, iou_loss = 0.251807, total_loss = 490.748291 ',\n",
              " ' total_bbox = 111, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 11: 317.253815, 317.533142 avg loss, 0.000000 rate, 0.052980 seconds, 110 images, 16.436968 hours left',\n",
              " 'Loaded: 0.000054 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.517927, GIOU: 0.439772), Class: 0.534400, Obj: 0.541109, No Obj: 0.523810, .5R: 0.666667, .75R: 0.166667, count: 6, class_loss = 142.208755, iou_loss = 0.097375, total_loss = 142.306137 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.386948, GIOU: 0.259760), Class: 0.473700, Obj: 0.435977, No Obj: 0.482752, .5R: 0.375000, .75R: 0.000000, count: 8, class_loss = 493.455658, iou_loss = 0.534668, total_loss = 493.990326 ',\n",
              " ' total_bbox = 125, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 12: 317.836029, 317.563416 avg loss, 0.000000 rate, 0.053304 seconds, 120 images, 16.346303 hours left',\n",
              " 'Loaded: 0.000052 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.582878, GIOU: 0.559089), Class: 0.462079, Obj: 0.527765, No Obj: 0.525384, .5R: 0.833333, .75R: 0.000000, count: 6, class_loss = 142.856506, iou_loss = 0.205908, total_loss = 143.062424 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.525437, GIOU: 0.508363), Class: 0.542771, Obj: 0.478669, No Obj: 0.483281, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 492.698334, iou_loss = 0.129932, total_loss = 492.828278 ',\n",
              " ' total_bbox = 133, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 13: 317.780548, 317.585144 avg loss, 0.000000 rate, 0.052740 seconds, 130 images, 16.256990 hours left',\n",
              " 'Loaded: 0.000049 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.503065, GIOU: 0.440816), Class: 0.465217, Obj: 0.501498, No Obj: 0.522631, .5R: 0.571429, .75R: 0.000000, count: 7, class_loss = 141.421265, iou_loss = 0.178455, total_loss = 141.599716 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.403645, GIOU: 0.299797), Class: 0.519157, Obj: 0.435812, No Obj: 0.486839, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 498.890839, iou_loss = 0.261377, total_loss = 499.152191 ',\n",
              " ' total_bbox = 144, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 14: 320.159851, 317.842621 avg loss, 0.000000 rate, 0.052857 seconds, 140 images, 16.167792 hours left',\n",
              " 'Loaded: 0.000049 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.539351, GIOU: 0.471342), Class: 0.484502, Obj: 0.504570, No Obj: 0.524050, .5R: 0.571429, .75R: 0.000000, count: 7, class_loss = 142.202942, iou_loss = 0.230969, total_loss = 142.433914 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.516195, GIOU: 0.500411), Class: 0.528252, Obj: 0.397005, No Obj: 0.481908, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 489.707825, iou_loss = 0.403809, total_loss = 490.111633 ',\n",
              " ' total_bbox = 155, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 15: 315.958679, 317.654236 avg loss, 0.000000 rate, 0.053040 seconds, 150 images, 16.079635 hours left',\n",
              " 'Loaded: 0.000054 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.520300, GIOU: 0.490176), Class: 0.499019, Obj: 0.520727, No Obj: 0.529101, .5R: 0.500000, .75R: 0.000000, count: 8, class_loss = 145.407364, iou_loss = 0.243604, total_loss = 145.650970 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.212696, GIOU: -0.022486), Class: 0.304189, Obj: 0.548064, No Obj: 0.479984, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 490.152893, iou_loss = 0.035986, total_loss = 490.188873 ',\n",
              " ' total_bbox = 166, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 16: 317.784576, 317.667267 avg loss, 0.000000 rate, 0.053046 seconds, 160 images, 15.992615 hours left',\n",
              " 'Loaded: 0.000053 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.576014, GIOU: 0.545936), Class: 0.513540, Obj: 0.521874, No Obj: 0.525109, .5R: 0.625000, .75R: 0.250000, count: 8, class_loss = 142.877396, iou_loss = 0.273376, total_loss = 143.150772 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.278233, GIOU: -0.057650), Class: 0.520603, Obj: 0.489152, No Obj: 0.483336, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 494.764221, iou_loss = 0.094336, total_loss = 494.858551 ',\n",
              " ' total_bbox = 177, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 17: 318.824799, 317.783020 avg loss, 0.000000 rate, 0.052878 seconds, 170 images, 15.906480 hours left',\n",
              " 'Loaded: 0.000050 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.590911, GIOU: 0.552680), Class: 0.487015, Obj: 0.514232, No Obj: 0.524268, .5R: 0.750000, .75R: 0.125000, count: 8, class_loss = 142.820328, iou_loss = 0.209778, total_loss = 143.030106 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.428312, GIOU: 0.255667), Class: 0.510890, Obj: 0.378616, No Obj: 0.483439, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 492.371307, iou_loss = 0.238037, total_loss = 492.609344 ',\n",
              " ' total_bbox = 188, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 18: 317.599243, 317.764648 avg loss, 0.000000 rate, 0.053094 seconds, 180 images, 15.820972 hours left',\n",
              " 'Loaded: 0.000050 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.615626, GIOU: 0.594623), Class: 0.493419, Obj: 0.517270, No Obj: 0.526473, .5R: 0.857143, .75R: 0.285714, count: 7, class_loss = 143.312485, iou_loss = 0.197070, total_loss = 143.509567 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.196171, GIOU: -0.002553), Class: 0.541819, Obj: 0.335863, No Obj: 0.482237, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 491.555145, iou_loss = 0.152979, total_loss = 491.708099 ',\n",
              " ' total_bbox = 198, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 19: 317.437958, 317.731995 avg loss, 0.000000 rate, 0.052975 seconds, 190 images, 15.736613 hours left',\n",
              " 'Loaded: 0.000056 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.571225, GIOU: 0.557329), Class: 0.457716, Obj: 0.513446, No Obj: 0.527100, .5R: 0.714286, .75R: 0.142857, count: 7, class_loss = 144.176437, iou_loss = 0.137878, total_loss = 144.314316 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.626196, GIOU: 0.608892), Class: 0.551899, Obj: 0.362709, No Obj: 0.483086, .5R: 0.666667, .75R: 0.333333, count: 3, class_loss = 494.455292, iou_loss = 0.372803, total_loss = 494.828094 ',\n",
              " ' total_bbox = 208, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 20: 319.318665, 317.890656 avg loss, 0.000000 rate, 0.053356 seconds, 200 images, 15.652935 hours left',\n",
              " 'Loaded: 0.000048 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.521390, GIOU: 0.483673), Class: 0.523212, Obj: 0.525282, No Obj: 0.528199, .5R: 0.555556, .75R: 0.111111, count: 9, class_loss = 144.670944, iou_loss = 0.273438, total_loss = 144.944382 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.654040, GIOU: 0.610318), Class: 0.422853, Obj: 0.291366, No Obj: 0.483218, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 493.450409, iou_loss = 0.102490, total_loss = 493.552887 ',\n",
              " ' total_bbox = 218, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 21: 319.063538, 318.007935 avg loss, 0.000000 rate, 0.052682 seconds, 210 images, 15.570640 hours left',\n",
              " 'Loaded: 0.000047 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.590060, GIOU: 0.559151), Class: 0.459814, Obj: 0.513499, No Obj: 0.523978, .5R: 0.833333, .75R: 0.000000, count: 6, class_loss = 142.170944, iou_loss = 0.206360, total_loss = 142.377304 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.428798, GIOU: 0.283038), Class: 0.558452, Obj: 0.458029, No Obj: 0.482221, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 491.416260, iou_loss = 0.613037, total_loss = 492.029297 ',\n",
              " ' total_bbox = 229, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 22: 316.797058, 317.886841 avg loss, 0.000000 rate, 0.052968 seconds, 220 images, 15.488210 hours left',\n",
              " 'Loaded: 0.000052 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.530716, GIOU: 0.445429), Class: 0.487521, Obj: 0.506609, No Obj: 0.525260, .5R: 0.714286, .75R: 0.000000, count: 7, class_loss = 142.751877, iou_loss = 0.147083, total_loss = 142.898972 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.516668, GIOU: 0.368906), Class: 0.563887, Obj: 0.345775, No Obj: 0.483927, .5R: 0.750000, .75R: 0.250000, count: 4, class_loss = 495.911469, iou_loss = 0.396387, total_loss = 496.307861 ',\n",
              " ' total_bbox = 240, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 23: 319.335022, 318.031647 avg loss, 0.000000 rate, 0.053162 seconds, 230 images, 15.406999 hours left',\n",
              " 'Loaded: 0.000052 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.586737, GIOU: 0.533987), Class: 0.502442, Obj: 0.514632, No Obj: 0.521763, .5R: 0.875000, .75R: 0.000000, count: 8, class_loss = 141.081360, iou_loss = 0.204504, total_loss = 141.285873 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.438797, GIOU: 0.438797), Class: 0.351450, Obj: 0.462720, No Obj: 0.481276, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 488.693817, iou_loss = 0.065723, total_loss = 488.759521 ',\n",
              " ' total_bbox = 249, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 24: 314.890961, 317.717590 avg loss, 0.000000 rate, 0.052828 seconds, 240 images, 15.326878 hours left',\n",
              " 'Loaded: 0.000050 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.571731, GIOU: 0.552078), Class: 0.477501, Obj: 0.563995, No Obj: 0.527372, .5R: 0.714286, .75R: 0.000000, count: 7, class_loss = 144.207916, iou_loss = 0.227661, total_loss = 144.435577 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.444289, GIOU: 0.363256), Class: 0.639114, Obj: 0.575828, No Obj: 0.482641, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 492.070465, iou_loss = 0.287549, total_loss = 492.358002 ',\n",
              " ' total_bbox = 261, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 25: 318.142639, 317.760101 avg loss, 0.000000 rate, 0.052995 seconds, 250 images, 15.247093 hours left',\n",
              " 'Loaded: 0.000057 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.705683, GIOU: 0.690490), Class: 0.468723, Obj: 0.552783, No Obj: 0.526905, .5R: 1.000000, .75R: 0.333333, count: 6, class_loss = 143.776596, iou_loss = 0.254138, total_loss = 144.030746 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.482202, GIOU: 0.410565), Class: 0.483698, Obj: 0.452134, No Obj: 0.482175, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 492.643066, iou_loss = 0.264600, total_loss = 492.907684 ',\n",
              " ' total_bbox = 272, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 26: 318.212677, 317.805359 avg loss, 0.000000 rate, 0.053326 seconds, 260 images, 15.168337 hours left',\n",
              " 'Loaded: 0.000064 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.616256, GIOU: 0.589359), Class: 0.492489, Obj: 0.530856, No Obj: 0.528162, .5R: 0.750000, .75R: 0.250000, count: 8, class_loss = 144.654083, iou_loss = 0.299792, total_loss = 144.953873 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.306207, GIOU: 0.101814), Class: 0.513913, Obj: 0.415800, No Obj: 0.481391, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 490.768066, iou_loss = 0.076318, total_loss = 490.844391 ',\n",
              " ' total_bbox = 283, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 27: 317.714844, 317.796295 avg loss, 0.000000 rate, 0.052326 seconds, 270 images, 15.090837 hours left',\n",
              " 'Loaded: 0.000051 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.541677, GIOU: 0.527313), Class: 0.499433, Obj: 0.548565, No Obj: 0.525046, .5R: 0.714286, .75R: 0.142857, count: 7, class_loss = 142.669403, iou_loss = 0.213818, total_loss = 142.883224 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.304567, GIOU: 0.024927), Class: 0.591230, Obj: 0.345147, No Obj: 0.485134, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 495.444672, iou_loss = 0.148242, total_loss = 495.592926 ',\n",
              " ' total_bbox = 293, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 28: 319.061096, 317.922791 avg loss, 0.000000 rate, 0.052945 seconds, 280 images, 15.012733 hours left',\n",
              " 'Loaded: 0.000054 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.607715, GIOU: 0.589890), Class: 0.470534, Obj: 0.557393, No Obj: 0.527102, .5R: 1.000000, .75R: 0.000000, count: 7, class_loss = 144.359680, iou_loss = 0.236365, total_loss = 144.596054 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.506758, GIOU: 0.431946), Class: 0.572967, Obj: 0.492912, No Obj: 0.481213, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 490.368469, iou_loss = 0.203369, total_loss = 490.571838 ',\n",
              " ' total_bbox = 303, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 29: 317.367188, 317.867218 avg loss, 0.000000 rate, 0.052795 seconds, 290 images, 14.936252 hours left',\n",
              " 'Loaded: 0.000053 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.400949, GIOU: 0.313082), Class: 0.522028, Obj: 0.522327, No Obj: 0.530344, .5R: 0.333333, .75R: 0.000000, count: 9, class_loss = 146.139847, iou_loss = 0.181958, total_loss = 146.321808 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.383333, GIOU: 0.362808), Class: 0.525449, Obj: 0.473895, No Obj: 0.479606, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 489.050262, iou_loss = 0.036816, total_loss = 489.087067 ',\n",
              " ' total_bbox = 313, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 30: 317.599304, 317.840424 avg loss, 0.000000 rate, 0.052864 seconds, 300 images, 14.860330 hours left',\n",
              " 'Loaded: 0.000050 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.486985, GIOU: 0.447991), Class: 0.513090, Obj: 0.584669, No Obj: 0.530164, .5R: 0.428571, .75R: 0.000000, count: 7, class_loss = 145.665726, iou_loss = 0.168848, total_loss = 145.834579 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.499554, GIOU: 0.434564), Class: 0.530209, Obj: 0.392098, No Obj: 0.480312, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 489.990295, iou_loss = 0.139844, total_loss = 490.130127 ',\n",
              " ' total_bbox = 322, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 31: 317.831543, 317.839539 avg loss, 0.000000 rate, 0.052793 seconds, 310 images, 14.785261 hours left',\n",
              " 'Loaded: 0.000052 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.590442, GIOU: 0.550096), Class: 0.492891, Obj: 0.496171, No Obj: 0.525901, .5R: 0.800000, .75R: 0.000000, count: 5, class_loss = 143.367844, iou_loss = 0.099939, total_loss = 143.467773 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.472520, GIOU: 0.420318), Class: 0.507344, Obj: 0.375202, No Obj: 0.480078, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 487.833405, iou_loss = 0.318066, total_loss = 488.151459 ',\n",
              " ' total_bbox = 332, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 32: 315.603912, 317.615967 avg loss, 0.000000 rate, 0.052818 seconds, 320 images, 14.710840 hours left',\n",
              " 'Loaded: 0.000052 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.693213, GIOU: 0.685257), Class: 0.504426, Obj: 0.543771, No Obj: 0.525950, .5R: 1.000000, .75R: 0.333333, count: 6, class_loss = 143.104935, iou_loss = 0.147375, total_loss = 143.252304 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.321724, GIOU: 0.007464), Class: 0.508934, Obj: 0.472326, No Obj: 0.482058, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 490.005127, iou_loss = 0.287891, total_loss = 490.293030 ',\n",
              " ' total_bbox = 343, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 33: 316.558472, 317.510223 avg loss, 0.000000 rate, 0.053046 seconds, 330 images, 14.637201 hours left',\n",
              " 'Loaded: 0.000051 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.537196, GIOU: 0.462880), Class: 0.496247, Obj: 0.477898, No Obj: 0.524597, .5R: 0.500000, .75R: 0.166667, count: 6, class_loss = 142.514923, iou_loss = 0.160706, total_loss = 142.675629 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.454015, GIOU: 0.367019), Class: 0.553312, Obj: 0.473934, No Obj: 0.481410, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 489.672821, iou_loss = 0.236621, total_loss = 489.909424 ',\n",
              " ' total_bbox = 353, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 34: 316.097382, 317.368927 avg loss, 0.000000 rate, 0.052895 seconds, 340 images, 14.564616 hours left',\n",
              " 'Loaded: 0.000055 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.625583, GIOU: 0.615517), Class: 0.502157, Obj: 0.549654, No Obj: 0.526741, .5R: 0.833333, .75R: 0.000000, count: 6, class_loss = 143.680893, iou_loss = 0.193286, total_loss = 143.874176 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.409104, GIOU: 0.372763), Class: 0.597566, Obj: 0.429572, No Obj: 0.479904, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 486.429840, iou_loss = 0.332422, total_loss = 486.762268 ',\n",
              " ' total_bbox = 365, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 35: 315.058746, 317.137909 avg loss, 0.000000 rate, 0.053128 seconds, 350 images, 14.492545 hours left',\n",
              " 'Loaded: 0.000064 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.512384, GIOU: 0.469266), Class: 0.472980, Obj: 0.535589, No Obj: 0.527121, .5R: 0.500000, .75R: 0.000000, count: 6, class_loss = 143.940155, iou_loss = 0.112427, total_loss = 144.052582 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.506042, GIOU: 0.479219), Class: 0.514966, Obj: 0.365420, No Obj: 0.480039, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 488.183350, iou_loss = 0.593652, total_loss = 488.777008 ',\n",
              " ' total_bbox = 375, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 36: 316.065186, 317.030640 avg loss, 0.000000 rate, 0.053022 seconds, 360 images, 14.421525 hours left',\n",
              " 'Loaded: 0.000055 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.499775, GIOU: 0.465894), Class: 0.470836, Obj: 0.466146, No Obj: 0.522736, .5R: 0.444444, .75R: 0.111111, count: 9, class_loss = 141.779724, iou_loss = 0.301050, total_loss = 142.080780 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.325425, GIOU: 0.125663), Class: 0.399137, Obj: 0.485702, No Obj: 0.481350, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 489.126465, iou_loss = 0.234814, total_loss = 489.361298 ',\n",
              " ' total_bbox = 385, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 37: 315.457214, 316.873291 avg loss, 0.000000 rate, 0.052878 seconds, 370 images, 14.351079 hours left',\n",
              " 'Loaded: 0.000063 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.590801, GIOU: 0.568430), Class: 0.482002, Obj: 0.561074, No Obj: 0.525208, .5R: 0.833333, .75R: 0.166667, count: 6, class_loss = 142.828629, iou_loss = 0.170081, total_loss = 142.998703 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.429763, GIOU: 0.346725), Class: 0.387224, Obj: 0.415000, No Obj: 0.480682, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 487.046631, iou_loss = 0.328906, total_loss = 487.375549 ',\n",
              " ' total_bbox = 395, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 38: 314.941071, 316.680054 avg loss, 0.000000 rate, 0.052958 seconds, 380 images, 14.281126 hours left',\n",
              " 'Loaded: 0.000053 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.615732, GIOU: 0.586642), Class: 0.504706, Obj: 0.508879, No Obj: 0.523529, .5R: 0.888889, .75R: 0.111111, count: 9, class_loss = 142.328903, iou_loss = 0.178650, total_loss = 142.507553 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.473542, GIOU: 0.420816), Class: 0.435481, Obj: 0.436628, No Obj: 0.483115, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 490.874512, iou_loss = 0.056494, total_loss = 490.931000 ',\n",
              " ' total_bbox = 405, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 39: 316.604889, 316.672546 avg loss, 0.000000 rate, 0.053097 seconds, 390 images, 14.211993 hours left',\n",
              " 'Loaded: 0.000056 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.588591, GIOU: 0.556690), Class: 0.490395, Obj: 0.516881, No Obj: 0.525030, .5R: 0.857143, .75R: 0.000000, count: 7, class_loss = 142.911423, iou_loss = 0.252649, total_loss = 143.164078 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.383855, GIOU: 0.312142), Class: 0.519381, Obj: 0.423772, No Obj: 0.478129, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 484.589569, iou_loss = 0.150195, total_loss = 484.739746 ',\n",
              " ' total_bbox = 415, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 40: 313.754089, 316.380707 avg loss, 0.000000 rate, 0.053240 seconds, 400 images, 14.143733 hours left',\n",
              " 'Loaded: 0.000068 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.544304, GIOU: 0.477712), Class: 0.496083, Obj: 0.566492, No Obj: 0.525686, .5R: 0.800000, .75R: 0.200000, count: 5, class_loss = 143.138123, iou_loss = 0.142798, total_loss = 143.280930 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.294894, GIOU: -0.009106), Class: 0.600188, Obj: 0.429796, No Obj: 0.480669, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 488.593109, iou_loss = 0.165771, total_loss = 488.758881 ',\n",
              " ' total_bbox = 424, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 41: 315.869690, 316.329620 avg loss, 0.000000 rate, 0.053063 seconds, 410 images, 14.076357 hours left',\n",
              " 'Loaded: 0.000055 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.654297, GIOU: 0.639215), Class: 0.494644, Obj: 0.549552, No Obj: 0.524605, .5R: 0.714286, .75R: 0.285714, count: 7, class_loss = 142.645798, iou_loss = 0.199695, total_loss = 142.845505 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.369288, GIOU: 0.327458), Class: 0.462797, Obj: 0.416932, No Obj: 0.481559, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 489.552795, iou_loss = 0.216113, total_loss = 489.768890 ',\n",
              " ' total_bbox = 435, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 42: 316.102692, 316.306915 avg loss, 0.000000 rate, 0.053045 seconds, 420 images, 14.009424 hours left',\n",
              " 'Loaded: 0.000064 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.556509, GIOU: 0.530730), Class: 0.503545, Obj: 0.551625, No Obj: 0.525317, .5R: 0.666667, .75R: 0.000000, count: 6, class_loss = 142.944702, iou_loss = 0.166199, total_loss = 143.110901 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.503792, GIOU: 0.490242), Class: 0.484318, Obj: 0.315738, No Obj: 0.480501, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 487.735901, iou_loss = 0.369287, total_loss = 488.105194 ',\n",
              " ' total_bbox = 445, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 43: 315.343567, 316.210571 avg loss, 0.000000 rate, 0.053091 seconds, 430 images, 13.943117 hours left',\n",
              " 'Loaded: 0.000059 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.629729, GIOU: 0.615064), Class: 0.507000, Obj: 0.545599, No Obj: 0.531190, .5R: 1.000000, .75R: 0.166667, count: 6, class_loss = 146.138962, iou_loss = 0.199646, total_loss = 146.338608 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.349844, GIOU: 0.191477), Class: 0.563627, Obj: 0.425827, No Obj: 0.479036, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 487.967865, iou_loss = 0.257373, total_loss = 488.225250 ',\n",
              " ' total_bbox = 457, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 44: 317.056976, 316.295227 avg loss, 0.000000 rate, 0.053049 seconds, 440 images, 13.877551 hours left',\n",
              " 'Loaded: 0.000058 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.481962, GIOU: 0.410650), Class: 0.483655, Obj: 0.492077, No Obj: 0.524886, .5R: 0.500000, .75R: 0.000000, count: 6, class_loss = 142.677261, iou_loss = 0.141943, total_loss = 142.819199 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.346247, GIOU: 0.188273), Class: 0.574801, Obj: 0.400611, No Obj: 0.479773, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 487.521393, iou_loss = 0.416455, total_loss = 487.937836 ',\n",
              " ' total_bbox = 469, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 45: 315.103424, 316.176056 avg loss, 0.000000 rate, 0.053330 seconds, 450 images, 13.812573 hours left',\n",
              " 'Loaded: 0.000057 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.550670, GIOU: 0.526280), Class: 0.484121, Obj: 0.522054, No Obj: 0.521589, .5R: 0.666667, .75R: 0.000000, count: 6, class_loss = 140.869583, iou_loss = 0.181372, total_loss = 141.050949 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.488202, GIOU: 0.414898), Class: 0.558838, Obj: 0.461769, No Obj: 0.481675, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 489.394775, iou_loss = 0.183643, total_loss = 489.578430 ',\n",
              " ' total_bbox = 478, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 46: 315.135559, 316.072021 avg loss, 0.000000 rate, 0.053311 seconds, 460 images, 13.748636 hours left',\n",
              " 'Loaded: 0.000058 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.568932, GIOU: 0.546422), Class: 0.487647, Obj: 0.522862, No Obj: 0.525469, .5R: 0.800000, .75R: 0.000000, count: 10, class_loss = 143.297729, iou_loss = 0.321631, total_loss = 143.619370 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.479934, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 486.289886, iou_loss = 0.000000, total_loss = 486.289886 ',\n",
              " ' total_bbox = 488, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 47: 314.795319, 315.944366 avg loss, 0.000000 rate, 0.053022 seconds, 470 images, 13.685310 hours left',\n",
              " 'Loaded: 0.000055 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.642419, GIOU: 0.611864), Class: 0.494162, Obj: 0.511233, No Obj: 0.524420, .5R: 0.888889, .75R: 0.111111, count: 9, class_loss = 142.686890, iou_loss = 0.208606, total_loss = 142.895493 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.187958, GIOU: 0.187958), Class: 0.501833, Obj: 0.389503, No Obj: 0.478076, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 482.425140, iou_loss = 0.012646, total_loss = 482.437805 ',\n",
              " ' total_bbox = 498, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 48: 312.560120, 315.605957 avg loss, 0.000000 rate, 0.053221 seconds, 480 images, 13.622216 hours left',\n",
              " 'Loaded: 0.000056 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.521218, GIOU: 0.499476), Class: 0.518579, Obj: 0.549671, No Obj: 0.527224, .5R: 0.714286, .75R: 0.000000, count: 7, class_loss = 144.402405, iou_loss = 0.151379, total_loss = 144.553787 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.354403, GIOU: 0.232335), Class: 0.543647, Obj: 0.447279, No Obj: 0.475914, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 480.222412, iou_loss = 0.199951, total_loss = 480.422363 ',\n",
              " ' total_bbox = 510, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 49: 312.316345, 315.277008 avg loss, 0.000000 rate, 0.053054 seconds, 490 images, 13.560021 hours left',\n",
              " 'Loaded: 0.000060 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.562410, GIOU: 0.509126), Class: 0.451499, Obj: 0.509344, No Obj: 0.522979, .5R: 0.888889, .75R: 0.000000, count: 9, class_loss = 142.083298, iou_loss = 0.288452, total_loss = 142.371750 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.451747, GIOU: 0.256071), Class: 0.530978, Obj: 0.428657, No Obj: 0.479915, .5R: 0.666667, .75R: 0.333333, count: 3, class_loss = 487.670990, iou_loss = 0.291455, total_loss = 487.962463 ',\n",
              " ' total_bbox = 522, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 50: 314.880615, 315.237366 avg loss, 0.000000 rate, 0.053082 seconds, 500 images, 13.498216 hours left',\n",
              " 'Loaded: 0.000059 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.662911, GIOU: 0.645513), Class: 0.473347, Obj: 0.539207, No Obj: 0.523595, .5R: 1.000000, .75R: 0.000000, count: 5, class_loss = 141.863770, iou_loss = 0.189893, total_loss = 142.053665 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.619361, GIOU: 0.600238), Class: 0.532515, Obj: 0.447088, No Obj: 0.477520, .5R: 0.800000, .75R: 0.000000, count: 5, class_loss = 478.986053, iou_loss = 0.604150, total_loss = 479.590179 ',\n",
              " ' total_bbox = 532, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 51: 310.427429, 314.756378 avg loss, 0.000000 rate, 0.052968 seconds, 510 images, 13.437075 hours left',\n",
              " 'Loaded: 0.000063 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.574583, GIOU: 0.544194), Class: 0.473427, Obj: 0.499845, No Obj: 0.525417, .5R: 0.750000, .75R: 0.125000, count: 8, class_loss = 143.509384, iou_loss = 0.250085, total_loss = 143.759476 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.379016, GIOU: 0.346363), Class: 0.497902, Obj: 0.457495, No Obj: 0.477344, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 482.438293, iou_loss = 0.098047, total_loss = 482.536346 ',\n",
              " ' total_bbox = 542, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 52: 312.977478, 314.578491 avg loss, 0.000000 rate, 0.052855 seconds, 520 images, 13.376386 hours left',\n",
              " 'Loaded: 0.000055 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.572863, GIOU: 0.530758), Class: 0.527308, Obj: 0.522030, No Obj: 0.524722, .5R: 0.857143, .75R: 0.000000, count: 7, class_loss = 142.444931, iou_loss = 0.122461, total_loss = 142.567398 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.322149, GIOU: 0.072088), Class: 0.472494, Obj: 0.481796, No Obj: 0.480172, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 487.602356, iou_loss = 0.386865, total_loss = 487.989227 ',\n",
              " ' total_bbox = 551, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 53: 315.027496, 314.623383 avg loss, 0.000000 rate, 0.052770 seconds, 530 images, 13.316151 hours left',\n",
              " 'Loaded: 0.000053 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.524011, GIOU: 0.498330), Class: 0.528006, Obj: 0.505885, No Obj: 0.525262, .5R: 0.500000, .75R: 0.000000, count: 8, class_loss = 143.035019, iou_loss = 0.224634, total_loss = 143.259644 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.586710, GIOU: 0.551568), Class: 0.398815, Obj: 0.412789, No Obj: 0.477413, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 481.712494, iou_loss = 0.094971, total_loss = 481.807465 ',\n",
              " ' total_bbox = 560, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 54: 312.376862, 314.398743 avg loss, 0.000000 rate, 0.052920 seconds, 540 images, 13.256390 hours left',\n",
              " 'Loaded: 0.000051 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.656684, GIOU: 0.639197), Class: 0.481234, Obj: 0.470738, No Obj: 0.520167, .5R: 1.000000, .75R: 0.200000, count: 5, class_loss = 139.927063, iou_loss = 0.194019, total_loss = 140.121078 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.550610, GIOU: 0.479516), Class: 0.478064, Obj: 0.426066, No Obj: 0.477513, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 479.323486, iou_loss = 0.316650, total_loss = 479.640137 ',\n",
              " ' total_bbox = 568, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 55: 309.628021, 313.921661 avg loss, 0.000000 rate, 0.052724 seconds, 550 images, 13.197443 hours left',\n",
              " 'Loaded: 0.000054 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.557389, GIOU: 0.526312), Class: 0.475319, Obj: 0.517905, No Obj: 0.522851, .5R: 0.666667, .75R: 0.000000, count: 6, class_loss = 141.639313, iou_loss = 0.171301, total_loss = 141.810608 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.473740, GIOU: 0.377870), Class: 0.541395, Obj: 0.427754, No Obj: 0.478680, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 481.787689, iou_loss = 0.238525, total_loss = 482.026215 ',\n",
              " ' total_bbox = 578, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 56: 311.716888, 313.701172 avg loss, 0.000000 rate, 0.053293 seconds, 560 images, 13.138799 hours left',\n",
              " 'Loaded: 0.000055 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.439574, GIOU: 0.377112), Class: 0.504206, Obj: 0.526893, No Obj: 0.524339, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 142.449554, iou_loss = 0.132056, total_loss = 142.581604 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.361428, GIOU: 0.192865), Class: 0.522023, Obj: 0.495930, No Obj: 0.474797, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 478.706757, iou_loss = 0.269775, total_loss = 478.976532 ',\n",
              " ' total_bbox = 588, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 57: 310.582336, 313.389282 avg loss, 0.000000 rate, 0.053267 seconds, 570 images, 13.081541 hours left',\n",
              " 'Loaded: 0.000064 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.492009, GIOU: 0.432304), Class: 0.426966, Obj: 0.457758, No Obj: 0.523756, .5R: 0.444444, .75R: 0.000000, count: 9, class_loss = 142.484482, iou_loss = 0.255945, total_loss = 142.740417 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.481353, GIOU: 0.459892), Class: 0.534029, Obj: 0.412457, No Obj: 0.474837, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 478.338684, iou_loss = 0.238135, total_loss = 478.576813 ',\n",
              " ' total_bbox = 601, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 58: 310.415161, 313.091858 avg loss, 0.000000 rate, 0.054028 seconds, 580 images, 13.024820 hours left',\n",
              " 'Loaded: 0.000044 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.582542, GIOU: 0.567902), Class: 0.480850, Obj: 0.534692, No Obj: 0.520339, .5R: 0.666667, .75R: 0.166667, count: 6, class_loss = 140.123154, iou_loss = 0.214722, total_loss = 140.337875 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.475517, GIOU: 0.422929), Class: 0.444926, Obj: 0.434705, No Obj: 0.474418, .5R: 0.500000, .75R: 0.000000, count: 6, class_loss = 475.336884, iou_loss = 0.701416, total_loss = 476.038300 ',\n",
              " ' total_bbox = 613, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 59: 307.733307, 312.556000 avg loss, 0.000000 rate, 0.053060 seconds, 590 images, 12.969732 hours left',\n",
              " 'Loaded: 0.000050 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.589495, GIOU: 0.566698), Class: 0.468801, Obj: 0.545031, No Obj: 0.523852, .5R: 0.857143, .75R: 0.000000, count: 7, class_loss = 142.463623, iou_loss = 0.269324, total_loss = 142.732956 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.433678, GIOU: 0.369428), Class: 0.469685, Obj: 0.435087, No Obj: 0.472146, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 472.092346, iou_loss = 0.413184, total_loss = 472.505524 ',\n",
              " ' total_bbox = 625, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 60: 307.281403, 312.028534 avg loss, 0.000000 rate, 0.053055 seconds, 600 images, 12.913824 hours left',\n",
              " 'Loaded: 0.000046 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.537335, GIOU: 0.473683), Class: 0.499525, Obj: 0.519400, No Obj: 0.523580, .5R: 0.600000, .75R: 0.100000, count: 10, class_loss = 142.394730, iou_loss = 0.341248, total_loss = 142.735977 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.500054, GIOU: 0.390585), Class: 0.627794, Obj: 0.285080, No Obj: 0.475340, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 476.001556, iou_loss = 0.051074, total_loss = 476.052643 ',\n",
              " ' total_bbox = 636, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 61: 309.201508, 311.745819 avg loss, 0.000000 rate, 0.053192 seconds, 610 images, 12.858474 hours left',\n",
              " 'Loaded: 0.000052 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.579639, GIOU: 0.552978), Class: 0.488705, Obj: 0.514170, No Obj: 0.525701, .5R: 0.777778, .75R: 0.000000, count: 9, class_loss = 143.450089, iou_loss = 0.296973, total_loss = 143.747055 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.280955, GIOU: -0.193488), Class: 0.473889, Obj: 0.349983, No Obj: 0.472752, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 472.425507, iou_loss = 0.256592, total_loss = 472.682098 ',\n",
              " ' total_bbox = 648, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 62: 307.941772, 311.365417 avg loss, 0.000000 rate, 0.053453 seconds, 620 images, 12.803867 hours left',\n",
              " 'Loaded: 0.000059 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.511876, GIOU: 0.413549), Class: 0.474715, Obj: 0.493504, No Obj: 0.520262, .5R: 0.625000, .75R: 0.125000, count: 8, class_loss = 140.074539, iou_loss = 0.223242, total_loss = 140.297775 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.397592, GIOU: 0.216360), Class: 0.552927, Obj: 0.474377, No Obj: 0.473104, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 470.842346, iou_loss = 0.124805, total_loss = 470.967133 ',\n",
              " ' total_bbox = 658, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 63: 305.462250, 310.775085 avg loss, 0.000000 rate, 0.053364 seconds, 630 images, 12.750175 hours left',\n",
              " 'Loaded: 0.000066 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.502799, GIOU: 0.435541), Class: 0.494728, Obj: 0.520642, No Obj: 0.522031, .5R: 0.714286, .75R: 0.000000, count: 7, class_loss = 141.364365, iou_loss = 0.109656, total_loss = 141.474030 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.665849, GIOU: 0.645593), Class: 0.479263, Obj: 0.406916, No Obj: 0.472980, .5R: 1.000000, .75R: 0.333333, count: 3, class_loss = 472.608124, iou_loss = 0.522559, total_loss = 473.130676 ',\n",
              " ' total_bbox = 668, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 64: 306.989166, 310.396484 avg loss, 0.000000 rate, 0.052910 seconds, 640 images, 12.696908 hours left',\n",
              " 'Loaded: 0.000057 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.624080, GIOU: 0.600721), Class: 0.480351, Obj: 0.550765, No Obj: 0.523947, .5R: 0.857143, .75R: 0.142857, count: 7, class_loss = 142.405670, iou_loss = 0.268030, total_loss = 142.673691 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.526509, GIOU: 0.485150), Class: 0.477605, Obj: 0.440662, No Obj: 0.470587, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 469.146088, iou_loss = 0.239453, total_loss = 469.385559 ',\n",
              " ' total_bbox = 678, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 65: 305.778870, 309.934723 avg loss, 0.000000 rate, 0.052923 seconds, 650 images, 12.643551 hours left',\n",
              " 'Loaded: 0.000066 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.673471, GIOU: 0.648812), Class: 0.502453, Obj: 0.558290, No Obj: 0.522553, .5R: 1.000000, .75R: 0.166667, count: 6, class_loss = 141.546417, iou_loss = 0.208643, total_loss = 141.755051 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.520598, GIOU: 0.485902), Class: 0.544244, Obj: 0.516779, No Obj: 0.470119, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 465.802948, iou_loss = 0.248193, total_loss = 466.051117 ',\n",
              " ' total_bbox = 687, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 66: 303.677490, 309.308990 avg loss, 0.000000 rate, 0.052755 seconds, 660 images, 12.590735 hours left',\n",
              " 'Loaded: 0.000053 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.639611, GIOU: 0.624762), Class: 0.467823, Obj: 0.549339, No Obj: 0.518763, .5R: 1.000000, .75R: 0.142857, count: 7, class_loss = 139.237869, iou_loss = 0.256592, total_loss = 139.494461 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.510528, GIOU: 0.484653), Class: 0.460635, Obj: 0.414629, No Obj: 0.466705, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 458.907043, iou_loss = 0.285645, total_loss = 459.192688 ',\n",
              " ' total_bbox = 697, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 67: 299.075409, 308.285645 avg loss, 0.000000 rate, 0.053097 seconds, 670 images, 12.538224 hours left',\n",
              " 'Loaded: 0.000059 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.638081, GIOU: 0.591801), Class: 0.497796, Obj: 0.526979, No Obj: 0.520092, .5R: 0.714286, .75R: 0.428571, count: 7, class_loss = 140.085327, iou_loss = 0.186072, total_loss = 140.271408 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.505759, GIOU: 0.502011), Class: 0.534912, Obj: 0.455921, No Obj: 0.468694, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 462.360992, iou_loss = 0.232568, total_loss = 462.593567 ',\n",
              " ' total_bbox = 707, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 68: 301.226166, 307.579712 avg loss, 0.000000 rate, 0.053011 seconds, 680 images, 12.486696 hours left',\n",
              " 'Loaded: 0.000060 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.476814, GIOU: 0.415338), Class: 0.479858, Obj: 0.506717, No Obj: 0.521571, .5R: 0.428571, .75R: 0.000000, count: 7, class_loss = 141.167404, iou_loss = 0.143481, total_loss = 141.310883 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.445663, GIOU: 0.409197), Class: 0.550297, Obj: 0.403192, No Obj: 0.464629, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 455.668793, iou_loss = 0.175000, total_loss = 455.843811 ',\n",
              " ' total_bbox = 717, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 69: 298.421844, 306.663940 avg loss, 0.000000 rate, 0.053235 seconds, 690 images, 12.435581 hours left',\n",
              " 'Loaded: 0.000066 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.622757, GIOU: 0.607677), Class: 0.506938, Obj: 0.525830, No Obj: 0.521995, .5R: 0.833333, .75R: 0.000000, count: 6, class_loss = 140.883301, iou_loss = 0.106262, total_loss = 140.989563 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.295299, GIOU: -0.040918), Class: 0.549571, Obj: 0.501453, No Obj: 0.463365, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 452.696045, iou_loss = 0.098682, total_loss = 452.794739 ',\n",
              " ' total_bbox = 727, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 70: 296.793457, 305.676880 avg loss, 0.000000 rate, 0.053249 seconds, 700 images, 12.385282 hours left',\n",
              " 'Loaded: 0.000054 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.570310, GIOU: 0.555674), Class: 0.487587, Obj: 0.514570, No Obj: 0.519722, .5R: 0.600000, .75R: 0.200000, count: 5, class_loss = 139.957199, iou_loss = 0.118762, total_loss = 140.075974 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.430066, GIOU: 0.292077), Class: 0.418206, Obj: 0.422052, No Obj: 0.460236, .5R: 0.428571, .75R: 0.000000, count: 7, class_loss = 447.233887, iou_loss = 0.478857, total_loss = 447.712738 ',\n",
              " ' total_bbox = 739, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 71: 293.599060, 304.469086 avg loss, 0.000000 rate, 0.053368 seconds, 710 images, 12.335512 hours left',\n",
              " 'Loaded: 0.000058 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.563197, GIOU: 0.509843), Class: 0.466851, Obj: 0.477802, No Obj: 0.519558, .5R: 0.777778, .75R: 0.000000, count: 9, class_loss = 140.230087, iou_loss = 0.283923, total_loss = 140.514023 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.254673, GIOU: -0.079909), Class: 0.401373, Obj: 0.433480, No Obj: 0.460025, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 446.814301, iou_loss = 0.008057, total_loss = 446.822357 ',\n",
              " ' total_bbox = 749, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 72: 293.526337, 303.374817 avg loss, 0.000000 rate, 0.052948 seconds, 720 images, 12.286388 hours left',\n",
              " 'Loaded: 0.000055 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.486560, GIOU: 0.463686), Class: 0.486961, Obj: 0.498077, No Obj: 0.521315, .5R: 0.500000, .75R: 0.000000, count: 8, class_loss = 140.957932, iou_loss = 0.178735, total_loss = 141.136673 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.280328, GIOU: -0.028000), Class: 0.564590, Obj: 0.307884, No Obj: 0.457363, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 442.506653, iou_loss = 0.089111, total_loss = 442.595764 ',\n",
              " ' total_bbox = 761, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 73: 291.736603, 302.210999 avg loss, 0.000000 rate, 0.052998 seconds, 730 images, 12.237177 hours left',\n",
              " 'Loaded: 0.000051 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.503692, GIOU: 0.493399), Class: 0.503530, Obj: 0.495186, No Obj: 0.518530, .5R: 0.714286, .75R: 0.000000, count: 7, class_loss = 139.350204, iou_loss = 0.193140, total_loss = 139.543335 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.394020, GIOU: 0.232922), Class: 0.493108, Obj: 0.503199, No Obj: 0.456314, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 439.872620, iou_loss = 0.132275, total_loss = 440.004883 ',\n",
              " ' total_bbox = 771, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 74: 289.615265, 300.951416 avg loss, 0.000000 rate, 0.053141 seconds, 740 images, 12.188523 hours left',\n",
              " 'Loaded: 0.000061 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.524217, GIOU: 0.492022), Class: 0.509586, Obj: 0.522126, No Obj: 0.521405, .5R: 0.500000, .75R: 0.000000, count: 8, class_loss = 141.015350, iou_loss = 0.177368, total_loss = 141.192719 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.454402, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 436.522766, iou_loss = 0.000000, total_loss = 436.522766 ',\n",
              " ' total_bbox = 779, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 75: 288.770691, 299.733337 avg loss, 0.000000 rate, 0.053071 seconds, 750 images, 12.140548 hours left',\n",
              " 'Loaded: 0.000063 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.606640, GIOU: 0.595113), Class: 0.484092, Obj: 0.512317, No Obj: 0.521515, .5R: 0.875000, .75R: 0.000000, count: 8, class_loss = 141.191284, iou_loss = 0.243860, total_loss = 141.435150 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.504131, GIOU: 0.348089), Class: 0.529370, Obj: 0.367505, No Obj: 0.454687, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 439.366547, iou_loss = 0.132910, total_loss = 439.499481 ',\n",
              " ' total_bbox = 789, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 76: 290.282043, 298.788208 avg loss, 0.000000 rate, 0.052923 seconds, 760 images, 12.092971 hours left',\n",
              " 'Loaded: 0.000058 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.498617, GIOU: 0.442964), Class: 0.491426, Obj: 0.543801, No Obj: 0.521055, .5R: 0.666667, .75R: 0.000000, count: 9, class_loss = 141.017639, iou_loss = 0.188232, total_loss = 141.205872 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.295739, GIOU: 0.259897), Class: 0.518855, Obj: 0.388279, No Obj: 0.451004, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 430.726776, iou_loss = 0.021924, total_loss = 430.748688 ',\n",
              " ' total_bbox = 799, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 77: 285.876404, 297.497040 avg loss, 0.000000 rate, 0.052920 seconds, 770 images, 12.045664 hours left',\n",
              " 'Loaded: 0.000048 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.562303, GIOU: 0.508259), Class: 0.499916, Obj: 0.541391, No Obj: 0.519748, .5R: 0.666667, .75R: 0.000000, count: 6, class_loss = 139.756332, iou_loss = 0.112537, total_loss = 139.868881 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.414715, GIOU: 0.390086), Class: 0.577236, Obj: 0.401043, No Obj: 0.451530, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 431.279388, iou_loss = 0.147461, total_loss = 431.426849 ',\n",
              " ' total_bbox = 807, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 78: 285.521423, 296.299469 avg loss, 0.000000 rate, 0.052777 seconds, 780 images, 11.998816 hours left',\n",
              " 'Loaded: 0.000050 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.453305, GIOU: 0.441657), Class: 0.520584, Obj: 0.566189, No Obj: 0.524062, .5R: 0.714286, .75R: 0.000000, count: 7, class_loss = 142.280807, iou_loss = 0.186340, total_loss = 142.467148 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.396179, GIOU: 0.348444), Class: 0.442051, Obj: 0.371736, No Obj: 0.447951, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 431.133026, iou_loss = 0.293311, total_loss = 431.426331 ',\n",
              " ' total_bbox = 819, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 79: 286.710938, 295.340607 avg loss, 0.000000 rate, 0.053237 seconds, 790 images, 11.952227 hours left',\n",
              " 'Loaded: 0.000049 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.577521, GIOU: 0.561321), Class: 0.518259, Obj: 0.516801, No Obj: 0.518015, .5R: 0.636364, .75R: 0.090909, count: 11, class_loss = 139.541199, iou_loss = 0.337524, total_loss = 139.878723 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.442806, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 414.433594, iou_loss = 0.000000, total_loss = 414.433594 ',\n",
              " ' total_bbox = 830, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 80: 276.988861, 293.505432 avg loss, 0.000000 rate, 0.052869 seconds, 800 images, 11.906748 hours left',\n",
              " 'Loaded: 0.000062 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.594432, GIOU: 0.571646), Class: 0.496301, Obj: 0.506387, No Obj: 0.519504, .5R: 1.000000, .75R: 0.000000, count: 6, class_loss = 139.752625, iou_loss = 0.142053, total_loss = 139.894684 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.476590, GIOU: 0.406309), Class: 0.582571, Obj: 0.390233, No Obj: 0.443929, .5R: 0.400000, .75R: 0.200000, count: 5, class_loss = 418.203339, iou_loss = 0.349561, total_loss = 418.552887 ',\n",
              " ' total_bbox = 841, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 81: 278.981232, 292.053009 avg loss, 0.000000 rate, 0.053082 seconds, 810 images, 11.861214 hours left',\n",
              " 'Loaded: 0.000066 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.629026, GIOU: 0.592275), Class: 0.528124, Obj: 0.509881, No Obj: 0.518201, .5R: 1.000000, .75R: 0.166667, count: 6, class_loss = 139.176010, iou_loss = 0.181738, total_loss = 139.357758 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.326430, GIOU: 0.115652), Class: 0.488872, Obj: 0.343265, No Obj: 0.438136, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 406.020111, iou_loss = 0.173657, total_loss = 406.193787 ',\n",
              " ' total_bbox = 851, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 82: 272.601715, 290.107880 avg loss, 0.000000 rate, 0.052943 seconds, 820 images, 11.816446 hours left',\n",
              " 'Loaded: 0.000059 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.447626, GIOU: 0.322094), Class: 0.463724, Obj: 0.527314, No Obj: 0.517715, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 138.798462, iou_loss = 0.105493, total_loss = 138.903961 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.425396, GIOU: 0.333611), Class: 0.558380, Obj: 0.371387, No Obj: 0.441888, .5R: 0.285714, .75R: 0.000000, count: 7, class_loss = 413.312897, iou_loss = 0.422119, total_loss = 413.735016 ',\n",
              " ' total_bbox = 862, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 83: 276.059631, 288.703064 avg loss, 0.000000 rate, 0.053157 seconds, 830 images, 11.771935 hours left',\n",
              " 'Loaded: 0.000061 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.608285, GIOU: 0.571232), Class: 0.486525, Obj: 0.507635, No Obj: 0.517589, .5R: 0.750000, .75R: 0.125000, count: 8, class_loss = 138.935974, iou_loss = 0.260730, total_loss = 139.196701 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.424246, GIOU: 0.351965), Class: 0.470448, Obj: 0.381903, No Obj: 0.435115, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 400.606415, iou_loss = 0.094873, total_loss = 400.701294 ',\n",
              " ' total_bbox = 872, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 84: 269.774597, 286.810211 avg loss, 0.000000 rate, 0.052997 seconds, 840 images, 11.728160 hours left',\n",
              " 'Loaded: 0.000054 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.562001, GIOU: 0.536828), Class: 0.479116, Obj: 0.521922, No Obj: 0.511191, .5R: 0.750000, .75R: 0.000000, count: 8, class_loss = 135.379059, iou_loss = 0.232532, total_loss = 135.611588 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.767735, GIOU: 0.760312), Class: 0.945702, Obj: 0.358927, No Obj: 0.438959, .5R: 1.000000, .75R: 1.000000, count: 1, class_loss = 405.691376, iou_loss = 0.175537, total_loss = 405.866913 ',\n",
              " ' total_bbox = 881, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 85: 270.537537, 285.182953 avg loss, 0.000000 rate, 0.052641 seconds, 850 images, 11.684602 hours left',\n",
              " 'Loaded: 0.000054 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.567402, GIOU: 0.521803), Class: 0.502809, Obj: 0.516994, No Obj: 0.512160, .5R: 0.750000, .75R: 0.000000, count: 8, class_loss = 135.919174, iou_loss = 0.177173, total_loss = 136.096359 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.449714, GIOU: 0.321264), Class: 0.499486, Obj: 0.373009, No Obj: 0.429446, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 389.777039, iou_loss = 0.156055, total_loss = 389.933075 ',\n",
              " ' total_bbox = 892, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 86: 262.851532, 282.949799 avg loss, 0.000000 rate, 0.053216 seconds, 860 images, 11.640976 hours left',\n",
              " 'Loaded: 0.000080 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.547871, GIOU: 0.484284), Class: 0.471066, Obj: 0.532955, No Obj: 0.514745, .5R: 0.571429, .75R: 0.000000, count: 7, class_loss = 137.515182, iou_loss = 0.150928, total_loss = 137.666107 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.473082, GIOU: 0.419171), Class: 0.531381, Obj: 0.510936, No Obj: 0.425010, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 382.135101, iou_loss = 0.218384, total_loss = 382.353485 ',\n",
              " ' total_bbox = 902, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 87: 259.828552, 280.637665 avg loss, 0.000000 rate, 0.052840 seconds, 870 images, 11.598583 hours left',\n",
              " 'Loaded: 0.000065 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.500317, GIOU: 0.439103), Class: 0.488519, Obj: 0.556063, No Obj: 0.512370, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 136.073532, iou_loss = 0.119275, total_loss = 136.192795 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.553833, GIOU: 0.449743), Class: 0.447196, Obj: 0.386748, No Obj: 0.423165, .5R: 0.666667, .75R: 0.166667, count: 6, class_loss = 378.992401, iou_loss = 0.598657, total_loss = 379.591064 ',\n",
              " ' total_bbox = 913, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 88: 257.536285, 278.327515 avg loss, 0.000000 rate, 0.053139 seconds, 880 images, 11.556123 hours left',\n",
              " 'Loaded: 0.000060 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.630708, GIOU: 0.571589), Class: 0.487886, Obj: 0.493714, No Obj: 0.513107, .5R: 0.857143, .75R: 0.285714, count: 7, class_loss = 136.155304, iou_loss = 0.223779, total_loss = 136.379074 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.600358, GIOU: 0.550698), Class: 0.456814, Obj: 0.400948, No Obj: 0.421786, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 374.799225, iou_loss = 0.405542, total_loss = 375.204773 ',\n",
              " ' total_bbox = 924, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 89: 255.479950, 276.042755 avg loss, 0.000000 rate, 0.052828 seconds, 890 images, 11.514484 hours left',\n",
              " 'Loaded: 0.000057 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.570756, GIOU: 0.539056), Class: 0.491152, Obj: 0.508487, No Obj: 0.510159, .5R: 0.625000, .75R: 0.125000, count: 8, class_loss = 134.763229, iou_loss = 0.260168, total_loss = 135.023392 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.519655, GIOU: 0.415096), Class: 0.628437, Obj: 0.502488, No Obj: 0.416459, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 364.841400, iou_loss = 0.252710, total_loss = 365.094116 ',\n",
              " ' total_bbox = 935, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 90: 249.805496, 273.419037 avg loss, 0.000000 rate, 0.053038 seconds, 900 images, 11.472824 hours left',\n",
              " 'Loaded: 0.000051 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.540515, GIOU: 0.518013), Class: 0.498070, Obj: 0.528550, No Obj: 0.512446, .5R: 0.777778, .75R: 0.000000, count: 9, class_loss = 136.303223, iou_loss = 0.220264, total_loss = 136.523483 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.301316, GIOU: -0.028011), Class: 0.578707, Obj: 0.475916, No Obj: 0.413355, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 362.167694, iou_loss = 0.059033, total_loss = 362.226746 ',\n",
              " ' total_bbox = 947, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 91: 249.239502, 271.001099 avg loss, 0.000000 rate, 0.053030 seconds, 910 images, 11.431866 hours left',\n",
              " 'Loaded: 0.000055 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.575995, GIOU: 0.543454), Class: 0.501098, Obj: 0.565468, No Obj: 0.511581, .5R: 0.833333, .75R: 0.166667, count: 6, class_loss = 135.555649, iou_loss = 0.175024, total_loss = 135.730682 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.607818, GIOU: 0.597962), Class: 0.458640, Obj: 0.371107, No Obj: 0.412067, .5R: 0.666667, .75R: 0.333333, count: 3, class_loss = 360.504639, iou_loss = 0.325806, total_loss = 360.830444 ',\n",
              " ' total_bbox = 956, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 92: 248.033005, 268.704285 avg loss, 0.000000 rate, 0.052969 seconds, 920 images, 11.391301 hours left',\n",
              " 'Loaded: 0.000057 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.545318, GIOU: 0.518126), Class: 0.473683, Obj: 0.524085, No Obj: 0.509770, .5R: 0.777778, .75R: 0.000000, count: 9, class_loss = 134.978745, iou_loss = 0.204443, total_loss = 135.183182 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.123170, GIOU: 0.123169), Class: 0.422879, Obj: 0.554007, No Obj: 0.410659, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 355.940521, iou_loss = 0.007275, total_loss = 355.947815 ',\n",
              " ' total_bbox = 966, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 93: 245.464279, 266.380280 avg loss, 0.000000 rate, 0.053202 seconds, 930 images, 11.351062 hours left',\n",
              " 'Loaded: 0.000073 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.673610, GIOU: 0.646118), Class: 0.488508, Obj: 0.535710, No Obj: 0.510401, .5R: 1.000000, .75R: 0.000000, count: 4, class_loss = 134.909424, iou_loss = 0.158826, total_loss = 135.068253 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.410330, GIOU: 0.229416), Class: 0.528044, Obj: 0.445733, No Obj: 0.404106, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 348.144379, iou_loss = 0.275024, total_loss = 348.419403 ',\n",
              " ' total_bbox = 975, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 94: 241.530106, 263.895264 avg loss, 0.000000 rate, 0.053137 seconds, 940 images, 11.311554 hours left',\n",
              " 'Loaded: 0.000069 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.517002, GIOU: 0.477502), Class: 0.505727, Obj: 0.489809, No Obj: 0.507446, .5R: 0.571429, .75R: 0.142857, count: 7, class_loss = 133.558823, iou_loss = 0.188416, total_loss = 133.747238 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.482269, GIOU: 0.426743), Class: 0.626956, Obj: 0.317232, No Obj: 0.398331, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 337.242920, iou_loss = 0.238867, total_loss = 337.481781 ',\n",
              " ' total_bbox = 986, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 95: 235.404373, 261.046173 avg loss, 0.000000 rate, 0.053797 seconds, 950 images, 11.272372 hours left',\n",
              " 'Loaded: 0.000069 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.564747, GIOU: 0.530422), Class: 0.466430, Obj: 0.500199, No Obj: 0.508679, .5R: 0.833333, .75R: 0.000000, count: 6, class_loss = 134.016678, iou_loss = 0.128931, total_loss = 134.145615 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.514413, GIOU: 0.410364), Class: 0.528551, Obj: 0.372562, No Obj: 0.397089, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 334.068115, iou_loss = 0.328076, total_loss = 334.396210 ',\n",
              " ' total_bbox = 996, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 96: 234.045609, 258.346130 avg loss, 0.000000 rate, 0.052522 seconds, 960 images, 11.234493 hours left',\n",
              " 'Loaded: 0.000050 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.613117, GIOU: 0.588503), Class: 0.481968, Obj: 0.485309, No Obj: 0.504527, .5R: 0.666667, .75R: 0.111111, count: 9, class_loss = 132.371567, iou_loss = 0.253882, total_loss = 132.625458 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.476032, GIOU: 0.439229), Class: 0.463248, Obj: 0.496926, No Obj: 0.388995, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 321.468231, iou_loss = 0.114502, total_loss = 321.582733 ',\n",
              " ' total_bbox = 1007, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 97: 226.923065, 255.203827 avg loss, 0.000000 rate, 0.052878 seconds, 970 images, 11.195216 hours left',\n",
              " 'Loaded: 0.000054 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.537405, GIOU: 0.471701), Class: 0.463751, Obj: 0.501455, No Obj: 0.503507, .5R: 0.714286, .75R: 0.000000, count: 7, class_loss = 131.164566, iou_loss = 0.223877, total_loss = 131.388443 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.563632, GIOU: 0.527742), Class: 0.567283, Obj: 0.319710, No Obj: 0.385712, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 315.070282, iou_loss = 0.367749, total_loss = 315.438049 ',\n",
              " ' total_bbox = 1018, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 98: 223.120560, 251.995499 avg loss, 0.000000 rate, 0.053356 seconds, 980 images, 11.156805 hours left',\n",
              " 'Loaded: 0.000055 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.539361, GIOU: 0.516254), Class: 0.537858, Obj: 0.514712, No Obj: 0.506510, .5R: 0.625000, .75R: 0.000000, count: 8, class_loss = 133.167419, iou_loss = 0.111499, total_loss = 133.278915 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.523115, GIOU: 0.467600), Class: 0.490225, Obj: 0.345879, No Obj: 0.383414, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 315.845795, iou_loss = 0.240479, total_loss = 316.086273 ',\n",
              " ' total_bbox = 1029, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 99: 224.509888, 249.246933 avg loss, 0.000000 rate, 0.052973 seconds, 990 images, 11.119448 hours left',\n",
              " 'Loaded: 0.000052 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.637962, GIOU: 0.625396), Class: 0.466225, Obj: 0.480526, No Obj: 0.502743, .5R: 0.833333, .75R: 0.166667, count: 6, class_loss = 130.897675, iou_loss = 0.170422, total_loss = 131.068100 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.524584, GIOU: 0.455158), Class: 0.453348, Obj: 0.337135, No Obj: 0.381419, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 308.806305, iou_loss = 0.544653, total_loss = 309.350952 ',\n",
              " ' total_bbox = 1040, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 100: 219.854904, 246.307724 avg loss, 0.000000 rate, 0.052932 seconds, 1000 images, 11.081933 hours left',\n",
              " 'Saving weights to backup/yolov4_tiny_custom_train_last.weights',\n",
              " 'Loaded: 0.000031 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.533849, GIOU: 0.494227), Class: 0.495599, Obj: 0.550373, No Obj: 0.501148, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 130.110947, iou_loss = 0.057275, total_loss = 130.168228 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.464413, GIOU: 0.344793), Class: 0.561289, Obj: 0.385492, No Obj: 0.372398, .5R: 0.571429, .75R: 0.142857, count: 7, class_loss = 296.937592, iou_loss = 0.666577, total_loss = 297.604156 ',\n",
              " ' total_bbox = 1051, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 101: 213.527786, 243.029724 avg loss, 0.000000 rate, 0.026765 seconds, 1010 images, 11.044733 hours left',\n",
              " 'Loaded: 0.000036 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.550275, GIOU: 0.506437), Class: 0.488244, Obj: 0.514537, No Obj: 0.499177, .5R: 0.750000, .75R: 0.125000, count: 8, class_loss = 129.289597, iou_loss = 0.218164, total_loss = 129.507767 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.533183, GIOU: 0.485803), Class: 0.510926, Obj: 0.460419, No Obj: 0.365904, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 283.859833, iou_loss = 0.286548, total_loss = 284.146362 ',\n",
              " ' total_bbox = 1062, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 102: 206.577927, 239.384552 avg loss, 0.000000 rate, 0.053014 seconds, 1020 images, 10.971521 hours left',\n",
              " 'Loaded: 0.000054 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.496025, GIOU: 0.450382), Class: 0.476858, Obj: 0.534414, No Obj: 0.498132, .5R: 0.500000, .75R: 0.000000, count: 6, class_loss = 128.369492, iou_loss = 0.177502, total_loss = 128.546997 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.505160, GIOU: 0.499215), Class: 0.548698, Obj: 0.356069, No Obj: 0.367710, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 286.469574, iou_loss = 0.313647, total_loss = 286.783203 ',\n",
              " ' total_bbox = 1072, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 103: 207.423019, 236.188400 avg loss, 0.000000 rate, 0.052911 seconds, 1030 images, 10.935511 hours left',\n",
              " 'Loaded: 0.000057 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.536674, GIOU: 0.477716), Class: 0.495803, Obj: 0.499915, No Obj: 0.495881, .5R: 0.375000, .75R: 0.000000, count: 8, class_loss = 127.553238, iou_loss = 0.172791, total_loss = 127.726028 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.577893, GIOU: 0.542353), Class: 0.531051, Obj: 0.254699, No Obj: 0.361429, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 276.997986, iou_loss = 0.348364, total_loss = 277.346344 ',\n",
              " ' total_bbox = 1083, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 104: 202.278717, 232.797440 avg loss, 0.000000 rate, 0.053010 seconds, 1040 images, 10.899744 hours left',\n",
              " 'Loaded: 0.000057 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.591105, GIOU: 0.557944), Class: 0.524041, Obj: 0.498589, No Obj: 0.495174, .5R: 0.777778, .75R: 0.000000, count: 9, class_loss = 127.241737, iou_loss = 0.256714, total_loss = 127.498451 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.262441, GIOU: -0.078752), Class: 0.348315, Obj: 0.301933, No Obj: 0.358347, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 271.994354, iou_loss = 0.009521, total_loss = 272.003876 ',\n",
              " ' total_bbox = 1093, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 105: 199.622040, 229.479904 avg loss, 0.000000 rate, 0.052986 seconds, 1050 images, 10.864476 hours left',\n",
              " 'Loaded: 0.000053 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.400733, GIOU: 0.371412), Class: 0.531265, Obj: 0.482315, No Obj: 0.497722, .5R: 0.428571, .75R: 0.000000, count: 7, class_loss = 128.142044, iou_loss = 0.131531, total_loss = 128.273575 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.390777, GIOU: 0.369678), Class: 0.504357, Obj: 0.346764, No Obj: 0.350936, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 263.464020, iou_loss = 0.435498, total_loss = 263.899506 ',\n",
              " ' total_bbox = 1105, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 106: 195.807266, 226.112640 avg loss, 0.000000 rate, 0.054340 seconds, 1060 images, 10.829525 hours left',\n",
              " 'Loaded: 0.000052 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.606952, GIOU: 0.586230), Class: 0.490807, Obj: 0.478069, No Obj: 0.491658, .5R: 1.000000, .75R: 0.000000, count: 7, class_loss = 125.628128, iou_loss = 0.240808, total_loss = 125.868935 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.551325, GIOU: 0.442552), Class: 0.495573, Obj: 0.291165, No Obj: 0.342405, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 250.361374, iou_loss = 0.313525, total_loss = 250.674911 ',\n",
              " ' total_bbox = 1116, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 107: 187.997711, 222.301147 avg loss, 0.000000 rate, 0.052053 seconds, 1070 images, 10.796806 hours left',\n",
              " 'Loaded: 0.000053 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.496741, GIOU: 0.434116), Class: 0.498638, Obj: 0.471440, No Obj: 0.491314, .5R: 0.375000, .75R: 0.000000, count: 8, class_loss = 125.434959, iou_loss = 0.185645, total_loss = 125.620605 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.324593, GIOU: 0.302204), Class: 0.682433, Obj: 0.338999, No Obj: 0.340027, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 246.605957, iou_loss = 0.143408, total_loss = 246.749374 ',\n",
              " ' total_bbox = 1128, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 108: 186.024582, 218.673492 avg loss, 0.000000 rate, 0.053045 seconds, 1080 images, 10.761229 hours left',\n",
              " 'Loaded: 0.000054 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.438439, GIOU: 0.352481), Class: 0.499851, Obj: 0.488848, No Obj: 0.491182, .5R: 0.428571, .75R: 0.000000, count: 7, class_loss = 125.002190, iou_loss = 0.113098, total_loss = 125.115288 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.396833, GIOU: 0.231571), Class: 0.514562, Obj: 0.328483, No Obj: 0.337459, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 242.747543, iou_loss = 0.199512, total_loss = 242.947052 ',\n",
              " ' total_bbox = 1138, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 109: 183.878937, 215.194031 avg loss, 0.000000 rate, 0.052824 seconds, 1090 images, 10.727387 hours left',\n",
              " 'Loaded: 0.000055 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.594409, GIOU: 0.543623), Class: 0.494056, Obj: 0.491821, No Obj: 0.489907, .5R: 0.875000, .75R: 0.000000, count: 8, class_loss = 124.751213, iou_loss = 0.249841, total_loss = 125.001053 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.265572, GIOU: 0.117466), Class: 0.484782, Obj: 0.380354, No Obj: 0.328593, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 230.579620, iou_loss = 0.060059, total_loss = 230.639679 ',\n",
              " ' total_bbox = 1148, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 110: 177.669403, 211.441574 avg loss, 0.000000 rate, 0.053310 seconds, 1100 images, 10.693579 hours left',\n",
              " 'Loaded: 0.000043 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.482789, GIOU: 0.415996), Class: 0.529009, Obj: 0.463838, No Obj: 0.488557, .5R: 0.333333, .75R: 0.111111, count: 9, class_loss = 124.057373, iou_loss = 0.247961, total_loss = 124.305336 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.492988, GIOU: 0.398295), Class: 0.503950, Obj: 0.348687, No Obj: 0.326204, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 227.350082, iou_loss = 0.085864, total_loss = 227.435944 ',\n",
              " ' total_bbox = 1159, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 111: 175.707306, 207.868149 avg loss, 0.000000 rate, 0.052838 seconds, 1110 images, 10.660787 hours left',\n",
              " 'Loaded: 0.000048 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.614963, GIOU: 0.554745), Class: 0.484608, Obj: 0.492278, No Obj: 0.485753, .5R: 0.666667, .75R: 0.333333, count: 6, class_loss = 122.463844, iou_loss = 0.111047, total_loss = 122.574890 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.520498, GIOU: 0.458034), Class: 0.494139, Obj: 0.323311, No Obj: 0.316684, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 215.864120, iou_loss = 0.280054, total_loss = 216.144165 ',\n",
              " ' total_bbox = 1169, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 112: 169.166992, 203.998032 avg loss, 0.000000 rate, 0.052861 seconds, 1120 images, 10.627649 hours left',\n",
              " 'Loaded: 0.000047 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.598926, GIOU: 0.543111), Class: 0.522072, Obj: 0.502720, No Obj: 0.482807, .5R: 0.857143, .75R: 0.000000, count: 7, class_loss = 120.991844, iou_loss = 0.152673, total_loss = 121.144524 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.328633, GIOU: 0.208911), Class: 0.425927, Obj: 0.287020, No Obj: 0.310292, .5R: 0.111111, .75R: 0.000000, count: 9, class_loss = 208.907516, iou_loss = 0.506909, total_loss = 209.414429 ',\n",
              " ' total_bbox = 1185, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 113: 164.953430, 200.093567 avg loss, 0.000000 rate, 0.053122 seconds, 1130 images, 10.594881 hours left',\n",
              " 'Loaded: 0.000049 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.635292, GIOU: 0.606326), Class: 0.512426, Obj: 0.498701, No Obj: 0.482421, .5R: 1.000000, .75R: 0.166667, count: 6, class_loss = 120.611465, iou_loss = 0.148315, total_loss = 120.759781 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.400010, GIOU: 0.335319), Class: 0.576859, Obj: 0.288767, No Obj: 0.305054, .5R: 0.400000, .75R: 0.200000, count: 5, class_loss = 200.348068, iou_loss = 0.456323, total_loss = 200.804398 ',\n",
              " ' total_bbox = 1196, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 114: 160.483139, 196.132523 avg loss, 0.000000 rate, 0.053028 seconds, 1140 images, 10.562801 hours left',\n",
              " 'Loaded: 0.000051 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.523654, GIOU: 0.511920), Class: 0.488394, Obj: 0.464746, No Obj: 0.479256, .5R: 0.833333, .75R: 0.000000, count: 6, class_loss = 118.799797, iou_loss = 0.150464, total_loss = 118.950256 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.588722, GIOU: 0.521603), Class: 0.569760, Obj: 0.302932, No Obj: 0.301908, .5R: 0.750000, .75R: 0.250000, count: 4, class_loss = 194.971451, iou_loss = 0.292749, total_loss = 195.264206 ',\n",
              " ' total_bbox = 1206, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 115: 156.888718, 192.208145 avg loss, 0.000000 rate, 0.052902 seconds, 1150 images, 10.530915 hours left',\n",
              " 'Loaded: 0.000052 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.561476, GIOU: 0.523475), Class: 0.491355, Obj: 0.467953, No Obj: 0.476963, .5R: 0.857143, .75R: 0.000000, count: 7, class_loss = 118.169678, iou_loss = 0.223425, total_loss = 118.393105 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.650588, GIOU: 0.627532), Class: 0.445989, Obj: 0.313682, No Obj: 0.290070, .5R: 1.000000, .75R: 0.000000, count: 5, class_loss = 181.440964, iou_loss = 0.643250, total_loss = 182.084213 ',\n",
              " ' total_bbox = 1218, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 116: 149.808075, 187.968140 avg loss, 0.000000 rate, 0.053103 seconds, 1160 images, 10.499173 hours left',\n",
              " 'Loaded: 0.000047 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.547777, GIOU: 0.469910), Class: 0.489565, Obj: 0.460168, No Obj: 0.473380, .5R: 0.875000, .75R: 0.000000, count: 8, class_loss = 116.415115, iou_loss = 0.176062, total_loss = 116.591179 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.261805, GIOU: -0.132004), Class: 0.515221, Obj: 0.294813, No Obj: 0.286199, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 174.661789, iou_loss = 0.076978, total_loss = 174.738754 ',\n",
              " ' total_bbox = 1228, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 117: 145.542603, 183.725586 avg loss, 0.000000 rate, 0.052839 seconds, 1170 images, 10.468031 hours left',\n",
              " 'Loaded: 0.000058 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.543676, GIOU: 0.502445), Class: 0.480105, Obj: 0.478504, No Obj: 0.475925, .5R: 0.750000, .75R: 0.000000, count: 8, class_loss = 117.632072, iou_loss = 0.154773, total_loss = 117.786842 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.628694, GIOU: 0.603789), Class: 0.467021, Obj: 0.213320, No Obj: 0.277308, .5R: 1.000000, .75R: 0.000000, count: 4, class_loss = 168.024216, iou_loss = 0.541711, total_loss = 168.565933 ',\n",
              " ' total_bbox = 1240, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 118: 142.831039, 179.636139 avg loss, 0.000001 rate, 0.053085 seconds, 1180 images, 10.436827 hours left',\n",
              " 'Loaded: 0.000059 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.498439, GIOU: 0.462738), Class: 0.494428, Obj: 0.471481, No Obj: 0.467312, .5R: 0.555556, .75R: 0.000000, count: 9, class_loss = 113.857132, iou_loss = 0.185303, total_loss = 114.042435 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.552166, GIOU: 0.505869), Class: 0.565709, Obj: 0.189506, No Obj: 0.266357, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 155.456375, iou_loss = 0.053662, total_loss = 155.510040 ',\n",
              " ' total_bbox = 1250, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 119: 134.660080, 175.138535 avg loss, 0.000001 rate, 0.052991 seconds, 1190 images, 10.406296 hours left',\n",
              " 'Loaded: 0.000069 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.479613, GIOU: 0.415053), Class: 0.481750, Obj: 0.468436, No Obj: 0.470085, .5R: 0.375000, .75R: 0.000000, count: 8, class_loss = 115.405968, iou_loss = 0.246802, total_loss = 115.652771 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.269674, GIOU: 0.058285), Class: 0.492875, Obj: 0.252752, No Obj: 0.267489, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 157.882736, iou_loss = 0.101025, total_loss = 157.983749 ',\n",
              " ' total_bbox = 1262, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 120: 136.648727, 171.289551 avg loss, 0.000001 rate, 0.053069 seconds, 1200 images, 10.375937 hours left',\n",
              " 'Loaded: 0.000057 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.568312, GIOU: 0.511287), Class: 0.450069, Obj: 0.436145, No Obj: 0.462507, .5R: 0.727273, .75R: 0.090909, count: 11, class_loss = 111.735863, iou_loss = 0.410913, total_loss = 112.146782 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.502763, GIOU: 0.478394), Class: 0.591624, Obj: 0.258664, No Obj: 0.257125, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 142.106888, iou_loss = 0.451245, total_loss = 142.558136 ',\n",
              " ' total_bbox = 1275, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 121: 126.924637, 166.853058 avg loss, 0.000001 rate, 0.054103 seconds, 1210 images, 10.346004 hours left',\n",
              " 'Loaded: 0.000054 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.555887, GIOU: 0.476817), Class: 0.511919, Obj: 0.469741, No Obj: 0.462052, .5R: 0.500000, .75R: 0.250000, count: 8, class_loss = 111.288025, iou_loss = 0.161597, total_loss = 111.449623 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.318859, GIOU: 0.168061), Class: 0.297640, Obj: 0.224599, No Obj: 0.250666, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 138.081207, iou_loss = 0.092065, total_loss = 138.173264 ',\n",
              " ' total_bbox = 1287, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 122: 124.688553, 162.636612 avg loss, 0.000001 rate, 0.052905 seconds, 1220 images, 10.317804 hours left',\n",
              " 'Loaded: 0.000053 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.577150, GIOU: 0.548529), Class: 0.471973, Obj: 0.463542, No Obj: 0.461187, .5R: 0.750000, .75R: 0.125000, count: 8, class_loss = 110.749870, iou_loss = 0.261462, total_loss = 111.011330 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.349321, GIOU: 0.160058), Class: 0.444969, Obj: 0.219404, No Obj: 0.246004, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 131.082428, iou_loss = 0.196973, total_loss = 131.279404 ',\n",
              " ' total_bbox = 1299, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 123: 120.919899, 158.464935 avg loss, 0.000001 rate, 0.053036 seconds, 1230 images, 10.288207 hours left',\n",
              " 'Loaded: 0.000054 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.462970, GIOU: 0.408556), Class: 0.486848, Obj: 0.449417, No Obj: 0.457940, .5R: 0.333333, .75R: 0.000000, count: 9, class_loss = 109.356316, iou_loss = 0.157617, total_loss = 109.513931 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.304235, GIOU: 0.227760), Class: 0.628436, Obj: 0.296804, No Obj: 0.241534, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 126.430679, iou_loss = 0.038379, total_loss = 126.469055 ',\n",
              " ' total_bbox = 1310, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 124: 117.897804, 154.408218 avg loss, 0.000001 rate, 0.052777 seconds, 1240 images, 10.259086 hours left',\n",
              " 'Loaded: 0.000058 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.602124, GIOU: 0.554217), Class: 0.484876, Obj: 0.461877, No Obj: 0.456343, .5R: 0.888889, .75R: 0.111111, count: 9, class_loss = 108.435120, iou_loss = 0.302478, total_loss = 108.737602 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.448134, GIOU: 0.311071), Class: 0.408660, Obj: 0.249791, No Obj: 0.240836, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 126.316872, iou_loss = 0.034387, total_loss = 126.351257 ',\n",
              " ' total_bbox = 1320, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 125: 117.379318, 150.705322 avg loss, 0.000001 rate, 0.052777 seconds, 1250 images, 10.229898 hours left',\n",
              " 'Loaded: 0.000059 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.578581, GIOU: 0.552011), Class: 0.464358, Obj: 0.448686, No Obj: 0.453437, .5R: 0.666667, .75R: 0.166667, count: 6, class_loss = 107.059143, iou_loss = 0.168848, total_loss = 107.227989 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.330239, GIOU: 0.138391), Class: 0.644657, Obj: 0.261921, No Obj: 0.226597, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 112.762650, iou_loss = 0.129578, total_loss = 112.892227 ',\n",
              " ' total_bbox = 1330, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 126: 109.914719, 146.626266 avg loss, 0.000001 rate, 0.052884 seconds, 1260 images, 10.201006 hours left',\n",
              " 'Loaded: 0.000050 seconds',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.471515, GIOU: 0.438593), Class: 0.467100, Obj: 0.495623, No Obj: 0.448724, .5R: 0.285714, .75R: 0.000000, count: 7, class_loss = 104.769531, iou_loss = 0.142590, total_loss = 104.912125 ',\n",
              " 'v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.311909, GIOU: 0.126023), Class: 0.544284, Obj: 0.214916, No Obj: 0.225601, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 110.973694, iou_loss = 0.093494, total_loss = 111.067192 ',\n",
              " ' total_bbox = 1341, rewritten_bbox = 0.000000 % ',\n",
              " '',\n",
              " ' (next mAP calculation at 1120 iterations) ',\n",
              " ' 127: 107.875877, 142.751221 avg loss, 0.000001 rate, 0.052880 seconds, 1270 images, 10.172553 hours left',\n",
              " 'Loaded: 0.000054 seconds',\n",
              " ...]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ouQHipZul0v"
      },
      "source": [
        "with open(\"output.txt\", \"w\") as f:\n",
        "  for line in Out[14]:\n",
        "    f.write(\"%s\\n\" % line)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "azzJCBjMZO6Y"
      },
      "source": [
        "## Visualize training result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "R4ruwfrV9GUR",
        "outputId": "1edbc59d-198c-46be-b387-46532a5947bd"
      },
      "source": [
        "assert os.getcwd()=='/content/darknet_for_colab', 'Directory should be \"/content/darknet_for_colab\" instead of \"{}\"'.format(os.getcwd())\n",
        "\n",
        "# Plotting training result after 2000 epochs\n",
        "import matplotlib.pyplot as plt\n",
        "fig = plt.figure(figsize=(10,10))\n",
        "train_result = plt.imread(\"chart.png\")\n",
        "plt.axis(False)\n",
        "plt.imshow(train_result)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fe909ad28d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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