Number_Plate_Detection_YOLOv3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Number_Plate_Detection_YOLOv3.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "RhqL2ThH1dOi",
        "colab_type": "code",
        "outputId": "aa29380f-7ea7-4367-fcf2-968c05194efb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "source": [
        "! pip install pydrive \n",
        "import os\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "# 1. Authenticate and create the PyDrive client.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "local_download_path = os.path.expanduser('~/data')\n",
        "try:\n",
        "  os.makedirs(local_download_path)\n",
        "except: pass\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive',force_remount=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pydrive\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/52/e0/0e64788e5dd58ce2d6934549676243dc69d982f198524be9b99e9c2a4fd5/PyDrive-1.3.1.tar.gz (987kB)\n",
            "\u001b[K     |████████████████████████████████| 993kB 1.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: google-api-python-client>=1.2 in /usr/local/lib/python3.6/dist-packages (from pydrive) (1.7.10)\n",
            "Requirement already satisfied: oauth2client>=4.0.0 in /usr/local/lib/python3.6/dist-packages (from pydrive) (4.1.3)\n",
            "Requirement already satisfied: PyYAML>=3.0 in /usr/local/lib/python3.6/dist-packages (from pydrive) (3.13)\n",
            "Requirement already satisfied: google-auth>=1.4.1 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive) (1.4.2)\n",
            "Requirement already satisfied: httplib2<1dev,>=0.9.2 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive) (0.11.3)\n",
            "Requirement already satisfied: six<2dev,>=1.6.1 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive) (1.12.0)\n",
            "Requirement already satisfied: uritemplate<4dev,>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive) (3.0.0)\n",
            "Requirement already satisfied: google-auth-httplib2>=0.0.3 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive) (0.0.3)\n",
            "Requirement already satisfied: rsa>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->pydrive) (4.0)\n",
            "Requirement already satisfied: pyasn1-modules>=0.0.5 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->pydrive) (0.2.6)\n",
            "Requirement already satisfied: pyasn1>=0.1.7 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->pydrive) (0.4.6)\n",
            "Requirement already satisfied: cachetools>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth>=1.4.1->google-api-python-client>=1.2->pydrive) (3.1.1)\n",
            "Building wheels for collected packages: pydrive\n",
            "  Building wheel for pydrive (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pydrive: filename=PyDrive-1.3.1-cp36-none-any.whl size=27435 sha256=841d553d9129ae6b9281eaa6d6a4733ed67f90beec144c8404b2b9a5fc4d22e5\n",
            "  Stored in directory: /root/.cache/pip/wheels/fa/d2/9a/d3b6b506c2da98289e5d417215ce34b696db856643bad779f4\n",
            "Successfully built pydrive\n",
            "Installing collected packages: pydrive\n",
            "Successfully installed pydrive-1.3.1\n",
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eK4yik0Y2jht",
        "colab_type": "code",
        "outputId": "09c70c9d-7dde-4acb-d578-53506efaca55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd 'drive/My Drive'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DrdyBxyZvLpM",
        "colab_type": "code",
        "outputId": "4a40f595-5a95-4590-8e20-258e429bcb5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "!apt-get update > /dev/null\n",
        "!apt-get upgrade > /dev/null\n",
        "!apt-get install build-essential > /dev/null\n",
        "!apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev > /dev/null\n",
        "!apt-get install libopencv-dev > /dev/null\n",
        "!apt-get install libavcodec-dev libavformat-dev libswscale-d > /dev/null"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Extracting templates from packages: 100%\n",
            "E: Unable to locate package libswscale-d\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N0xmqTzt42KM",
        "colab_type": "code",
        "outputId": "fb93132a-4092-47dd-af48-caa1d077376a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "%cd darknet\n",
        "!sed -i 's/OPENCV=1/OPENCV=0/g' Makefile\n",
        "!sed -i 's/GPU=0/GPU=1/g' Makefile\n",
        "!sed -i 's/CUDNN=0/CUDNN=1/g' Makefile\n",
        "!sed -i 's/CUDNN_HALF=0/CUDNN_HALF=1/g' Makefile\n",
        "\n",
        "!make"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Errno 20] Not a directory: 'darknet'\n",
            "/content/drive/My Drive/darknet\n",
            "chmod +x *.sh\n",
            "g++ -std=c++11 -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/image_opencv.cpp -o obj/image_opencv.o\n",
            "g++ -std=c++11 -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/http_stream.cpp -o obj/http_stream.o\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:228:21:\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",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -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:2037:15:\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:2036:15:\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 -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/utils.c -o obj/utils.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -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[Kget_cuda_stream\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:120:18:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbuffer\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "             char \u001b[01;35m\u001b[Kbuffer\u001b[m\u001b[K[256];\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[Kget_cuda_memcpy_stream\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:141:18:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbuffer\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "             char \u001b[01;35m\u001b[Kbuffer\u001b[m\u001b[K[256];\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[Kcudnn_handle\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:161:23:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kstatus\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         cudnnStatus_t \u001b[01;35m\u001b[Kstatus\u001b[m\u001b[K = cudnnSetStream(handle[i], get_cuda_stream());\n",
            "                       \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -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:1019:32:\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",
            "\u001b[01m\u001b[K./src/convolutional_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kassisted_excitation_forward\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/convolutional_layer.c:1101:17:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kepoch\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     const float \u001b[01;35m\u001b[Kepoch\u001b[m\u001b[K = (float)(*state.net.seen) / state.net.train_images_num;\n",
            "                 \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/list.c -o obj/list.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/image.c -o obj/image.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -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:68:5:\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:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgradient\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/activations.c:138:5:\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",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/im2col.c -o obj/im2col.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/col2im.c -o obj/col2im.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/blas.c -o obj/blas.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/crop_layer.c -o obj/crop_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/dropout_layer.c -o obj/dropout_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/maxpool_layer.c -o obj/maxpool_layer.o\n",
            "\u001b[01m\u001b[K./src/maxpool_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcudnn_maxpool_setup\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/maxpool_layer.c:26:19:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kmaxpool_status\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "     cudnnStatus_t \u001b[01;35m\u001b[Kmaxpool_status\u001b[m\u001b[K;\n",
            "                   \u001b[01;35m\u001b[K^~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/softmax_layer.c -o obj/softmax_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -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:1046:19:\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 = rand_precalc_random(.25, 2, r_scale); // unused currently\n",
            "                   \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/data.c:1046:19:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kr_scale\u001b[m\u001b[K’ may be used uninitialized in this function [\u001b[01;35m\u001b[K-Wmaybe-uninitialized\u001b[m\u001b[K]\n",
            "             float \u001b[01;35m\u001b[Kscale\u001b[m\u001b[K = rand_precalc_random(.25, 2, r_scale); // unused currently\n",
            "                   \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/matrix.c -o obj/matrix.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -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:562:42:\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:39\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:4412:39:\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 -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -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:335:11:\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:333:13:\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:332:13:\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:331:13:\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:330:9:\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:329:9:\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:328:9:\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 -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/cost_layer.c -o obj/cost_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -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:1007:42:\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:39\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:4412:39:\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 -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/option_list.c -o obj/option_list.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/darknet.c -o obj/darknet.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/detection_layer.c -o obj/detection_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/captcha.c -o obj/captcha.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/route_layer.c -o obj/route_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/writing.c -o obj/writing.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/box.c -o obj/box.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/nightmare.c -o obj/nightmare.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/normalization_layer.c -o obj/normalization_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/avgpool_layer.c -o obj/avgpool_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/coco.c -o obj/coco.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/dice.c -o obj/dice.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/yolo.c -o obj/yolo.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -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[Ktrain_detector\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:52:16:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Knames\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         char **\u001b[01;35m\u001b[Knames\u001b[m\u001b[K = get_labels_custom(name_list, &names_size);\n",
            "                \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:258:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kdraw_precision\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "         int \u001b[01;35m\u001b[Kdraw_precision\u001b[m\u001b[K = 0;\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:1046:15:\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:1045:15:\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",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/layer.c -o obj/layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/compare.c -o obj/compare.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -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:157:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kdraw_precision\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "         int \u001b[01;35m\u001b[Kdraw_precision\u001b[m\u001b[K = 0;\n",
            "             \u001b[01;35m\u001b[K^~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/local_layer.c -o obj/local_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/swag.c -o obj/swag.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/shortcut_layer.c -o obj/shortcut_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/activation_layer.c -o obj/activation_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/rnn_layer.c -o obj/rnn_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/gru_layer.c -o obj/gru_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/rnn.c -o obj/rnn.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/rnn_vid.c -o obj/rnn_vid.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/crnn_layer.c -o obj/crnn_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/demo.c -o obj/demo.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/tag.c -o obj/tag.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/cifar.c -o obj/cifar.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/go.c -o obj/go.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/batchnorm_layer.c -o obj/batchnorm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/art.c -o obj/art.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/region_layer.c -o obj/region_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/reorg_layer.c -o obj/reorg_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/reorg_old_layer.c -o obj/reorg_old_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/super.c -o obj/super.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/voxel.c -o obj/voxel.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/tree.c -o obj/tree.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -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:58:38:\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:39\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:4412:39:\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:65:38:\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:39\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:4412:39:\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:92:42:\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:39\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:4412:39:\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:101:42:\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:39\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:4412:39:\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 -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/upsample_layer.c -o obj/upsample_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/lstm_layer.c -o obj/lstm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/conv_lstm_layer.c -o obj/conv_lstm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/scale_channels_layer.c -o obj/scale_channels_layer.o\n",
            "In file included from \u001b[01m\u001b[K./src/scale_channels_layer.c:5:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K./src/scale_channels_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_scale_channels_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/scale_channels_layer.c:16:14:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around comparison in operand of ‘\u001b[01m\u001b[K&\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n",
            "     assert(\u001b[01;35m\u001b[Kw == 1\u001b[m\u001b[K & h == 1);\n",
            "            \u001b[01;35m\u001b[K~~^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/scale_channels_layer.c:16:14:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around comparison in operand of ‘\u001b[01m\u001b[K&\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n",
            "     assert(\u001b[01;35m\u001b[Kw == 1\u001b[m\u001b[K & h == 1);\n",
            "            \u001b[01;35m\u001b[K~~^~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/sam_layer.c -o obj/sam_layer.o\n",
            "In file included from \u001b[01m\u001b[K./src/sam_layer.c:5:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K./src/sam_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_sam_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/sam_layer.c:21:16:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around comparison in operand of ‘\u001b[01m\u001b[K&\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n",
            "     assert(\u001b[01;35m\u001b[Kl.w == l.o\u001b[m\u001b[Kut_w & l.h == l.out_h);\n",
            "            \u001b[01;35m\u001b[K~~~~^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/sam_layer.c:21:16:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around comparison in operand of ‘\u001b[01m\u001b[K&\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n",
            "     assert(\u001b[01;35m\u001b[Kl.w == l.o\u001b[m\u001b[Kut_w & l.h == l.out_h);\n",
            "            \u001b[01;35m\u001b[K~~~~^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/sam_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_sam_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/sam_layer.c:63:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kchannel_size\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kchannel_size\u001b[m\u001b[K = 1;\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/sam_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbackward_sam_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/sam_layer.c:82:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kchannel_size\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kchannel_size\u001b[m\u001b[K = 1;\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n",
            "nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/convolutional_kernels.cu -o obj/convolutional_kernels.o\n",
            "nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/activation_kernels.cu -o obj/activation_kernels.o\n",
            "nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/im2col_kernels.cu -o obj/im2col_kernels.o\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",
            "\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_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/col2im_kernels.cu -o obj/col2im_kernels.o\n",
            "nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/blas_kernels.cu -o obj/blas_kernels.o\n",
            "nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/crop_layer_kernels.cu -o obj/crop_layer_kernels.o\n",
            "nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/dropout_layer_kernels.cu -o obj/dropout_layer_kernels.o\n",
            "./src/dropout_layer_kernels.cu(18): warning: variable \"iteration_num\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(39): warning: variable \"iteration_num\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(18): warning: variable \"iteration_num\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(39): warning: variable \"iteration_num\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(18): warning: variable \"iteration_num\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(39): warning: variable \"iteration_num\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(18): warning: variable \"iteration_num\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(39): warning: variable \"iteration_num\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(18): warning: variable \"iteration_num\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(39): warning: variable \"iteration_num\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(18): warning: variable \"iteration_num\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(39): warning: variable \"iteration_num\" was declared but never referenced\n",
            "\n",
            "nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/maxpool_layer_kernels.cu -o obj/maxpool_layer_kernels.o\n",
            "nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/network_kernels.cu -o obj/network_kernels.o\n",
            "nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/avgpool_layer_kernels.cu -o obj/avgpool_layer_kernels.o\n",
            "g++ -std=c++11 -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF 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/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 -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand -L/usr/local/cudnn/lib64 -lcudnn -lstdc++ \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a2EDh5hYXb5C",
        "colab_type": "code",
        "outputId": "e972890e-8ff6-44be-8c73-23e3ef176d32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!apt install g++-5\n",
        "!apt install gcc-5\n",
        "!apt update\n",
        "!apt upgrade"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-410\n",
            "Use 'apt autoremove' to remove it.\n",
            "The following additional packages will be installed:\n",
            "  cpp-5 gcc-5 gcc-5-base libasan2 libgcc-5-dev libisl15 libmpx0\n",
            "  libstdc++-5-dev\n",
            "Suggested packages:\n",
            "  gcc-5-locales g++-5-multilib gcc-5-doc libstdc++6-5-dbg gcc-5-multilib\n",
            "  libgcc1-dbg libgomp1-dbg libitm1-dbg libatomic1-dbg libasan2-dbg\n",
            "  liblsan0-dbg libtsan0-dbg libubsan0-dbg libcilkrts5-dbg libmpx0-dbg\n",
            "  libquadmath0-dbg libstdc++-5-doc\n",
            "The following NEW packages will be installed:\n",
            "  cpp-5 g++-5 gcc-5 gcc-5-base libasan2 libgcc-5-dev libisl15 libmpx0\n",
            "  libstdc++-5-dev\n",
            "0 upgraded, 9 newly installed, 0 to remove and 4 not upgraded.\n",
            "Need to get 29.1 MB of archives.\n",
            "After this operation, 100 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 gcc-5-base amd64 5.5.0-12ubuntu1 [17.1 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libisl15 amd64 0.18-4 [548 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu bionic/universe amd64 cpp-5 amd64 5.5.0-12ubuntu1 [7,785 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libasan2 amd64 5.5.0-12ubuntu1 [264 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libmpx0 amd64 5.5.0-12ubuntu1 [9,888 B]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libgcc-5-dev amd64 5.5.0-12ubuntu1 [2,224 kB]\n",
            "Get:7 http://archive.ubuntu.com/ubuntu bionic/universe amd64 gcc-5 amd64 5.5.0-12ubuntu1 [8,357 kB]\n",
            "Get:8 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libstdc++-5-dev amd64 5.5.0-12ubuntu1 [1,415 kB]\n",
            "Get:9 http://archive.ubuntu.com/ubuntu bionic/universe amd64 g++-5 amd64 5.5.0-12ubuntu1 [8,450 kB]\n",
            "Fetched 29.1 MB in 2s (14.9 MB/s)\n",
            "Selecting previously unselected package gcc-5-base:amd64.\n",
            "(Reading database ... 133231 files and directories currently installed.)\n",
            "Preparing to unpack .../0-gcc-5-base_5.5.0-12ubuntu1_amd64.deb ...\n",
            "Unpacking gcc-5-base:amd64 (5.5.0-12ubuntu1) ...\n",
            "Selecting previously unselected package libisl15:amd64.\n",
            "Preparing to unpack .../1-libisl15_0.18-4_amd64.deb ...\n",
            "Unpacking libisl15:amd64 (0.18-4) ...\n",
            "Selecting previously unselected package cpp-5.\n",
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            "Unpacking cpp-5 (5.5.0-12ubuntu1) ...\n",
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            "Selecting previously unselected package libmpx0:amd64.\n",
            "Preparing to unpack .../4-libmpx0_5.5.0-12ubuntu1_amd64.deb ...\n",
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            "Unpacking g++-5 (5.5.0-12ubuntu1) ...\n",
            "Setting up libisl15:amd64 (0.18-4) ...\n",
            "Setting up gcc-5-base:amd64 (5.5.0-12ubuntu1) ...\n",
            "Setting up libmpx0:amd64 (5.5.0-12ubuntu1) ...\n",
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            "Setting up libgcc-5-dev:amd64 (5.5.0-12ubuntu1) ...\n",
            "Setting up cpp-5 (5.5.0-12ubuntu1) ...\n",
            "Setting up libstdc++-5-dev:amd64 (5.5.0-12ubuntu1) ...\n",
            "Setting up gcc-5 (5.5.0-12ubuntu1) ...\n",
            "Setting up g++-5 (5.5.0-12ubuntu1) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1) ...\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "gcc-5 is already the newest version (5.5.0-12ubuntu1).\n",
            "gcc-5 set to manually installed.\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-410\n",
            "Use 'apt autoremove' to remove it.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 4 not upgraded.\n",
            "Ign:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n",
            "Hit:2 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\n",
            "Hit:3 http://archive.ubuntu.com/ubuntu bionic InRelease\n",
            "Hit:4 http://security.ubuntu.com/ubuntu bionic-security InRelease\n",
            "Hit:5 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran35/ InRelease\n",
            "Ign:6 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n",
            "Hit:7 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release\n",
            "Hit:8 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n",
            "Hit:9 http://archive.ubuntu.com/ubuntu bionic-updates InRelease\n",
            "Hit:10 http://ppa.launchpad.net/marutter/c2d4u3.5/ubuntu bionic InRelease\n",
            "Hit:11 http://archive.ubuntu.com/ubuntu bionic-backports InRelease\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "4 packages can be upgraded. Run 'apt list --upgradable' to see them.\n",
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            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "Calculating upgrade... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-410\n",
            "Use 'apt autoremove' to remove it.\n",
            "The following packages have been kept back:\n",
            "  libcudnn7 libcudnn7-dev libnccl-dev libnccl2\n",
            "0 upgraded, 0 newly installed, 0 to remove and 4 not upgraded.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "se7Wkedem6H5",
        "colab_type": "code",
        "outputId": "f34d6f49-de26-4346-beda-1723602d654b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!./darknet detector test data/obj.data yolo-obj.cfg backup/yolo-obj_final.weights -ext_output -dont_show -out result_plates.json < data/valid.txt"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " compute_capability = 370, cudnn_half = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 conv     32       3 x 3/ 1    608 x 608 x   3 ->  608 x 608 x  32 0.639 BF\n",
            "   1 conv     64       3 x 3/ 2    608 x 608 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   2 conv     32       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  32 0.379 BF\n",
            "   3 conv     64       3 x 3/ 1    304 x 304 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   4 Shortcut Layer: 1\n",
            "   5 conv    128       3 x 3/ 2    304 x 304 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   6 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "   7 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   8 Shortcut Layer: 5\n",
            "   9 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "  10 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "  11 Shortcut Layer: 8\n",
            "  12 conv    256       3 x 3/ 2    152 x 152 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  13 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  14 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  15 Shortcut Layer: 12\n",
            "  16 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  17 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  18 Shortcut Layer: 15\n",
            "  19 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  20 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  21 Shortcut Layer: 18\n",
            "  22 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  23 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  24 Shortcut Layer: 21\n",
            "  25 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  26 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  27 Shortcut Layer: 24\n",
            "  28 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  29 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  30 Shortcut Layer: 27\n",
            "  31 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  32 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  33 Shortcut Layer: 30\n",
            "  34 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  35 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  36 Shortcut Layer: 33\n",
            "  37 conv    512       3 x 3/ 2     76 x  76 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  38 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  39 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  40 Shortcut Layer: 37\n",
            "  41 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  42 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  43 Shortcut Layer: 40\n",
            "  44 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  45 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  46 Shortcut Layer: 43\n",
            "  47 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  48 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  49 Shortcut Layer: 46\n",
            "  50 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  51 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  52 Shortcut Layer: 49\n",
            "  53 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  54 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  55 Shortcut Layer: 52\n",
            "  56 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  57 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  58 Shortcut Layer: 55\n",
            "  59 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  60 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  61 Shortcut Layer: 58\n",
            "  62 conv   1024       3 x 3/ 2     38 x  38 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  63 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  64 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  65 Shortcut Layer: 62\n",
            "  66 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  67 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  68 Shortcut Layer: 65\n",
            "  69 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  70 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  71 Shortcut Layer: 68\n",
            "  72 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  73 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  74 Shortcut Layer: 71\n",
            "  75 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  76 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  77 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  78 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  79 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  80 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  81 conv     18       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x  18 0.013 BF\n",
            "  82 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  83 route  79\n",
            "  84 conv    256       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 256 0.095 BF\n",
            "  85 upsample                 2x    19 x  19 x 256 ->   38 x  38 x 256\n",
            "  86 route  85 61\n",
            "  87 conv    256       1 x 1/ 1     38 x  38 x 768 ->   38 x  38 x 256 0.568 BF\n",
            "  88 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  89 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  90 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  91 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  92 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  93 conv     18       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x  18 0.027 BF\n",
            "  94 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  95 route  91\n",
            "  96 conv    128       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 128 0.095 BF\n",
            "  97 upsample                 2x    38 x  38 x 128 ->   76 x  76 x 128\n",
            "  98 route  97 36\n",
            "  99 conv    128       1 x 1/ 1     76 x  76 x 384 ->   76 x  76 x 128 0.568 BF\n",
            " 100 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 101 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 102 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 103 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 104 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 105 conv     18       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x  18 0.053 BF\n",
            " 106 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "Total BFLOPS 139.465 \n",
            " Allocate additional workspace_size = 18.91 MB \n",
            "Loading weights from backup/yolo-obj_final.weights...\n",
            " seen 64 \n",
            "Done!\n",
            "Enter Image Path: data/img2/0.jpg: Predicted in 159.048000 milli-seconds.\n",
            "NumberPlate: 47%\t(left_x: 1362   top_y: 1942   width:  274   height:  133)\n",
            "Enter Image Path: data/img2/1.jpg: Predicted in 142.025000 milli-seconds.\n",
            "NumberPlate: 93%\t(left_x:  688   top_y: 1955   width:  479   height:  171)\n",
            "Enter Image Path: data/img2/10.jpg: Predicted in 130.037000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  551   top_y:  562   width:  601   height:  179)\n",
            "Enter Image Path: data/img2/100.jpg: Predicted in 130.265000 milli-seconds.\n",
            "NumberPlate: 95%\t(left_x:  352   top_y:  334   width:  226   height:   46)\n",
            "Enter Image Path: data/img2/101.jpg: Predicted in 156.024000 milli-seconds.\n",
            "Enter Image Path: data/img2/102.jpg: Predicted in 141.008000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  306   top_y:  515   width:  269   height:   66)\n",
            "Enter Image Path: data/img2/103.jpg: Predicted in 141.564000 milli-seconds.\n",
            "NumberPlate: 97%\t(left_x:  302   top_y:  527   width:  248   height:   53)\n",
            "Enter Image Path: data/img2/104.jpg: Predicted in 127.467000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  392   top_y:  527   width:  225   height:   59)\n",
            "Enter Image Path: data/img2/105.jpg: Predicted in 118.865000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:   96   top_y:  339   width:  140   height:  106)\n",
            "Enter Image Path: data/img2/106.jpg: Predicted in 119.016000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  336   top_y:  384   width:  226   height:   60)\n",
            "Enter Image Path: data/img2/107.jpg: Predicted in 115.778000 milli-seconds.\n",
            "NumberPlate: 70%\t(left_x:  710   top_y:  344   width:  150   height:   73)\n",
            "NumberPlate: 99%\t(left_x:  756   top_y:  344   width:   60   height:   74)\n",
            "Enter Image Path: data/img2/108.jpg: Predicted in 112.198000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  361   top_y:  499   width:  263   height:   87)\n",
            "Enter Image Path: data/img2/109.jpg: Predicted in 111.699000 milli-seconds.\n",
            "NumberPlate: 97%\t(left_x:   51   top_y:  386   width:   92   height:   86)\n",
            "Enter Image Path: data/img2/11.jpg: Predicted in 111.739000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  359   top_y:  267   width:  135   height:   34)\n",
            "Enter Image Path: data/img2/110.jpg: Predicted in 106.521000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  275   top_y:  512   width:  313   height:   64)\n",
            "Enter Image Path: data/img2/111.jpg: Predicted in 106.259000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  671   top_y:  446   width:  116   height:   84)\n",
            "Enter Image Path: data/img2/112.jpg: Predicted in 107.255000 milli-seconds.\n",
            "NumberPlate: 94%\t(left_x:  339   top_y:  547   width:  217   height:   57)\n",
            "Enter Image Path: data/img2/113.jpg: Predicted in 106.674000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  137   top_y:  316   width:  120   height:   89)\n",
            "Enter Image Path: data/img2/114.jpg: Predicted in 109.143000 milli-seconds.\n",
            "NumberPlate: 96%\t(left_x:  424   top_y:  383   width:  194   height:   64)\n",
            "NumberPlate: 74%\t(left_x:  600   top_y:  710   width:  126   height:   28)\n",
            "Enter Image Path: data/img2/115.jpg: Predicted in 108.022000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  118   top_y:  349   width:  135   height:   84)\n",
            "NumberPlate: 46%\t(left_x:  609   top_y:  711   width:  108   height:   25)\n",
            "Enter Image Path: data/img2/116.jpg: Predicted in 107.916000 milli-seconds.\n",
            "NumberPlate: 79%\t(left_x:  609   top_y:  710   width:  105   height:   30)\n",
            "NumberPlate: 89%\t(left_x:  718   top_y:  338   width:   70   height:   66)\n",
            "Enter Image Path: data/img2/117.jpg: Predicted in 106.567000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  391   top_y:  489   width:  230   height:   60)\n",
            "NumberPlate: 77%\t(left_x:  609   top_y:  711   width:  106   height:   27)\n",
            "Enter Image Path: data/img2/118.jpg: Predicted in 106.444000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:   71   top_y:  348   width:  101   height:   80)\n",
            "Enter Image Path: data/img2/119.jpg: Predicted in 105.802000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  311   top_y:  373   width:  263   height:   59)\n",
            "Enter Image Path: data/img2/12.jpg: Predicted in 107.151000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  761   top_y:  496   width:  131   height:   29)\n",
            "Enter Image Path: data/img2/120.jpg: Predicted in 108.186000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  682   top_y:  369   width:  100   height:   76)\n",
            "Enter Image Path: data/img2/121.jpg: Predicted in 107.529000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  326   top_y:  527   width:  243   height:   56)\n",
            "Enter Image Path: data/img2/122.jpg: Predicted in 107.674000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:   99   top_y:  445   width:  112   height:   80)\n",
            "Enter Image Path: data/img2/123.jpg: Predicted in 110.294000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  313   top_y:  514   width:  281   height:   67)\n",
            "Enter Image Path: data/img2/124.jpg: Predicted in 109.981000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  320   top_y:  363   width:  289   height:   53)\n",
            "Enter Image Path: data/img2/125.jpg: Predicted in 110.479000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  723   top_y:  444   width:   93   height:   81)\n",
            "Enter Image Path: data/img2/126.jpg: Predicted in 107.487000 milli-seconds.\n",
            "NumberPlate: 96%\t(left_x:  392   top_y:  517   width:  198   height:   57)\n",
            "Enter Image Path: data/img2/127.jpg: Predicted in 107.428000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  131   top_y:  388   width:  113   height:   71)\n",
            "Enter Image Path: data/img2/128.jpg: Predicted in 107.589000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  405   top_y:  436   width:  172   height:   40)\n",
            "Enter Image Path: data/img2/129.jpg: Predicted in 106.492000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  595   top_y:  431   width:  122   height:   73)\n",
            "Enter Image Path: data/img2/13.jpg: Predicted in 105.957000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  406   top_y:  185   width:  138   height:   65)\n",
            "Enter Image Path: data/img2/130.jpg: Predicted in 107.106000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  300   top_y:  526   width:  252   height:   57)\n",
            "Enter Image Path: data/img2/131.jpg: Predicted in 106.903000 milli-seconds.\n",
            "NumberPlate: 98%\t(left_x:  295   top_y:  380   width:  256   height:   56)\n",
            "Enter Image Path: data/img2/132.jpg: Predicted in 110.634000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  725   top_y:  348   width:  104   height:   69)\n",
            "Enter Image Path: data/img2/133.jpg: Predicted in 110.493000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  317   top_y:  467   width:  307   height:   75)\n",
            "Enter Image Path: data/img2/134.jpg: Predicted in 107.490000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  323   top_y:  494   width:  213   height:   46)\n",
            "Enter Image Path: data/img2/135.jpg: Predicted in 108.056000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  798   top_y:  325   width:   93   height:   67)\n",
            "Enter Image Path: data/img2/136.jpg: Predicted in 110.492000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  324   top_y:  461   width:  227   height:   58)\n",
            "Enter Image Path: data/img2/137.jpg: Predicted in 110.368000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  188   top_y:  475   width:  156   height:   60)\n",
            "Enter Image Path: data/img2/138.jpg: Predicted in 106.207000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  723   top_y:  361   width:   99   height:   48)\n",
            "Enter Image Path: data/img2/139.jpg: Predicted in 106.077000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:   61   top_y:  469   width:  114   height:   49)\n",
            "Enter Image Path: data/img2/14.jpg: Predicted in 106.417000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  236   top_y:  194   width:  148   height:   36)\n",
            "Enter Image Path: data/img2/140.jpg: Predicted in 108.130000 milli-seconds.\n",
            "NumberPlate: 84%\t(left_x:  586   top_y:  450   width:  105   height:   37)\n",
            "Enter Image Path: data/img2/141.jpg: Predicted in 108.012000 milli-seconds.\n",
            "Enter Image Path: data/img2/15.jpg: Predicted in 108.344000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  322   top_y:  299   width:  128   height:   24)\n",
            "Enter Image Path: data/img2/16.jpg: Predicted in 106.490000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  332   top_y:  253   width:  138   height:   31)\n",
            "Enter Image Path: data/img2/17.jpg: Predicted in 107.402000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  251   top_y:  265   width:  135   height:   27)\n",
            "Enter Image Path: data/img2/18.jpg: Predicted in 106.675000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  299   top_y:  213   width:   70   height:   19)\n",
            "Enter Image Path: data/img2/19.jpg: Predicted in 107.917000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  216   top_y:  322   width:  184   height:   36)\n",
            "Enter Image Path: data/img2/2.jpg: Predicted in 107.707000 milli-seconds.\n",
            "NumberPlate: 76%\t(left_x: 1524   top_y: 3102   width:  821   height:  321)\n",
            "Enter Image Path: data/img2/20.jpg: Predicted in 108.449000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  400   top_y:  148   width:   98   height:   46)\n",
            "Enter Image Path: data/img2/21.jpg: Predicted in 107.824000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  504   top_y:  375   width:   90   height:   46)\n",
            "Enter Image Path: data/img2/22.jpg: Predicted in 107.605000 milli-seconds.\n",
            "NumberPlate: 45%\t(left_x:  503   top_y:  171   width:   39   height:  167)\n",
            "Enter Image Path: data/img2/23.jpg: Predicted in 108.457000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  266   top_y:  417   width:  178   height:   33)\n",
            "Enter Image Path: data/img2/24.jpg: Predicted in 107.635000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  424   top_y:  347   width:  149   height:   75)\n",
            "Enter Image Path: data/img2/25.jpg: Predicted in 110.159000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  244   top_y:  406   width:  157   height:   35)\n",
            "Enter Image Path: data/img2/26.jpg: Predicted in 110.586000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  263   top_y:  367   width:  154   height:   41)\n",
            "Enter Image Path: data/img2/27.jpg: Predicted in 106.313000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  271   top_y:  265   width:  136   height:   33)\n",
            "Enter Image Path: data/img2/28.jpg: Predicted in 106.660000 milli-seconds.\n",
            "NumberPlate: 98%\t(left_x:  193   top_y:  185   width:   98   height:   36)\n",
            "Enter Image Path: data/img2/29.jpg: Predicted in 107.286000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  528   top_y:  222   width:   91   height:   56)\n",
            "Enter Image Path: data/img2/3.jpg: Predicted in 107.036000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x: 1446   top_y: 3469   width:  689   height:  189)\n",
            "Enter Image Path: data/img2/30.jpg: Predicted in 109.019000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:   68   top_y:  255   width:   90   height:   67)\n",
            "Enter Image Path: data/img2/31.jpg: Predicted in 109.087000 milli-seconds.\n",
            "NumberPlate: 98%\t(left_x:  282   top_y:  364   width:  130   height:   31)\n",
            "Enter Image Path: data/img2/32.jpg: Predicted in 107.030000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  149   top_y:  367   width:  159   height:   58)\n",
            "Enter Image Path: data/img2/33.jpg: Predicted in 107.109000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  263   top_y:  355   width:  130   height:   30)\n",
            "Enter Image Path: data/img2/34.jpg: Predicted in 106.585000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  266   top_y:  371   width:  157   height:   38)\n",
            "Enter Image Path: data/img2/35.jpg: Predicted in 106.803000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  261   top_y:  288   width:  185   height:   43)\n",
            "Enter Image Path: data/img2/36.jpg: Predicted in 107.010000 milli-seconds.\n",
            "NumberPlate: 91%\t(left_x:  259   top_y:  251   width:  143   height:   29)\n",
            "Enter Image Path: data/img2/37.jpg: Predicted in 112.346000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  328   top_y:  212   width:   78   height:   22)\n",
            "Enter Image Path: data/img2/38.jpg: Predicted in 111.133000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  266   top_y:  299   width:  100   height:   23)\n",
            "Enter Image Path: data/img2/39.jpg: Predicted in 108.490000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  225   top_y:  375   width:  206   height:   56)\n",
            "Enter Image Path: data/img2/4.jpg: Predicted in 108.387000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  623   top_y:  292   width:  169   height:   41)\n",
            "Enter Image Path: data/img2/40.jpg: Predicted in 110.949000 milli-seconds.\n",
            "NumberPlate: 96%\t(left_x:  330   top_y:  329   width:   99   height:   19)\n",
            "Enter Image Path: data/img2/41.jpg: Predicted in 109.772000 milli-seconds.\n",
            "NumberPlate: 97%\t(left_x:  227   top_y:  286   width:  123   height:   31)\n",
            "Enter Image Path: data/img2/42.jpg: Predicted in 107.363000 milli-seconds.\n",
            "NumberPlate: 98%\t(left_x:  142   top_y:  346   width:  147   height:   50)\n",
            "Enter Image Path: data/img2/43.jpg: Predicted in 109.193000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  279   top_y:  298   width:  136   height:   33)\n",
            "Enter Image Path: data/img2/44.jpg: Predicted in 106.925000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  174   top_y:  290   width:  121   height:   35)\n",
            "Enter Image Path: data/img2/45.jpg: Predicted in 112.370000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  290   top_y:  282   width:  123   height:   26)\n",
            "Enter Image Path: data/img2/46.jpg: Predicted in 110.787000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  283   top_y:  292   width:  106   height:   20)\n",
            "Enter Image Path: data/img2/47.jpg: Predicted in 110.829000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  404   top_y:  257   width:  124   height:   34)\n",
            "Enter Image Path: data/img2/48.jpg: Predicted in 106.700000 milli-seconds.\n",
            "NumberPlate: 98%\t(left_x:   51   top_y:  263   width:  115   height:   79)\n",
            "Enter Image Path: data/img2/49.jpg: Predicted in 108.895000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  166   top_y:  414   width:   99   height:   49)\n",
            "Enter Image Path: data/img2/5.jpg: Predicted in 108.453000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x: 1161   top_y: 2066   width:  652   height:  226)\n",
            "Enter Image Path: data/img2/50.jpg: Predicted in 108.771000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  284   top_y:  237   width:  117   height:   26)\n",
            "Enter Image Path: data/img2/51.jpg: Predicted in 108.453000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  286   top_y:  363   width:  173   height:   52)\n",
            "Enter Image Path: data/img2/52.jpg: Predicted in 108.111000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  411   top_y:  295   width:  110   height:   56)\n",
            "Enter Image Path: data/img2/53.jpg: Predicted in 108.482000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  260   top_y:  369   width:  160   height:   37)\n",
            "Enter Image Path: data/img2/54.jpg: Predicted in 111.081000 milli-seconds.\n",
            "NumberPlate: 97%\t(left_x:  181   top_y:  244   width:   76   height:   39)\n",
            "Enter Image Path: data/img2/55.jpg: Predicted in 110.924000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  513   top_y:  287   width:   75   height:   49)\n",
            "Enter Image Path: data/img2/56.jpg: Predicted in 110.566000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  259   top_y:  407   width:  202   height:   49)\n",
            "Enter Image Path: data/img2/57.jpg: Predicted in 110.351000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  116   top_y:  285   width:  113   height:   60)\n",
            "Enter Image Path: data/img2/58.jpg: Predicted in 111.334000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  216   top_y:  366   width:  178   height:   44)\n",
            "Enter Image Path: data/img2/59.jpg: Predicted in 110.975000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  261   top_y:  316   width:  114   height:   22)\n",
            "Enter Image Path: data/img2/6.jpg: Predicted in 108.423000 milli-seconds.\n",
            "NumberPlate: 94%\t(left_x:  413   top_y:  621   width:  193   height:   58)\n",
            "Enter Image Path: data/img2/60.jpg: Predicted in 108.731000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:   90   top_y:  323   width:  152   height:   64)\n",
            "Enter Image Path: data/img2/61.jpg: Predicted in 108.987000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  167   top_y:  135   width:   72   height:   46)\n",
            "Enter Image Path: data/img2/62.jpg: Predicted in 108.804000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  360   top_y:  346   width:  119   height:   34)\n",
            "Enter Image Path: data/img2/63.jpg: Predicted in 109.307000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  263   top_y:  225   width:  118   height:   27)\n",
            "Enter Image Path: data/img2/64.jpg: Predicted in 107.059000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  233   top_y:  226   width:  129   height:   32)\n",
            "Enter Image Path: data/img2/65.jpg: Predicted in 107.048000 milli-seconds.\n",
            "NumberPlate: 98%\t(left_x:  236   top_y:  219   width:  133   height:   31)\n",
            "Enter Image Path: data/img2/66.jpg: Predicted in 107.615000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  181   top_y:  265   width:  126   height:   29)\n",
            "Enter Image Path: data/img2/67.jpg: Predicted in 106.019000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  530   top_y:  304   width:   63   height:   38)\n",
            "Enter Image Path: data/img2/68.jpg: Predicted in 107.620000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  266   top_y:  306   width:  100   height:   25)\n",
            "Enter Image Path: data/img2/69.jpg: Predicted in 108.113000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  434   top_y:  153   width:   97   height:   38)\n",
            "Enter Image Path: data/img2/7.jpg: Predicted in 160.549000 milli-seconds.\n",
            "NumberPlate: 93%\t(left_x: 1148   top_y: 1638   width:  513   height:  156)\n",
            "Enter Image Path: data/img2/70.jpg: Predicted in 143.019000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  501   top_y:  334   width:  131   height:   82)\n",
            "Enter Image Path: data/img2/71.jpg: Predicted in 131.065000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  205   top_y:  358   width:  209   height:   47)\n",
            "Enter Image Path: data/img2/72.jpg: Predicted in 126.565000 milli-seconds.\n",
            "NumberPlate: 98%\t(left_x:  254   top_y:  213   width:  141   height:   36)\n",
            "Enter Image Path: data/img2/73.jpg: Predicted in 127.350000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  114   top_y:  362   width:  141   height:   40)\n",
            "Enter Image Path: data/img2/74.jpg: Predicted in 127.432000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  245   top_y:  226   width:   92   height:   23)\n",
            "Enter Image Path: data/img2/75.jpg: Predicted in 116.811000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  540   top_y:  262   width:  113   height:   47)\n",
            "Enter Image Path: data/img2/76.jpg: Predicted in 116.442000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  430   top_y:  183   width:   65   height:   35)\n",
            "Enter Image Path: data/img2/77.jpg: Predicted in 108.638000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  259   top_y:  241   width:  117   height:   23)\n",
            "Enter Image Path: data/img2/78.jpg: Predicted in 107.547000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  201   top_y:  393   width:  188   height:   38)\n",
            "Enter Image Path: data/img2/79.jpg: Predicted in 107.435000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  264   top_y:  299   width:  193   height:   41)\n",
            "Enter Image Path: data/img2/8.jpg: Predicted in 107.410000 milli-seconds.\n",
            "NumberPlate: 70%\t(left_x: 1694   top_y: 1830   width:  236   height:  183)\n",
            "Enter Image Path: data/img2/80.jpg: Predicted in 108.153000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  305   top_y:  291   width:  122   height:   36)\n",
            "Enter Image Path: data/img2/81.jpg: Predicted in 108.985000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  218   top_y:  307   width:   96   height:   30)\n",
            "Enter Image Path: data/img2/82.jpg: Predicted in 108.684000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  278   top_y:  269   width:  151   height:   38)\n",
            "Enter Image Path: data/img2/83.jpg: Predicted in 110.844000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  280   top_y:  330   width:  105   height:   25)\n",
            "Enter Image Path: data/img2/84.jpg: Predicted in 111.294000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  275   top_y:  342   width:  113   height:   24)\n",
            "Enter Image Path: data/img2/85.jpg: Predicted in 108.315000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  458   top_y:  275   width:   66   height:   40)\n",
            "Enter Image Path: data/img2/86.jpg: Predicted in 111.381000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  280   top_y:  327   width:  145   height:   30)\n",
            "Enter Image Path: data/img2/87.jpg: Predicted in 110.316000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  369   top_y:  164   width:   90   height:   30)\n",
            "Enter Image Path: data/img2/88.jpg: Predicted in 113.119000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  368   top_y:  290   width:  119   height:   27)\n",
            "Enter Image Path: data/img2/89.jpg: Predicted in 111.472000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  268   top_y:  413   width:  189   height:   49)\n",
            "Enter Image Path: data/img2/9.jpg: Predicted in 111.437000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  370   top_y:  239   width:  137   height:   28)\n",
            "Enter Image Path: data/img2/90.jpg: Predicted in 113.807000 milli-seconds.\n",
            "NumberPlate: 98%\t(left_x:  166   top_y:  261   width:   99   height:   44)\n",
            "Enter Image Path: data/img2/91.jpg: Predicted in 107.502000 milli-seconds.\n",
            "NumberPlate: 85%\t(left_x:  217   top_y:  294   width:   48   height:   19)\n",
            "Enter Image Path: data/img2/92.jpg: Predicted in 107.844000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  311   top_y:  348   width:  146   height:   45)\n",
            "Enter Image Path: data/img2/93.jpg: Predicted in 108.999000 milli-seconds.\n",
            "NumberPlate: 97%\t(left_x:  177   top_y:  342   width:  116   height:   41)\n",
            "Enter Image Path: data/img2/94.jpg: Predicted in 108.863000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  303   top_y:  306   width:  122   height:   27)\n",
            "Enter Image Path: data/img2/95.jpg: Predicted in 109.530000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  510   top_y:  339   width:  129   height:   83)\n",
            "Enter Image Path: data/img2/96.jpg: Predicted in 107.700000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  291   top_y:  309   width:  114   height:   26)\n",
            "Enter Image Path: data/img2/97.jpg: Predicted in 107.477000 milli-seconds.\n",
            "NumberPlate: 100%\t(left_x:  248   top_y:  270   width:   90   height:   23)\n",
            "Enter Image Path: data/img2/98.jpg: Predicted in 109.530000 milli-seconds.\n",
            "NumberPlate: 85%\t(left_x:   48   top_y:  352   width:   62   height:   71)\n",
            "Enter Image Path: data/img2/99.jpg: Predicted in 109.083000 milli-seconds.\n",
            "NumberPlate: 99%\t(left_x:  293   top_y:  383   width:  138   height:   30)\n",
            "Enter Image Path: "
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wMiARsZ-nHfG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import json\n",
        "with open('result_plates.json') as file:\n",
        "  data=json.load(file)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fv_82eh_H0Ad",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data=pd.DataFrame(data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tNzKObNnH2s_",
        "colab_type": "code",
        "outputId": "000f0deb-f0dc-4e08-8e89-2cd57904743d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>frame_id</th>\n",
              "      <th>objects</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>data/img2/0.jpg</td>\n",
              "      <td>1</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>data/img2/1.jpg</td>\n",
              "      <td>2</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>data/img2/10.jpg</td>\n",
              "      <td>3</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>data/img2/100.jpg</td>\n",
              "      <td>4</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>data/img2/101.jpg</td>\n",
              "      <td>5</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>data/img2/102.jpg</td>\n",
              "      <td>6</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>data/img2/103.jpg</td>\n",
              "      <td>7</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>data/img2/104.jpg</td>\n",
              "      <td>8</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>data/img2/105.jpg</td>\n",
              "      <td>9</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>data/img2/106.jpg</td>\n",
              "      <td>10</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>data/img2/107.jpg</td>\n",
              "      <td>11</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>data/img2/108.jpg</td>\n",
              "      <td>12</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>data/img2/109.jpg</td>\n",
              "      <td>13</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>data/img2/11.jpg</td>\n",
              "      <td>14</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>data/img2/110.jpg</td>\n",
              "      <td>15</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>data/img2/111.jpg</td>\n",
              "      <td>16</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>data/img2/112.jpg</td>\n",
              "      <td>17</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>data/img2/113.jpg</td>\n",
              "      <td>18</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>data/img2/114.jpg</td>\n",
              "      <td>19</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>data/img2/115.jpg</td>\n",
              "      <td>20</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>data/img2/116.jpg</td>\n",
              "      <td>21</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>data/img2/117.jpg</td>\n",
              "      <td>22</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>data/img2/118.jpg</td>\n",
              "      <td>23</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>data/img2/119.jpg</td>\n",
              "      <td>24</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>data/img2/12.jpg</td>\n",
              "      <td>25</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>data/img2/120.jpg</td>\n",
              "      <td>26</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>data/img2/121.jpg</td>\n",
              "      <td>27</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>data/img2/122.jpg</td>\n",
              "      <td>28</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>data/img2/123.jpg</td>\n",
              "      <td>29</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>data/img2/124.jpg</td>\n",
              "      <td>30</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>data/img2/72.jpg</td>\n",
              "      <td>113</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>data/img2/73.jpg</td>\n",
              "      <td>114</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>data/img2/74.jpg</td>\n",
              "      <td>115</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>data/img2/75.jpg</td>\n",
              "      <td>116</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>data/img2/76.jpg</td>\n",
              "      <td>117</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>data/img2/77.jpg</td>\n",
              "      <td>118</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>data/img2/78.jpg</td>\n",
              "      <td>119</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>data/img2/79.jpg</td>\n",
              "      <td>120</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>data/img2/8.jpg</td>\n",
              "      <td>121</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>data/img2/80.jpg</td>\n",
              "      <td>122</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>data/img2/81.jpg</td>\n",
              "      <td>123</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>data/img2/82.jpg</td>\n",
              "      <td>124</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>data/img2/83.jpg</td>\n",
              "      <td>125</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>data/img2/84.jpg</td>\n",
              "      <td>126</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>data/img2/85.jpg</td>\n",
              "      <td>127</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>data/img2/86.jpg</td>\n",
              "      <td>128</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>data/img2/87.jpg</td>\n",
              "      <td>129</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>data/img2/88.jpg</td>\n",
              "      <td>130</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>data/img2/89.jpg</td>\n",
              "      <td>131</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>data/img2/9.jpg</td>\n",
              "      <td>132</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>data/img2/90.jpg</td>\n",
              "      <td>133</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>data/img2/91.jpg</td>\n",
              "      <td>134</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>data/img2/92.jpg</td>\n",
              "      <td>135</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>data/img2/93.jpg</td>\n",
              "      <td>136</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>data/img2/94.jpg</td>\n",
              "      <td>137</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>data/img2/95.jpg</td>\n",
              "      <td>138</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>data/img2/96.jpg</td>\n",
              "      <td>139</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>data/img2/97.jpg</td>\n",
              "      <td>140</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>data/img2/98.jpg</td>\n",
              "      <td>141</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>data/img2/99.jpg</td>\n",
              "      <td>142</td>\n",
              "      <td>[{'class_id': 0, 'name': 'NumberPlate', 'relat...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>142 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              filename  ...                                            objects\n",
              "0      data/img2/0.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "1      data/img2/1.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "2     data/img2/10.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "3    data/img2/100.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "4    data/img2/101.jpg  ...                                                 []\n",
              "5    data/img2/102.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "6    data/img2/103.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "7    data/img2/104.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "8    data/img2/105.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "9    data/img2/106.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "10   data/img2/107.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "11   data/img2/108.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "12   data/img2/109.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "13    data/img2/11.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "14   data/img2/110.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "15   data/img2/111.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "16   data/img2/112.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "17   data/img2/113.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "18   data/img2/114.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "19   data/img2/115.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "20   data/img2/116.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "21   data/img2/117.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "22   data/img2/118.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "23   data/img2/119.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "24    data/img2/12.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "25   data/img2/120.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "26   data/img2/121.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "27   data/img2/122.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "28   data/img2/123.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "29   data/img2/124.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "..                 ...  ...                                                ...\n",
              "112   data/img2/72.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "113   data/img2/73.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "114   data/img2/74.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "115   data/img2/75.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "116   data/img2/76.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "117   data/img2/77.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "118   data/img2/78.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "119   data/img2/79.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "120    data/img2/8.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "121   data/img2/80.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "122   data/img2/81.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "123   data/img2/82.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "124   data/img2/83.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "125   data/img2/84.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "126   data/img2/85.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "127   data/img2/86.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "128   data/img2/87.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "129   data/img2/88.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "130   data/img2/89.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "131    data/img2/9.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "132   data/img2/90.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "133   data/img2/91.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "134   data/img2/92.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "135   data/img2/93.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "136   data/img2/94.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "137   data/img2/95.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "138   data/img2/96.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "139   data/img2/97.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "140   data/img2/98.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "141   data/img2/99.jpg  ...  [{'class_id': 0, 'name': 'NumberPlate', 'relat...\n",
              "\n",
              "[142 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sz81BRFtH6y7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%cd data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OMC9PJ7aSaZ8",
        "colab_type": "code",
        "outputId": "3bd0faed-e47f-4d3f-993d-b3c381a63042",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!./darknet detector map data/obj.data yolo-obj.cfg backup/yolo-obj_last.weights -points 0 -iou_thresh 0.1 -thresh 0.2"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " compute_capability = 370, cudnn_half = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 conv     32       3 x 3/ 1    608 x 608 x   3 ->  608 x 608 x  32 0.639 BF\n",
            "   1 conv     64       3 x 3/ 2    608 x 608 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   2 conv     32       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  32 0.379 BF\n",
            "   3 conv     64       3 x 3/ 1    304 x 304 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   4 Shortcut Layer: 1\n",
            "   5 conv    128       3 x 3/ 2    304 x 304 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   6 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "   7 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   8 Shortcut Layer: 5\n",
            "   9 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "  10 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "  11 Shortcut Layer: 8\n",
            "  12 conv    256       3 x 3/ 2    152 x 152 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  13 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  14 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  15 Shortcut Layer: 12\n",
            "  16 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  17 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  18 Shortcut Layer: 15\n",
            "  19 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  20 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  21 Shortcut Layer: 18\n",
            "  22 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  23 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  24 Shortcut Layer: 21\n",
            "  25 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  26 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  27 Shortcut Layer: 24\n",
            "  28 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  29 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  30 Shortcut Layer: 27\n",
            "  31 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  32 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  33 Shortcut Layer: 30\n",
            "  34 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  35 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  36 Shortcut Layer: 33\n",
            "  37 conv    512       3 x 3/ 2     76 x  76 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  38 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  39 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  40 Shortcut Layer: 37\n",
            "  41 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  42 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  43 Shortcut Layer: 40\n",
            "  44 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  45 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  46 Shortcut Layer: 43\n",
            "  47 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  48 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  49 Shortcut Layer: 46\n",
            "  50 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  51 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  52 Shortcut Layer: 49\n",
            "  53 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  54 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  55 Shortcut Layer: 52\n",
            "  56 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  57 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  58 Shortcut Layer: 55\n",
            "  59 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  60 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  61 Shortcut Layer: 58\n",
            "  62 conv   1024       3 x 3/ 2     38 x  38 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  63 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  64 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  65 Shortcut Layer: 62\n",
            "  66 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  67 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  68 Shortcut Layer: 65\n",
            "  69 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  70 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  71 Shortcut Layer: 68\n",
            "  72 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  73 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  74 Shortcut Layer: 71\n",
            "  75 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  76 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  77 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  78 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  79 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  80 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  81 conv     18       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x  18 0.013 BF\n",
            "  82 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  83 route  79\n",
            "  84 conv    256       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 256 0.095 BF\n",
            "  85 upsample                 2x    19 x  19 x 256 ->   38 x  38 x 256\n",
            "  86 route  85 61\n",
            "  87 conv    256       1 x 1/ 1     38 x  38 x 768 ->   38 x  38 x 256 0.568 BF\n",
            "  88 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  89 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  90 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  91 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  92 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  93 conv     18       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x  18 0.027 BF\n",
            "  94 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  95 route  91\n",
            "  96 conv    128       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 128 0.095 BF\n",
            "  97 upsample                 2x    38 x  38 x 128 ->   76 x  76 x 128\n",
            "  98 route  97 36\n",
            "  99 conv    128       1 x 1/ 1     76 x  76 x 384 ->   76 x  76 x 128 0.568 BF\n",
            " 100 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 101 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 102 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 103 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 104 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 105 conv     18       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x  18 0.053 BF\n",
            " 106 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "Total BFLOPS 139.465 \n",
            " Allocate additional workspace_size = 18.91 MB \n",
            "Loading weights from backup/yolo-obj_last.weights...\n",
            " seen 64 \n",
            "Done!\n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "4Cannot load image \"data/img2/100.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/0.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/10.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/1.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/102.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/104.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/103.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/101.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/0.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/1.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/10.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/100.txt \n",
            "8Cannot load image \"data/img2/105.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/108.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/107.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/106.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/101.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/102.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/103.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/104.txt \n",
            "12Cannot load image \"data/img2/110.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/111.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/11.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/109.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/105.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/106.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/107.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/108.txt \n",
            "16Cannot load image \"data/img2/112.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/115.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/114.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/113.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/109.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/11.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/110.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/111.txt \n",
            "20Cannot load image \"data/img2/116.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/119.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/118.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/117.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/112.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/113.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/114.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/115.txt \n",
            "24Cannot load image \"data/img2/12.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/122.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/121.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/120.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/116.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/117.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/118.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/119.txt \n",
            "28Cannot load image \"data/img2/123.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/126.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/124.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/125.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/12.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/120.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/121.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/122.txt \n",
            "32Cannot load image \"data/img2/127.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/13.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/129.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/128.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/123.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/124.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/125.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/126.txt \n",
            "36Cannot load image \"data/img2/130.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/133.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/132.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/131.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/127.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/128.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/129.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/13.txt \n",
            "40Cannot load image \"data/img2/134.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/136.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/137.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/135.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/130.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/131.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/132.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/133.txt \n",
            "44Cannot load image \"data/img2/138.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/14.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/140.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/139.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/134.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/135.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/136.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/137.txt \n",
            "48Cannot load image \"data/img2/17.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/16.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/15.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/141.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/138.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/139.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/14.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/140.txt \n",
            "52Cannot load image \"data/img2/18.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/20.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/2.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/19.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/141.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/15.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/16.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/17.txt \n",
            "56Cannot load image \"data/img2/21.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/24.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/23.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/22.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/18.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/19.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/2.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/20.txt \n",
            "60Cannot load image \"data/img2/25.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/28.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/27.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/26.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/21.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/22.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/23.txt \n",
            "Can't open label file. (This can be normal only if you use MSCOCO): data/img2/24.txt \n",
            "64Cannot load image \"data/img2/29.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/31.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/30.jpg\"\n",
            "STB Reason: can't fopen\n",
            "Cannot load image \"data/img2/3.jpg\"\n",
            "STB Reason: can't fopen\n",
            "^C\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kRuaCFmMcq-U",
        "colab_type": "code",
        "outputId": "94b52c68-23f6-49dc-94f3-e53ac52778e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "ITERATION = \"2500\"\n",
        "!./darknet detector map data/obj.data yolo-obj.cfg backup/yolo-obj_last.weights -points 0 -iou_thresh 0.1 -thresh 0.2 > yolo-test_{ITERATION}.log\n",
        "!./darknet detector map data/obj.data yolo-obj.cfg backup/yolo-obj_last.weights -points 0 -iou_thresh 0.1 -thresh 0.15 >> yolo-test_{ITERATION}.log\n",
        "!./darknet detector map data/obj.data yolo-obj.cfg backup/yolo-obj_last.weights -points 0 -iou_thresh 0.1 -thresh 0.1 >> yolo-test_{ITERATION}.log\n",
        "!./darknet detector map data/obj.data yolo-obj.cfg backup/yolo-obj_last.weights -points 0 -iou_thresh 0.1 -thresh 0.05 >> yolo-test_{ITERATION}.log\n",
        "!./darknet detector map data/obj.data yolo-obj.cfg backup/yolo-obj_last.weights -points 0 -iou_thresh 0.05 -thresh 0.2 >> yolo-test_{ITERATION}.log\n",
        "!./darknet detector map data/obj.data yolo-obj.cfg backup/yolo-obj_last.weights -points 0 -iou_thresh 0.1 -thresh 0.2 >> yolo-test_{ITERATION}.log\n",
        "!./darknet detector map data/obj.data yolo-obj.cfg backup/yolo-obj_last.weights -points 0 -iou_thresh 0.15 -thresh 0.2 >> yolo-test_{ITERATION}.log\n",
        "!./darknet detector map data/obj.data yolo-obj.cfg backup/yolo-obj_last.weights -points 0 -iou_thresh 0.2 -thresh 0.2 >> yolo-test_{ITERATION}.log\n",
        "!./darknet detector map data/obj.data yolo-obj.cfg backup/yolo-obj_last.weights -points 0 -iou_thresh 0.3 -thresh 0.2 >> yolo-test_{ITERATION}.log\n",
        "!./darknet detector map data/obj.data yolo-obj.cfg backup/yolo-obj_last.weights -points 0 -iou_thresh 0.5 -thresh 0.2 >> yolo-test_{ITERATION}.log\n",
        "#!grep 'conf_thresh\\|mAP@' yolo-test_{ITERATION}.log | awk -F' ' 'BEGIN{l=\"conf,pre,rec,f1-score,avg_iou,iou_thr,map\"}{if($1==\"for\"){if($5==\"precision\"){l=l \"\\n\" $4 $7 $10 $13} else{l=l \",\" $17}} else{l=l \",\" $4 \",\" $8}}END{print l}'\n",
        "from subprocess import Popen, PIPE\n",
        "cmd = '''grep 'conf_thresh\\|mAP@' yolo-test_{}.log'''.format(ITERATION) + ''' | awk -F' ' 'BEGIN{l=\"conf,pre,rec,f1-score,avg_iou,iou_thr,map\"}{if($1==\"for\"){if($5==\"precision\"){l=l \"\\\\n\" $4 $7 $10 $13} else{l=l \",\" $17}} else{l=l \",\" $4 \",\" $8}}END{print l}' '''\n",
        "p=Popen(cmd, shell=True, stdout=PIPE)\n",
        "p.wait()\n",
        "print(p.communicate()[0].decode())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " compute_capability = 370, cudnn_half = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 conv     32       3 x 3/ 1    608 x 608 x   3 ->  608 x 608 x  32 0.639 BF\n",
            "   1 conv     64       3 x 3/ 2    608 x 608 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   2 conv     32       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  32 0.379 BF\n",
            "   3 conv     64       3 x 3/ 1    304 x 304 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   4 Shortcut Layer: 1\n",
            "   5 conv    128       3 x 3/ 2    304 x 304 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   6 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "   7 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   8 Shortcut Layer: 5\n",
            "   9 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "  10 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "  11 Shortcut Layer: 8\n",
            "  12 conv    256       3 x 3/ 2    152 x 152 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  13 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  14 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  15 Shortcut Layer: 12\n",
            "  16 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  17 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  18 Shortcut Layer: 15\n",
            "  19 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  20 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  21 Shortcut Layer: 18\n",
            "  22 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  23 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  24 Shortcut Layer: 21\n",
            "  25 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  26 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  27 Shortcut Layer: 24\n",
            "  28 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  29 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  30 Shortcut Layer: 27\n",
            "  31 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  32 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  33 Shortcut Layer: 30\n",
            "  34 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  35 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  36 Shortcut Layer: 33\n",
            "  37 conv    512       3 x 3/ 2     76 x  76 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  38 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  39 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  40 Shortcut Layer: 37\n",
            "  41 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  42 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  43 Shortcut Layer: 40\n",
            "  44 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  45 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  46 Shortcut Layer: 43\n",
            "  47 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  48 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  49 Shortcut Layer: 46\n",
            "  50 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  51 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  52 Shortcut Layer: 49\n",
            "  53 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  54 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  55 Shortcut Layer: 52\n",
            "  56 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  57 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  58 Shortcut Layer: 55\n",
            "  59 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  60 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  61 Shortcut Layer: 58\n",
            "  62 conv   1024       3 x 3/ 2     38 x  38 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  63 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  64 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  65 Shortcut Layer: 62\n",
            "  66 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  67 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  68 Shortcut Layer: 65\n",
            "  69 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  70 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  71 Shortcut Layer: 68\n",
            "  72 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  73 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  74 Shortcut Layer: 71\n",
            "  75 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  76 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  77 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  78 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  79 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  80 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  81 conv     18       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x  18 0.013 BF\n",
            "  82 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  83 route  79\n",
            "  84 conv    256       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 256 0.095 BF\n",
            "  85 upsample                 2x    19 x  19 x 256 ->   38 x  38 x 256\n",
            "  86 route  85 61\n",
            "  87 conv    256       1 x 1/ 1     38 x  38 x 768 ->   38 x  38 x 256 0.568 BF\n",
            "  88 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  89 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  90 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  91 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  92 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  93 conv     18       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x  18 0.027 BF\n",
            "  94 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  95 route  91\n",
            "  96 conv    128       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 128 0.095 BF\n",
            "  97 upsample                 2x    38 x  38 x 128 ->   76 x  76 x 128\n",
            "  98 route  97 36\n",
            "  99 conv    128       1 x 1/ 1     76 x  76 x 384 ->   76 x  76 x 128 0.568 BF\n",
            " 100 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 101 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 102 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 103 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 104 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 105 conv     18       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x  18 0.053 BF\n",
            " 106 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "Total BFLOPS 139.465 \n",
            " Allocate additional workspace_size = 18.91 MB \n",
            "Loading weights from backup/yolo-obj_last.weights...Done!\n",
            "144Total Detection Time: 17.000000 Seconds\n",
            " compute_capability = 370, cudnn_half = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 conv     32       3 x 3/ 1    608 x 608 x   3 ->  608 x 608 x  32 0.639 BF\n",
            "   1 conv     64       3 x 3/ 2    608 x 608 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   2 conv     32       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  32 0.379 BF\n",
            "   3 conv     64       3 x 3/ 1    304 x 304 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   4 Shortcut Layer: 1\n",
            "   5 conv    128       3 x 3/ 2    304 x 304 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   6 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "   7 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   8 Shortcut Layer: 5\n",
            "   9 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "  10 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "  11 Shortcut Layer: 8\n",
            "  12 conv    256       3 x 3/ 2    152 x 152 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  13 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  14 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  15 Shortcut Layer: 12\n",
            "  16 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  17 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  18 Shortcut Layer: 15\n",
            "  19 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  20 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  21 Shortcut Layer: 18\n",
            "  22 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  23 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  24 Shortcut Layer: 21\n",
            "  25 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  26 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  27 Shortcut Layer: 24\n",
            "  28 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  29 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  30 Shortcut Layer: 27\n",
            "  31 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  32 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  33 Shortcut Layer: 30\n",
            "  34 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  35 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  36 Shortcut Layer: 33\n",
            "  37 conv    512       3 x 3/ 2     76 x  76 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  38 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  39 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  40 Shortcut Layer: 37\n",
            "  41 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  42 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  43 Shortcut Layer: 40\n",
            "  44 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  45 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  46 Shortcut Layer: 43\n",
            "  47 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  48 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  49 Shortcut Layer: 46\n",
            "  50 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  51 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  52 Shortcut Layer: 49\n",
            "  53 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  54 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  55 Shortcut Layer: 52\n",
            "  56 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  57 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  58 Shortcut Layer: 55\n",
            "  59 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  60 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  61 Shortcut Layer: 58\n",
            "  62 conv   1024       3 x 3/ 2     38 x  38 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  63 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  64 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  65 Shortcut Layer: 62\n",
            "  66 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  67 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  68 Shortcut Layer: 65\n",
            "  69 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  70 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  71 Shortcut Layer: 68\n",
            "  72 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  73 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  74 Shortcut Layer: 71\n",
            "  75 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  76 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  77 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  78 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  79 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  80 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  81 conv     18       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x  18 0.013 BF\n",
            "  82 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  83 route  79\n",
            "  84 conv    256       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 256 0.095 BF\n",
            "  85 upsample                 2x    19 x  19 x 256 ->   38 x  38 x 256\n",
            "  86 route  85 61\n",
            "  87 conv    256       1 x 1/ 1     38 x  38 x 768 ->   38 x  38 x 256 0.568 BF\n",
            "  88 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  89 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  90 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  91 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  92 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  93 conv     18       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x  18 0.027 BF\n",
            "  94 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  95 route  91\n",
            "  96 conv    128       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 128 0.095 BF\n",
            "  97 upsample                 2x    38 x  38 x 128 ->   76 x  76 x 128\n",
            "  98 route  97 36\n",
            "  99 conv    128       1 x 1/ 1     76 x  76 x 384 ->   76 x  76 x 128 0.568 BF\n",
            " 100 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 101 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 102 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 103 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 104 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 105 conv     18       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x  18 0.053 BF\n",
            " 106 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "Total BFLOPS 139.465 \n",
            " Allocate additional workspace_size = 18.91 MB \n",
            "Loading weights from backup/yolo-obj_last.weights...Done!\n",
            "144Total Detection Time: 17.000000 Seconds\n",
            " compute_capability = 370, cudnn_half = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 conv     32       3 x 3/ 1    608 x 608 x   3 ->  608 x 608 x  32 0.639 BF\n",
            "   1 conv     64       3 x 3/ 2    608 x 608 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   2 conv     32       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  32 0.379 BF\n",
            "   3 conv     64       3 x 3/ 1    304 x 304 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   4 Shortcut Layer: 1\n",
            "   5 conv    128       3 x 3/ 2    304 x 304 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   6 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "   7 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   8 Shortcut Layer: 5\n",
            "   9 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "  10 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "  11 Shortcut Layer: 8\n",
            "  12 conv    256       3 x 3/ 2    152 x 152 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  13 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  14 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  15 Shortcut Layer: 12\n",
            "  16 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  17 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  18 Shortcut Layer: 15\n",
            "  19 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  20 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  21 Shortcut Layer: 18\n",
            "  22 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  23 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  24 Shortcut Layer: 21\n",
            "  25 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  26 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  27 Shortcut Layer: 24\n",
            "  28 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  29 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  30 Shortcut Layer: 27\n",
            "  31 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  32 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  33 Shortcut Layer: 30\n",
            "  34 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  35 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  36 Shortcut Layer: 33\n",
            "  37 conv    512       3 x 3/ 2     76 x  76 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  38 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  39 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  40 Shortcut Layer: 37\n",
            "  41 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  42 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  43 Shortcut Layer: 40\n",
            "  44 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  45 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  46 Shortcut Layer: 43\n",
            "  47 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  48 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  49 Shortcut Layer: 46\n",
            "  50 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  51 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  52 Shortcut Layer: 49\n",
            "  53 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  54 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  55 Shortcut Layer: 52\n",
            "  56 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  57 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  58 Shortcut Layer: 55\n",
            "  59 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  60 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  61 Shortcut Layer: 58\n",
            "  62 conv   1024       3 x 3/ 2     38 x  38 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  63 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  64 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  65 Shortcut Layer: 62\n",
            "  66 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  67 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  68 Shortcut Layer: 65\n",
            "  69 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  70 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  71 Shortcut Layer: 68\n",
            "  72 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  73 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  74 Shortcut Layer: 71\n",
            "  75 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  76 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  77 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  78 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  79 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  80 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  81 conv     18       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x  18 0.013 BF\n",
            "  82 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  83 route  79\n",
            "  84 conv    256       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 256 0.095 BF\n",
            "  85 upsample                 2x    19 x  19 x 256 ->   38 x  38 x 256\n",
            "  86 route  85 61\n",
            "  87 conv    256       1 x 1/ 1     38 x  38 x 768 ->   38 x  38 x 256 0.568 BF\n",
            "  88 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  89 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  90 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  91 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  92 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  93 conv     18       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x  18 0.027 BF\n",
            "  94 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  95 route  91\n",
            "  96 conv    128       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 128 0.095 BF\n",
            "  97 upsample                 2x    38 x  38 x 128 ->   76 x  76 x 128\n",
            "  98 route  97 36\n",
            "  99 conv    128       1 x 1/ 1     76 x  76 x 384 ->   76 x  76 x 128 0.568 BF\n",
            " 100 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 101 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 102 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 103 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 104 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 105 conv     18       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x  18 0.053 BF\n",
            " 106 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "Total BFLOPS 139.465 \n",
            " Allocate additional workspace_size = 18.91 MB \n",
            "Loading weights from backup/yolo-obj_last.weights...Done!\n",
            "144Total Detection Time: 17.000000 Seconds\n",
            " compute_capability = 370, cudnn_half = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 conv     32       3 x 3/ 1    608 x 608 x   3 ->  608 x 608 x  32 0.639 BF\n",
            "   1 conv     64       3 x 3/ 2    608 x 608 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   2 conv     32       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  32 0.379 BF\n",
            "   3 conv     64       3 x 3/ 1    304 x 304 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   4 Shortcut Layer: 1\n",
            "   5 conv    128       3 x 3/ 2    304 x 304 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   6 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "   7 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   8 Shortcut Layer: 5\n",
            "   9 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "  10 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "  11 Shortcut Layer: 8\n",
            "  12 conv    256       3 x 3/ 2    152 x 152 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  13 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  14 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  15 Shortcut Layer: 12\n",
            "  16 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  17 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  18 Shortcut Layer: 15\n",
            "  19 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  20 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  21 Shortcut Layer: 18\n",
            "  22 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  23 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  24 Shortcut Layer: 21\n",
            "  25 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  26 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  27 Shortcut Layer: 24\n",
            "  28 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  29 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  30 Shortcut Layer: 27\n",
            "  31 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  32 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  33 Shortcut Layer: 30\n",
            "  34 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  35 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  36 Shortcut Layer: 33\n",
            "  37 conv    512       3 x 3/ 2     76 x  76 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  38 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  39 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  40 Shortcut Layer: 37\n",
            "  41 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  42 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  43 Shortcut Layer: 40\n",
            "  44 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  45 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  46 Shortcut Layer: 43\n",
            "  47 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  48 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  49 Shortcut Layer: 46\n",
            "  50 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  51 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  52 Shortcut Layer: 49\n",
            "  53 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  54 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  55 Shortcut Layer: 52\n",
            "  56 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  57 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  58 Shortcut Layer: 55\n",
            "  59 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  60 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  61 Shortcut Layer: 58\n",
            "  62 conv   1024       3 x 3/ 2     38 x  38 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  63 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  64 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  65 Shortcut Layer: 62\n",
            "  66 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  67 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  68 Shortcut Layer: 65\n",
            "  69 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  70 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  71 Shortcut Layer: 68\n",
            "  72 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  73 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  74 Shortcut Layer: 71\n",
            "  75 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  76 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  77 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  78 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  79 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  80 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  81 conv     18       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x  18 0.013 BF\n",
            "  82 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  83 route  79\n",
            "  84 conv    256       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 256 0.095 BF\n",
            "  85 upsample                 2x    19 x  19 x 256 ->   38 x  38 x 256\n",
            "  86 route  85 61\n",
            "  87 conv    256       1 x 1/ 1     38 x  38 x 768 ->   38 x  38 x 256 0.568 BF\n",
            "  88 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  89 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  90 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  91 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  92 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  93 conv     18       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x  18 0.027 BF\n",
            "  94 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  95 route  91\n",
            "  96 conv    128       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 128 0.095 BF\n",
            "  97 upsample                 2x    38 x  38 x 128 ->   76 x  76 x 128\n",
            "  98 route  97 36\n",
            "  99 conv    128       1 x 1/ 1     76 x  76 x 384 ->   76 x  76 x 128 0.568 BF\n",
            " 100 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 101 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 102 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 103 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 104 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 105 conv     18       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x  18 0.053 BF\n",
            " 106 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "Total BFLOPS 139.465 \n",
            " Allocate additional workspace_size = 18.91 MB \n",
            "Loading weights from backup/yolo-obj_last.weights...Done!\n",
            "144Total Detection Time: 18.000000 Seconds\n",
            " compute_capability = 370, cudnn_half = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 conv     32       3 x 3/ 1    608 x 608 x   3 ->  608 x 608 x  32 0.639 BF\n",
            "   1 conv     64       3 x 3/ 2    608 x 608 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   2 conv     32       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  32 0.379 BF\n",
            "   3 conv     64       3 x 3/ 1    304 x 304 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   4 Shortcut Layer: 1\n",
            "   5 conv    128       3 x 3/ 2    304 x 304 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   6 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "   7 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   8 Shortcut Layer: 5\n",
            "   9 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "  10 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "  11 Shortcut Layer: 8\n",
            "  12 conv    256       3 x 3/ 2    152 x 152 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  13 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  14 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  15 Shortcut Layer: 12\n",
            "  16 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  17 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  18 Shortcut Layer: 15\n",
            "  19 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  20 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  21 Shortcut Layer: 18\n",
            "  22 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  23 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  24 Shortcut Layer: 21\n",
            "  25 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  26 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  27 Shortcut Layer: 24\n",
            "  28 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  29 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  30 Shortcut Layer: 27\n",
            "  31 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  32 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  33 Shortcut Layer: 30\n",
            "  34 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  35 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  36 Shortcut Layer: 33\n",
            "  37 conv    512       3 x 3/ 2     76 x  76 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  38 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  39 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  40 Shortcut Layer: 37\n",
            "  41 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  42 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  43 Shortcut Layer: 40\n",
            "  44 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  45 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  46 Shortcut Layer: 43\n",
            "  47 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  48 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  49 Shortcut Layer: 46\n",
            "  50 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  51 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  52 Shortcut Layer: 49\n",
            "  53 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  54 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  55 Shortcut Layer: 52\n",
            "  56 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  57 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  58 Shortcut Layer: 55\n",
            "  59 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  60 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  61 Shortcut Layer: 58\n",
            "  62 conv   1024       3 x 3/ 2     38 x  38 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  63 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  64 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  65 Shortcut Layer: 62\n",
            "  66 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  67 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  68 Shortcut Layer: 65\n",
            "  69 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  70 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  71 Shortcut Layer: 68\n",
            "  72 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  73 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  74 Shortcut Layer: 71\n",
            "  75 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  76 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  77 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  78 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  79 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  80 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  81 conv     18       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x  18 0.013 BF\n",
            "  82 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  83 route  79\n",
            "  84 conv    256       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 256 0.095 BF\n",
            "  85 upsample                 2x    19 x  19 x 256 ->   38 x  38 x 256\n",
            "  86 route  85 61\n",
            "  87 conv    256       1 x 1/ 1     38 x  38 x 768 ->   38 x  38 x 256 0.568 BF\n",
            "  88 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  89 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  90 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  91 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  92 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  93 conv     18       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x  18 0.027 BF\n",
            "  94 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  95 route  91\n",
            "  96 conv    128       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 128 0.095 BF\n",
            "  97 upsample                 2x    38 x  38 x 128 ->   76 x  76 x 128\n",
            "  98 route  97 36\n",
            "  99 conv    128       1 x 1/ 1     76 x  76 x 384 ->   76 x  76 x 128 0.568 BF\n",
            " 100 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 101 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 102 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 103 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 104 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 105 conv     18       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x  18 0.053 BF\n",
            " 106 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "Total BFLOPS 139.465 \n",
            " Allocate additional workspace_size = 18.91 MB \n",
            "Loading weights from backup/yolo-obj_last.weights...Done!\n",
            "144Total Detection Time: 17.000000 Seconds\n",
            " compute_capability = 370, cudnn_half = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 conv     32       3 x 3/ 1    608 x 608 x   3 ->  608 x 608 x  32 0.639 BF\n",
            "   1 conv     64       3 x 3/ 2    608 x 608 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   2 conv     32       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  32 0.379 BF\n",
            "   3 conv     64       3 x 3/ 1    304 x 304 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   4 Shortcut Layer: 1\n",
            "   5 conv    128       3 x 3/ 2    304 x 304 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   6 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "   7 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   8 Shortcut Layer: 5\n",
            "   9 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "  10 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "  11 Shortcut Layer: 8\n",
            "  12 conv    256       3 x 3/ 2    152 x 152 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  13 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  14 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  15 Shortcut Layer: 12\n",
            "  16 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  17 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  18 Shortcut Layer: 15\n",
            "  19 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  20 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  21 Shortcut Layer: 18\n",
            "  22 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  23 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  24 Shortcut Layer: 21\n",
            "  25 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  26 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  27 Shortcut Layer: 24\n",
            "  28 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  29 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  30 Shortcut Layer: 27\n",
            "  31 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  32 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  33 Shortcut Layer: 30\n",
            "  34 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  35 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  36 Shortcut Layer: 33\n",
            "  37 conv    512       3 x 3/ 2     76 x  76 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  38 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  39 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  40 Shortcut Layer: 37\n",
            "  41 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  42 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  43 Shortcut Layer: 40\n",
            "  44 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  45 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  46 Shortcut Layer: 43\n",
            "  47 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  48 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  49 Shortcut Layer: 46\n",
            "  50 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  51 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  52 Shortcut Layer: 49\n",
            "  53 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  54 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  55 Shortcut Layer: 52\n",
            "  56 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  57 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  58 Shortcut Layer: 55\n",
            "  59 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  60 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  61 Shortcut Layer: 58\n",
            "  62 conv   1024       3 x 3/ 2     38 x  38 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  63 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  64 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  65 Shortcut Layer: 62\n",
            "  66 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  67 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  68 Shortcut Layer: 65\n",
            "  69 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  70 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  71 Shortcut Layer: 68\n",
            "  72 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  73 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  74 Shortcut Layer: 71\n",
            "  75 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  76 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  77 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  78 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  79 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  80 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  81 conv     18       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x  18 0.013 BF\n",
            "  82 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  83 route  79\n",
            "  84 conv    256       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 256 0.095 BF\n",
            "  85 upsample                 2x    19 x  19 x 256 ->   38 x  38 x 256\n",
            "  86 route  85 61\n",
            "  87 conv    256       1 x 1/ 1     38 x  38 x 768 ->   38 x  38 x 256 0.568 BF\n",
            "  88 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  89 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  90 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  91 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  92 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  93 conv     18       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x  18 0.027 BF\n",
            "  94 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  95 route  91\n",
            "  96 conv    128       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 128 0.095 BF\n",
            "  97 upsample                 2x    38 x  38 x 128 ->   76 x  76 x 128\n",
            "  98 route  97 36\n",
            "  99 conv    128       1 x 1/ 1     76 x  76 x 384 ->   76 x  76 x 128 0.568 BF\n",
            " 100 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 101 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 102 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 103 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 104 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 105 conv     18       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x  18 0.053 BF\n",
            " 106 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "Total BFLOPS 139.465 \n",
            " Allocate additional workspace_size = 18.91 MB \n",
            "Loading weights from backup/yolo-obj_last.weights...Done!\n",
            "144Total Detection Time: 17.000000 Seconds\n",
            " compute_capability = 370, cudnn_half = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 conv     32       3 x 3/ 1    608 x 608 x   3 ->  608 x 608 x  32 0.639 BF\n",
            "   1 conv     64       3 x 3/ 2    608 x 608 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   2 conv     32       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  32 0.379 BF\n",
            "   3 conv     64       3 x 3/ 1    304 x 304 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   4 Shortcut Layer: 1\n",
            "   5 conv    128       3 x 3/ 2    304 x 304 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   6 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "   7 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   8 Shortcut Layer: 5\n",
            "   9 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "  10 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "  11 Shortcut Layer: 8\n",
            "  12 conv    256       3 x 3/ 2    152 x 152 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  13 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  14 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  15 Shortcut Layer: 12\n",
            "  16 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  17 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  18 Shortcut Layer: 15\n",
            "  19 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  20 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  21 Shortcut Layer: 18\n",
            "  22 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  23 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  24 Shortcut Layer: 21\n",
            "  25 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  26 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  27 Shortcut Layer: 24\n",
            "  28 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  29 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  30 Shortcut Layer: 27\n",
            "  31 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  32 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  33 Shortcut Layer: 30\n",
            "  34 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  35 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  36 Shortcut Layer: 33\n",
            "  37 conv    512       3 x 3/ 2     76 x  76 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  38 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  39 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  40 Shortcut Layer: 37\n",
            "  41 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  42 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  43 Shortcut Layer: 40\n",
            "  44 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  45 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  46 Shortcut Layer: 43\n",
            "  47 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  48 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  49 Shortcut Layer: 46\n",
            "  50 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  51 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  52 Shortcut Layer: 49\n",
            "  53 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  54 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  55 Shortcut Layer: 52\n",
            "  56 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  57 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  58 Shortcut Layer: 55\n",
            "  59 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  60 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  61 Shortcut Layer: 58\n",
            "  62 conv   1024       3 x 3/ 2     38 x  38 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  63 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  64 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  65 Shortcut Layer: 62\n",
            "  66 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  67 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  68 Shortcut Layer: 65\n",
            "  69 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  70 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  71 Shortcut Layer: 68\n",
            "  72 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  73 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  74 Shortcut Layer: 71\n",
            "  75 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  76 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  77 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  78 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  79 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  80 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  81 conv     18       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x  18 0.013 BF\n",
            "  82 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  83 route  79\n",
            "  84 conv    256       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 256 0.095 BF\n",
            "  85 upsample                 2x    19 x  19 x 256 ->   38 x  38 x 256\n",
            "  86 route  85 61\n",
            "  87 conv    256       1 x 1/ 1     38 x  38 x 768 ->   38 x  38 x 256 0.568 BF\n",
            "  88 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  89 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  90 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  91 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  92 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  93 conv     18       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x  18 0.027 BF\n",
            "  94 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  95 route  91\n",
            "  96 conv    128       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 128 0.095 BF\n",
            "  97 upsample                 2x    38 x  38 x 128 ->   76 x  76 x 128\n",
            "  98 route  97 36\n",
            "  99 conv    128       1 x 1/ 1     76 x  76 x 384 ->   76 x  76 x 128 0.568 BF\n",
            " 100 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 101 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 102 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 103 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 104 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 105 conv     18       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x  18 0.053 BF\n",
            " 106 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "Total BFLOPS 139.465 \n",
            " Allocate additional workspace_size = 18.91 MB \n",
            "Loading weights from backup/yolo-obj_last.weights...Done!\n",
            "144Total Detection Time: 18.000000 Seconds\n",
            " compute_capability = 370, cudnn_half = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 conv     32       3 x 3/ 1    608 x 608 x   3 ->  608 x 608 x  32 0.639 BF\n",
            "   1 conv     64       3 x 3/ 2    608 x 608 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   2 conv     32       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  32 0.379 BF\n",
            "   3 conv     64       3 x 3/ 1    304 x 304 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   4 Shortcut Layer: 1\n",
            "   5 conv    128       3 x 3/ 2    304 x 304 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   6 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "   7 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   8 Shortcut Layer: 5\n",
            "   9 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "  10 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "  11 Shortcut Layer: 8\n",
            "  12 conv    256       3 x 3/ 2    152 x 152 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  13 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  14 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  15 Shortcut Layer: 12\n",
            "  16 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  17 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  18 Shortcut Layer: 15\n",
            "  19 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  20 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  21 Shortcut Layer: 18\n",
            "  22 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  23 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  24 Shortcut Layer: 21\n",
            "  25 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  26 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  27 Shortcut Layer: 24\n",
            "  28 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  29 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  30 Shortcut Layer: 27\n",
            "  31 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  32 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  33 Shortcut Layer: 30\n",
            "  34 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  35 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  36 Shortcut Layer: 33\n",
            "  37 conv    512       3 x 3/ 2     76 x  76 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  38 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  39 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  40 Shortcut Layer: 37\n",
            "  41 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  42 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  43 Shortcut Layer: 40\n",
            "  44 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  45 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  46 Shortcut Layer: 43\n",
            "  47 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  48 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  49 Shortcut Layer: 46\n",
            "  50 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  51 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  52 Shortcut Layer: 49\n",
            "  53 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  54 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  55 Shortcut Layer: 52\n",
            "  56 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  57 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  58 Shortcut Layer: 55\n",
            "  59 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  60 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  61 Shortcut Layer: 58\n",
            "  62 conv   1024       3 x 3/ 2     38 x  38 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  63 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  64 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  65 Shortcut Layer: 62\n",
            "  66 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  67 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  68 Shortcut Layer: 65\n",
            "  69 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  70 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  71 Shortcut Layer: 68\n",
            "  72 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  73 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  74 Shortcut Layer: 71\n",
            "  75 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  76 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  77 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  78 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  79 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  80 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  81 conv     18       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x  18 0.013 BF\n",
            "  82 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  83 route  79\n",
            "  84 conv    256       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 256 0.095 BF\n",
            "  85 upsample                 2x    19 x  19 x 256 ->   38 x  38 x 256\n",
            "  86 route  85 61\n",
            "  87 conv    256       1 x 1/ 1     38 x  38 x 768 ->   38 x  38 x 256 0.568 BF\n",
            "  88 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  89 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  90 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  91 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  92 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  93 conv     18       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x  18 0.027 BF\n",
            "  94 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  95 route  91\n",
            "  96 conv    128       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 128 0.095 BF\n",
            "  97 upsample                 2x    38 x  38 x 128 ->   76 x  76 x 128\n",
            "  98 route  97 36\n",
            "  99 conv    128       1 x 1/ 1     76 x  76 x 384 ->   76 x  76 x 128 0.568 BF\n",
            " 100 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 101 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 102 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 103 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 104 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 105 conv     18       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x  18 0.053 BF\n",
            " 106 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "Total BFLOPS 139.465 \n",
            " Allocate additional workspace_size = 18.91 MB \n",
            "Loading weights from backup/yolo-obj_last.weights...Done!\n",
            "144Total Detection Time: 18.000000 Seconds\n",
            " compute_capability = 370, cudnn_half = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 conv     32       3 x 3/ 1    608 x 608 x   3 ->  608 x 608 x  32 0.639 BF\n",
            "   1 conv     64       3 x 3/ 2    608 x 608 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   2 conv     32       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  32 0.379 BF\n",
            "   3 conv     64       3 x 3/ 1    304 x 304 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   4 Shortcut Layer: 1\n",
            "   5 conv    128       3 x 3/ 2    304 x 304 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   6 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "   7 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   8 Shortcut Layer: 5\n",
            "   9 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "  10 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "  11 Shortcut Layer: 8\n",
            "  12 conv    256       3 x 3/ 2    152 x 152 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  13 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  14 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  15 Shortcut Layer: 12\n",
            "  16 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  17 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  18 Shortcut Layer: 15\n",
            "  19 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  20 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  21 Shortcut Layer: 18\n",
            "  22 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  23 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  24 Shortcut Layer: 21\n",
            "  25 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  26 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  27 Shortcut Layer: 24\n",
            "  28 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  29 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  30 Shortcut Layer: 27\n",
            "  31 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  32 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  33 Shortcut Layer: 30\n",
            "  34 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  35 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  36 Shortcut Layer: 33\n",
            "  37 conv    512       3 x 3/ 2     76 x  76 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  38 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  39 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  40 Shortcut Layer: 37\n",
            "  41 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  42 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  43 Shortcut Layer: 40\n",
            "  44 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  45 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  46 Shortcut Layer: 43\n",
            "  47 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  48 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  49 Shortcut Layer: 46\n",
            "  50 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  51 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  52 Shortcut Layer: 49\n",
            "  53 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  54 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  55 Shortcut Layer: 52\n",
            "  56 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  57 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  58 Shortcut Layer: 55\n",
            "  59 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  60 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  61 Shortcut Layer: 58\n",
            "  62 conv   1024       3 x 3/ 2     38 x  38 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  63 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  64 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  65 Shortcut Layer: 62\n",
            "  66 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  67 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  68 Shortcut Layer: 65\n",
            "  69 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  70 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  71 Shortcut Layer: 68\n",
            "  72 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  73 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  74 Shortcut Layer: 71\n",
            "  75 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  76 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  77 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  78 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  79 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  80 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  81 conv     18       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x  18 0.013 BF\n",
            "  82 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  83 route  79\n",
            "  84 conv    256       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 256 0.095 BF\n",
            "  85 upsample                 2x    19 x  19 x 256 ->   38 x  38 x 256\n",
            "  86 route  85 61\n",
            "  87 conv    256       1 x 1/ 1     38 x  38 x 768 ->   38 x  38 x 256 0.568 BF\n",
            "  88 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  89 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  90 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  91 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  92 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  93 conv     18       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x  18 0.027 BF\n",
            "  94 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  95 route  91\n",
            "  96 conv    128       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 128 0.095 BF\n",
            "  97 upsample                 2x    38 x  38 x 128 ->   76 x  76 x 128\n",
            "  98 route  97 36\n",
            "  99 conv    128       1 x 1/ 1     76 x  76 x 384 ->   76 x  76 x 128 0.568 BF\n",
            " 100 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 101 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 102 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 103 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 104 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 105 conv     18       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x  18 0.053 BF\n",
            " 106 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "Total BFLOPS 139.465 \n",
            " Allocate additional workspace_size = 18.91 MB \n",
            "Loading weights from backup/yolo-obj_last.weights...Done!\n",
            "144Total Detection Time: 17.000000 Seconds\n",
            " compute_capability = 370, cudnn_half = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 conv     32       3 x 3/ 1    608 x 608 x   3 ->  608 x 608 x  32 0.639 BF\n",
            "   1 conv     64       3 x 3/ 2    608 x 608 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   2 conv     32       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  32 0.379 BF\n",
            "   3 conv     64       3 x 3/ 1    304 x 304 x  32 ->  304 x 304 x  64 3.407 BF\n",
            "   4 Shortcut Layer: 1\n",
            "   5 conv    128       3 x 3/ 2    304 x 304 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   6 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "   7 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "   8 Shortcut Layer: 5\n",
            "   9 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF\n",
            "  10 conv    128       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x 128 3.407 BF\n",
            "  11 Shortcut Layer: 8\n",
            "  12 conv    256       3 x 3/ 2    152 x 152 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  13 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  14 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  15 Shortcut Layer: 12\n",
            "  16 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  17 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  18 Shortcut Layer: 15\n",
            "  19 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  20 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  21 Shortcut Layer: 18\n",
            "  22 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  23 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  24 Shortcut Layer: 21\n",
            "  25 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  26 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  27 Shortcut Layer: 24\n",
            "  28 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  29 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  30 Shortcut Layer: 27\n",
            "  31 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  32 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  33 Shortcut Layer: 30\n",
            "  34 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            "  35 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            "  36 Shortcut Layer: 33\n",
            "  37 conv    512       3 x 3/ 2     76 x  76 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  38 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  39 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  40 Shortcut Layer: 37\n",
            "  41 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  42 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  43 Shortcut Layer: 40\n",
            "  44 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  45 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  46 Shortcut Layer: 43\n",
            "  47 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  48 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  49 Shortcut Layer: 46\n",
            "  50 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  51 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  52 Shortcut Layer: 49\n",
            "  53 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  54 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  55 Shortcut Layer: 52\n",
            "  56 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  57 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  58 Shortcut Layer: 55\n",
            "  59 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  60 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  61 Shortcut Layer: 58\n",
            "  62 conv   1024       3 x 3/ 2     38 x  38 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  63 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  64 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  65 Shortcut Layer: 62\n",
            "  66 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  67 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  68 Shortcut Layer: 65\n",
            "  69 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  70 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  71 Shortcut Layer: 68\n",
            "  72 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  73 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  74 Shortcut Layer: 71\n",
            "  75 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  76 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  77 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  78 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  79 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF\n",
            "  80 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF\n",
            "  81 conv     18       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x  18 0.013 BF\n",
            "  82 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  83 route  79\n",
            "  84 conv    256       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 256 0.095 BF\n",
            "  85 upsample                 2x    19 x  19 x 256 ->   38 x  38 x 256\n",
            "  86 route  85 61\n",
            "  87 conv    256       1 x 1/ 1     38 x  38 x 768 ->   38 x  38 x 256 0.568 BF\n",
            "  88 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  89 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  90 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  91 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF\n",
            "  92 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF\n",
            "  93 conv     18       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x  18 0.027 BF\n",
            "  94 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "  95 route  91\n",
            "  96 conv    128       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 128 0.095 BF\n",
            "  97 upsample                 2x    38 x  38 x 128 ->   76 x  76 x 128\n",
            "  98 route  97 36\n",
            "  99 conv    128       1 x 1/ 1     76 x  76 x 384 ->   76 x  76 x 128 0.568 BF\n",
            " 100 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 101 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 102 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 103 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF\n",
            " 104 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF\n",
            " 105 conv     18       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x  18 0.053 BF\n",
            " 106 yolo\n",
            "[yolo] params: iou loss: mse, iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00\n",
            "Total BFLOPS 139.465 \n",
            " Allocate additional workspace_size = 18.91 MB \n",
            "Loading weights from backup/yolo-obj_last.weights...Done!\n",
            "144Total Detection Time: 17.000000 Seconds\n",
            "conf,pre,rec,f1-score,avg_iou,iou_thr,map\n",
            "0.20,0.97,0.99,0.98,78.98,(mAP@0.10),99.16\n",
            "0.15,0.96,0.99,0.97,78.44,(mAP@0.10),99.16\n",
            "0.10,0.96,0.99,0.98,78.34,(mAP@0.10),99.16\n",
            "0.05,0.95,0.99,0.97,77.81,(mAP@0.10),99.16\n",
            "0.20,0.97,0.99,0.98,78.98,(mAP@0.05),99.16\n",
            "0.20,0.97,0.99,0.98,78.98,(mAP@0.10),99.16\n",
            "0.20,0.97,0.99,0.98,78.98,(mAP@0.15),99.16\n",
            "0.20,0.97,0.99,0.98,78.98,(mAP@0.20),99.16\n",
            "0.20,0.97,0.99,0.98,78.98,(mAP@0.30),99.16\n",
            "0.20,0.96,0.98,0.97,78.65,(mAP@0.50),98.46\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Pjx_Uv5cvUA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!./darknet detector test data/obj.data cfg/yolo-obj.cfg backup/yolo-obj_final.weights -ext_output -dont_show -out result.json < data/valid.txt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0PWWumowhNoM",
        "colab_type": "code",
        "outputId": "d447b4ce-847d-4650-b5c9-5242ebe7168f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "!apt-get install unzip\n",
        "!unzip img2.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "unzip is already the newest version (6.0-21ubuntu1).\n",
            "The following packages were automatically installed and are no longer required:\n",
            "  libnvidia-common-410 linux-headers-4.15.0-55 linux-headers-4.15.0-55-generic\n",
            "Use 'apt autoremove' to remove them.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 18 not upgraded.\n",
            "unzip:  cannot find or open img2.zip, img2.zip.zip or img2.zip.ZIP.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8krRiFAzibhT",
        "colab_type": "code",
        "outputId": "1ebfe376-ac83-4bfd-a81b-f05f0b53dcd2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd .."
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/darknet\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fLBKZyDtBMer",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}