ME470_ALICE_Sensors_YOLOv4_Darknet_Roboflow.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "ME470_ALICE_Sensors_YOLOv4-Darknet-Roboflow.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ma53ma/ME470/blob/main/ME470_ALICE_Sensors_YOLOv4_Darknet_Roboflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GNVU7eu9CQj3"
      },
      "source": [
        "# Introduction\n",
        "\n",
        "\n",
        "In this notebook, we implement [YOLOv4](https://arxiv.org/pdf/2004.10934.pdf) for training on your own dataset.\n",
        "\n",
        "We also recommend reading our blog post on [Training YOLOv4 on custom data](https://blog.roboflow.ai/training-yolov4-on-a-custom-dataset/) side by side.\n",
        "\n",
        "We will take the following steps to implement YOLOv4 on our custom data:\n",
        "* Configure our GPU environment on Google Colab\n",
        "* Install the Darknet YOLOv4 training environment\n",
        "* Download our custom dataset for YOLOv4 and set up directories\n",
        "* Configure a custom YOLOv4 training config file for Darknet\n",
        "* Train our custom YOLOv4 object detector\n",
        "* Reload YOLOv4 trained weights and make inference on test images\n",
        "\n",
        "When you are done you will have a custom detector that you can use. It will make inference like this:\n",
        "\n",
        "#### ![Roboflow Workmark](https://i.imgur.com/L0n564N.png)\n",
        "\n",
        "### **Reach out for support**\n",
        "\n",
        "If you run into any hurdles on your own data set or just want to share some cool results in your own domain, [reach out!](https://roboflow.ai) \n",
        "\n",
        "\n",
        "\n",
        "#### ![Roboflow Workmark](https://i.imgur.com/WHFqYSJ.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mDTvGt2zt7cm"
      },
      "source": [
        "# Configuring cuDNN on Colab for YOLOv4\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u-bguKWgtxSx",
        "outputId": "2042c7e8-3671-422c-d036-46519e176a01"
      },
      "source": [
        "# CUDA: Let's check that Nvidia CUDA drivers are already pre-installed and which version is it.\n",
        "!/usr/local/cuda/bin/nvcc --version\n",
        "# We need to install the correct cuDNN according to this output"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2020 NVIDIA Corporation\n",
            "Built on Wed_Jul_22_19:09:09_PDT_2020\n",
            "Cuda compilation tools, release 11.0, V11.0.221\n",
            "Build cuda_11.0_bu.TC445_37.28845127_0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KJYM7-_Had0Q",
        "outputId": "b63b9f2c-c9f3-4b5d-9bc7-f757197da57f"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mon Apr 19 02:38:31 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.67       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   49C    P8     7W /  75W |      0MiB /  7611MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k_qFPIBgvlkn",
        "outputId": "f174f51a-c5f9-4dbb-82fc-8a02375987b8"
      },
      "source": [
        "# This cell ensures you have the correct architecture for your respective GPU\n",
        "# If you command is not found, look through these GPUs, find the respective\n",
        "# GPU and add them to the archTypes dictionary\n",
        "\n",
        "# Tesla V100\n",
        "# ARCH= -gencode arch=compute_70,code=[sm_70,compute_70]\n",
        "\n",
        "# Tesla K80 \n",
        "# ARCH= -gencode arch=compute_37,code=sm_37\n",
        "\n",
        "# GeForce RTX 2080 Ti, RTX 2080, RTX 2070, Quadro RTX 8000, Quadro RTX 6000, Quadro RTX 5000, Tesla T4, XNOR Tensor Cores\n",
        "# ARCH= -gencode arch=compute_75,code=[sm_75,compute_75]\n",
        "\n",
        "# Jetson XAVIER\n",
        "# ARCH= -gencode arch=compute_72,code=[sm_72,compute_72]\n",
        "\n",
        "# GTX 1080, GTX 1070, GTX 1060, GTX 1050, GTX 1030, Titan Xp, Tesla P40, Tesla P4\n",
        "# ARCH= -gencode arch=compute_61,code=sm_61\n",
        "\n",
        "# GP100/Tesla P100 - DGX-1\n",
        "# ARCH= -gencode arch=compute_60,code=sm_60\n",
        "\n",
        "# For Jetson TX1, Tegra X1, DRIVE CX, DRIVE PX - uncomment:\n",
        "# ARCH= -gencode arch=compute_53,code=[sm_53,compute_53]\n",
        "\n",
        "# For Jetson Tx2 or Drive-PX2 uncomment:\n",
        "# ARCH= -gencode arch=compute_62,code=[sm_62,compute_62]\n",
        "import os\n",
        "os.environ['GPU_TYPE'] = str(os.popen('nvidia-smi --query-gpu=name --format=csv,noheader').read())\n",
        "\n",
        "def getGPUArch(argument):\n",
        "  try:\n",
        "    argument = argument.strip()\n",
        "    # All Colab GPUs\n",
        "    archTypes = {\n",
        "        \"Tesla V100-SXM2-16GB\": \"-gencode arch=compute_70,code=[sm_70,compute_70]\",\n",
        "        \"Tesla K80\": \"-gencode arch=compute_37,code=sm_37\",\n",
        "        \"Tesla T4\": \"-gencode arch=compute_75,code=[sm_75,compute_75]\",\n",
        "        \"Tesla P40\": \"-gencode arch=compute_61,code=sm_61\",\n",
        "        \"Tesla P4\": \"-gencode arch=compute_61,code=sm_61\",\n",
        "        \"Tesla P100-PCIE-16GB\": \"-gencode arch=compute_60,code=sm_60\"\n",
        "\n",
        "      }\n",
        "    return archTypes[argument]\n",
        "  except KeyError:\n",
        "    return \"GPU must be added to GPU Commands\"\n",
        "os.environ['ARCH_VALUE'] = getGPUArch(os.environ['GPU_TYPE'])\n",
        "\n",
        "print(\"GPU Type: \" + os.environ['GPU_TYPE'])\n",
        "print(\"ARCH Value: \" + os.environ['ARCH_VALUE'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GPU Type: Tesla T4\n",
            "\n",
            "ARCH Value: -gencode arch=compute_75,code=[sm_75,compute_75]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v3nkYzWwMuBk"
      },
      "source": [
        "## STEP 1. Install cuDNN according to the current CUDA version\n",
        "Colab added cuDNN as an inherent install - so you don't have to do a thing - major win\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "16pvdFMa1FEe"
      },
      "source": [
        "# Step 2: Installing Darknet for YOLOv4 on Colab\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K9uY-38P93oz",
        "outputId": "316ae026-a3cc-4c0b-d0ac-22fa219d18cd"
      },
      "source": [
        "%cd /content/\n",
        "%rm -rf darknet"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HQEktcfj9y9O",
        "outputId": "ccf7e28e-e18e-4cfb-eda5-7ca1193d0c88"
      },
      "source": [
        "#we clone the fork of darknet maintained by roboflow\n",
        "#small changes have been made to configure darknet for training\n",
        "!git clone https://github.com/roboflow-ai/darknet.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'darknet'...\n",
            "remote: Enumerating objects: 13289, done.\u001b[K\n",
            "remote: Total 13289 (delta 0), reused 0 (delta 0), pack-reused 13289\u001b[K\n",
            "Receiving objects: 100% (13289/13289), 12.13 MiB | 23.56 MiB/s, done.\n",
            "Resolving deltas: 100% (9104/9104), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9O6dTiq5ga0I",
        "outputId": "4af8f5f6-c9d4-4fb3-deee-18fdb9a81e5c"
      },
      "source": [
        "%cd /content/darknet/\n",
        "%rm Makefile"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/darknet\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OyTAyEhOgd__",
        "outputId": "34e1ec66-bf45-4aeb-a371-10443bf766c9"
      },
      "source": [
        "#colab occasionally shifts dependencies around, at the time of authorship, this Makefile works for building Darknet on Colab\n",
        "\n",
        "%%writefile Makefile\n",
        "GPU=1\n",
        "CUDNN=1\n",
        "CUDNN_HALF=0\n",
        "OPENCV=1\n",
        "AVX=0\n",
        "OPENMP=0\n",
        "LIBSO=1\n",
        "ZED_CAMERA=0\n",
        "ZED_CAMERA_v2_8=0\n",
        "\n",
        "# set GPU=1 and CUDNN=1 to speedup on GPU\n",
        "# set CUDNN_HALF=1 to further speedup 3 x times (Mixed-precision on Tensor Cores) GPU: Volta, Xavier, Turing and higher\n",
        "# set AVX=1 and OPENMP=1 to speedup on CPU (if error occurs then set AVX=0)\n",
        "# set ZED_CAMERA=1 to enable ZED SDK 3.0 and above\n",
        "# set ZED_CAMERA_v2_8=1 to enable ZED SDK 2.X\n",
        "\n",
        "USE_CPP=0\n",
        "DEBUG=0\n",
        "\n",
        "ARCH= -gencode arch=compute_35,code=sm_35 \\\n",
        "      -gencode arch=compute_50,code=[sm_50,compute_50] \\\n",
        "      -gencode arch=compute_52,code=[sm_52,compute_52] \\\n",
        "\t    -gencode arch=compute_61,code=[sm_61,compute_61] \\\n",
        "      -gencode arch=compute_37,code=sm_37\n",
        "\n",
        "ARCH= -gencode arch=compute_60,code=sm_60\n",
        "\n",
        "OS := $(shell uname)\n",
        "\n",
        "VPATH=./src/\n",
        "EXEC=darknet\n",
        "OBJDIR=./obj/\n",
        "\n",
        "ifeq ($(LIBSO), 1)\n",
        "LIBNAMESO=libdarknet.so\n",
        "APPNAMESO=uselib\n",
        "endif\n",
        "\n",
        "ifeq ($(USE_CPP), 1)\n",
        "CC=g++\n",
        "else\n",
        "CC=gcc\n",
        "endif\n",
        "\n",
        "CPP=g++ -std=c++11\n",
        "NVCC=nvcc\n",
        "OPTS=-Ofast\n",
        "LDFLAGS= -lm -pthread\n",
        "COMMON= -Iinclude/ -I3rdparty/stb/include\n",
        "CFLAGS=-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC\n",
        "\n",
        "ifeq ($(DEBUG), 1)\n",
        "#OPTS= -O0 -g\n",
        "#OPTS= -Og -g\n",
        "COMMON+= -DDEBUG\n",
        "CFLAGS+= -DDEBUG\n",
        "else\n",
        "ifeq ($(AVX), 1)\n",
        "CFLAGS+= -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a\n",
        "endif\n",
        "endif\n",
        "\n",
        "CFLAGS+=$(OPTS)\n",
        "\n",
        "ifneq (,$(findstring MSYS_NT,$(OS)))\n",
        "LDFLAGS+=-lws2_32\n",
        "endif\n",
        "\n",
        "ifeq ($(OPENCV), 1)\n",
        "COMMON+= -DOPENCV\n",
        "CFLAGS+= -DOPENCV\n",
        "LDFLAGS+= `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv`\n",
        "COMMON+= `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv`\n",
        "endif\n",
        "\n",
        "ifeq ($(OPENMP), 1)\n",
        "CFLAGS+= -fopenmp\n",
        "LDFLAGS+= -lgomp\n",
        "endif\n",
        "\n",
        "ifeq ($(GPU), 1)\n",
        "COMMON+= -DGPU -I/usr/local/cuda/include/\n",
        "CFLAGS+= -DGPU\n",
        "ifeq ($(OS),Darwin) #MAC\n",
        "LDFLAGS+= -L/usr/local/cuda/lib -lcuda -lcudart -lcublas -lcurand\n",
        "else\n",
        "LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand\n",
        "endif\n",
        "endif\n",
        "\n",
        "ifeq ($(CUDNN), 1)\n",
        "COMMON+= -DCUDNN\n",
        "ifeq ($(OS),Darwin) #MAC\n",
        "CFLAGS+= -DCUDNN -I/usr/local/cuda/include\n",
        "LDFLAGS+= -L/usr/local/cuda/lib -lcudnn\n",
        "else\n",
        "CFLAGS+= -DCUDNN -I/usr/local/cudnn/include\n",
        "LDFLAGS+= -L/usr/local/cudnn/lib64 -lcudnn\n",
        "endif\n",
        "endif\n",
        "\n",
        "ifeq ($(CUDNN_HALF), 1)\n",
        "COMMON+= -DCUDNN_HALF\n",
        "CFLAGS+= -DCUDNN_HALF\n",
        "ARCH+= -gencode arch=compute_70,code=[sm_70,compute_70]\n",
        "endif\n",
        "\n",
        "ifeq ($(ZED_CAMERA), 1)\n",
        "CFLAGS+= -DZED_STEREO -I/usr/local/zed/include\n",
        "ifeq ($(ZED_CAMERA_v2_8), 1)\n",
        "LDFLAGS+= -L/usr/local/zed/lib -lsl_core -lsl_input -lsl_zed\n",
        "#-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0\n",
        "else\n",
        "LDFLAGS+= -L/usr/local/zed/lib -lsl_zed\n",
        "#-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0\n",
        "endif\n",
        "endif\n",
        "\n",
        "OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o gaussian_yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o scale_channels_layer.o sam_layer.o\n",
        "ifeq ($(GPU), 1)\n",
        "LDFLAGS+= -lstdc++\n",
        "OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o\n",
        "endif\n",
        "\n",
        "OBJS = $(addprefix $(OBJDIR), $(OBJ))\n",
        "DEPS = $(wildcard src/*.h) Makefile include/darknet.h\n",
        "\n",
        "all: $(OBJDIR) backup results setchmod $(EXEC) $(LIBNAMESO) $(APPNAMESO)\n",
        "\n",
        "ifeq ($(LIBSO), 1)\n",
        "CFLAGS+= -fPIC\n",
        "\n",
        "$(LIBNAMESO): $(OBJDIR) $(OBJS) include/yolo_v2_class.hpp src/yolo_v2_class.cpp\n",
        "\t$(CPP) -shared -std=c++11 -fvisibility=hidden -DLIB_EXPORTS $(COMMON) $(CFLAGS) $(OBJS) src/yolo_v2_class.cpp -o $@ $(LDFLAGS)\n",
        "\n",
        "$(APPNAMESO): $(LIBNAMESO) include/yolo_v2_class.hpp src/yolo_console_dll.cpp\n",
        "\t$(CPP) -std=c++11 $(COMMON) $(CFLAGS) -o $@ src/yolo_console_dll.cpp $(LDFLAGS) -L ./ -l:$(LIBNAMESO)\n",
        "endif\n",
        "\n",
        "$(EXEC): $(OBJS)\n",
        "\t$(CPP) -std=c++11 $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS)\n",
        "\n",
        "$(OBJDIR)%.o: %.c $(DEPS)\n",
        "\t$(CC) $(COMMON) $(CFLAGS) -c $< -o $@\n",
        "\n",
        "$(OBJDIR)%.o: %.cpp $(DEPS)\n",
        "\t$(CPP) -std=c++11 $(COMMON) $(CFLAGS) -c $< -o $@\n",
        "\n",
        "$(OBJDIR)%.o: %.cu $(DEPS)\n",
        "\t$(NVCC) $(ARCH) $(COMMON) --compiler-options \"$(CFLAGS)\" -c $< -o $@\n",
        "\n",
        "$(OBJDIR):\n",
        "\tmkdir -p $(OBJDIR)\n",
        "backup:\n",
        "\tmkdir -p backup\n",
        "results:\n",
        "\tmkdir -p results\n",
        "setchmod:\n",
        "\tchmod +x *.sh\n",
        "\n",
        ".PHONY: clean\n",
        "\n",
        "clean:\n",
        "\trm -rf $(OBJS) $(EXEC) $(LIBNAMESO) $(APPNAMESO)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Writing Makefile\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QyMBDkaL-Aep",
        "outputId": "105a19f9-fa29-4a10-e7a8-9ee3a2b312e3"
      },
      "source": [
        "#install environment from the Makefile\n",
        "#note if you are on Colab Pro this works on a P100 GPU\n",
        "#if you are on Colab free, you may need to change the Makefile for the K80 GPU\n",
        "#this goes for any GPU, you need to change the Makefile to inform darknet which GPU you are running on.\n",
        "#note the Makefile above should work for you, if you need to tweak, try the below\n",
        "%cd /content/darknet/\n",
        "!sed -i 's/OPENCV=0/OPENCV=1/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/ARCH= -gencode arch=compute_60,code=sm_60/ARCH= ${ARCH_VALUE}/g\" Makefile\n",
        "!make"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/darknet\n",
            "mkdir -p ./obj/\n",
            "mkdir -p backup\n",
            "chmod +x *.sh\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/image_opencv.cpp -o obj/image_opencv.o\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid draw_detections_cv_v3(void**, detection*, int, float, char**, image**, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:910:23:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Krgb\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "                 float \u001b[01;35m\u001b[Krgb\u001b[m\u001b[K[3];\n",
            "                       \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid cv_draw_object(image, float*, int, int, int*, float*, int*, int, char**)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1391:14:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbuff\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         char \u001b[01;35m\u001b[Kbuff\u001b[m\u001b[K[100];\n",
            "              \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1367:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kit_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kit_tb_res\u001b[m\u001b[K = cv::createTrackbar(it_trackbar_name, window_name, &it_trackbar_value, 1000);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1371:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Klr_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Klr_tb_res\u001b[m\u001b[K = cv::createTrackbar(lr_trackbar_name, window_name, &lr_trackbar_value, 20);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1375:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcl_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kcl_tb_res\u001b[m\u001b[K = cv::createTrackbar(cl_trackbar_name, window_name, &cl_trackbar_value, classes-1);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1378:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbo_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kbo_tb_res\u001b[m\u001b[K = cv::createTrackbar(bo_trackbar_name, window_name, boxonly, 1);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/http_stream.cpp -o obj/http_stream.o\n",
            "In file included from \u001b[01m\u001b[K./src/http_stream.cpp:580:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K./src/httplib.h:129:0:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"INVALID_SOCKET\" redefined\n",
            " #define INVALID_SOCKET (-1)\n",
            " \n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:73:0:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kthis is the location of the previous definition\n",
            " #define INVALID_SOCKET -1\n",
            " \n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kbool JSON_sender::write(const char*)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:249:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                 int \u001b[01;35m\u001b[Kn\u001b[m\u001b[K = _write(client, outputbuf, outlen);\n",
            "                     \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kbool MJPG_sender::write(const cv::Mat&)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:507:113:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%zu\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "                 sprintf(head, \"--mjpegstream\\r\\nContent-Type: image/jpeg\\r\\nContent-Length: %zu\\r\\n\\r\\n\", outlen\u001b[01;35m\u001b[K)\u001b[m\u001b[K;\n",
            "                                                                                                                 \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/gemm.c -o obj/gemm.o\n",
            "\u001b[01m\u001b[K./src/gemm.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kconvolution_2d\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gemm.c:2039:15:\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:2038:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     const int \u001b[01;35m\u001b[Kout_h\u001b[m\u001b[K = (h + 2 * pad - ksize) / stride + 1;    // output_height=input_height for stride=1 and pad=1\n",
            "               \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/utils.c -o obj/utils.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/dark_cuda.c -o obj/dark_cuda.o\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcudnn_check_error_extended\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:224:20:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between ‘\u001b[01m\u001b[KcudaError_t {aka enum cudaError}\u001b[m\u001b[K’ and ‘\u001b[01m\u001b[Kenum <anonymous>\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wenum-compare\u001b[m\u001b[K]\n",
            "         if (status \u001b[01;35m\u001b[K!=\u001b[m\u001b[K CUDNN_STATUS_SUCCESS)\n",
            "                    \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kpre_allocate_pinned_memory\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:276:40:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\"pre_allocate: size = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K MB, num_of_blocks = %Iu, block_size = %Iu MB \\n\",\n",
            "                                      \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
            "                                      \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n",
            "             \u001b[32m\u001b[Ksize / (1024*1024)\u001b[m\u001b[K, num_of_blocks, pinned_block_size / (1024 * 1024));\n",
            "             \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K          \n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:276:64:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\"pre_allocate: size = %Iu MB, num_of_blocks = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K, block_size = %Iu MB \\n\",\n",
            "                                                              \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
            "                                                              \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:276:82:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\"pre_allocate: size = %Iu MB, num_of_blocks = %Iu, block_size = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K MB \\n\",\n",
            "                                                                                \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
            "                                                                                \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:286:37:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "                 printf(\" Allocated \u001b[01;35m\u001b[K%d\u001b[m\u001b[K pinned block \\n\", pinned_block_size);\n",
            "                                    \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                    \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcuda_make_array_pinned_preallocated\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:307:43:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "             printf(\"\\n Pinned block_id = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, filled = %f %% \\n\", pinned_block_id, filled);\n",
            "                                          \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                          \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:322:64:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "             printf(\"Try to allocate new pinned memory, size = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K MB \\n\", \u001b[32m\u001b[Ksize / (1024 * 1024)\u001b[m\u001b[K);\n",
            "                                                               \u001b[01;35m\u001b[K~^\u001b[m\u001b[K         \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "                                                               \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:328:63:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "             printf(\"Try to allocate new pinned BLOCK, size = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K MB \\n\", \u001b[32m\u001b[Ksize / (1024 * 1024)\u001b[m\u001b[K);\n",
            "                                                              \u001b[01;35m\u001b[K~^\u001b[m\u001b[K         \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "                                                              \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/convolutional_layer.c -o obj/convolutional_layer.o\n",
            "\u001b[01m\u001b[K./src/convolutional_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_convolutional_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/convolutional_layer.c:1204:32:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kt_intput_size\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                         size_t \u001b[01;35m\u001b[Kt_intput_size\u001b[m\u001b[K = binary_transpose_align_input(k, n, state.workspace, &l.t_bit_input, ldb_align, l.bit_align);\n",
            "                                \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/list.c -o obj/list.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/image.c -o obj/image.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/activations.c -o obj/activations.o\n",
            "\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kactivate\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/activations.c:77:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KRELU6\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(a){\n",
            "     \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/activations.c:77:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KSWISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:77:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:77:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:77:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN_SOFTMAX\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:77:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN_SOFTMAX_MAXVAL\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgradient\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/activations.c:287:5:\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:287:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/im2col.c -o obj/im2col.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/col2im.c -o obj/col2im.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/blas.c -o obj/blas.o\n",
            "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbackward_shortcut_multilayer_cpu\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas.c:207:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_index\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                 int \u001b[01;35m\u001b[Kout_index\u001b[m\u001b[K = id;\n",
            "                     \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/crop_layer.c -o obj/crop_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/dropout_layer.c -o obj/dropout_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/maxpool_layer.c -o obj/maxpool_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/softmax_layer.c -o obj/softmax_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/data.c -o obj/data.o\n",
            "\u001b[01m\u001b[K./src/data.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kload_data_detection\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/data.c:1134:24:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kx\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                 int k, \u001b[01;35m\u001b[Kx\u001b[m\u001b[K, y;\n",
            "                        \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/data.c:984:43:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kr_scale\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "     float r1 = 0, r2 = 0, r3 = 0, r4 = 0, \u001b[01;35m\u001b[Kr_scale\u001b[m\u001b[K = 0;\n",
            "                                           \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/matrix.c -o obj/matrix.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/network.c -o obj/network.o\n",
            "\u001b[01m\u001b[K./src/network.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_network\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/network.c:610:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Knet->input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped))\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/network.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4707:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/connected_layer.c -o obj/connected_layer.o\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_connected_layer_gpu\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:346:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kone\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Kone\u001b[m\u001b[K = 1;    // alpha[0], beta[0]\n",
            "           \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:344:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kc\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float * \u001b[01;35m\u001b[Kc\u001b[m\u001b[K = l.output_gpu;\n",
            "             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:343:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kb\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float * \u001b[01;35m\u001b[Kb\u001b[m\u001b[K = l.weights_gpu;\n",
            "             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:342:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ka\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float * \u001b[01;35m\u001b[Ka\u001b[m\u001b[K = state.input;\n",
            "             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:341:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kn\u001b[m\u001b[K = l.outputs;\n",
            "         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:340:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kk\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kk\u001b[m\u001b[K = l.inputs;\n",
            "         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:339:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Km\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Km\u001b[m\u001b[K = l.batch;\n",
            "         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/cost_layer.c -o obj/cost_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/parser.c -o obj/parser.o\n",
            "\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kparse_network_cfg_custom\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/parser.c:1573:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Knet.input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1;\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activation_layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/parser.c:6\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4707:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/option_list.c -o obj/option_list.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/darknet.c -o obj/darknet.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/detection_layer.c -o obj/detection_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/captcha.c -o obj/captcha.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/route_layer.c -o obj/route_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/writing.c -o obj/writing.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/box.c -o obj/box.o\n",
            "\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbox_iou_kind\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/box.c:154:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMSE\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(iou_kind) {\n",
            "     \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdiounms_sort\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/box.c:898:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbeta_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                     float \u001b[01;35m\u001b[Kbeta_prob\u001b[m\u001b[K = pow(dets[j].prob[k], 2) / sum_prob;\n",
            "                           \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/box.c:897:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kalpha_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                     float \u001b[01;35m\u001b[Kalpha_prob\u001b[m\u001b[K = pow(dets[i].prob[k], 2) / sum_prob;\n",
            "                           \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/nightmare.c -o obj/nightmare.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/normalization_layer.c -o obj/normalization_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/avgpool_layer.c -o obj/avgpool_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/coco.c -o obj/coco.o\n",
            "\u001b[01m\u001b[K./src/coco.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_coco_recall\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/coco.c:248:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbase\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     char *\u001b[01;35m\u001b[Kbase\u001b[m\u001b[K = \"results/comp4_det_test_\";\n",
            "           \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/dice.c -o obj/dice.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/yolo.c -o obj/yolo.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/detector.c -o obj/detector.o\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kprint_cocos\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:451:29:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat not a string literal and no format arguments [\u001b[01;35m\u001b[K-Wformat-security\u001b[m\u001b[K]\n",
            "                 fprintf(fp, \u001b[01;35m\u001b[Kbuff\u001b[m\u001b[K);\n",
            "                             \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Keliminate_bdd\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:544:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kstatement with no effect [\u001b[01;35m\u001b[K-Wunused-value\u001b[m\u001b[K]\n",
            "                     \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K (k; buf[k + n] != '\\0'; k++)\n",
            "                     \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_detector\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:657:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmkd2\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         int \u001b[01;35m\u001b[Kmkd2\u001b[m\u001b[K = make_directory(buff2, 0777);\n",
            "             \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:655:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmkd\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         int \u001b[01;35m\u001b[Kmkd\u001b[m\u001b[K = make_directory(buff, 0777);\n",
            "             \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_detector_map\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:1276:15:\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:1275:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kclass_precision\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         float \u001b[01;35m\u001b[Kclass_precision\u001b[m\u001b[K = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)fp_for_thresh_per_class[i]);\n",
            "               \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdraw_object\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:1792:19:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kinv_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "             float \u001b[01;35m\u001b[Kinv_loss\u001b[m\u001b[K = 1.0 / max_val_cmp(0.01, avg_loss);\n",
            "                   \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/layer.c -o obj/layer.o\n",
            "\u001b[01m\u001b[K./src/layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfree_layer_custom\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/layer.c:191:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "         \u001b[01;35m\u001b[Kif\u001b[m\u001b[K (l.x_gpu)                   cuda_free(l.x_gpu);  l.x_gpu = NULL;\n",
            "         \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/layer.c:191:61:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
            "         if (l.x_gpu)                   cuda_free(l.x_gpu);  \u001b[01;36m\u001b[Kl\u001b[m\u001b[K.x_gpu = NULL;\n",
            "                                                             \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/compare.c -o obj/compare.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/classifier.c -o obj/classifier.o\n",
            "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_classifier\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/classifier.c:138:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcount\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kcount\u001b[m\u001b[K = 0;\n",
            "         \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kpredict_classifier\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/classifier.c:831:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktime\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     clock_t \u001b[01;35m\u001b[Ktime\u001b[m\u001b[K;\n",
            "             \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdemo_classifier\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/classifier.c:1261:49:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktval_result\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         struct timeval tval_before, tval_after, \u001b[01;35m\u001b[Ktval_result\u001b[m\u001b[K;\n",
            "                                                 \u001b[01;35m\u001b[K^~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/classifier.c:1261:37:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktval_after\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         struct timeval tval_before, \u001b[01;35m\u001b[Ktval_after\u001b[m\u001b[K, tval_result;\n",
            "                                     \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/local_layer.c -o obj/local_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/swag.c -o obj/swag.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/shortcut_layer.c -o obj/shortcut_layer.o\n",
            "\u001b[01m\u001b[K./src/shortcut_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_shortcut_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/shortcut_layer.c:55:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kscale\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         float \u001b[01;35m\u001b[Kscale\u001b[m\u001b[K = sqrt(2. / l.nweights);\n",
            "               \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/activation_layer.c -o obj/activation_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/rnn_layer.c -o obj/rnn_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/gru_layer.c -o obj/gru_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/rnn.c -o obj/rnn.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/rnn_vid.c -o obj/rnn_vid.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/crnn_layer.c -o obj/crnn_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/demo.c -o obj/demo.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/tag.c -o obj/tag.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/cifar.c -o obj/cifar.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/go.c -o obj/go.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/batchnorm_layer.c -o obj/batchnorm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/art.c -o obj/art.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/region_layer.c -o obj/region_layer.o\n",
            "\u001b[01m\u001b[K./src/region_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_region_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/region_layer.c:58:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kold_h\u001b[m\u001b[K = l->h;\n",
            "         \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/region_layer.c:57:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_w\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kold_w\u001b[m\u001b[K = l->w;\n",
            "         \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/reorg_layer.c -o obj/reorg_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/reorg_old_layer.c -o obj/reorg_old_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/super.c -o obj/super.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/voxel.c -o obj/voxel.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/tree.c -o obj/tree.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/yolo_layer.c -o obj/yolo_layer.o\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:61:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.output, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4707:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:68:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.delta, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4707:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:95:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4707:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:104:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4707:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:371:25:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kbest_match_t\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "                     int \u001b[01;35m\u001b[Kbest_match_t\u001b[m\u001b[K = 0;\n",
            "                         \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/gaussian_yolo_layer.c -o obj/gaussian_yolo_layer.o\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_gaussian_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:71:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.output, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4707:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:78:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.delta, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4707:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_gaussian_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:110:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4707:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:119:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4707:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_gaussian_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:460:25:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kbest_match_t\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "                     int \u001b[01;35m\u001b[Kbest_match_t\u001b[m\u001b[K = 0;\n",
            "                         \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/upsample_layer.c -o obj/upsample_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/lstm_layer.c -o obj/lstm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/conv_lstm_layer.c -o obj/conv_lstm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/scale_channels_layer.c -o obj/scale_channels_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/sam_layer.c -o obj/sam_layer.o\n",
            "nvcc -gencode arch=compute_75,code=[sm_75,compute_75] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/convolutional_kernels.cu -o obj/convolutional_kernels.o\n",
            "nvcc -gencode arch=compute_75,code=[sm_75,compute_75] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/activation_kernels.cu -o obj/activation_kernels.o\n",
            "nvcc -gencode arch=compute_75,code=[sm_75,compute_75] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/im2col_kernels.cu -o obj/im2col_kernels.o\n",
            "\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_75,code=[sm_75,compute_75] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/col2im_kernels.cu -o obj/col2im_kernels.o\n",
            "nvcc -gencode arch=compute_75,code=[sm_75,compute_75] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/blas_kernels.cu -o obj/blas_kernels.o\n",
            "./src/blas_kernels.cu(1038): warning: variable \"out_index\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1082): warning: variable \"step\" was set but never used\n",
            "\n",
            "./src/blas_kernels.cu(1686): warning: variable \"stage_id\" was declared but never referenced\n",
            "\n",
            "\u001b[01m\u001b[K./src/blas_kernels.cu:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid backward_shortcut_multilayer_gpu(int, int, int, int*, float**, float*, float*, float*, float*, int, float*, float**, WEIGHTS_NORMALIZATION_T)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas_kernels.cu:1082:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kstep\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kint \u001b[m\u001b[Kstep = 0;\n",
            "     \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "nvcc -gencode arch=compute_75,code=[sm_75,compute_75] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/crop_layer_kernels.cu -o obj/crop_layer_kernels.o\n",
            "nvcc -gencode arch=compute_75,code=[sm_75,compute_75] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/dropout_layer_kernels.cu -o obj/dropout_layer_kernels.o\n",
            "nvcc -gencode arch=compute_75,code=[sm_75,compute_75] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/maxpool_layer_kernels.cu -o obj/maxpool_layer_kernels.o\n",
            "nvcc -gencode arch=compute_75,code=[sm_75,compute_75] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/network_kernels.cu -o obj/network_kernels.o\n",
            "./src/network_kernels.cu(359): warning: variable \"l\" was declared but never referenced\n",
            "\n",
            "./src/network_kernels.cu(368): warning: variable \"im\" was set but never used\n",
            "\n",
            "\u001b[01m\u001b[K./src/network_kernels.cu:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfloat train_network_datum_gpu(network, float*, float*)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/network_kernels.cu:359:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kl\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "       \u001b[01;35m\u001b[K \u001b[m\u001b[K layer l = net.layers[net.n - 1];\n",
            "       \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/network_kernels.cu:368:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kim\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "       \u001b[01;35m\u001b[K  \u001b[m\u001b[Kimage im;\n",
            "       \u001b[01;35m\u001b[K^\u001b[m\u001b[K \n",
            "nvcc -gencode arch=compute_75,code=[sm_75,compute_75] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/avgpool_layer_kernels.cu -o obj/avgpool_layer_kernels.o\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC obj/image_opencv.o obj/http_stream.o obj/gemm.o obj/utils.o obj/dark_cuda.o obj/convolutional_layer.o obj/list.o obj/image.o obj/activations.o obj/im2col.o obj/col2im.o obj/blas.o obj/crop_layer.o obj/dropout_layer.o obj/maxpool_layer.o obj/softmax_layer.o obj/data.o obj/matrix.o obj/network.o obj/connected_layer.o obj/cost_layer.o obj/parser.o obj/option_list.o obj/darknet.o obj/detection_layer.o obj/captcha.o obj/route_layer.o obj/writing.o obj/box.o obj/nightmare.o obj/normalization_layer.o obj/avgpool_layer.o obj/coco.o obj/dice.o obj/yolo.o obj/detector.o obj/layer.o obj/compare.o obj/classifier.o obj/local_layer.o obj/swag.o obj/shortcut_layer.o obj/activation_layer.o obj/rnn_layer.o obj/gru_layer.o obj/rnn.o obj/rnn_vid.o obj/crnn_layer.o obj/demo.o obj/tag.o obj/cifar.o obj/go.o obj/batchnorm_layer.o obj/art.o obj/region_layer.o obj/reorg_layer.o obj/reorg_old_layer.o obj/super.o obj/voxel.o obj/tree.o obj/yolo_layer.o obj/gaussian_yolo_layer.o obj/upsample_layer.o obj/lstm_layer.o obj/conv_lstm_layer.o obj/scale_channels_layer.o obj/sam_layer.o obj/convolutional_kernels.o obj/activation_kernels.o obj/im2col_kernels.o obj/col2im_kernels.o obj/blas_kernels.o obj/crop_layer_kernels.o obj/dropout_layer_kernels.o obj/maxpool_layer_kernels.o obj/network_kernels.o obj/avgpool_layer_kernels.o -o darknet -lm -pthread `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv` -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand -L/usr/local/cudnn/lib64 -lcudnn -lstdc++\n",
            "g++ -std=c++11 -shared -std=c++11 -fvisibility=hidden -DLIB_EXPORTS -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC ./obj/image_opencv.o ./obj/http_stream.o ./obj/gemm.o ./obj/utils.o ./obj/dark_cuda.o ./obj/convolutional_layer.o ./obj/list.o ./obj/image.o ./obj/activations.o ./obj/im2col.o ./obj/col2im.o ./obj/blas.o ./obj/crop_layer.o ./obj/dropout_layer.o ./obj/maxpool_layer.o ./obj/softmax_layer.o ./obj/data.o ./obj/matrix.o ./obj/network.o ./obj/connected_layer.o ./obj/cost_layer.o ./obj/parser.o ./obj/option_list.o ./obj/darknet.o ./obj/detection_layer.o ./obj/captcha.o ./obj/route_layer.o ./obj/writing.o ./obj/box.o ./obj/nightmare.o ./obj/normalization_layer.o ./obj/avgpool_layer.o ./obj/coco.o ./obj/dice.o ./obj/yolo.o ./obj/detector.o ./obj/layer.o ./obj/compare.o ./obj/classifier.o ./obj/local_layer.o ./obj/swag.o ./obj/shortcut_layer.o ./obj/activation_layer.o ./obj/rnn_layer.o ./obj/gru_layer.o ./obj/rnn.o ./obj/rnn_vid.o ./obj/crnn_layer.o ./obj/demo.o ./obj/tag.o ./obj/cifar.o ./obj/go.o ./obj/batchnorm_layer.o ./obj/art.o ./obj/region_layer.o ./obj/reorg_layer.o ./obj/reorg_old_layer.o ./obj/super.o ./obj/voxel.o ./obj/tree.o ./obj/yolo_layer.o ./obj/gaussian_yolo_layer.o ./obj/upsample_layer.o ./obj/lstm_layer.o ./obj/conv_lstm_layer.o ./obj/scale_channels_layer.o ./obj/sam_layer.o ./obj/convolutional_kernels.o ./obj/activation_kernels.o ./obj/im2col_kernels.o ./obj/col2im_kernels.o ./obj/blas_kernels.o ./obj/crop_layer_kernels.o ./obj/dropout_layer_kernels.o ./obj/maxpool_layer_kernels.o ./obj/network_kernels.o ./obj/avgpool_layer_kernels.o src/yolo_v2_class.cpp -o libdarknet.so -lm -pthread `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv` -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand -L/usr/local/cudnn/lib64 -lcudnn -lstdc++\n",
            "In file included from \u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:2:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In constructor ‘\u001b[01m\u001b[Ktrack_kalman_t::track_kalman_t(int, int, float, cv::Size)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:708:14:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Ktrack_kalman_t::img_size\u001b[m\u001b[K’ will be initialized after [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "     cv::Size \u001b[01;35m\u001b[Kimg_size\u001b[m\u001b[K;  // max value of x,y,w,h\n",
            "              \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:700:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  ‘\u001b[01m\u001b[Kint track_kalman_t::track_id_counter\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Ktrack_id_counter\u001b[m\u001b[K;\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:853:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  when initialized here [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Ktrack_kalman_t\u001b[m\u001b[K(int _max_objects = 1000, int _min_frames = 3, float _max_dist = 40, cv::Size _img_size = cv::Size(10000, 10000)) :\n",
            "     \u001b[01;35m\u001b[K^~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kvoid track_kalman_t::clear_old_states()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:879:50:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "                 if ((result_vec_pred[state_id].x > img_size.width) ||\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:880:50:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "                     (result_vec_pred[state_id].y > img_size.height))\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Ktrack_kalman_t::tst_t track_kalman_t::get_state_id(bbox_t, std::vector<bool>&)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:900:30:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::predict()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:990:30:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::correct(std::vector<bbox_t>)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:1025:30:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> Detector::tracking_id(std::vector<bbox_t>, bool, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:370:40:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         if (\u001b[01;35m\u001b[Kprev_bbox_vec_deque.size() > frames_story\u001b[m\u001b[K) prev_bbox_vec_deque.pop_back();\n",
            "             \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:385:34:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "                     if (\u001b[01;35m\u001b[Kcur_dist < max_dist\u001b[m\u001b[K && (k.track_id == 0 || dist_vec[m] > cur_dist)) {\n",
            "                         \u001b[01;35m\u001b[K~~~~~~~~~^~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:409:40:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         if (\u001b[01;35m\u001b[Kprev_bbox_vec_deque.size() > frames_story\u001b[m\u001b[K) prev_bbox_vec_deque.pop_back();\n",
            "             \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -o uselib src/yolo_console_dll.cpp -lm -pthread `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv` -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand -L/usr/local/cudnn/lib64 -lcudnn -lstdc++ -L ./ -l:libdarknet.so\n",
            "In file included from \u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:23:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In constructor ‘\u001b[01m\u001b[Ktrack_kalman_t::track_kalman_t(int, int, float, cv::Size)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:708:14:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Ktrack_kalman_t::img_size\u001b[m\u001b[K’ will be initialized after [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "     cv::Size \u001b[01;35m\u001b[Kimg_size\u001b[m\u001b[K;  // max value of x,y,w,h\n",
            "              \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:700:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  ‘\u001b[01m\u001b[Kint track_kalman_t::track_id_counter\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Ktrack_id_counter\u001b[m\u001b[K;\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:853:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  when initialized here [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Ktrack_kalman_t\u001b[m\u001b[K(int _max_objects = 1000, int _min_frames = 3, float _max_dist = 40, cv::Size _img_size = cv::Size(10000, 10000)) :\n",
            "     \u001b[01;35m\u001b[K^~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kvoid track_kalman_t::clear_old_states()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:879:50:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "                 if ((result_vec_pred[state_id].x > img_size.width) ||\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:880:50:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "                     (result_vec_pred[state_id].y > img_size.height))\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Ktrack_kalman_t::tst_t track_kalman_t::get_state_id(bbox_t, std::vector<bool>&)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:900:30:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::predict()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:990:30:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::correct(std::vector<bbox_t>)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:1025:30:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid draw_boxes(cv::Mat, std::vector<bbox_t>, std::vector<std::__cxx11::basic_string<char> >, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:192:46:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "             int max_width = (\u001b[01;35m\u001b[Ktext_size.width > i.w + 2\u001b[m\u001b[K) ? text_size.width : (i.w + 2);\n",
            "                              \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:201:62:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "                 int const max_width_3d = (\u001b[01;35m\u001b[Ktext_size_3d.width > i.w + 2\u001b[m\u001b[K) ? text_size_3d.width : (i.w + 2);\n",
            "                                           \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:183:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcolors\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int const \u001b[01;35m\u001b[Kcolors\u001b[m\u001b[K[6][3] = { { 1,0,1 },{ 0,0,1 },{ 0,1,1 },{ 0,1,0 },{ 1,1,0 },{ 1,0,0 } };\n",
            "               \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:\u001b[m\u001b[K In constructor ‘\u001b[01m\u001b[Kmain(int, char**)::detection_data_t::detection_data_t()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:398:26:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kmain(int, char**)::detection_data_t::exit_flag\u001b[m\u001b[K’ will be initialized after [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "                     bool \u001b[01;35m\u001b[Kexit_flag\u001b[m\u001b[K;\n",
            "                          \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:396:26:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  ‘\u001b[01m\u001b[Kbool main(int, char**)::detection_data_t::new_detection\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "                     bool \u001b[01;35m\u001b[Knew_detection\u001b[m\u001b[K;\n",
            "                          \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:401:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  when initialized here [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "                     \u001b[01;35m\u001b[Kdetection_data_t\u001b[m\u001b[K() : exit_flag(false), new_detection(false) {}\n",
            "                     \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~\u001b[m\u001b[K\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LGPDEjfAALrQ",
        "outputId": "1d8c9465-0dfe-46c3-bba0-058ef7873ef0"
      },
      "source": [
        "#download the newly released yolov4 ConvNet weights\n",
        "%cd /content/darknet\n",
        "#!wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137\n",
        "!wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights # for tiny yolov4"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/darknet\n",
            "--2021-04-20 14:46:04--  https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights\n",
            "Resolving github.com (github.com)... 192.30.255.113\n",
            "Connecting to github.com (github.com)|192.30.255.113|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github-releases.githubusercontent.com/75388965/228a9c00-3ea4-11eb-8e80-28d71569f56c?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210420%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210420T144604Z&X-Amz-Expires=300&X-Amz-Signature=f007da99aeac656f870d32b49372c1f37e228fafbb658468e564649bb89faf9b&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=75388965&response-content-disposition=attachment%3B%20filename%3Dyolov4-tiny.weights&response-content-type=application%2Foctet-stream [following]\n",
            "--2021-04-20 14:46:04--  https://github-releases.githubusercontent.com/75388965/228a9c00-3ea4-11eb-8e80-28d71569f56c?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210420%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210420T144604Z&X-Amz-Expires=300&X-Amz-Signature=f007da99aeac656f870d32b49372c1f37e228fafbb658468e564649bb89faf9b&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=75388965&response-content-disposition=attachment%3B%20filename%3Dyolov4-tiny.weights&response-content-type=application%2Foctet-stream\n",
            "Resolving github-releases.githubusercontent.com (github-releases.githubusercontent.com)... 185.199.108.154, 185.199.109.154, 185.199.110.154, ...\n",
            "Connecting to github-releases.githubusercontent.com (github-releases.githubusercontent.com)|185.199.108.154|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 24251276 (23M) [application/octet-stream]\n",
            "Saving to: ‘yolov4-tiny.weights’\n",
            "\n",
            "yolov4-tiny.weights 100%[===================>]  23.13M  34.1MB/s    in 0.7s    \n",
            "\n",
            "2021-04-20 14:46:05 (34.1 MB/s) - ‘yolov4-tiny.weights’ saved [24251276/24251276]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kWOiKj37l4wW"
      },
      "source": [
        "# Set up Custom Dataset for YOLOv4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zbniFj-eSimL"
      },
      "source": [
        "We'll use Roboflow to convert our dataset from any format to the YOLO Darknet format. \n",
        "\n",
        "1. To do so, create a free [Roboflow account](https://app.roboflow.ai).\n",
        "2. Upload your images and their annotations (in any format: VOC XML, COCO JSON, TensorFlow CSV, etc).\n",
        "3. Apply preprocessing and augmentation steps you may like. We recommend at least `auto-orient` and a `resize` to 416x416. Generate your dataset.\n",
        "4. Export your dataset in the **YOLO Darknet format**.\n",
        "5. Copy your download link, and paste it below.\n",
        "\n",
        "See our [blog post](https://blog.roboflow.ai/training-yolov4-on-a-custom-dataset/) for greater detail.\n",
        "\n",
        "In this example, I used the open source [BCCD Dataset](https://public.roboflow.ai/object-detection/bccd). (You can `fork` it to your Roboflow account to follow along.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cdj4tmT5Cmdl",
        "outputId": "69a4740c-c489-4e62-9bd2-96c540fcb6d8"
      },
      "source": [
        "\n",
        "#if you already have YOLO darknet format, you can skip this step\n",
        "%cd /content/darknet\n",
        "#!curl -L [YOUR LINK HERE] > roboflow.zip; unzip roboflow.zip; rm roboflow.zip\n",
        "!curl -L \"https://app.roboflow.com/ds/qZVA2n36pu?key=wzg6ZUF8OP\" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/darknet\n",
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100   890  100   890    0     0   1410      0 --:--:-- --:--:-- --:--:--  1408\n",
            "100 19.1M  100 19.1M    0     0  15.0M      0  0:00:01  0:00:01 --:--:--  214M\n",
            "Archive:  roboflow.zip\n",
            " extracting: README.roboflow.txt     \n",
            "   creating: test/\n",
            " extracting: test/20200212emPotholes02-1-1582068901_jpg.rf.b40d7df6f26540de4951702ba60f5fd0.jpg  \n",
            " extracting: test/20200212emPotholes02-1-1582068901_jpg.rf.b40d7df6f26540de4951702ba60f5fd0.txt  \n",
            " extracting: test/IMG_9252_jpg.rf.1ca8e76a89bd986f5c870f9702c66916.jpg  \n",
            " extracting: test/IMG_9252_jpg.rf.1ca8e76a89bd986f5c870f9702c66916.txt  \n",
            " extracting: test/IMG_9258_jpg.rf.6b4848a67d573e5f34a4929eb2b13963.jpg  \n",
            " extracting: test/IMG_9258_jpg.rf.6b4848a67d573e5f34a4929eb2b13963.txt  \n",
            " extracting: test/IMG_9280_jpg.rf.94615a0ea40603297ac1b63d91d876c3.jpg  \n",
            " extracting: test/IMG_9280_jpg.rf.94615a0ea40603297ac1b63d91d876c3.txt  \n",
            " extracting: test/IMG_9283_jpg.rf.010a22e090b6b655dd0f199a81855f7d.jpg  \n",
            " extracting: test/IMG_9283_jpg.rf.010a22e090b6b655dd0f199a81855f7d.txt  \n",
            " extracting: test/IMG_9294_jpg.rf.2aec2f7cef89231b01146044ea4125e6.jpg  \n",
            " extracting: test/IMG_9294_jpg.rf.2aec2f7cef89231b01146044ea4125e6.txt  \n",
            " extracting: test/IMG_9344_jpg.rf.395888b089767786f367be4d78c75bcd.jpg  \n",
            " extracting: test/IMG_9344_jpg.rf.395888b089767786f367be4d78c75bcd.txt  \n",
            " extracting: test/IMG_9348_jpg.rf.87eab1d3eaee217363628b54ceec393c.jpg  \n",
            " extracting: test/IMG_9348_jpg.rf.87eab1d3eaee217363628b54ceec393c.txt  \n",
            " extracting: test/IMG_9361_jpg.rf.269e5e116f7cb70c4337a7fe4fdfbece.jpg  \n",
            " extracting: test/IMG_9361_jpg.rf.269e5e116f7cb70c4337a7fe4fdfbece.txt  \n",
            " extracting: test/IMG_9398_jpg.rf.87a143658e5823e8bd5f88f7a1b9dea6.jpg  \n",
            " extracting: test/IMG_9398_jpg.rf.87a143658e5823e8bd5f88f7a1b9dea6.txt  \n",
            " extracting: test/IMG_9414_jpg.rf.53ed6ebc7d2d2b2126c2b619990594b2.jpg  \n",
            " extracting: test/IMG_9414_jpg.rf.53ed6ebc7d2d2b2126c2b619990594b2.txt  \n",
            " extracting: test/IMG_9419_jpg.rf.372ddce17450af086335862f6d6ca8b2.jpg  \n",
            " extracting: test/IMG_9419_jpg.rf.372ddce17450af086335862f6d6ca8b2.txt  \n",
            " extracting: test/IMG_9438_jpg.rf.76b5530f37b456b62b90ad1e09f6eaee.jpg  \n",
            " extracting: test/IMG_9438_jpg.rf.76b5530f37b456b62b90ad1e09f6eaee.txt  \n",
            " extracting: test/IMG_9441_jpg.rf.5e2c39e005961f0a94674180d7b9dd24.jpg  \n",
            " extracting: test/IMG_9441_jpg.rf.5e2c39e005961f0a94674180d7b9dd24.txt  \n",
            " extracting: test/IMG_9463_jpg.rf.41163b461b47b117848aecac5b290c24.jpg  \n",
            " extracting: test/IMG_9463_jpg.rf.41163b461b47b117848aecac5b290c24.txt  \n",
            " extracting: test/IMG_9470_jpg.rf.d0e1e2eda2dce0598b0df7acf9ff05d0.jpg  \n",
            " extracting: test/IMG_9470_jpg.rf.d0e1e2eda2dce0598b0df7acf9ff05d0.txt  \n",
            " extracting: test/IMG_9478_jpg.rf.1310cc1a43a77170b9dfea61570bde28.jpg  \n",
            " extracting: test/IMG_9478_jpg.rf.1310cc1a43a77170b9dfea61570bde28.txt  \n",
            " extracting: test/IMG_9636_jpg.rf.53ba83740accabd7f01ff7cfe3c8ef63.jpg  \n",
            " extracting: test/IMG_9636_jpg.rf.53ba83740accabd7f01ff7cfe3c8ef63.txt  \n",
            " extracting: test/IMG_9651_jpg.rf.f14d908f146cef21257b350a91d4ff01.jpg  \n",
            " extracting: test/IMG_9651_jpg.rf.f14d908f146cef21257b350a91d4ff01.txt  \n",
            " extracting: test/IMG_9656_jpg.rf.0e8bebfd6b36c52ad8bc4a74e53e2ad4.jpg  \n",
            " extracting: test/IMG_9656_jpg.rf.0e8bebfd6b36c52ad8bc4a74e53e2ad4.txt  \n",
            " extracting: test/IMG_9660_jpg.rf.bdaef34c2011b4c5b04b0f0bd1579256.jpg  \n",
            " extracting: test/IMG_9660_jpg.rf.bdaef34c2011b4c5b04b0f0bd1579256.txt  \n",
            " extracting: test/IMG_9663_jpg.rf.6ad497fc9ba02dd90a0589bd6037184a.jpg  \n",
            " extracting: test/IMG_9663_jpg.rf.6ad497fc9ba02dd90a0589bd6037184a.txt  \n",
            " extracting: test/IMG_9670_jpg.rf.828337864f4e82b3ba4278ee68644f7f.jpg  \n",
            " extracting: test/IMG_9670_jpg.rf.828337864f4e82b3ba4278ee68644f7f.txt  \n",
            " extracting: test/PXL_20210209_133656643_jpg.rf.aa8ad36cd72cd0f41b718fa2259bc468.jpg  \n",
            " extracting: test/PXL_20210209_133656643_jpg.rf.aa8ad36cd72cd0f41b718fa2259bc468.txt  \n",
            " extracting: test/PXL_20210209_135032706_jpg.rf.07e577971629c773a5d8e46428e44139.jpg  \n",
            " extracting: test/PXL_20210209_135032706_jpg.rf.07e577971629c773a5d8e46428e44139.txt  \n",
            " extracting: test/PXL_20210219_174644879_jpg.rf.3ded704bdcd029a728cbe786a55a7aeb.jpg  \n",
            " extracting: test/PXL_20210219_174644879_jpg.rf.3ded704bdcd029a728cbe786a55a7aeb.txt  \n",
            " extracting: test/PXL_20210222_163100148_jpg.rf.4f207f2fe552034288bb5051e287ffe2.jpg  \n",
            " extracting: test/PXL_20210222_163100148_jpg.rf.4f207f2fe552034288bb5051e287ffe2.txt  \n",
            " extracting: test/PXL_20210223_230042557_jpg.rf.f0c345ca5bb3f7cfb27bb6541b0a79ae.jpg  \n",
            " extracting: test/PXL_20210223_230042557_jpg.rf.f0c345ca5bb3f7cfb27bb6541b0a79ae.txt  \n",
            " extracting: test/PXL_20210226_181633946_jpg.rf.d0f455a153d08c12ffe6eddb3c7c8015.jpg  \n",
            " extracting: test/PXL_20210226_181633946_jpg.rf.d0f455a153d08c12ffe6eddb3c7c8015.txt  \n",
            " extracting: test/PXL_20210228_173757805_jpg.rf.53a6121d90f11c1c2e8fa7bccbbce2ee.jpg  \n",
            " extracting: test/PXL_20210228_173757805_jpg.rf.53a6121d90f11c1c2e8fa7bccbbce2ee.txt  \n",
            " extracting: test/PXL_20210228_173834625_jpg.rf.59f5611c89504c696c72b5ea3e87758f.jpg  \n",
            " extracting: test/PXL_20210228_173834625_jpg.rf.59f5611c89504c696c72b5ea3e87758f.txt  \n",
            " extracting: test/PXL_20210301_170423096_jpg.rf.e537bd884961fa61136e6505e76318ca.jpg  \n",
            " extracting: test/PXL_20210301_170423096_jpg.rf.e537bd884961fa61136e6505e76318ca.txt  \n",
            " extracting: test/PXL_20210302_134822119_jpg.rf.adb341589c85f96d5b08659601ea79e6.jpg  \n",
            " extracting: test/PXL_20210302_134822119_jpg.rf.adb341589c85f96d5b08659601ea79e6.txt  \n",
            " extracting: test/PXL_20210302_150325727_jpg.rf.875d32efdb224694094829039419bd01.jpg  \n",
            " extracting: test/PXL_20210302_150325727_jpg.rf.875d32efdb224694094829039419bd01.txt  \n",
            " extracting: test/PXL_20210302_151505360_jpg.rf.80b578bbb7cec3bb1953a670d702a830.jpg  \n",
            " extracting: test/PXL_20210302_151505360_jpg.rf.80b578bbb7cec3bb1953a670d702a830.txt  \n",
            " extracting: test/PXL_20210307_181326500_jpg.rf.91694fcb23b4ce1396dbca0a070913cd.jpg  \n",
            " extracting: test/PXL_20210307_181326500_jpg.rf.91694fcb23b4ce1396dbca0a070913cd.txt  \n",
            " extracting: test/PXL_20210307_215531973_jpg.rf.92f0908d421f4e01546cb4d865d04bca.jpg  \n",
            " extracting: test/PXL_20210307_215531973_jpg.rf.92f0908d421f4e01546cb4d865d04bca.txt  \n",
            " extracting: test/PXL_20210312_181509933_jpg.rf.23120fb991e688143785fd9566ad705a.jpg  \n",
            " extracting: test/PXL_20210312_181509933_jpg.rf.23120fb991e688143785fd9566ad705a.txt  \n",
            " extracting: test/PXL_20210316_123249675_jpg.rf.8adbda414656aaf246c5f7f78769ed76.jpg  \n",
            " extracting: test/PXL_20210316_123249675_jpg.rf.8adbda414656aaf246c5f7f78769ed76.txt  \n",
            " extracting: test/PXL_20210316_123645957_jpg.rf.386be56845f60539400e858db4e0e00c.jpg  \n",
            " extracting: test/PXL_20210316_123645957_jpg.rf.386be56845f60539400e858db4e0e00c.txt  \n",
            " extracting: test/PXL_20210320_085649444_jpg.rf.e375191694b640fe30b41ed2b64d4cfa.jpg  \n",
            " extracting: test/PXL_20210320_085649444_jpg.rf.e375191694b640fe30b41ed2b64d4cfa.txt  \n",
            " extracting: test/PXL_20210321_195231016_jpg.rf.c57b448c2824a8e7499f83b2ba374d1e.jpg  \n",
            " extracting: test/PXL_20210321_195231016_jpg.rf.c57b448c2824a8e7499f83b2ba374d1e.txt  \n",
            " extracting: test/Who-Is-Responsible-for-Crumbling-Cracked-Sidewalks-and-Walkways-300x199_jpeg.rf.6d4e7102cca319aa50095a3200649ca1.jpg  \n",
            " extracting: test/Who-Is-Responsible-for-Crumbling-Cracked-Sidewalks-and-Walkways-300x199_jpeg.rf.6d4e7102cca319aa50095a3200649ca1.txt  \n",
            " extracting: test/_darknet.labels    \n",
            " extracting: test/curbcut_jpg.rf.dac2d138987a5a7f8901451ec4da802c.jpg  \n",
            " extracting: test/curbcut_jpg.rf.dac2d138987a5a7f8901451ec4da802c.txt  \n",
            "   creating: train/\n",
            " extracting: train/08050_mb_may_2019_blog_potholesissues5_jpg.rf.bc916264fb70bd29086d1ea264300a93.jpg  \n",
            " extracting: train/08050_mb_may_2019_blog_potholesissues5_jpg.rf.bc916264fb70bd29086d1ea264300a93.txt  \n",
            " extracting: train/1112_n13_eta_villages_jpg.rf.a8fdd19a7e03d7952bc80f2331847bec.jpg  \n",
            " extracting: train/1112_n13_eta_villages_jpg.rf.a8fdd19a7e03d7952bc80f2331847bec.txt  \n",
            " extracting: train/1200px-Pram_Ramp_jpg.rf.cc9c10a566a584e785b0d1e639a93234.jpg  \n",
            " extracting: train/1200px-Pram_Ramp_jpg.rf.cc9c10a566a584e785b0d1e639a93234.txt  \n",
            " extracting: train/5d0212f1-c265-48af-badd-83b42e587363-large16x9_TreeBranchesDown_jpg.rf.ac018d35745ea8eeff327b6b19671120.jpg  \n",
            " extracting: train/5d0212f1-c265-48af-badd-83b42e587363-large16x9_TreeBranchesDown_jpg.rf.ac018d35745ea8eeff327b6b19671120.txt  \n",
            " extracting: train/Best-Pothole-Repair-Practices-In-Canada_jpg.rf.de66a8067d319b6976bc8cbbca9e6349.jpg  \n",
            " extracting: train/Best-Pothole-Repair-Practices-In-Canada_jpg.rf.de66a8067d319b6976bc8cbbca9e6349.txt  \n",
            " extracting: train/DSC00711_jpg.rf.3dad41e3d1226d523878f7261415fce8.jpg  \n",
            " extracting: train/DSC00711_jpg.rf.3dad41e3d1226d523878f7261415fce8.txt  \n",
            " extracting: train/IMG_20210305_142538_jpg.rf.cdc81968a67e562a499aa287f92b9722.jpg  \n",
            " extracting: train/IMG_20210305_142538_jpg.rf.cdc81968a67e562a499aa287f92b9722.txt  \n",
            " extracting: train/IMG_20210305_143913_jpg.rf.846b05cddaf774251a30653b88b853b8.jpg  \n",
            " extracting: train/IMG_20210305_143913_jpg.rf.846b05cddaf774251a30653b88b853b8.txt  \n",
            " extracting: train/IMG_9243_jpg.rf.2cd5a5384c5ee3bad7effd9472f6f491.jpg  \n",
            " extracting: train/IMG_9243_jpg.rf.2cd5a5384c5ee3bad7effd9472f6f491.txt  \n",
            " extracting: train/IMG_9244_jpg.rf.65ad38864a5907c829764cca68176b57.jpg  \n",
            " extracting: train/IMG_9244_jpg.rf.65ad38864a5907c829764cca68176b57.txt  \n",
            " extracting: train/IMG_9247_jpg.rf.62c8be61ba4b197c58fee5a944f45b91.jpg  \n",
            " extracting: train/IMG_9247_jpg.rf.62c8be61ba4b197c58fee5a944f45b91.txt  \n",
            " extracting: train/IMG_9248_jpg.rf.dd1a7045b18fdcdd972d3866f15ca795.jpg  \n",
            " extracting: train/IMG_9248_jpg.rf.dd1a7045b18fdcdd972d3866f15ca795.txt  \n",
            " extracting: train/IMG_9250_jpg.rf.7e773b30b81c091888d2bab6c31281a9.jpg  \n",
            " extracting: train/IMG_9250_jpg.rf.7e773b30b81c091888d2bab6c31281a9.txt  \n",
            " extracting: train/IMG_9254_jpg.rf.2da1a3dc9884fdb61adcee7bab388242.jpg  \n",
            " extracting: train/IMG_9254_jpg.rf.2da1a3dc9884fdb61adcee7bab388242.txt  \n",
            " extracting: train/IMG_9256_jpg.rf.0de1fd060c498d4b6e1150cdbd37531f.jpg  \n",
            " extracting: train/IMG_9256_jpg.rf.0de1fd060c498d4b6e1150cdbd37531f.txt  \n",
            " extracting: train/IMG_9259_jpg.rf.e0325d21a21191f3d4eccf94ec46a80e.jpg  \n",
            " extracting: train/IMG_9259_jpg.rf.e0325d21a21191f3d4eccf94ec46a80e.txt  \n",
            " extracting: train/IMG_9261_jpg.rf.02ac8adfdea635bfd34c15f029731c40.jpg  \n",
            " extracting: train/IMG_9261_jpg.rf.02ac8adfdea635bfd34c15f029731c40.txt  \n",
            " extracting: train/IMG_9263_jpg.rf.1afae74750705c4f2dbcf895a71121a0.jpg  \n",
            " extracting: train/IMG_9263_jpg.rf.1afae74750705c4f2dbcf895a71121a0.txt  \n",
            " extracting: train/IMG_9264_jpg.rf.4b73a77f87c669cbb48ae5466c509d8d.jpg  \n",
            " extracting: train/IMG_9264_jpg.rf.4b73a77f87c669cbb48ae5466c509d8d.txt  \n",
            " extracting: train/IMG_9270_jpg.rf.c11c4187d6f1248f444bc01b0f4c82a7.jpg  \n",
            " extracting: train/IMG_9270_jpg.rf.c11c4187d6f1248f444bc01b0f4c82a7.txt  \n",
            " extracting: train/IMG_9273_jpg.rf.67a0e3e01b40abfdcee58634ac418386.jpg  \n",
            " extracting: train/IMG_9273_jpg.rf.67a0e3e01b40abfdcee58634ac418386.txt  \n",
            " extracting: train/IMG_9275_jpg.rf.9e84f0f605ec730e27dd910b7d55fad0.jpg  \n",
            " extracting: train/IMG_9275_jpg.rf.9e84f0f605ec730e27dd910b7d55fad0.txt  \n",
            " extracting: train/IMG_9276_jpg.rf.f320d894ca803259a41571039c28a95c.jpg  \n",
            " extracting: train/IMG_9276_jpg.rf.f320d894ca803259a41571039c28a95c.txt  \n",
            " extracting: train/IMG_9277_jpg.rf.57fd0b85c92c05ecaf68ebb157435c31.jpg  \n",
            " extracting: train/IMG_9277_jpg.rf.57fd0b85c92c05ecaf68ebb157435c31.txt  \n",
            " extracting: train/IMG_9279_jpg.rf.ae7a6c23f34147d429ce7402517b0d01.jpg  \n",
            " extracting: train/IMG_9279_jpg.rf.ae7a6c23f34147d429ce7402517b0d01.txt  \n",
            " extracting: train/IMG_9282_jpg.rf.02c1b2714756fa8b47056f1c42f1ae2e.jpg  \n",
            " extracting: train/IMG_9282_jpg.rf.02c1b2714756fa8b47056f1c42f1ae2e.txt  \n",
            " extracting: train/IMG_9287_jpg.rf.e642f82289d8365b8b6b1009d79bf9ec.jpg  \n",
            " extracting: train/IMG_9287_jpg.rf.e642f82289d8365b8b6b1009d79bf9ec.txt  \n",
            " extracting: train/IMG_9288_jpg.rf.648ea4d40e8da7d810b39ebf553af8f1.jpg  \n",
            " extracting: train/IMG_9288_jpg.rf.648ea4d40e8da7d810b39ebf553af8f1.txt  \n",
            " extracting: train/IMG_9289_jpg.rf.ef54ec091382be1309fef14d979f460d.jpg  \n",
            " extracting: train/IMG_9289_jpg.rf.ef54ec091382be1309fef14d979f460d.txt  \n",
            " extracting: train/IMG_9296_jpg.rf.5b6bfab9667c900887b3b1d1cf9a6c31.jpg  \n",
            " extracting: train/IMG_9296_jpg.rf.5b6bfab9667c900887b3b1d1cf9a6c31.txt  \n",
            " extracting: train/IMG_9297_jpg.rf.8426ca75ce0b269e9d732d690ce3e4fc.jpg  \n",
            " extracting: train/IMG_9297_jpg.rf.8426ca75ce0b269e9d732d690ce3e4fc.txt  \n",
            " extracting: train/IMG_9299_jpg.rf.7eaf00343a33ccf82f9af0219c88f132.jpg  \n",
            " extracting: train/IMG_9299_jpg.rf.7eaf00343a33ccf82f9af0219c88f132.txt  \n",
            " extracting: train/IMG_9328_jpg.rf.7b58ce3f417d8788f1f8fa899acce6bf.jpg  \n",
            " extracting: train/IMG_9328_jpg.rf.7b58ce3f417d8788f1f8fa899acce6bf.txt  \n",
            " extracting: train/IMG_9334_jpg.rf.f5bacc42940203f754bfa5dafb197d6a.jpg  \n",
            " extracting: train/IMG_9334_jpg.rf.f5bacc42940203f754bfa5dafb197d6a.txt  \n",
            " extracting: train/IMG_9335_jpg.rf.4e916ea24a197257d3821f2fabc1cf6e.jpg  \n",
            " extracting: train/IMG_9335_jpg.rf.4e916ea24a197257d3821f2fabc1cf6e.txt  \n",
            " extracting: train/IMG_9336_jpg.rf.5a1a1c23b5bb880304a62ec47f16612c.jpg  \n",
            " extracting: train/IMG_9336_jpg.rf.5a1a1c23b5bb880304a62ec47f16612c.txt  \n",
            " extracting: train/IMG_9337_jpg.rf.eae488401de41e48e8916f001d9acc0a.jpg  \n",
            " extracting: train/IMG_9337_jpg.rf.eae488401de41e48e8916f001d9acc0a.txt  \n",
            " extracting: train/IMG_9339_jpg.rf.1d5c778b6f6fde112f74516aa05e94ad.jpg  \n",
            " extracting: train/IMG_9339_jpg.rf.1d5c778b6f6fde112f74516aa05e94ad.txt  \n",
            " extracting: train/IMG_9341_jpg.rf.aac7257beb9788aa5e0d483cb8211863.jpg  \n",
            " extracting: train/IMG_9341_jpg.rf.aac7257beb9788aa5e0d483cb8211863.txt  \n",
            " extracting: train/IMG_9343_jpg.rf.cb96b76956b192f97898ecd0a259dc40.jpg  \n",
            " extracting: train/IMG_9343_jpg.rf.cb96b76956b192f97898ecd0a259dc40.txt  \n",
            " extracting: train/IMG_9346_jpg.rf.9da2237e12c373e74c0a35e6a90e7ef1.jpg  \n",
            " extracting: train/IMG_9346_jpg.rf.9da2237e12c373e74c0a35e6a90e7ef1.txt  \n",
            " extracting: train/IMG_9347_jpg.rf.2c7b2ac2e7c636a5f3780eb798a1d076.jpg  \n",
            " extracting: train/IMG_9347_jpg.rf.2c7b2ac2e7c636a5f3780eb798a1d076.txt  \n",
            " extracting: train/IMG_9353_jpg.rf.2f3142d19241a0648b20c517b9397752.jpg  \n",
            " extracting: train/IMG_9353_jpg.rf.2f3142d19241a0648b20c517b9397752.txt  \n",
            " extracting: train/IMG_9354_jpg.rf.ada7c51e97c9d155a50edd66bcd6459f.jpg  \n",
            " extracting: train/IMG_9354_jpg.rf.ada7c51e97c9d155a50edd66bcd6459f.txt  \n",
            " extracting: train/IMG_9355_jpg.rf.f0039b3f1b451a59614b43303e229411.jpg  \n",
            " extracting: train/IMG_9355_jpg.rf.f0039b3f1b451a59614b43303e229411.txt  \n",
            " extracting: train/IMG_9358_jpg.rf.3fc0f5ed05636b1f736834bee44dc278.jpg  \n",
            " extracting: train/IMG_9358_jpg.rf.3fc0f5ed05636b1f736834bee44dc278.txt  \n",
            " extracting: train/IMG_9359_jpg.rf.d4b75bcb4783c7ff4cda4a30ac70cfe8.jpg  \n",
            " extracting: train/IMG_9359_jpg.rf.d4b75bcb4783c7ff4cda4a30ac70cfe8.txt  \n",
            " extracting: train/IMG_9360_jpg.rf.16ae0f7f8bcd9a408c59b7e0ef1be987.jpg  \n",
            " extracting: train/IMG_9360_jpg.rf.16ae0f7f8bcd9a408c59b7e0ef1be987.txt  \n",
            " extracting: train/IMG_9362_jpg.rf.dbcd1135300f2ec5f96f8e79f63e912c.jpg  \n",
            " extracting: train/IMG_9362_jpg.rf.dbcd1135300f2ec5f96f8e79f63e912c.txt  \n",
            " extracting: train/IMG_9363_jpg.rf.fbd8c1c376511b5fc2335202dbcec4fd.jpg  \n",
            " extracting: train/IMG_9363_jpg.rf.fbd8c1c376511b5fc2335202dbcec4fd.txt  \n",
            " extracting: train/IMG_9364_jpg.rf.a2698039e204d523766ba256898fddd6.jpg  \n",
            " extracting: train/IMG_9364_jpg.rf.a2698039e204d523766ba256898fddd6.txt  \n",
            " extracting: train/IMG_9366_jpg.rf.a48db113f1a334bd27a8a4e9a25ee9fa.jpg  \n",
            " extracting: train/IMG_9366_jpg.rf.a48db113f1a334bd27a8a4e9a25ee9fa.txt  \n",
            " extracting: train/IMG_9367_jpg.rf.fab32cf0f7a8a3da969bf4f2855e7363.jpg  \n",
            " extracting: train/IMG_9367_jpg.rf.fab32cf0f7a8a3da969bf4f2855e7363.txt  \n",
            " extracting: train/IMG_9369_jpg.rf.8b2b4c826373a6fb5fdc7cd5d20a4c20.jpg  \n",
            " extracting: train/IMG_9369_jpg.rf.8b2b4c826373a6fb5fdc7cd5d20a4c20.txt  \n",
            " extracting: train/IMG_9370_jpg.rf.817745fd479c5a72b85cc8726da98db3.jpg  \n",
            " extracting: train/IMG_9370_jpg.rf.817745fd479c5a72b85cc8726da98db3.txt  \n",
            " extracting: train/IMG_9397_jpg.rf.d89cb5801aa179948a361f097c57041d.jpg  \n",
            " extracting: train/IMG_9397_jpg.rf.d89cb5801aa179948a361f097c57041d.txt  \n",
            " extracting: train/IMG_9399_jpg.rf.1b81f02c66bdf2b9d0f1804d2dea97b6.jpg  \n",
            " extracting: train/IMG_9399_jpg.rf.1b81f02c66bdf2b9d0f1804d2dea97b6.txt  \n",
            " extracting: train/IMG_9400_jpg.rf.74325437d659a18b31ad76a0d0d1cec6.jpg  \n",
            " extracting: train/IMG_9400_jpg.rf.74325437d659a18b31ad76a0d0d1cec6.txt  \n",
            " extracting: train/IMG_9401_jpg.rf.fcfdf5be092e6716c631533baf056d49.jpg  \n",
            " extracting: train/IMG_9401_jpg.rf.fcfdf5be092e6716c631533baf056d49.txt  \n",
            " extracting: train/IMG_9403_jpg.rf.87ab2e113af55d9fdc751c5530d0c0b2.jpg  \n",
            " extracting: train/IMG_9403_jpg.rf.87ab2e113af55d9fdc751c5530d0c0b2.txt  \n",
            " extracting: train/IMG_9403_jpg.rf.8a68877d49735489e519f1a69968e27d.jpg  \n",
            " extracting: train/IMG_9403_jpg.rf.8a68877d49735489e519f1a69968e27d.txt  \n",
            " extracting: train/IMG_9404_jpg.rf.0334128a60dd42d7f7926cad8b3884a9.jpg  \n",
            " extracting: train/IMG_9404_jpg.rf.0334128a60dd42d7f7926cad8b3884a9.txt  \n",
            " extracting: train/IMG_9405_jpg.rf.4fd5e983fe72a01207dc1a826526a4e3.jpg  \n",
            " extracting: train/IMG_9405_jpg.rf.4fd5e983fe72a01207dc1a826526a4e3.txt  \n",
            " extracting: train/IMG_9406_jpg.rf.45e2951434f1a08ee64bcae678b33d1f.jpg  \n",
            " extracting: train/IMG_9406_jpg.rf.45e2951434f1a08ee64bcae678b33d1f.txt  \n",
            " extracting: train/IMG_9407_jpg.rf.2616c43a1c43775752b40357d0e56a59.jpg  \n",
            " extracting: train/IMG_9407_jpg.rf.2616c43a1c43775752b40357d0e56a59.txt  \n",
            " extracting: train/IMG_9408_jpg.rf.7f332595af04bdd3b02eaf17364ece0c.jpg  \n",
            " extracting: train/IMG_9408_jpg.rf.7f332595af04bdd3b02eaf17364ece0c.txt  \n",
            " extracting: train/IMG_9411_jpg.rf.b55654671b19d4b3fdba4b05dd54ccb7.jpg  \n",
            " extracting: train/IMG_9411_jpg.rf.b55654671b19d4b3fdba4b05dd54ccb7.txt  \n",
            " extracting: train/IMG_9412_jpg.rf.8b8e38079cc7f14fddc17e26caf290b6.jpg  \n",
            " extracting: train/IMG_9412_jpg.rf.8b8e38079cc7f14fddc17e26caf290b6.txt  \n",
            " extracting: train/IMG_9413_jpg.rf.7a64de2a1a61d2a09acc708435b3cb5a.jpg  \n",
            " extracting: train/IMG_9413_jpg.rf.7a64de2a1a61d2a09acc708435b3cb5a.txt  \n",
            " extracting: train/IMG_9417_jpg.rf.ae25fd818aa4050cc02e3e87eb6fafe9.jpg  \n",
            " extracting: train/IMG_9417_jpg.rf.ae25fd818aa4050cc02e3e87eb6fafe9.txt  \n",
            " extracting: train/IMG_9420_jpg.rf.8c6d9da84b26c59b37aca8a86b35cfb4.jpg  \n",
            " extracting: train/IMG_9420_jpg.rf.8c6d9da84b26c59b37aca8a86b35cfb4.txt  \n",
            " extracting: train/IMG_9421_jpg.rf.20108a4a1f62af61507aaa9c638232b3.jpg  \n",
            " extracting: train/IMG_9421_jpg.rf.20108a4a1f62af61507aaa9c638232b3.txt  \n",
            " extracting: train/IMG_9422_jpg.rf.e8afe86b972ab0d4fa9818a748464a1c.jpg  \n",
            " extracting: train/IMG_9422_jpg.rf.e8afe86b972ab0d4fa9818a748464a1c.txt  \n",
            " extracting: train/IMG_9423_jpg.rf.74e921229b69704acea821148d1440bb.jpg  \n",
            " extracting: train/IMG_9423_jpg.rf.74e921229b69704acea821148d1440bb.txt  \n",
            " extracting: train/IMG_9425_jpg.rf.4175a1d361555fe26540b88ce0b31a3d.jpg  \n",
            " extracting: train/IMG_9425_jpg.rf.4175a1d361555fe26540b88ce0b31a3d.txt  \n",
            " extracting: train/IMG_9426_jpg.rf.12975514df5ce72205d8f4c51262891c.jpg  \n",
            " extracting: train/IMG_9426_jpg.rf.12975514df5ce72205d8f4c51262891c.txt  \n",
            " extracting: train/IMG_9428_jpg.rf.f455fef2a792f7cbf03666bfcca9ad5f.jpg  \n",
            " extracting: train/IMG_9428_jpg.rf.f455fef2a792f7cbf03666bfcca9ad5f.txt  \n",
            " extracting: train/IMG_9429_jpg.rf.d4d4eaf0ab25ec919483ee8d1eed10e3.jpg  \n",
            " extracting: train/IMG_9429_jpg.rf.d4d4eaf0ab25ec919483ee8d1eed10e3.txt  \n",
            " extracting: train/IMG_9430_jpg.rf.98042ee8dc27ebb0a28e35de73c0402c.jpg  \n",
            " extracting: train/IMG_9430_jpg.rf.98042ee8dc27ebb0a28e35de73c0402c.txt  \n",
            " extracting: train/IMG_9431_jpg.rf.8d1db1213e65d3d913d6909663bba453.jpg  \n",
            " extracting: train/IMG_9431_jpg.rf.8d1db1213e65d3d913d6909663bba453.txt  \n",
            " extracting: train/IMG_9432_jpg.rf.35b42d647b92b4a13233cacf34cce77e.jpg  \n",
            " extracting: train/IMG_9432_jpg.rf.35b42d647b92b4a13233cacf34cce77e.txt  \n",
            " extracting: train/IMG_9433_jpg.rf.24e6acbd79bba5e76ac48b57c774de0e.jpg  \n",
            " extracting: train/IMG_9433_jpg.rf.24e6acbd79bba5e76ac48b57c774de0e.txt  \n",
            " extracting: train/IMG_9434_jpg.rf.688b7893ffe5e84223fd6b35d79082be.jpg  \n",
            " extracting: train/IMG_9434_jpg.rf.688b7893ffe5e84223fd6b35d79082be.txt  \n",
            " extracting: train/IMG_9435_jpg.rf.53eb55ec70e9cfabe6d7b39743302d94.jpg  \n",
            " extracting: train/IMG_9435_jpg.rf.53eb55ec70e9cfabe6d7b39743302d94.txt  \n",
            " extracting: train/IMG_9436_jpg.rf.338f4ffdfff75a82ccc48ba1b6557a49.jpg  \n",
            " extracting: train/IMG_9436_jpg.rf.338f4ffdfff75a82ccc48ba1b6557a49.txt  \n",
            " extracting: train/IMG_9437_jpg.rf.10aa5a8a47a8f2966a6a45e50569185c.jpg  \n",
            " extracting: train/IMG_9437_jpg.rf.10aa5a8a47a8f2966a6a45e50569185c.txt  \n",
            " extracting: train/IMG_9439_jpg.rf.88e57b35679edf389037ea6b42d4453a.jpg  \n",
            " extracting: train/IMG_9439_jpg.rf.88e57b35679edf389037ea6b42d4453a.txt  \n",
            " extracting: train/IMG_9442_jpg.rf.6fed68f75d17f01dce562407df164492.jpg  \n",
            " extracting: train/IMG_9442_jpg.rf.6fed68f75d17f01dce562407df164492.txt  \n",
            " extracting: train/IMG_9443_jpg.rf.8c66071c341e9a1b15ac6fa854550ada.jpg  \n",
            " extracting: train/IMG_9443_jpg.rf.8c66071c341e9a1b15ac6fa854550ada.txt  \n",
            " extracting: train/IMG_9444_jpg.rf.e40f975682d1966f8b0aeed98485550c.jpg  \n",
            " extracting: train/IMG_9444_jpg.rf.e40f975682d1966f8b0aeed98485550c.txt  \n",
            " extracting: train/IMG_9445_jpg.rf.6746d1b17c3116b8cc0971916b6b3b60.jpg  \n",
            " extracting: train/IMG_9445_jpg.rf.6746d1b17c3116b8cc0971916b6b3b60.txt  \n",
            " extracting: train/IMG_9456_jpg.rf.3f7895230c17c13f2612d42481b2ff89.jpg  \n",
            " extracting: train/IMG_9456_jpg.rf.3f7895230c17c13f2612d42481b2ff89.txt  \n",
            " extracting: train/IMG_9458_jpg.rf.f29812a0015d03a65460bff259203ce4.jpg  \n",
            " extracting: train/IMG_9458_jpg.rf.f29812a0015d03a65460bff259203ce4.txt  \n",
            " extracting: train/IMG_9459_jpg.rf.5d7b920b11022190c53a457585e47f40.jpg  \n",
            " extracting: train/IMG_9459_jpg.rf.5d7b920b11022190c53a457585e47f40.txt  \n",
            " extracting: train/IMG_9460_jpg.rf.8315ed0e24362814cb4a7585cd2ac088.jpg  \n",
            " extracting: train/IMG_9460_jpg.rf.8315ed0e24362814cb4a7585cd2ac088.txt  \n",
            " extracting: train/IMG_9461_jpg.rf.dc28c801e4d028da56cc5ac99e81d20e.jpg  \n",
            " extracting: train/IMG_9461_jpg.rf.dc28c801e4d028da56cc5ac99e81d20e.txt  \n",
            " extracting: train/IMG_9464_jpg.rf.56f07d2a3b9a00190e08e44fbc5b5789.jpg  \n",
            " extracting: train/IMG_9464_jpg.rf.56f07d2a3b9a00190e08e44fbc5b5789.txt  \n",
            " extracting: train/IMG_9465_jpg.rf.f9f718c409210f183fb1406ee56f1be3.jpg  \n",
            " extracting: train/IMG_9465_jpg.rf.f9f718c409210f183fb1406ee56f1be3.txt  \n",
            " extracting: train/IMG_9466_jpg.rf.5b106c000b1f5aa74f97bb6168afcdf1.jpg  \n",
            " extracting: train/IMG_9466_jpg.rf.5b106c000b1f5aa74f97bb6168afcdf1.txt  \n",
            " extracting: train/IMG_9467_jpg.rf.c590382d60006b77096aabaf19f9799a.jpg  \n",
            " extracting: train/IMG_9467_jpg.rf.c590382d60006b77096aabaf19f9799a.txt  \n",
            " extracting: train/IMG_9469_jpg.rf.be087c2ecda07dc0709b4c60f1b1e232.jpg  \n",
            " extracting: train/IMG_9469_jpg.rf.be087c2ecda07dc0709b4c60f1b1e232.txt  \n",
            " extracting: train/IMG_9473_jpg.rf.6469c340cb7adaaaf41770ba6fc626de.jpg  \n",
            " extracting: train/IMG_9473_jpg.rf.6469c340cb7adaaaf41770ba6fc626de.txt  \n",
            " extracting: train/IMG_9474_jpg.rf.0746d95934d8f9ae07e34f8bad03a957.jpg  \n",
            " extracting: train/IMG_9474_jpg.rf.0746d95934d8f9ae07e34f8bad03a957.txt  \n",
            " extracting: train/IMG_9475_jpg.rf.13407da487be0f0f36e91b7887fcbeaf.jpg  \n",
            " extracting: train/IMG_9475_jpg.rf.13407da487be0f0f36e91b7887fcbeaf.txt  \n",
            " extracting: train/IMG_9476_jpg.rf.60552b95cc1219d3afae903872c7e203.jpg  \n",
            " extracting: train/IMG_9476_jpg.rf.60552b95cc1219d3afae903872c7e203.txt  \n",
            " extracting: train/IMG_9479_jpg.rf.83bff2447ccc8456e132464a3dc9e21b.jpg  \n",
            " extracting: train/IMG_9479_jpg.rf.83bff2447ccc8456e132464a3dc9e21b.txt  \n",
            " extracting: train/IMG_9480_jpg.rf.8dd4512874682430959d004b91a805d8.jpg  \n",
            " extracting: train/IMG_9480_jpg.rf.8dd4512874682430959d004b91a805d8.txt  \n",
            " extracting: train/IMG_9482_jpg.rf.7f35cdf505e3de2bbf9123a464f68b42.jpg  \n",
            " extracting: train/IMG_9482_jpg.rf.7f35cdf505e3de2bbf9123a464f68b42.txt  \n",
            " extracting: train/IMG_9483_jpg.rf.eb4fb170b357e2971745395d480d9437.jpg  \n",
            " extracting: train/IMG_9483_jpg.rf.eb4fb170b357e2971745395d480d9437.txt  \n",
            " extracting: train/IMG_9528_jpg.rf.defb4506e08397074d87e399d9280bd2.jpg  \n",
            " extracting: train/IMG_9528_jpg.rf.defb4506e08397074d87e399d9280bd2.txt  \n",
            " extracting: train/IMG_9529_jpg.rf.2773d9f6e7b98db0ed235e8f4db7b0d9.jpg  \n",
            " extracting: train/IMG_9529_jpg.rf.2773d9f6e7b98db0ed235e8f4db7b0d9.txt  \n",
            " extracting: train/IMG_9530_jpg.rf.92715feb9c86edfb21aa18f5d7d7ad9c.jpg  \n",
            " extracting: train/IMG_9530_jpg.rf.92715feb9c86edfb21aa18f5d7d7ad9c.txt  \n",
            " extracting: train/IMG_9631_jpg.rf.b544d5f7ba3f11b7907635733138bb94.jpg  \n",
            " extracting: train/IMG_9631_jpg.rf.b544d5f7ba3f11b7907635733138bb94.txt  \n",
            " extracting: train/IMG_9632_jpg.rf.8006441d262a78e56de5abab28db4fc2.jpg  \n",
            " extracting: train/IMG_9632_jpg.rf.8006441d262a78e56de5abab28db4fc2.txt  \n",
            " extracting: train/IMG_9633_jpg.rf.ea1a804c905cba884aa6eeab01dbe30e.jpg  \n",
            " extracting: train/IMG_9633_jpg.rf.ea1a804c905cba884aa6eeab01dbe30e.txt  \n",
            " extracting: train/IMG_9634_jpg.rf.cf8759f6f049bd6a22b67070c19cf261.jpg  \n",
            " extracting: train/IMG_9634_jpg.rf.cf8759f6f049bd6a22b67070c19cf261.txt  \n",
            " extracting: train/IMG_9635_jpg.rf.6272ec27c4c354edbcf5261e287683cd.jpg  \n",
            " extracting: train/IMG_9635_jpg.rf.6272ec27c4c354edbcf5261e287683cd.txt  \n",
            " extracting: train/IMG_9637_jpg.rf.0702b0507d5c2a4dd814d00e0b869bae.jpg  \n",
            " extracting: train/IMG_9637_jpg.rf.0702b0507d5c2a4dd814d00e0b869bae.txt  \n",
            " extracting: train/IMG_9639_jpg.rf.2e1865b7a32147fe26deec9e38a91a83.jpg  \n",
            " extracting: train/IMG_9639_jpg.rf.2e1865b7a32147fe26deec9e38a91a83.txt  \n",
            " extracting: train/IMG_9640_jpg.rf.b4f7788a2d471b46aa07e78be2b303dc.jpg  \n",
            " extracting: train/IMG_9640_jpg.rf.b4f7788a2d471b46aa07e78be2b303dc.txt  \n",
            " extracting: train/IMG_9641_jpg.rf.1dcda624701ba19daf70fff2d84468a8.jpg  \n",
            " extracting: train/IMG_9641_jpg.rf.1dcda624701ba19daf70fff2d84468a8.txt  \n",
            " extracting: train/IMG_9642_jpg.rf.bf20572bbc6513c8509434869e17e9ce.jpg  \n",
            " extracting: train/IMG_9642_jpg.rf.bf20572bbc6513c8509434869e17e9ce.txt  \n",
            " extracting: train/IMG_9643_jpg.rf.f2ef0e1fb32e978a1819f3c4caf35d8b.jpg  \n",
            " extracting: train/IMG_9643_jpg.rf.f2ef0e1fb32e978a1819f3c4caf35d8b.txt  \n",
            " extracting: train/IMG_9644_jpg.rf.0853bb4661f7669c277b7a19fef0a68b.jpg  \n",
            " extracting: train/IMG_9644_jpg.rf.0853bb4661f7669c277b7a19fef0a68b.txt  \n",
            " extracting: train/IMG_9646_jpg.rf.f534616c547af6181df8db694a187d51.jpg  \n",
            " extracting: train/IMG_9646_jpg.rf.f534616c547af6181df8db694a187d51.txt  \n",
            " extracting: train/IMG_9647_jpg.rf.754bb1a29cf183fb490b17df5017414f.jpg  \n",
            " extracting: train/IMG_9647_jpg.rf.754bb1a29cf183fb490b17df5017414f.txt  \n",
            " extracting: train/IMG_9648_jpg.rf.6ee75afe6a4ef5d89271feefd280858e.jpg  \n",
            " extracting: train/IMG_9648_jpg.rf.6ee75afe6a4ef5d89271feefd280858e.txt  \n",
            " extracting: train/IMG_9650_jpg.rf.106d19f0faacb5b4705d9a63df10edac.jpg  \n",
            " extracting: train/IMG_9650_jpg.rf.106d19f0faacb5b4705d9a63df10edac.txt  \n",
            " extracting: train/IMG_9653_jpg.rf.bc9d2faf2ae45cf6489ece91b6606b9a.jpg  \n",
            " extracting: train/IMG_9653_jpg.rf.bc9d2faf2ae45cf6489ece91b6606b9a.txt  \n",
            " extracting: train/IMG_9654_jpg.rf.c05b9efd87dfb0d056c540fdd88a6f6c.jpg  \n",
            " extracting: train/IMG_9654_jpg.rf.c05b9efd87dfb0d056c540fdd88a6f6c.txt  \n",
            " extracting: train/IMG_9655_jpg.rf.b7cf1cc8b7198a4422ff1c7b047b19e2.jpg  \n",
            " extracting: train/IMG_9655_jpg.rf.b7cf1cc8b7198a4422ff1c7b047b19e2.txt  \n",
            " extracting: train/IMG_9657_jpg.rf.42cf540e74f64958715f9e9e6c75fc0a.jpg  \n",
            " extracting: train/IMG_9657_jpg.rf.42cf540e74f64958715f9e9e6c75fc0a.txt  \n",
            " extracting: train/IMG_9658_jpg.rf.ae3c9a61eec0ae5f9cf5455f243d69eb.jpg  \n",
            " extracting: train/IMG_9658_jpg.rf.ae3c9a61eec0ae5f9cf5455f243d69eb.txt  \n",
            " extracting: train/IMG_9659_jpg.rf.cdbd85503e50b62eb05e050c1dff85e7.jpg  \n",
            " extracting: train/IMG_9659_jpg.rf.cdbd85503e50b62eb05e050c1dff85e7.txt  \n",
            " extracting: train/IMG_9661_jpg.rf.d06ff9de012dcb2671a8bfe884c63aa4.jpg  \n",
            " extracting: train/IMG_9661_jpg.rf.d06ff9de012dcb2671a8bfe884c63aa4.txt  \n",
            " extracting: train/IMG_9662_jpg.rf.553ac6c12dc86083b3973940975ad614.jpg  \n",
            " extracting: train/IMG_9662_jpg.rf.553ac6c12dc86083b3973940975ad614.txt  \n",
            " extracting: train/IMG_9664_jpg.rf.5b4f4ec623c5c1a53f598d5f64dac4bd.jpg  \n",
            " extracting: train/IMG_9664_jpg.rf.5b4f4ec623c5c1a53f598d5f64dac4bd.txt  \n",
            " extracting: train/IMG_9665_jpg.rf.fe4a6c2eae6b093b6b6d902e9515de88.jpg  \n",
            " extracting: train/IMG_9665_jpg.rf.fe4a6c2eae6b093b6b6d902e9515de88.txt  \n",
            " extracting: train/IMG_9666_jpg.rf.117e446382eee4851da85a54bab1d365.jpg  \n",
            " extracting: train/IMG_9666_jpg.rf.117e446382eee4851da85a54bab1d365.txt  \n",
            " extracting: train/IMG_9668_jpg.rf.93f617f94310ad26c08959ae0eb53fca.jpg  \n",
            " extracting: train/IMG_9668_jpg.rf.93f617f94310ad26c08959ae0eb53fca.txt  \n",
            " extracting: train/IMG_9669_jpg.rf.0cd420b1a4bb0d23098b953dcf3b7ff2.jpg  \n",
            " extracting: train/IMG_9669_jpg.rf.0cd420b1a4bb0d23098b953dcf3b7ff2.txt  \n",
            " extracting: train/IMG_9671_jpg.rf.7f185e0d96b435476b63079a1d7a6f2f.jpg  \n",
            " extracting: train/IMG_9671_jpg.rf.7f185e0d96b435476b63079a1d7a6f2f.txt  \n",
            " extracting: train/IMG_9672_jpg.rf.5acaed1d47d79e0958ff965c19d40e39.jpg  \n",
            " extracting: train/IMG_9672_jpg.rf.5acaed1d47d79e0958ff965c19d40e39.txt  \n",
            " extracting: train/IMG_9673_jpg.rf.82b455e96c2031c81efbafe8e3d387a0.jpg  \n",
            " extracting: train/IMG_9673_jpg.rf.82b455e96c2031c81efbafe8e3d387a0.txt  \n",
            " extracting: train/IMG_9674_jpg.rf.607eb6224b0e37e8904d59dd66b5bb79.jpg  \n",
            " extracting: train/IMG_9674_jpg.rf.607eb6224b0e37e8904d59dd66b5bb79.txt  \n",
            " extracting: train/IMG_9675_jpg.rf.97c69f7f0121c6372e7f937fb79e1e15.jpg  \n",
            " extracting: train/IMG_9675_jpg.rf.97c69f7f0121c6372e7f937fb79e1e15.txt  \n",
            " extracting: train/IMG_9676_jpg.rf.7b57141883e4fa67bd287732fd51df9d.jpg  \n",
            " extracting: train/IMG_9676_jpg.rf.7b57141883e4fa67bd287732fd51df9d.txt  \n",
            " extracting: train/IMG_9677_jpg.rf.a4c86b73304211fa3b6fac86b62fba1e.jpg  \n",
            " extracting: train/IMG_9677_jpg.rf.a4c86b73304211fa3b6fac86b62fba1e.txt  \n",
            " extracting: train/IMG_9679_jpg.rf.be8ed5b1090d9f6e39c565f8a77f19bd.jpg  \n",
            " extracting: train/IMG_9679_jpg.rf.be8ed5b1090d9f6e39c565f8a77f19bd.txt  \n",
            " extracting: train/IMG_9681_jpg.rf.2ba843a5f182637bedf1e7e34e492d06.jpg  \n",
            " extracting: train/IMG_9681_jpg.rf.2ba843a5f182637bedf1e7e34e492d06.txt  \n",
            " extracting: train/IMG_9683_jpg.rf.21ddcd792be705f0ccb092121958087c.jpg  \n",
            " extracting: train/IMG_9683_jpg.rf.21ddcd792be705f0ccb092121958087c.txt  \n",
            " extracting: train/IMG_9686_jpg.rf.86bd758da67ae14a548e185edbae0575.jpg  \n",
            " extracting: train/IMG_9686_jpg.rf.86bd758da67ae14a548e185edbae0575.txt  \n",
            " extracting: train/IMG_9688_jpg.rf.77e5319704aa6e77fd275251f59b51a4.jpg  \n",
            " extracting: train/IMG_9688_jpg.rf.77e5319704aa6e77fd275251f59b51a4.txt  \n",
            " extracting: train/IMG_9689_jpg.rf.044b7cd3530a2af25e798334b87d293f.jpg  \n",
            " extracting: train/IMG_9689_jpg.rf.044b7cd3530a2af25e798334b87d293f.txt  \n",
            " extracting: train/PXL_20210208_184407167_jpg.rf.86e613cf615125b8a4290ce4594b749c.jpg  \n",
            " extracting: train/PXL_20210208_184407167_jpg.rf.86e613cf615125b8a4290ce4594b749c.txt  \n",
            " extracting: train/PXL_20210208_185742372_jpg.rf.3f10c59109f81f13519e846b339752dc.jpg  \n",
            " extracting: train/PXL_20210208_185742372_jpg.rf.3f10c59109f81f13519e846b339752dc.txt  \n",
            " extracting: train/PXL_20210208_185908534_jpg.rf.855ee8f36f81b2aec3bfa3f1e385d79e.jpg  \n",
            " extracting: train/PXL_20210208_185908534_jpg.rf.855ee8f36f81b2aec3bfa3f1e385d79e.txt  \n",
            " extracting: train/PXL_20210208_190326695_jpg.rf.592d32a36ed63b776c19a014308705cb.jpg  \n",
            " extracting: train/PXL_20210208_190326695_jpg.rf.592d32a36ed63b776c19a014308705cb.txt  \n",
            " extracting: train/PXL_20210208_190338702_jpg.rf.bd7b5256cde9f26376fa3521a957af35.jpg  \n",
            " extracting: train/PXL_20210208_190338702_jpg.rf.bd7b5256cde9f26376fa3521a957af35.txt  \n",
            " extracting: train/PXL_20210209_133627868_jpg.rf.af437acf42f879c81493e421291d8e29.jpg  \n",
            " extracting: train/PXL_20210209_133627868_jpg.rf.af437acf42f879c81493e421291d8e29.txt  \n",
            " extracting: train/PXL_20210209_134346517_jpg.rf.8608eea32eb2f35c3d799ff7c49b9258.jpg  \n",
            " extracting: train/PXL_20210209_134346517_jpg.rf.8608eea32eb2f35c3d799ff7c49b9258.txt  \n",
            " extracting: train/PXL_20210209_134547217_jpg.rf.b075fd22db497b915e5e6a8f77cc94b2.jpg  \n",
            " extracting: train/PXL_20210209_134547217_jpg.rf.b075fd22db497b915e5e6a8f77cc94b2.txt  \n",
            " extracting: train/PXL_20210209_152240603_jpg.rf.7c6a83fc0ba2387af390a1fae0c1353c.jpg  \n",
            " extracting: train/PXL_20210209_152240603_jpg.rf.7c6a83fc0ba2387af390a1fae0c1353c.txt  \n",
            " extracting: train/PXL_20210212_170857040_jpg.rf.9e98a7d58dcca1326d46005dbf73c466.jpg  \n",
            " extracting: train/PXL_20210212_170857040_jpg.rf.9e98a7d58dcca1326d46005dbf73c466.txt  \n",
            " extracting: train/PXL_20210212_171110133_jpg.rf.fe7336694e08c04187052fe97e631b79.jpg  \n",
            " extracting: train/PXL_20210212_171110133_jpg.rf.fe7336694e08c04187052fe97e631b79.txt  \n",
            " extracting: train/PXL_20210212_172232619_jpg.rf.bb6ee7d273e0616a62b3471112195cb0.jpg  \n",
            " extracting: train/PXL_20210212_172232619_jpg.rf.bb6ee7d273e0616a62b3471112195cb0.txt  \n",
            " extracting: train/PXL_20210218_201508887_jpg.rf.a83cbc5e31fb134756272b2917bfa06a.jpg  \n",
            " extracting: train/PXL_20210218_201508887_jpg.rf.a83cbc5e31fb134756272b2917bfa06a.txt  \n",
            " extracting: train/PXL_20210219_174210166_jpg.rf.0f9ef6ec2f90b7f6e3d6dc245db8fa25.jpg  \n",
            " extracting: train/PXL_20210219_174210166_jpg.rf.0f9ef6ec2f90b7f6e3d6dc245db8fa25.txt  \n",
            " extracting: train/PXL_20210219_174532326_jpg.rf.cbf11026640a604a7497b4f8dbf52ba2.jpg  \n",
            " extracting: train/PXL_20210219_174532326_jpg.rf.cbf11026640a604a7497b4f8dbf52ba2.txt  \n",
            " extracting: train/PXL_20210219_174627960_jpg.rf.b32cc53f00bca65de99d9763ba2974fd.jpg  \n",
            " extracting: train/PXL_20210219_174627960_jpg.rf.b32cc53f00bca65de99d9763ba2974fd.txt  \n",
            " extracting: train/PXL_20210219_174659869_jpg.rf.ff3c3c86ff294b27952658d5b529760f.jpg  \n",
            " extracting: train/PXL_20210219_174659869_jpg.rf.ff3c3c86ff294b27952658d5b529760f.txt  \n",
            " extracting: train/PXL_20210222_163306288_jpg.rf.5d72f53925196872c5e1cd47b7c4180b.jpg  \n",
            " extracting: train/PXL_20210222_163306288_jpg.rf.5d72f53925196872c5e1cd47b7c4180b.txt  \n",
            " extracting: train/PXL_20210222_163308167_jpg.rf.0ffc8f395e861af68dca4867ab83dbea.jpg  \n",
            " extracting: train/PXL_20210222_163308167_jpg.rf.0ffc8f395e861af68dca4867ab83dbea.txt  \n",
            " extracting: train/PXL_20210222_164933823_jpg.rf.9591b7df2319bb23df291fa58dac057e.jpg  \n",
            " extracting: train/PXL_20210222_164933823_jpg.rf.9591b7df2319bb23df291fa58dac057e.txt  \n",
            " extracting: train/PXL_20210222_164946950_jpg.rf.cfb521b9425286307549c4151fcc5181.jpg  \n",
            " extracting: train/PXL_20210222_164946950_jpg.rf.cfb521b9425286307549c4151fcc5181.txt  \n",
            " extracting: train/PXL_20210222_165342207_jpg.rf.cf8b7ed189451f15913bc5866492c948.jpg  \n",
            " extracting: train/PXL_20210222_165342207_jpg.rf.cf8b7ed189451f15913bc5866492c948.txt  \n",
            " extracting: train/PXL_20210222_165507539_jpg.rf.905fe0dc2f139321cbb5b837c3b963d5.jpg  \n",
            " extracting: train/PXL_20210222_165507539_jpg.rf.905fe0dc2f139321cbb5b837c3b963d5.txt  \n",
            " extracting: train/PXL_20210222_165531051_jpg.rf.b5ae01c7d565b81a56d9d7e43a3b0aaf.jpg  \n",
            " extracting: train/PXL_20210222_165531051_jpg.rf.b5ae01c7d565b81a56d9d7e43a3b0aaf.txt  \n",
            " extracting: train/PXL_20210222_165535312_jpg.rf.36d9be0c98d8ff90575e6670095794d5.jpg  \n",
            " extracting: train/PXL_20210222_165535312_jpg.rf.36d9be0c98d8ff90575e6670095794d5.txt  \n",
            " extracting: train/PXL_20210222_165542516_jpg.rf.a35835b0cddc6d0fdef564c1be43c18d.jpg  \n",
            " extracting: train/PXL_20210222_165542516_jpg.rf.a35835b0cddc6d0fdef564c1be43c18d.txt  \n",
            " extracting: train/PXL_20210222_165603519_jpg.rf.2e892b8beb27d3ba8d126f1e4175f00b.jpg  \n",
            " extracting: train/PXL_20210222_165603519_jpg.rf.2e892b8beb27d3ba8d126f1e4175f00b.txt  \n",
            " extracting: train/PXL_20210223_221824051_jpg.rf.5a9f5af113207fe98a3ae033677b22ec.jpg  \n",
            " extracting: train/PXL_20210223_221824051_jpg.rf.5a9f5af113207fe98a3ae033677b22ec.txt  \n",
            " extracting: train/PXL_20210223_223452981_jpg.rf.18ac0b8c87dac28220e579e049c91298.jpg  \n",
            " extracting: train/PXL_20210223_223452981_jpg.rf.18ac0b8c87dac28220e579e049c91298.txt  \n",
            " extracting: train/PXL_20210223_223505964_jpg.rf.0003aea3c055166dc5558453ade7cb8e.jpg  \n",
            " extracting: train/PXL_20210223_223505964_jpg.rf.0003aea3c055166dc5558453ade7cb8e.txt  \n",
            " extracting: train/PXL_20210223_225816499_jpg.rf.a2f803a3203744ba5d096338edcac426.jpg  \n",
            " extracting: train/PXL_20210223_225816499_jpg.rf.a2f803a3203744ba5d096338edcac426.txt  \n",
            " extracting: train/PXL_20210223_230023404_jpg.rf.18656a6e66b3da04fb357a196c82d3a8.jpg  \n",
            " extracting: train/PXL_20210223_230023404_jpg.rf.18656a6e66b3da04fb357a196c82d3a8.txt  \n",
            " extracting: train/PXL_20210223_230050907_jpg.rf.62b0b9f5b0acbe3536619495a5a0bd9f.jpg  \n",
            " extracting: train/PXL_20210223_230050907_jpg.rf.62b0b9f5b0acbe3536619495a5a0bd9f.txt  \n",
            " extracting: train/PXL_20210223_230139188_jpg.rf.918218e4d13ac2e97f7230bd863c011d.jpg  \n",
            " extracting: train/PXL_20210223_230139188_jpg.rf.918218e4d13ac2e97f7230bd863c011d.txt  \n",
            " extracting: train/PXL_20210223_230708706_jpg.rf.fa2ef679a956512a58c994be234ff795.jpg  \n",
            " extracting: train/PXL_20210223_230708706_jpg.rf.fa2ef679a956512a58c994be234ff795.txt  \n",
            " extracting: train/PXL_20210224_092157994_jpg.rf.8598360172d13c51d017591663496ced.jpg  \n",
            " extracting: train/PXL_20210224_092157994_jpg.rf.8598360172d13c51d017591663496ced.txt  \n",
            " extracting: train/PXL_20210225_102221165_jpg.rf.cec73a0a4c14a6489e9de18f4d7082dd.jpg  \n",
            " extracting: train/PXL_20210225_102221165_jpg.rf.cec73a0a4c14a6489e9de18f4d7082dd.txt  \n",
            " extracting: train/PXL_20210225_102245439_jpg.rf.8e96cd3cc68c1f483fb36d05ffdc5408.jpg  \n",
            " extracting: train/PXL_20210225_102245439_jpg.rf.8e96cd3cc68c1f483fb36d05ffdc5408.txt  \n",
            " extracting: train/PXL_20210225_103218302_jpg.rf.c39e9473ff4da1d9a13eb10e47c44925.jpg  \n",
            " extracting: train/PXL_20210225_103218302_jpg.rf.c39e9473ff4da1d9a13eb10e47c44925.txt  \n",
            " extracting: train/PXL_20210226_000048640_jpg.rf.bcb8e405baf226100613c0ede084c895.jpg  \n",
            " extracting: train/PXL_20210226_000048640_jpg.rf.bcb8e405baf226100613c0ede084c895.txt  \n",
            " extracting: train/PXL_20210226_181746288_jpg.rf.d9272d85a7987d48d7f4dae980b59807.jpg  \n",
            " extracting: train/PXL_20210226_181746288_jpg.rf.d9272d85a7987d48d7f4dae980b59807.txt  \n",
            " extracting: train/PXL_20210226_181754241_jpg.rf.30e169b1382ca2fb82f7ad83c4ec086a.jpg  \n",
            " extracting: train/PXL_20210226_181754241_jpg.rf.30e169b1382ca2fb82f7ad83c4ec086a.txt  \n",
            " extracting: train/PXL_20210226_181846534_jpg.rf.bedebbd867a2791018e6188b0678a7f2.jpg  \n",
            " extracting: train/PXL_20210226_181846534_jpg.rf.bedebbd867a2791018e6188b0678a7f2.txt  \n",
            " extracting: train/PXL_20210226_181909136_jpg.rf.4def5a76549dc6c6f6a668b6c05c40e2.jpg  \n",
            " extracting: train/PXL_20210226_181909136_jpg.rf.4def5a76549dc6c6f6a668b6c05c40e2.txt  \n",
            " extracting: train/PXL_20210226_181922413_jpg.rf.3f2d0bd0345e800dfc403c3aca40fc0c.jpg  \n",
            " extracting: train/PXL_20210226_181922413_jpg.rf.3f2d0bd0345e800dfc403c3aca40fc0c.txt  \n",
            " extracting: train/PXL_20210226_181954622_jpg.rf.64cf4e1abb0b742e5cd08fa8030632b6.jpg  \n",
            " extracting: train/PXL_20210226_181954622_jpg.rf.64cf4e1abb0b742e5cd08fa8030632b6.txt  \n",
            " extracting: train/PXL_20210226_183044551_jpg.rf.36151509b8d4dbad0d329fe9dbb09e30.jpg  \n",
            " extracting: train/PXL_20210226_183044551_jpg.rf.36151509b8d4dbad0d329fe9dbb09e30.txt  \n",
            " extracting: train/PXL_20210226_183130670_jpg.rf.60e2bacec1aa2258c0bdbb3e8cc1f5e5.jpg  \n",
            " extracting: train/PXL_20210226_183130670_jpg.rf.60e2bacec1aa2258c0bdbb3e8cc1f5e5.txt  \n",
            " extracting: train/PXL_20210226_183152053_jpg.rf.049a1f783c75f655b9c5fb4bb6300e8f.jpg  \n",
            " extracting: train/PXL_20210226_183152053_jpg.rf.049a1f783c75f655b9c5fb4bb6300e8f.txt  \n",
            " extracting: train/PXL_20210226_183158898_jpg.rf.c7969e0adee2cdde0d8fdbbb7041408c.jpg  \n",
            " extracting: train/PXL_20210226_183158898_jpg.rf.c7969e0adee2cdde0d8fdbbb7041408c.txt  \n",
            " extracting: train/PXL_20210226_183250020_jpg.rf.47012eb73cbd24d7dba9cf003c580afb.jpg  \n",
            " extracting: train/PXL_20210226_183250020_jpg.rf.47012eb73cbd24d7dba9cf003c580afb.txt  \n",
            " extracting: train/PXL_20210227_033501095_jpg.rf.01261d968ddc0fe84d3451dd46b7ef4d.jpg  \n",
            " extracting: train/PXL_20210227_033501095_jpg.rf.01261d968ddc0fe84d3451dd46b7ef4d.txt  \n",
            " extracting: train/PXL_20210227_033601061_jpg.rf.e47039628c5e5430c6bf703740c1fa29.jpg  \n",
            " extracting: train/PXL_20210227_033601061_jpg.rf.e47039628c5e5430c6bf703740c1fa29.txt  \n",
            " extracting: train/PXL_20210228_172932178_jpg.rf.91a9b4fdb3e97921ee0390b2aa33dbcc.jpg  \n",
            " extracting: train/PXL_20210228_172932178_jpg.rf.91a9b4fdb3e97921ee0390b2aa33dbcc.txt  \n",
            " extracting: train/PXL_20210228_172940624_jpg.rf.54ed64e5e0918d4cdd766ca96ea37d10.jpg  \n",
            " extracting: train/PXL_20210228_172940624_jpg.rf.54ed64e5e0918d4cdd766ca96ea37d10.txt  \n",
            " extracting: train/PXL_20210228_173324298_jpg.rf.618450e83347f666afcbd99229e9b1b4.jpg  \n",
            " extracting: train/PXL_20210228_173324298_jpg.rf.618450e83347f666afcbd99229e9b1b4.txt  \n",
            " extracting: train/PXL_20210228_173412938_jpg.rf.08a7c88f6c503ef0dd76c1f296d93d97.jpg  \n",
            " extracting: train/PXL_20210228_173412938_jpg.rf.08a7c88f6c503ef0dd76c1f296d93d97.txt  \n",
            " extracting: train/PXL_20210228_173504992_jpg.rf.e33ad25725e37b0da0d59b8aca198be9.jpg  \n",
            " extracting: train/PXL_20210228_173504992_jpg.rf.e33ad25725e37b0da0d59b8aca198be9.txt  \n",
            " extracting: train/PXL_20210228_173733090_jpg.rf.0a05274e375263d8eacd838f7da9e99b.jpg  \n",
            " extracting: train/PXL_20210228_173733090_jpg.rf.0a05274e375263d8eacd838f7da9e99b.txt  \n",
            " extracting: train/PXL_20210228_173744071_jpg.rf.29273287164632aa36bece587a7a216e.jpg  \n",
            " extracting: train/PXL_20210228_173744071_jpg.rf.29273287164632aa36bece587a7a216e.txt  \n",
            " extracting: train/PXL_20210228_173748536_jpg.rf.23fbd5ce1f7bf1606727c48d8a1dfdab.jpg  \n",
            " extracting: train/PXL_20210228_173748536_jpg.rf.23fbd5ce1f7bf1606727c48d8a1dfdab.txt  \n",
            " extracting: train/PXL_20210228_173854757_jpg.rf.b34b1fc1e42453092afd9f068850e672.jpg  \n",
            " extracting: train/PXL_20210228_173854757_jpg.rf.b34b1fc1e42453092afd9f068850e672.txt  \n",
            " extracting: train/PXL_20210228_173917600_jpg.rf.aef53f1057e808b1703fa3416d9129b0.jpg  \n",
            " extracting: train/PXL_20210228_173917600_jpg.rf.aef53f1057e808b1703fa3416d9129b0.txt  \n",
            " extracting: train/PXL_20210301_170739784_jpg.rf.7b02b000710c526d9c78ca9f1c99e293.jpg  \n",
            " extracting: train/PXL_20210301_170739784_jpg.rf.7b02b000710c526d9c78ca9f1c99e293.txt  \n",
            " extracting: train/PXL_20210301_171836684_jpg.rf.a578d1744ec4acfdd4777675af5090e6.jpg  \n",
            " extracting: train/PXL_20210301_171836684_jpg.rf.a578d1744ec4acfdd4777675af5090e6.txt  \n",
            " extracting: train/PXL_20210301_172002240_jpg.rf.9da2818445822a92273fec39aa4fbd5a.jpg  \n",
            " extracting: train/PXL_20210301_172002240_jpg.rf.9da2818445822a92273fec39aa4fbd5a.txt  \n",
            " extracting: train/PXL_20210301_172020003_jpg.rf.ad723c8a59cd4ea71d606309f400c6f9.jpg  \n",
            " extracting: train/PXL_20210301_172020003_jpg.rf.ad723c8a59cd4ea71d606309f400c6f9.txt  \n",
            " extracting: train/PXL_20210301_225705036_jpg.rf.d60598d52bf934e2b16a812d70de6fda.jpg  \n",
            " extracting: train/PXL_20210301_225705036_jpg.rf.d60598d52bf934e2b16a812d70de6fda.txt  \n",
            " extracting: train/PXL_20210302_133836341_jpg.rf.c8a952841bd9c93ee93cea7f0806a77f.jpg  \n",
            " extracting: train/PXL_20210302_133836341_jpg.rf.c8a952841bd9c93ee93cea7f0806a77f.txt  \n",
            " extracting: train/PXL_20210302_134154761_jpg.rf.5aac72a18f482103fbe9030335e3487d.jpg  \n",
            " extracting: train/PXL_20210302_134154761_jpg.rf.5aac72a18f482103fbe9030335e3487d.txt  \n",
            " extracting: train/PXL_20210302_134749343_jpg.rf.826f34f8db61f04a2925224da8c46006.jpg  \n",
            " extracting: train/PXL_20210302_134749343_jpg.rf.826f34f8db61f04a2925224da8c46006.txt  \n",
            " extracting: train/PXL_20210302_145943536_jpg.rf.54c3eaad216ad47bf1028c2b8fb843e8.jpg  \n",
            " extracting: train/PXL_20210302_145943536_jpg.rf.54c3eaad216ad47bf1028c2b8fb843e8.txt  \n",
            " extracting: train/PXL_20210302_150002877_jpg.rf.2334afde6684a7f829f6c7e5545067e5.jpg  \n",
            " extracting: train/PXL_20210302_150002877_jpg.rf.2334afde6684a7f829f6c7e5545067e5.txt  \n",
            " extracting: train/PXL_20210302_150231221_jpg.rf.ff9126265b0e84c476d8809e1a70507e.jpg  \n",
            " extracting: train/PXL_20210302_150231221_jpg.rf.ff9126265b0e84c476d8809e1a70507e.txt  \n",
            " extracting: train/PXL_20210302_150242681_jpg.rf.2de4a590f6743c347a30d9a2ca780dc7.jpg  \n",
            " extracting: train/PXL_20210302_150242681_jpg.rf.2de4a590f6743c347a30d9a2ca780dc7.txt  \n",
            " extracting: train/PXL_20210302_150249313_jpg.rf.920e2b023e472473a4948c4dd436c69c.jpg  \n",
            " extracting: train/PXL_20210302_150249313_jpg.rf.920e2b023e472473a4948c4dd436c69c.txt  \n",
            " extracting: train/PXL_20210302_150521062_jpg.rf.9996bb3c7fb526bd8a58e18b6e91f401.jpg  \n",
            " extracting: train/PXL_20210302_150521062_jpg.rf.9996bb3c7fb526bd8a58e18b6e91f401.txt  \n",
            " extracting: train/PXL_20210302_150749053_jpg.rf.a75b4e6bef7966a7906d5017c584be68.jpg  \n",
            " extracting: train/PXL_20210302_150749053_jpg.rf.a75b4e6bef7966a7906d5017c584be68.txt  \n",
            " extracting: train/PXL_20210302_151051087_jpg.rf.ec16b5e0291d71c29f5c04c358af95af.jpg  \n",
            " extracting: train/PXL_20210302_151051087_jpg.rf.ec16b5e0291d71c29f5c04c358af95af.txt  \n",
            " extracting: train/PXL_20210302_151441603_jpg.rf.ad0d08bf6b94fc116f3c3a3f5e9ee04a.jpg  \n",
            " extracting: train/PXL_20210302_151441603_jpg.rf.ad0d08bf6b94fc116f3c3a3f5e9ee04a.txt  \n",
            " extracting: train/PXL_20210303_010535674_jpg.rf.436ab4bd7bbc0f1021ae4cc697faae26.jpg  \n",
            " extracting: train/PXL_20210303_010535674_jpg.rf.436ab4bd7bbc0f1021ae4cc697faae26.txt  \n",
            " extracting: train/PXL_20210303_010543889_jpg.rf.23c65dde8df4abe78e17ab6a31f5ce41.jpg  \n",
            " extracting: train/PXL_20210303_010543889_jpg.rf.23c65dde8df4abe78e17ab6a31f5ce41.txt  \n",
            " extracting: train/PXL_20210303_011257870_jpg.rf.0becee5a67e57c1e845bb7fb73c8f7f8.jpg  \n",
            " extracting: train/PXL_20210303_011257870_jpg.rf.0becee5a67e57c1e845bb7fb73c8f7f8.txt  \n",
            " extracting: train/PXL_20210303_011306910_jpg.rf.ae858fd7c167d215488433e386c64449.jpg  \n",
            " extracting: train/PXL_20210303_011306910_jpg.rf.ae858fd7c167d215488433e386c64449.txt  \n",
            " extracting: train/PXL_20210303_011313115_jpg.rf.936e926a0892aebe944b88a406e23a55.jpg  \n",
            " extracting: train/PXL_20210303_011313115_jpg.rf.936e926a0892aebe944b88a406e23a55.txt  \n",
            " extracting: train/PXL_20210303_011341688_jpg.rf.b8e798095e0ac44d75df16f30a97d122.jpg  \n",
            " extracting: train/PXL_20210303_011341688_jpg.rf.b8e798095e0ac44d75df16f30a97d122.txt  \n",
            " extracting: train/PXL_20210303_011350161_jpg.rf.ddee83437fc1598026358d99987fbc56.jpg  \n",
            " extracting: train/PXL_20210303_011350161_jpg.rf.ddee83437fc1598026358d99987fbc56.txt  \n",
            " extracting: train/PXL_20210303_181913117_jpg.rf.dc1354e97e9199104b4868f54f0e58ca.jpg  \n",
            " extracting: train/PXL_20210303_181913117_jpg.rf.dc1354e97e9199104b4868f54f0e58ca.txt  \n",
            " extracting: train/PXL_20210307_180911327_jpg.rf.50e87ab1b407ea1ad533c4db21574e4f.jpg  \n",
            " extracting: train/PXL_20210307_180911327_jpg.rf.50e87ab1b407ea1ad533c4db21574e4f.txt  \n",
            " extracting: train/PXL_20210307_180919576_jpg.rf.f577322108c9e4338f98f4a88820b4d6.jpg  \n",
            " extracting: train/PXL_20210307_180919576_jpg.rf.f577322108c9e4338f98f4a88820b4d6.txt  \n",
            " extracting: train/PXL_20210307_181002063_jpg.rf.3ae1e454ae7aaa1ab1aed256365bbbc4.jpg  \n",
            " extracting: train/PXL_20210307_181002063_jpg.rf.3ae1e454ae7aaa1ab1aed256365bbbc4.txt  \n",
            " extracting: train/PXL_20210307_181259481_jpg.rf.01c30be8474b8cbf96add74ca0397507.jpg  \n",
            " extracting: train/PXL_20210307_181259481_jpg.rf.01c30be8474b8cbf96add74ca0397507.txt  \n",
            " extracting: train/PXL_20210307_181312522_jpg.rf.166843266853b9667c264c08f587aa95.jpg  \n",
            " extracting: train/PXL_20210307_181312522_jpg.rf.166843266853b9667c264c08f587aa95.txt  \n",
            " extracting: train/PXL_20210307_181405809_jpg.rf.bd403706027deab3e2fbe0ce592f35e5.jpg  \n",
            " extracting: train/PXL_20210307_181405809_jpg.rf.bd403706027deab3e2fbe0ce592f35e5.txt  \n",
            " extracting: train/PXL_20210307_181423380_jpg.rf.4ee2db50ba41b0986549b03bfab45347.jpg  \n",
            " extracting: train/PXL_20210307_181423380_jpg.rf.4ee2db50ba41b0986549b03bfab45347.txt  \n",
            " extracting: train/PXL_20210307_214756110_jpg.rf.f26a40c255aa55f94937f9bc0dd0ae21.jpg  \n",
            " extracting: train/PXL_20210307_214756110_jpg.rf.f26a40c255aa55f94937f9bc0dd0ae21.txt  \n",
            " extracting: train/PXL_20210307_215335742_jpg.rf.687ecb4d71925d5333747244f5dd9e8a.jpg  \n",
            " extracting: train/PXL_20210307_215335742_jpg.rf.687ecb4d71925d5333747244f5dd9e8a.txt  \n",
            " extracting: train/PXL_20210307_215714947_jpg.rf.c15b7d00f9c18ebc49c1ed45038ebff4.jpg  \n",
            " extracting: train/PXL_20210307_215714947_jpg.rf.c15b7d00f9c18ebc49c1ed45038ebff4.txt  \n",
            " extracting: train/PXL_20210307_215745860_jpg.rf.472602a001f5d3aebca22e5d36e374e8.jpg  \n",
            " extracting: train/PXL_20210307_215745860_jpg.rf.472602a001f5d3aebca22e5d36e374e8.txt  \n",
            " extracting: train/PXL_20210307_215806035_jpg.rf.fec6bdad7d914341fc490cce7df94748.jpg  \n",
            " extracting: train/PXL_20210307_215806035_jpg.rf.fec6bdad7d914341fc490cce7df94748.txt  \n",
            " extracting: train/PXL_20210308_010947770_jpg.rf.532507c4b6c9ed18674d1e0af299f79a.jpg  \n",
            " extracting: train/PXL_20210308_010947770_jpg.rf.532507c4b6c9ed18674d1e0af299f79a.txt  \n",
            " extracting: train/PXL_20210308_180206875_jpg.rf.ec652373b261b7de6831f73333e908f4.jpg  \n",
            " extracting: train/PXL_20210308_180206875_jpg.rf.ec652373b261b7de6831f73333e908f4.txt  \n",
            " extracting: train/PXL_20210310_192222772_jpg.rf.bf38db4dbc0e923585df07c796ff478c.jpg  \n",
            " extracting: train/PXL_20210310_192222772_jpg.rf.bf38db4dbc0e923585df07c796ff478c.txt  \n",
            " extracting: train/PXL_20210311_214207642_jpg.rf.45b0d99e3d0dba5c79c88c5894333646.jpg  \n",
            " extracting: train/PXL_20210311_214207642_jpg.rf.45b0d99e3d0dba5c79c88c5894333646.txt  \n",
            " extracting: train/PXL_20210311_214212986_jpg.rf.53ad4f73a0f7d33fd095b5e7216b42b4.jpg  \n",
            " extracting: train/PXL_20210311_214212986_jpg.rf.53ad4f73a0f7d33fd095b5e7216b42b4.txt  \n",
            " extracting: train/PXL_20210311_214227797_jpg.rf.3f2eb74e9b4e05ab7252050e6b34aa43.jpg  \n",
            " extracting: train/PXL_20210311_214227797_jpg.rf.3f2eb74e9b4e05ab7252050e6b34aa43.txt  \n",
            " extracting: train/PXL_20210312_181502302_jpg.rf.2f8619064bd7d2a6644718099a04a5c5.jpg  \n",
            " extracting: train/PXL_20210312_181502302_jpg.rf.2f8619064bd7d2a6644718099a04a5c5.txt  \n",
            " extracting: train/PXL_20210312_182902751_jpg.rf.510c0115ed577d7e9add1d5d5e0c41db.jpg  \n",
            " extracting: train/PXL_20210312_182902751_jpg.rf.510c0115ed577d7e9add1d5d5e0c41db.txt  \n",
            " extracting: train/PXL_20210312_225546722_jpg.rf.b74e4a0690c2f9ebe4bd69c703e28f61.jpg  \n",
            " extracting: train/PXL_20210312_225546722_jpg.rf.b74e4a0690c2f9ebe4bd69c703e28f61.txt  \n",
            " extracting: train/PXL_20210313_205053568_jpg.rf.7ef842ac05789ee1768dacde3318c1b9.jpg  \n",
            " extracting: train/PXL_20210313_205053568_jpg.rf.7ef842ac05789ee1768dacde3318c1b9.txt  \n",
            " extracting: train/PXL_20210313_205132793_jpg.rf.71054315e88d3b25ff59be62d1fa2425.jpg  \n",
            " extracting: train/PXL_20210313_205132793_jpg.rf.71054315e88d3b25ff59be62d1fa2425.txt  \n",
            " extracting: train/PXL_20210316_123300874_jpg.rf.2968eaacbd2cd6d539c99432837f7691.jpg  \n",
            " extracting: train/PXL_20210316_123300874_jpg.rf.2968eaacbd2cd6d539c99432837f7691.txt  \n",
            " extracting: train/PXL_20210316_123330238_jpg.rf.47c33f666e30fb8cd7b6555619128168.jpg  \n",
            " extracting: train/PXL_20210316_123330238_jpg.rf.47c33f666e30fb8cd7b6555619128168.txt  \n",
            " extracting: train/PXL_20210316_123342785_jpg.rf.c05d56db7b8ec1bc63c86a472c54c8d2.jpg  \n",
            " extracting: train/PXL_20210316_123342785_jpg.rf.c05d56db7b8ec1bc63c86a472c54c8d2.txt  \n",
            " extracting: train/PXL_20210316_123348034_jpg.rf.43df114b571dd357c6bf781a4737783b.jpg  \n",
            " extracting: train/PXL_20210316_123348034_jpg.rf.43df114b571dd357c6bf781a4737783b.txt  \n",
            " extracting: train/PXL_20210316_123634319_jpg.rf.f56ea90c8b3bad7071572cd397267f99.jpg  \n",
            " extracting: train/PXL_20210316_123634319_jpg.rf.f56ea90c8b3bad7071572cd397267f99.txt  \n",
            " extracting: train/PXL_20210316_123658679_jpg.rf.ac3868886be9e06dcdf6930d68ddc12b.jpg  \n",
            " extracting: train/PXL_20210316_123658679_jpg.rf.ac3868886be9e06dcdf6930d68ddc12b.txt  \n",
            " extracting: train/PXL_20210316_124254967_jpg.rf.f6b8c933d35e16b6addbfff83709b5d9.jpg  \n",
            " extracting: train/PXL_20210316_124254967_jpg.rf.f6b8c933d35e16b6addbfff83709b5d9.txt  \n",
            " extracting: train/PXL_20210316_124321333_jpg.rf.4a1caf22c60591934a91fe9bba482fe2.jpg  \n",
            " extracting: train/PXL_20210316_124321333_jpg.rf.4a1caf22c60591934a91fe9bba482fe2.txt  \n",
            " extracting: train/PXL_20210316_124333001_jpg.rf.eda190f50591fb8ee515faa623451d84.jpg  \n",
            " extracting: train/PXL_20210316_124333001_jpg.rf.eda190f50591fb8ee515faa623451d84.txt  \n",
            " extracting: train/PXL_20210316_124931862_jpg.rf.6bdc4866e6fa850e5a7c740d259a7ffa.jpg  \n",
            " extracting: train/PXL_20210316_124931862_jpg.rf.6bdc4866e6fa850e5a7c740d259a7ffa.txt  \n",
            " extracting: train/PXL_20210316_141153841_jpg.rf.4b39f953c25ec2471f419d23939d273d.jpg  \n",
            " extracting: train/PXL_20210316_141153841_jpg.rf.4b39f953c25ec2471f419d23939d273d.txt  \n",
            " extracting: train/PXL_20210316_141229424_jpg.rf.4f69bd68f06844404936436734605fdc.jpg  \n",
            " extracting: train/PXL_20210316_141229424_jpg.rf.4f69bd68f06844404936436734605fdc.txt  \n",
            " extracting: train/PXL_20210316_141248491_jpg.rf.9b08ac74ebec02620ad88430d5c04fd0.jpg  \n",
            " extracting: train/PXL_20210316_141248491_jpg.rf.9b08ac74ebec02620ad88430d5c04fd0.txt  \n",
            " extracting: train/PXL_20210316_143036215_jpg.rf.fd12ef5ea91bd9772bc155675a413291.jpg  \n",
            " extracting: train/PXL_20210316_143036215_jpg.rf.fd12ef5ea91bd9772bc155675a413291.txt  \n",
            " extracting: train/PXL_20210316_143041883_jpg.rf.9d8d26aa7967c7ecce6b49263dae0164.jpg  \n",
            " extracting: train/PXL_20210316_143041883_jpg.rf.9d8d26aa7967c7ecce6b49263dae0164.txt  \n",
            " extracting: train/PXL_20210316_143052338_jpg.rf.6e8c1ca81ee490b5aaa91e7e743b1fde.jpg  \n",
            " extracting: train/PXL_20210316_143052338_jpg.rf.6e8c1ca81ee490b5aaa91e7e743b1fde.txt  \n",
            " extracting: train/PXL_20210316_143117717_jpg.rf.43526ccb0fa0cda2ba410332e84171df.jpg  \n",
            " extracting: train/PXL_20210316_143117717_jpg.rf.43526ccb0fa0cda2ba410332e84171df.txt  \n",
            " extracting: train/PXL_20210320_085655940_jpg.rf.20d2026fdefa4c1c3a98aec3c32d2545.jpg  \n",
            " extracting: train/PXL_20210320_085655940_jpg.rf.20d2026fdefa4c1c3a98aec3c32d2545.txt  \n",
            " extracting: train/PXL_20210320_085705445_jpg.rf.b87ae23afa82ae9854e0b8d3b5839fe5.jpg  \n",
            " extracting: train/PXL_20210320_085705445_jpg.rf.b87ae23afa82ae9854e0b8d3b5839fe5.txt  \n",
            " extracting: train/PXL_20210320_171127413_jpg.rf.85612821f3358add6701e38aacb0f0cb.jpg  \n",
            " extracting: train/PXL_20210320_171127413_jpg.rf.85612821f3358add6701e38aacb0f0cb.txt  \n",
            " extracting: train/PXL_20210321_194854888_jpg.rf.32ee820cc046175cffcf03310abf62ba.jpg  \n",
            " extracting: train/PXL_20210321_194854888_jpg.rf.32ee820cc046175cffcf03310abf62ba.txt  \n",
            " extracting: train/PXL_20210321_195243628_jpg.rf.21886f48e591a151806aa7c1db560ee7.jpg  \n",
            " extracting: train/PXL_20210321_195243628_jpg.rf.21886f48e591a151806aa7c1db560ee7.txt  \n",
            " extracting: train/PXL_20210321_200507910_jpg.rf.ccc54f26e78f87bdb26ef5cb8de8a888.jpg  \n",
            " extracting: train/PXL_20210321_200507910_jpg.rf.ccc54f26e78f87bdb26ef5cb8de8a888.txt  \n",
            " extracting: train/PXL_20210321_200516331_jpg.rf.82164481e32ebf9f9f45b39229397f04.jpg  \n",
            " extracting: train/PXL_20210321_200516331_jpg.rf.82164481e32ebf9f9f45b39229397f04.txt  \n",
            " extracting: train/PXL_20210321_201905035_jpg.rf.cf465f537e5de0e45c6e60cc9d041bdb.jpg  \n",
            " extracting: train/PXL_20210321_201905035_jpg.rf.cf465f537e5de0e45c6e60cc9d041bdb.txt  \n",
            " extracting: train/PXL_20210321_201916847_jpg.rf.8dc84d9de17138f610075fcd8356e563.jpg  \n",
            " extracting: train/PXL_20210321_201916847_jpg.rf.8dc84d9de17138f610075fcd8356e563.txt  \n",
            " extracting: train/PXL_20210321_201922958_jpg.rf.3b1e3bf5392bba20e31523901a834bcb.jpg  \n",
            " extracting: train/PXL_20210321_201922958_jpg.rf.3b1e3bf5392bba20e31523901a834bcb.txt  \n",
            " extracting: train/PXL_20210321_202018177_jpg.rf.0dc2f0b64ed8c9f52c1e3dfc52f05b3c.jpg  \n",
            " extracting: train/PXL_20210321_202018177_jpg.rf.0dc2f0b64ed8c9f52c1e3dfc52f05b3c.txt  \n",
            " extracting: train/PXL_20210321_202029832_jpg.rf.cf6c16cd0b0cb3d48c485d57213cc55f.jpg  \n",
            " extracting: train/PXL_20210321_202029832_jpg.rf.cf6c16cd0b0cb3d48c485d57213cc55f.txt  \n",
            " extracting: train/PXL_20210321_202239275_jpg.rf.d8d7147da40fdf4b84cbbbd04515f4c7.jpg  \n",
            " extracting: train/PXL_20210321_202239275_jpg.rf.d8d7147da40fdf4b84cbbbd04515f4c7.txt  \n",
            " extracting: train/PXL_20210321_203249417_jpg.rf.f9fddd5198dea1722c772b1e4e1a4217.jpg  \n",
            " extracting: train/PXL_20210321_203249417_jpg.rf.f9fddd5198dea1722c772b1e4e1a4217.txt  \n",
            " extracting: train/PXL_20210321_203321343_jpg.rf.166b9a9210422218f73f61e1789c9746.jpg  \n",
            " extracting: train/PXL_20210321_203321343_jpg.rf.166b9a9210422218f73f61e1789c9746.txt  \n",
            " extracting: train/PXL_20210323_124155342_jpg.rf.6a80e77680b57c09f3087df9e8ebcbc5.jpg  \n",
            " extracting: train/PXL_20210323_124155342_jpg.rf.6a80e77680b57c09f3087df9e8ebcbc5.txt  \n",
            " extracting: train/PXL_20210323_124221757_jpg.rf.19db656271f8be7e0829ff00396f543b.jpg  \n",
            " extracting: train/PXL_20210323_124221757_jpg.rf.19db656271f8be7e0829ff00396f543b.txt  \n",
            " extracting: train/PXL_20210323_124513029_jpg.rf.c6111968839f04675ad82b9f0008b2fc.jpg  \n",
            " extracting: train/PXL_20210323_124513029_jpg.rf.c6111968839f04675ad82b9f0008b2fc.txt  \n",
            " extracting: train/PXL_20210323_124524639_jpg.rf.c7250c6d0dd83a7dad7f7ae7028b6cb6.jpg  \n",
            " extracting: train/PXL_20210323_124524639_jpg.rf.c7250c6d0dd83a7dad7f7ae7028b6cb6.txt  \n",
            " extracting: train/PXL_20210323_124537282_jpg.rf.73ce5cf5deb20acdf5853d2278ffed6d.jpg  \n",
            " extracting: train/PXL_20210323_124537282_jpg.rf.73ce5cf5deb20acdf5853d2278ffed6d.txt  \n",
            " extracting: train/PXL_20210323_125108142_jpg.rf.425db9694aa5fa16e4ceabdf9de82855.jpg  \n",
            " extracting: train/PXL_20210323_125108142_jpg.rf.425db9694aa5fa16e4ceabdf9de82855.txt  \n",
            " extracting: train/PXL_20210324_222710177_jpg.rf.9b38f5589753db1f082ceb9ad10f9f2a.jpg  \n",
            " extracting: train/PXL_20210324_222710177_jpg.rf.9b38f5589753db1f082ceb9ad10f9f2a.txt  \n",
            " extracting: train/_106177565_pothole_jpg.rf.66d87ed17d7fa50074cde643fae2a04b.jpg  \n",
            " extracting: train/_106177565_pothole_jpg.rf.66d87ed17d7fa50074cde643fae2a04b.txt  \n",
            " extracting: train/_darknet.labels   \n",
            " extracting: train/car-and-pothole_jpg.rf.857105778a24ea70c0b86b3eb1ed7ca6.jpg  \n",
            " extracting: train/car-and-pothole_jpg.rf.857105778a24ea70c0b86b3eb1ed7ca6.txt  \n",
            " extracting: train/curb-repair-1a_jpg.rf.acb3239c9dad140cc92a41b853015323.jpg  \n",
            " extracting: train/curb-repair-1a_jpg.rf.acb3239c9dad140cc92a41b853015323.txt  \n",
            " extracting: train/curb_cut_jpg.rf.cea93619911861d43f9e58b5e920a1ba.jpg  \n",
            " extracting: train/curb_cut_jpg.rf.cea93619911861d43f9e58b5e920a1ba.txt  \n",
            " extracting: train/download_jpeg.rf.faea5273cc021064b112c42cc2f52612.jpg  \n",
            " extracting: train/download_jpeg.rf.faea5273cc021064b112c42cc2f52612.txt  \n",
            " extracting: train/gettyimages-183851840_jpg.rf.8c36a6320d6df8b139937c97cc6f9518.jpg  \n",
            " extracting: train/gettyimages-183851840_jpg.rf.8c36a6320d6df8b139937c97cc6f9518.txt  \n",
            " extracting: train/how-to-repair-cracked-concrete-678x381_jpg.rf.77c9bcd367942862e42fb4af21113d2b.jpg  \n",
            " extracting: train/how-to-repair-cracked-concrete-678x381_jpg.rf.77c9bcd367942862e42fb4af21113d2b.txt  \n",
            " extracting: train/iStock-136736843_jpg.rf.23355e7f01e37e626817041d627486c1.jpg  \n",
            " extracting: train/iStock-136736843_jpg.rf.23355e7f01e37e626817041d627486c1.txt  \n",
            " extracting: train/istockphoto-96921064-612x612_jpg.rf.43248cf8a6af27d800cd76f47a3da522.jpg  \n",
            " extracting: train/istockphoto-96921064-612x612_jpg.rf.43248cf8a6af27d800cd76f47a3da522.txt  \n",
            " extracting: train/rocks_jpg.rf.163d39168f45c44a22ba8463bf314a6a.jpg  \n",
            " extracting: train/rocks_jpg.rf.163d39168f45c44a22ba8463bf314a6a.txt  \n",
            " extracting: train/shutterstock-fall-caused-by-broken-sidewalk_jpg.rf.5856449ce80ea91dc07842a30674fb2a.jpg  \n",
            " extracting: train/shutterstock-fall-caused-by-broken-sidewalk_jpg.rf.5856449ce80ea91dc07842a30674fb2a.txt  \n",
            " extracting: train/shutterstock_626273606-0_jpg.rf.09a9acf5cd4fd71c7e1821b82dfb8f49.jpg  \n",
            " extracting: train/shutterstock_626273606-0_jpg.rf.09a9acf5cd4fd71c7e1821b82dfb8f49.txt  \n",
            " extracting: train/stock-photo-road-sidewalk-fallen-tree-trees-houses-wooden-fence-ice-crystals-snow-pack-condominiums-3085046f-5991-4655-886d-66d8cfe71d3b_jpg.rf.fc9bcf976892f359c638d31006eda95e.jpg  \n",
            " extracting: train/stock-photo-road-sidewalk-fallen-tree-trees-houses-wooden-fence-ice-crystals-snow-pack-condominiums-3085046f-5991-4655-886d-66d8cfe71d3b_jpg.rf.fc9bcf976892f359c638d31006eda95e.txt  \n",
            "   creating: valid/\n",
            " extracting: valid/IMG_9251_jpg.rf.cd9b60f3e72faa2f743d17c491cddb6e.jpg  \n",
            " extracting: valid/IMG_9251_jpg.rf.cd9b60f3e72faa2f743d17c491cddb6e.txt  \n",
            " extracting: valid/IMG_9253_jpg.rf.e218b2dcff71dd3e4e4fcb2d3f4691a6.jpg  \n",
            " extracting: valid/IMG_9253_jpg.rf.e218b2dcff71dd3e4e4fcb2d3f4691a6.txt  \n",
            " extracting: valid/IMG_9255_jpg.rf.b6c58cc1502e06e683a948eb6dbb81f2.jpg  \n",
            " extracting: valid/IMG_9255_jpg.rf.b6c58cc1502e06e683a948eb6dbb81f2.txt  \n",
            " extracting: valid/IMG_9271_jpg.rf.702907427c6247de295641b88942934e.jpg  \n",
            " extracting: valid/IMG_9271_jpg.rf.702907427c6247de295641b88942934e.txt  \n",
            " extracting: valid/IMG_9272_jpg.rf.1406fab5d0ba8a65e826ca291879bcd0.jpg  \n",
            " extracting: valid/IMG_9272_jpg.rf.1406fab5d0ba8a65e826ca291879bcd0.txt  \n",
            " extracting: valid/IMG_9274_jpg.rf.ed17892fb37e5b411e6e85b4008dc715.jpg  \n",
            " extracting: valid/IMG_9274_jpg.rf.ed17892fb37e5b411e6e85b4008dc715.txt  \n",
            " extracting: valid/IMG_9290_jpg.rf.327cf3014a42755b622c86590d693956.jpg  \n",
            " extracting: valid/IMG_9290_jpg.rf.327cf3014a42755b622c86590d693956.txt  \n",
            " extracting: valid/IMG_9291_jpg.rf.0acfa2e995a1d79546809576cbdf5378.jpg  \n",
            " extracting: valid/IMG_9291_jpg.rf.0acfa2e995a1d79546809576cbdf5378.txt  \n",
            " extracting: valid/IMG_9295_jpg.rf.03695170e3689b73282d3e389ad1368a.jpg  \n",
            " extracting: valid/IMG_9295_jpg.rf.03695170e3689b73282d3e389ad1368a.txt  \n",
            " extracting: valid/IMG_9298_jpg.rf.306d721165dfdb78443e289133bc8481.jpg  \n",
            " extracting: valid/IMG_9298_jpg.rf.306d721165dfdb78443e289133bc8481.txt  \n",
            " extracting: valid/IMG_9342_jpg.rf.3f82320719e4ebbeac250bd1da8e3903.jpg  \n",
            " extracting: valid/IMG_9342_jpg.rf.3f82320719e4ebbeac250bd1da8e3903.txt  \n",
            " extracting: valid/IMG_9345_jpg.rf.2b9a5b0c4efb6355b38efe316765c6cd.jpg  \n",
            " extracting: valid/IMG_9345_jpg.rf.2b9a5b0c4efb6355b38efe316765c6cd.txt  \n",
            " extracting: valid/IMG_9352_jpg.rf.ba826fbc972ea1bdeea5470b929058a2.jpg  \n",
            " extracting: valid/IMG_9352_jpg.rf.ba826fbc972ea1bdeea5470b929058a2.txt  \n",
            " extracting: valid/IMG_9357_jpg.rf.e8e711cce40dfba260bf488fda02c8d8.jpg  \n",
            " extracting: valid/IMG_9357_jpg.rf.e8e711cce40dfba260bf488fda02c8d8.txt  \n",
            " extracting: valid/IMG_9365_jpg.rf.a3bc3e72351c8dc29a2f868a69517874.jpg  \n",
            " extracting: valid/IMG_9365_jpg.rf.a3bc3e72351c8dc29a2f868a69517874.txt  \n",
            " extracting: valid/IMG_9402_jpg.rf.aa883b55a95323a23dc39c96059aeea7.jpg  \n",
            " extracting: valid/IMG_9402_jpg.rf.aa883b55a95323a23dc39c96059aeea7.txt  \n",
            " extracting: valid/IMG_9405_jpg.rf.c055efcef6e1b695962eb2faf1eb3f41.jpg  \n",
            " extracting: valid/IMG_9405_jpg.rf.c055efcef6e1b695962eb2faf1eb3f41.txt  \n",
            " extracting: valid/IMG_9409_jpg.rf.befba2a4d09f278ab42d6ad7b9fb924b.jpg  \n",
            " extracting: valid/IMG_9409_jpg.rf.befba2a4d09f278ab42d6ad7b9fb924b.txt  \n",
            " extracting: valid/IMG_9410_jpg.rf.dc209bd9735ac249041912352f56734a.jpg  \n",
            " extracting: valid/IMG_9410_jpg.rf.dc209bd9735ac249041912352f56734a.txt  \n",
            " extracting: valid/IMG_9415_jpg.rf.5a29972bc4931d3e2cc4b62d86e75715.jpg  \n",
            " extracting: valid/IMG_9415_jpg.rf.5a29972bc4931d3e2cc4b62d86e75715.txt  \n",
            " extracting: valid/IMG_9416_jpg.rf.c21052e4bb2243ca3f89fa0843fe2036.jpg  \n",
            " extracting: valid/IMG_9416_jpg.rf.c21052e4bb2243ca3f89fa0843fe2036.txt  \n",
            " extracting: valid/IMG_9418_jpg.rf.1e1acd24213ddeb4a9c94d859f710187.jpg  \n",
            " extracting: valid/IMG_9418_jpg.rf.1e1acd24213ddeb4a9c94d859f710187.txt  \n",
            " extracting: valid/IMG_9427_jpg.rf.0269e97a3360bf8bd18d9d9d2c495cf3.jpg  \n",
            " extracting: valid/IMG_9427_jpg.rf.0269e97a3360bf8bd18d9d9d2c495cf3.txt  \n",
            " extracting: valid/IMG_9440_jpg.rf.f2afe6aef4f36421162718253d920595.jpg  \n",
            " extracting: valid/IMG_9440_jpg.rf.f2afe6aef4f36421162718253d920595.txt  \n",
            " extracting: valid/IMG_9443_jpg.rf.afa30d8ae7d27bc49860e1a91eff2a8e.jpg  \n",
            " extracting: valid/IMG_9443_jpg.rf.afa30d8ae7d27bc49860e1a91eff2a8e.txt  \n",
            " extracting: valid/IMG_9462_jpg.rf.2da3b940596501c4998ecfa0c84381a8.jpg  \n",
            " extracting: valid/IMG_9462_jpg.rf.2da3b940596501c4998ecfa0c84381a8.txt  \n",
            " extracting: valid/IMG_9468_jpg.rf.59fc696d0dda018dbef3fcf2e8460140.jpg  \n",
            " extracting: valid/IMG_9468_jpg.rf.59fc696d0dda018dbef3fcf2e8460140.txt  \n",
            " extracting: valid/IMG_9471_jpg.rf.6f02cd13491cf196d0dc6df90e8ac53b.jpg  \n",
            " extracting: valid/IMG_9471_jpg.rf.6f02cd13491cf196d0dc6df90e8ac53b.txt  \n",
            " extracting: valid/IMG_9472_jpg.rf.dd7aa04ab90a5c54318741dcf758b903.jpg  \n",
            " extracting: valid/IMG_9472_jpg.rf.dd7aa04ab90a5c54318741dcf758b903.txt  \n",
            " extracting: valid/IMG_9477_jpg.rf.10fd397aca2deca40684c4d10746c3c7.jpg  \n",
            " extracting: valid/IMG_9477_jpg.rf.10fd397aca2deca40684c4d10746c3c7.txt  \n",
            " extracting: valid/IMG_9481_jpg.rf.9ee39198473dc4bab007af82336611d5.jpg  \n",
            " extracting: valid/IMG_9481_jpg.rf.9ee39198473dc4bab007af82336611d5.txt  \n",
            " extracting: valid/IMG_9531_jpg.rf.c09264b5174e49b084e586e9cbbf6278.jpg  \n",
            " extracting: valid/IMG_9531_jpg.rf.c09264b5174e49b084e586e9cbbf6278.txt  \n",
            " extracting: valid/IMG_9638_jpg.rf.b8775426c4f6f8160e407634906bce99.jpg  \n",
            " extracting: valid/IMG_9638_jpg.rf.b8775426c4f6f8160e407634906bce99.txt  \n",
            " extracting: valid/IMG_9645_jpg.rf.f89db8fcb94f058e6a83d59ec306f936.jpg  \n",
            " extracting: valid/IMG_9645_jpg.rf.f89db8fcb94f058e6a83d59ec306f936.txt  \n",
            " extracting: valid/IMG_9649_jpg.rf.4c4569b1a7fc0ef12009e5ce967e4b72.jpg  \n",
            " extracting: valid/IMG_9649_jpg.rf.4c4569b1a7fc0ef12009e5ce967e4b72.txt  \n",
            " extracting: valid/IMG_9652_jpg.rf.168cbf11adc89e90f52dc0e508a82ff1.jpg  \n",
            " extracting: valid/IMG_9652_jpg.rf.168cbf11adc89e90f52dc0e508a82ff1.txt  \n",
            " extracting: valid/IMG_9667_jpg.rf.e91e58b8aebf0e77f9076525c05a400c.jpg  \n",
            " extracting: valid/IMG_9667_jpg.rf.e91e58b8aebf0e77f9076525c05a400c.txt  \n",
            " extracting: valid/IMG_9678_jpg.rf.e2afc6d8c740a6ce09bc1c6e167e2c50.jpg  \n",
            " extracting: valid/IMG_9678_jpg.rf.e2afc6d8c740a6ce09bc1c6e167e2c50.txt  \n",
            " extracting: valid/IMG_9680_jpg.rf.2c9086ee4275d6e72a12fdba4453fe62.jpg  \n",
            " extracting: valid/IMG_9680_jpg.rf.2c9086ee4275d6e72a12fdba4453fe62.txt  \n",
            " extracting: valid/IMG_9682_jpg.rf.f2bdc59700abfe9f57d0a1c5e6c06f68.jpg  \n",
            " extracting: valid/IMG_9682_jpg.rf.f2bdc59700abfe9f57d0a1c5e6c06f68.txt  \n",
            " extracting: valid/IMG_9684_jpg.rf.ccd8738d0733480ab68092d7fead7203.jpg  \n",
            " extracting: valid/IMG_9684_jpg.rf.ccd8738d0733480ab68092d7fead7203.txt  \n",
            " extracting: valid/IMG_9685_jpg.rf.85a60c54d6f5f9d4378ccf13705b5a3f.jpg  \n",
            " extracting: valid/IMG_9685_jpg.rf.85a60c54d6f5f9d4378ccf13705b5a3f.txt  \n",
            " extracting: valid/IMG_9687_jpg.rf.c9a49ddda3abd76949a33087dbaab27d.jpg  \n",
            " extracting: valid/IMG_9687_jpg.rf.c9a49ddda3abd76949a33087dbaab27d.txt  \n",
            " extracting: valid/IMG_9690_jpg.rf.bd4efdeb87bd1a4668f2d54e0c11d47a.jpg  \n",
            " extracting: valid/IMG_9690_jpg.rf.bd4efdeb87bd1a4668f2d54e0c11d47a.txt  \n",
            " extracting: valid/PXL_20210208_184423824_jpg.rf.12664d448c0f29156ee3338b3d587f72.jpg  \n",
            " extracting: valid/PXL_20210208_184423824_jpg.rf.12664d448c0f29156ee3338b3d587f72.txt  \n",
            " extracting: valid/PXL_20210208_190346787_jpg.rf.af49724b1da7f0d9405e56c22a6cd436.jpg  \n",
            " extracting: valid/PXL_20210208_190346787_jpg.rf.af49724b1da7f0d9405e56c22a6cd436.txt  \n",
            " extracting: valid/PXL_20210209_134942024_jpg.rf.f637662559bf07f51dcb69e3fb1c1268.jpg  \n",
            " extracting: valid/PXL_20210209_134942024_jpg.rf.f637662559bf07f51dcb69e3fb1c1268.txt  \n",
            " extracting: valid/PXL_20210219_174214925_jpg.rf.3ba3a45a93b13f0b5216000af82f9ea4.jpg  \n",
            " extracting: valid/PXL_20210219_174214925_jpg.rf.3ba3a45a93b13f0b5216000af82f9ea4.txt  \n",
            " extracting: valid/PXL_20210219_174633781_jpg.rf.74d56e199c14a4e665c30ec908d5b23c.jpg  \n",
            " extracting: valid/PXL_20210219_174633781_jpg.rf.74d56e199c14a4e665c30ec908d5b23c.txt  \n",
            " extracting: valid/PXL_20210222_163218561_jpg.rf.d74e4227a4eda51b353c169bdbc13573.jpg  \n",
            " extracting: valid/PXL_20210222_163218561_jpg.rf.d74e4227a4eda51b353c169bdbc13573.txt  \n",
            " extracting: valid/PXL_20210222_165409269_jpg.rf.5beb7d1128ff0c0e8faf184dd3106ee3.jpg  \n",
            " extracting: valid/PXL_20210222_165409269_jpg.rf.5beb7d1128ff0c0e8faf184dd3106ee3.txt  \n",
            " extracting: valid/PXL_20210222_165615630_jpg.rf.aa8b883941c26a65f1d42af011b486f6.jpg  \n",
            " extracting: valid/PXL_20210222_165615630_jpg.rf.aa8b883941c26a65f1d42af011b486f6.txt  \n",
            " extracting: valid/PXL_20210222_165625250_jpg.rf.a22e71ab290de2a55f87b388432cbf11.jpg  \n",
            " extracting: valid/PXL_20210222_165625250_jpg.rf.a22e71ab290de2a55f87b388432cbf11.txt  \n",
            " extracting: valid/PXL_20210223_221841791_jpg.rf.637bc1e563edc521853e5bc2deb61253.jpg  \n",
            " extracting: valid/PXL_20210223_221841791_jpg.rf.637bc1e563edc521853e5bc2deb61253.txt  \n",
            " extracting: valid/PXL_20210223_221933300_jpg.rf.b8edde626cb6b014f1d2826b3277242f.jpg  \n",
            " extracting: valid/PXL_20210223_221933300_jpg.rf.b8edde626cb6b014f1d2826b3277242f.txt  \n",
            " extracting: valid/PXL_20210223_230130756_jpg.rf.64a863f4623ab0ac203842ecb6cac50e.jpg  \n",
            " extracting: valid/PXL_20210223_230130756_jpg.rf.64a863f4623ab0ac203842ecb6cac50e.txt  \n",
            " extracting: valid/PXL_20210223_231444920_jpg.rf.44d15f658069852ea4379aaa013d0c4c.jpg  \n",
            " extracting: valid/PXL_20210223_231444920_jpg.rf.44d15f658069852ea4379aaa013d0c4c.txt  \n",
            " extracting: valid/PXL_20210224_092203936_jpg.rf.8d62608c4cb32df3184c0baf501ab9b7.jpg  \n",
            " extracting: valid/PXL_20210224_092203936_jpg.rf.8d62608c4cb32df3184c0baf501ab9b7.txt  \n",
            " extracting: valid/PXL_20210226_183120515_jpg.rf.16ec80e9c4db43c15fc2b9f9a6ada584.jpg  \n",
            " extracting: valid/PXL_20210226_183120515_jpg.rf.16ec80e9c4db43c15fc2b9f9a6ada584.txt  \n",
            " extracting: valid/PXL_20210228_172954473_jpg.rf.4745899645e44f2c2add19a4b2a79f19.jpg  \n",
            " extracting: valid/PXL_20210228_172954473_jpg.rf.4745899645e44f2c2add19a4b2a79f19.txt  \n",
            " extracting: valid/PXL_20210228_173248500_jpg.rf.4ed5f2868f9f548294870b948d94dca3.jpg  \n",
            " extracting: valid/PXL_20210228_173248500_jpg.rf.4ed5f2868f9f548294870b948d94dca3.txt  \n",
            " extracting: valid/PXL_20210228_173819977_jpg.rf.88c3cf2a64a9136805c45d930b5b6238.jpg  \n",
            " extracting: valid/PXL_20210228_173819977_jpg.rf.88c3cf2a64a9136805c45d930b5b6238.txt  \n",
            " extracting: valid/PXL_20210301_171821576_jpg.rf.760ad8072db5fe143be6ca912565d46a.jpg  \n",
            " extracting: valid/PXL_20210301_171821576_jpg.rf.760ad8072db5fe143be6ca912565d46a.txt  \n",
            " extracting: valid/PXL_20210302_134307261_jpg.rf.9db3e34ca99a9eb842a6d346e225a5cd.jpg  \n",
            " extracting: valid/PXL_20210302_134307261_jpg.rf.9db3e34ca99a9eb842a6d346e225a5cd.txt  \n",
            " extracting: valid/PXL_20210302_134448773_jpg.rf.8f71eae6b568299f759624a98da69887.jpg  \n",
            " extracting: valid/PXL_20210302_134448773_jpg.rf.8f71eae6b568299f759624a98da69887.txt  \n",
            " extracting: valid/PXL_20210302_134511006_jpg.rf.db837a36b3cfc363277b8206dcdd4dce.jpg  \n",
            " extracting: valid/PXL_20210302_134511006_jpg.rf.db837a36b3cfc363277b8206dcdd4dce.txt  \n",
            " extracting: valid/PXL_20210302_145950606_jpg.rf.0a2c96efb2d9fb7e9ee7cde326728567.jpg  \n",
            " extracting: valid/PXL_20210302_145950606_jpg.rf.0a2c96efb2d9fb7e9ee7cde326728567.txt  \n",
            " extracting: valid/PXL_20210302_150320580_jpg.rf.c2844aa6863d50971b2ce93bfbda516a.jpg  \n",
            " extracting: valid/PXL_20210302_150320580_jpg.rf.c2844aa6863d50971b2ce93bfbda516a.txt  \n",
            " extracting: valid/PXL_20210302_150758895_jpg.rf.626a6cc6e1459b852a9cb4a160d5e291.jpg  \n",
            " extracting: valid/PXL_20210302_150758895_jpg.rf.626a6cc6e1459b852a9cb4a160d5e291.txt  \n",
            " extracting: valid/PXL_20210302_215227933_jpg.rf.414812528807420841cbb52421521292.jpg  \n",
            " extracting: valid/PXL_20210302_215227933_jpg.rf.414812528807420841cbb52421521292.txt  \n",
            " extracting: valid/PXL_20210307_180948877_jpg.rf.ee65f86ab42af55693d67ecafdf9e6ed.jpg  \n",
            " extracting: valid/PXL_20210307_180948877_jpg.rf.ee65f86ab42af55693d67ecafdf9e6ed.txt  \n",
            " extracting: valid/PXL_20210307_181317890_jpg.rf.5b7711d30a97fd70a8277d71bfd627af.jpg  \n",
            " extracting: valid/PXL_20210307_181317890_jpg.rf.5b7711d30a97fd70a8277d71bfd627af.txt  \n",
            " extracting: valid/PXL_20210307_181347432_jpg.rf.d4869aa1c4fc2710134236464ec022d2.jpg  \n",
            " extracting: valid/PXL_20210307_181347432_jpg.rf.d4869aa1c4fc2710134236464ec022d2.txt  \n",
            " extracting: valid/PXL_20210307_215836632_jpg.rf.7b3bcb02b40d033e8c37e53ee14867f5.jpg  \n",
            " extracting: valid/PXL_20210307_215836632_jpg.rf.7b3bcb02b40d033e8c37e53ee14867f5.txt  \n",
            " extracting: valid/PXL_20210308_180150550_jpg.rf.f911ab11d7fe66c8b2ad78ad6b26c6dc.jpg  \n",
            " extracting: valid/PXL_20210308_180150550_jpg.rf.f911ab11d7fe66c8b2ad78ad6b26c6dc.txt  \n",
            " extracting: valid/PXL_20210311_214221917_jpg.rf.d9b459b677aadf8f7418bc7954df6bb5.jpg  \n",
            " extracting: valid/PXL_20210311_214221917_jpg.rf.d9b459b677aadf8f7418bc7954df6bb5.txt  \n",
            " extracting: valid/PXL_20210311_214233709_jpg.rf.1370cc3fc1f23e35e73670fc51d26337.jpg  \n",
            " extracting: valid/PXL_20210311_214233709_jpg.rf.1370cc3fc1f23e35e73670fc51d26337.txt  \n",
            " extracting: valid/PXL_20210316_123305742_jpg.rf.c5d776aacff9d60e0d2171f90a25fc59.jpg  \n",
            " extracting: valid/PXL_20210316_123305742_jpg.rf.c5d776aacff9d60e0d2171f90a25fc59.txt  \n",
            " extracting: valid/PXL_20210316_124238853_jpg.rf.e9e232265b81b5a84d5dcfd029f88991.jpg  \n",
            " extracting: valid/PXL_20210316_124238853_jpg.rf.e9e232265b81b5a84d5dcfd029f88991.txt  \n",
            " extracting: valid/PXL_20210316_124243905_jpg.rf.4decd4fd7a043d9304041a945a88098d.jpg  \n",
            " extracting: valid/PXL_20210316_124243905_jpg.rf.4decd4fd7a043d9304041a945a88098d.txt  \n",
            " extracting: valid/PXL_20210316_124927170_jpg.rf.5091a139765d51bf38674249670a0362.jpg  \n",
            " extracting: valid/PXL_20210316_124927170_jpg.rf.5091a139765d51bf38674249670a0362.txt  \n",
            " extracting: valid/PXL_20210316_141149246_jpg.rf.c800ff35bed7cd49d9aa0ca3ed7817bd.jpg  \n",
            " extracting: valid/PXL_20210316_141149246_jpg.rf.c800ff35bed7cd49d9aa0ca3ed7817bd.txt  \n",
            " extracting: valid/PXL_20210319_173620764_jpg.rf.ba6711216dcb2869bb4a3cdd3131ef72.jpg  \n",
            " extracting: valid/PXL_20210319_173620764_jpg.rf.ba6711216dcb2869bb4a3cdd3131ef72.txt  \n",
            " extracting: valid/PXL_20210320_051316954_jpg.rf.f05ab1e60c56a68225201390af7ba3b8.jpg  \n",
            " extracting: valid/PXL_20210320_051316954_jpg.rf.f05ab1e60c56a68225201390af7ba3b8.txt  \n",
            " extracting: valid/PXL_20210321_202057417_jpg.rf.253a94e8b173ef0ec7278373be40bbf8.jpg  \n",
            " extracting: valid/PXL_20210321_202057417_jpg.rf.253a94e8b173ef0ec7278373be40bbf8.txt  \n",
            " extracting: valid/PXL_20210321_203240584_jpg.rf.f281cbc4b8fa0d5c7ff85ecfca2e7c72.jpg  \n",
            " extracting: valid/PXL_20210321_203240584_jpg.rf.f281cbc4b8fa0d5c7ff85ecfca2e7c72.txt  \n",
            " extracting: valid/_darknet.labels   \n",
            " extracting: valid/cracks-in-sidewalk_jpg.rf.0e02bd86dd99d03a764618c4d8a269f9.jpg  \n",
            " extracting: valid/cracks-in-sidewalk_jpg.rf.0e02bd86dd99d03a764618c4d8a269f9.txt  \n",
            " extracting: valid/dot-dipped-sidewalk_jpg.rf.c0001998e1e68031b3d7e459d33e9fc6.jpg  \n",
            " extracting: valid/dot-dipped-sidewalk_jpg.rf.c0001998e1e68031b3d7e459d33e9fc6.txt  \n",
            " extracting: valid/images--2-_jpeg.rf.ab996228f40e2224d96a5b2860c2cabb.jpg  \n",
            " extracting: valid/images--2-_jpeg.rf.ab996228f40e2224d96a5b2860c2cabb.txt  \n",
            " extracting: valid/rkyh1bwb4fl31_jpg.rf.de71471d59cfbd0098e2dcf534270675.jpg  \n",
            " extracting: valid/rkyh1bwb4fl31_jpg.rf.de71471d59cfbd0098e2dcf534270675.txt  \n",
            " extracting: valid/sidewalkdamage_jpg.rf.9127ab3580510562502c5e338a921fe7.jpg  \n",
            " extracting: valid/sidewalkdamage_jpg.rf.9127ab3580510562502c5e338a921fe7.txt  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KiCILEbs1NII",
        "outputId": "6cc4fb19-ddf7-49dc-a915-755851cf83fa"
      },
      "source": [
        "#Set up training file directories for custom dataset\n",
        "%cd /content/darknet/\n",
        "%cp train/_darknet.labels data/obj.names\n",
        "%mkdir data/obj\n",
        "#copy image and labels\n",
        "%cp train/*.jpg data/obj/\n",
        "%cp valid/*.jpg data/obj/\n",
        "\n",
        "%cp train/*.txt data/obj/\n",
        "%cp valid/*.txt data/obj/\n",
        "\n",
        "with open('data/obj.data', 'w') as out:\n",
        "  out.write('classes = 3\\n')\n",
        "  out.write('train = data/train.txt\\n')\n",
        "  out.write('valid = data/valid.txt\\n')\n",
        "  out.write('names = data/obj.names\\n')\n",
        "  out.write('backup = backup/')\n",
        "\n",
        "#write train file (just the image list)\n",
        "import os\n",
        "\n",
        "with open('data/train.txt', 'w') as out:\n",
        "  for img in [f for f in os.listdir('train') if f.endswith('jpg')]:\n",
        "    out.write('data/obj/' + img + '\\n')\n",
        "\n",
        "#write the valid file (just the image list)\n",
        "import os\n",
        "\n",
        "with open('data/valid.txt', 'w') as out:\n",
        "  for img in [f for f in os.listdir('valid') if f.endswith('jpg')]:\n",
        "    out.write('data/obj/' + img + '\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/darknet\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5HtRqO3QvjkP"
      },
      "source": [
        "# Write Custom Training Config for YOLOv4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U_WJcqHhpeVr",
        "outputId": "2c34a696-da20-4570-f339-4bda07b5024e"
      },
      "source": [
        "#we build config dynamically based on number of classes\n",
        "#we build iteratively from base config files. This is the same file shape as cfg/yolo-obj.cfg\n",
        "def file_len(fname):\n",
        "  with open(fname) as f:\n",
        "    for i, l in enumerate(f):\n",
        "      pass\n",
        "  return i + 1\n",
        "\n",
        "num_classes = file_len('train/_darknet.labels')\n",
        "print(\"writing config for a custom YOLOv4 detector detecting number of classes: \" + str(num_classes))\n",
        "\n",
        "#Instructions from the darknet repo\n",
        "#change line max_batches to (classes*2000 but not less than number of training images, and not less than 6000), f.e. max_batches=6000 if you train for 3 classes\n",
        "#change line steps to 80% and 90% of max_batches, f.e. steps=4800,5400\n",
        "if os.path.exists('./cfg/custom-yolov4-detector.cfg'): os.remove('./cfg/custom-yolov4-detector.cfg')\n",
        "\n",
        "\n",
        "with open('./cfg/custom-yolov4-detector.cfg', 'a') as f:\n",
        "  f.write('[net]' + '\\n')\n",
        "  f.write('batch=64' + '\\n')\n",
        "  #####smaller subdivisions help the GPU run faster. 12 is optimal, but you might need to change to 24,36,64####\n",
        "  f.write('subdivisions=24' + '\\n')\n",
        "  f.write('width=416' + '\\n')\n",
        "  f.write('height=416' + '\\n')\n",
        "  f.write('channels=3' + '\\n')\n",
        "  f.write('momentum=0.949' + '\\n')\n",
        "  f.write('decay=0.0005' + '\\n')\n",
        "  f.write('angle=0' + '\\n')\n",
        "  f.write('saturation = 1.5' + '\\n')\n",
        "  f.write('exposure = 1.5' + '\\n')\n",
        "  f.write('hue = .1' + '\\n')\n",
        "  f.write('\\n')\n",
        "  f.write('learning_rate=0.001' + '\\n')\n",
        "  f.write('burn_in=1000' + '\\n')\n",
        "  ######you can adjust up and down to change training time#####\n",
        "  ##Darknet does iterations with batches, not epochs####\n",
        "  #max_batches = num_classes*2000\n",
        "  max_batches = 1000\n",
        "  f.write('max_batches=' + str(max_batches) + '\\n')\n",
        "  f.write('policy=steps' + '\\n')\n",
        "  steps1 = .8 * max_batches\n",
        "  steps2 = .9 * max_batches\n",
        "  f.write('steps='+str(steps1)+','+str(steps2) + '\\n')\n",
        "\n",
        "#Instructions from the darknet repo\n",
        "#change line classes=80 to your number of objects in each of 3 [yolo]-layers:\n",
        "#change [filters=255] to filters=(classes + 5)x3 in the 3 [convolutional] before each [yolo] layer, keep in mind that it only has to be the last [convolutional] before each of the [yolo] layers.\n",
        "\n",
        "  with open('cfg/yolov4-custom2.cfg', 'r') as f2:\n",
        "    content = f2.readlines()\n",
        "    for line in content:\n",
        "      f.write(line)    \n",
        "    num_filters = (num_classes + 5) * 3\n",
        "    f.write('filters='+str(num_filters) + '\\n')\n",
        "    f.write('activation=linear')\n",
        "    f.write('\\n')\n",
        "    f.write('\\n')\n",
        "    f.write('[yolo]' + '\\n')\n",
        "    f.write('mask = 0,1,2' + '\\n')\n",
        "    f.write('anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401' + '\\n')\n",
        "    f.write('classes=' + str(num_classes) + '\\n')\n",
        "\n",
        "  with open('cfg/yolov4-custom3.cfg', 'r') as f3:\n",
        "    content = f3.readlines()\n",
        "    for line in content:\n",
        "      f.write(line)    \n",
        "    num_filters = (num_classes + 5) * 3\n",
        "    f.write('filters='+str(num_filters) + '\\n')\n",
        "    f.write('activation=linear')\n",
        "    f.write('\\n')\n",
        "    f.write('\\n')\n",
        "    f.write('[yolo]' + '\\n')\n",
        "    f.write('mask = 3,4,5' + '\\n')\n",
        "    f.write('anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401' + '\\n')\n",
        "    f.write('classes=' + str(num_classes) + '\\n')\n",
        "\n",
        "  with open('cfg/yolov4-custom4.cfg', 'r') as f4:\n",
        "    content = f4.readlines()\n",
        "    for line in content:\n",
        "      f.write(line)    \n",
        "    num_filters = (num_classes + 5) * 3\n",
        "    f.write('filters='+str(num_filters) + '\\n')\n",
        "    f.write('activation=linear')\n",
        "    f.write('\\n')\n",
        "    f.write('\\n')\n",
        "    f.write('[yolo]' + '\\n')\n",
        "    f.write('mask = 6,7,8' + '\\n')\n",
        "    f.write('anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401' + '\\n')\n",
        "    f.write('classes=' + str(num_classes) + '\\n')\n",
        "    \n",
        "  with open('cfg/yolov4-custom5.cfg', 'r') as f5:\n",
        "    content = f5.readlines()\n",
        "    for line in content:\n",
        "      f.write(line)\n",
        "\n",
        "print(\"file is written!\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "writing config for a custom YOLOv4 detector detecting number of classes: 9\n",
            "file is written!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u2LAciMh4Cut",
        "outputId": "24b2989a-233f-4850-ed0f-74518d40ccaa"
      },
      "source": [
        "#here is the file that was just written. \n",
        "#you may consider adjusting certain things\n",
        "\n",
        "#like the number of subdivisions 64 runs faster but Colab GPU may not be big enough\n",
        "#if Colab GPU memory is too small, you will need to adjust subdivisions to 16\n",
        "%cat cfg/yolov4-tiny.cfg"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[net]\n",
            "# Testing\n",
            "#batch=1\n",
            "#subdivisions=1\n",
            "# Training\n",
            "batch=64\n",
            "subdivisions=1\n",
            "width=416\n",
            "height=416\n",
            "channels=3\n",
            "momentum=0.9\n",
            "decay=0.0005\n",
            "angle=0\n",
            "saturation = 1.5\n",
            "exposure = 1.5\n",
            "hue=.1\n",
            "\n",
            "learning_rate=0.00261\n",
            "burn_in=1000\n",
            "\n",
            "max_batches = 1200\n",
            "policy=steps\n",
            "steps=1000,1200\n",
            "scales=.1,.1\n",
            "\n",
            "\n",
            "#weights_reject_freq=1001\n",
            "#ema_alpha=0.9998\n",
            "#equidistant_point=1000\n",
            "#num_sigmas_reject_badlabels=3\n",
            "#badlabels_rejection_percentage=0.2\n",
            "\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=32\n",
            "size=3\n",
            "stride=2\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=3\n",
            "stride=2\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers=-1\n",
            "groups=2\n",
            "group_id=1\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=32\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=32\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers = -1,-2\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers = -6,-1\n",
            "\n",
            "[maxpool]\n",
            "size=2\n",
            "stride=2\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers=-1\n",
            "groups=2\n",
            "group_id=1\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers = -1,-2\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers = -6,-1\n",
            "\n",
            "[maxpool]\n",
            "size=2\n",
            "stride=2\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers=-1\n",
            "groups=2\n",
            "group_id=1\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers = -1,-2\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers = -6,-1\n",
            "\n",
            "[maxpool]\n",
            "size=2\n",
            "stride=2\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "##################################\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "filters=255\n",
            "activation=linear\n",
            "\n",
            "\n",
            "\n",
            "[yolo]\n",
            "mask = 3,4,5\n",
            "anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319\n",
            "classes=80\n",
            "num=6\n",
            "jitter=.3\n",
            "scale_x_y = 1.05\n",
            "cls_normalizer=1.0\n",
            "iou_normalizer=0.07\n",
            "iou_loss=ciou\n",
            "ignore_thresh = .7\n",
            "truth_thresh = 1\n",
            "random=0\n",
            "resize=1.5\n",
            "nms_kind=greedynms\n",
            "beta_nms=0.6\n",
            "#new_coords=1\n",
            "#scale_x_y = 2.0\n",
            "\n",
            "[route]\n",
            "layers = -4\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[upsample]\n",
            "stride=2\n",
            "\n",
            "[route]\n",
            "layers = -1, 23\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "filters=255\n",
            "activation=linear\n",
            "\n",
            "[yolo]\n",
            "mask = 1,2,3\n",
            "anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319\n",
            "classes=80\n",
            "num=6\n",
            "jitter=.3\n",
            "scale_x_y = 1.05\n",
            "cls_normalizer=1.0\n",
            "iou_normalizer=0.07\n",
            "iou_loss=ciou\n",
            "ignore_thresh = .7\n",
            "truth_thresh = 1\n",
            "random=0\n",
            "resize=1.5\n",
            "nms_kind=greedynms\n",
            "beta_nms=0.6\n",
            "#new_coords=1\n",
            "#scale_x_y = 2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vWrG9EGamSpH"
      },
      "source": [
        "# Train Custom YOLOv4 Detector"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6miYFbvExqMd",
        "outputId": "3fcda8be-f1e1-4b52-8db2-1843bdc742a1"
      },
      "source": [
        "!./darknet detector train data/obj.data cfg/yolov4-tiny.cfg yolov4-tiny.weights -dont_show -map\n",
        "#!./darknet detector train data/obj.data cfg/custom-yolov4-detector.cfg yolov4.conv.137 -dont_show >> custom_yolov4_loss.log\n",
        "#If you get CUDA out of memory adjust subdivisions above!\n",
        "#adjust max batches down for shorter training above"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " CUDA-version: 11000 (11020), cuDNN: 7.6.5, GPU count: 1  \n",
            " OpenCV version: 3.2.0\n",
            " Prepare additional network for mAP calculation...\n",
            " compute_capability = 750, cudnn_half = 0 \n",
            "net.optimized_memory = 0 \n",
            "mini_batch = 1, batch = 1, time_steps = 1, train = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 conv     32       3 x 3/ 2    416 x 416 x   3 ->  208 x 208 x  32 0.075 BF\n",
            "   1 conv     64       3 x 3/ 2    208 x 208 x  32 ->  104 x 104 x  64 0.399 BF\n",
            "   2 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF\n",
            "   3 route  2 \t\t                       1/2 ->  104 x 104 x  32 \n",
            "   4 conv     32       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  32 0.199 BF\n",
            "   5 conv     32       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  32 0.199 BF\n",
            "   6 route  5 4 \t                           ->  104 x 104 x  64 \n",
            "   7 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n",
            "   8 route  2 7 \t                           ->  104 x 104 x 128 \n",
            "   9 max                2x 2/ 2    104 x 104 x 128 ->   52 x  52 x 128 0.001 BF\n",
            "  10 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  11 route  10 \t\t                       1/2 ->   52 x  52 x  64 \n",
            "  12 conv     64       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x  64 0.199 BF\n",
            "  13 conv     64       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x  64 0.199 BF\n",
            "  14 route  13 12 \t                           ->   52 x  52 x 128 \n",
            "  15 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  16 route  10 15 \t                           ->   52 x  52 x 256 \n",
            "  17 max                2x 2/ 2     52 x  52 x 256 ->   26 x  26 x 256 0.001 BF\n",
            "  18 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  19 route  18 \t\t                       1/2 ->   26 x  26 x 128 \n",
            "  20 conv    128       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 128 0.199 BF\n",
            "  21 conv    128       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 128 0.199 BF\n",
            "  22 route  21 20 \t                           ->   26 x  26 x 256 \n",
            "  23 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  24 route  18 23 \t                           ->   26 x  26 x 512 \n",
            "  25 max                2x 2/ 2     26 x  26 x 512 ->   13 x  13 x 512 0.000 BF\n",
            "  26 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n",
            "  27 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF\n",
            "  28 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF\n",
            "  29 conv    255       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 255 0.044 BF\n",
            "  30 yolo\n",
            "Error: l.outputs == params.inputs \n",
            "filters= in the [convolutional]-layer doesn't correspond to classes= or mask= in [yolo]-layer \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "7hxHBZrIBBCX",
        "outputId": "a3a38341-1502-460b-c32b-de6ecf0c5c96"
      },
      "source": [
        "import argparse\n",
        "import sys\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "def yolo_plot_log(log_file):\n",
        "    f = open(log_file)\n",
        "\n",
        "    lines = [line.rstrip(\"\\n\") for line in f.readlines()]\n",
        "\n",
        "    numbers = {'1', '2', '3', '4', '5', '6', '7', '8', '9'}\n",
        "\n",
        "    iters = []\n",
        "    loss = []\n",
        "\n",
        "    fig, ax = plt.subplots()\n",
        "\n",
        "    prev_line = \"\"\n",
        "    for line in lines:\n",
        "        args = line.split(' ')\n",
        "        if len(args) < 2:\n",
        "          continue\n",
        "        if args[1][-1:] == ':' and args[2][0] in numbers:\n",
        "            iters.append(int(args[1][0:-1]))\n",
        "            loss.append(float(args[2][0:-1]))\n",
        "\n",
        "    ax.plot(iters, loss)\n",
        "    plt.xlabel('iters')\n",
        "    plt.ylabel('loss')\n",
        "    plt.grid()\n",
        "\n",
        "    ticks = range(0, 250, 10)\n",
        "\n",
        "    # ax.set_yticks(ticks)\n",
        "    plt.show()\n",
        "\n",
        "yolo_plot_log(\"custom_yolov4_loss.log\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-7229b5ea93ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m \u001b[0myolo_plot_log\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"custom_yolov4_loss.log\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-1-7229b5ea93ae>\u001b[0m in \u001b[0;36myolo_plot_log\u001b[0;34m(log_file)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0myolo_plot_log\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'custom_yolov4_loss.log'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nBnwpBV5ZXxQ"
      },
      "source": [
        "# Infer Custom Objects with Saved YOLOv4 Weights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzoJQQw8Zdco"
      },
      "source": [
        "#define utility function\n",
        "def imShow(path):\n",
        "  import cv2\n",
        "  import matplotlib.pyplot as plt\n",
        "  %matplotlib inline\n",
        "\n",
        "  image = cv2.imread(path)\n",
        "  height, width = image.shape[:2]\n",
        "  resized_image = cv2.resize(image,(3*width, 3*height), interpolation = cv2.INTER_CUBIC)\n",
        "\n",
        "  fig = plt.gcf()\n",
        "  fig.set_size_inches(18, 10)\n",
        "  plt.axis(\"off\")\n",
        "  #plt.rcParams['figure.figsize'] = [10, 5]\n",
        "  plt.imshow(cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB))\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e3dJB6NZv4kh",
        "outputId": "9761008f-0237-4c48-c91a-9b514f6dcf57"
      },
      "source": [
        "#check if weigths have saved yet\n",
        "#backup houses the last weights for our detector\n",
        "#(file yolo-obj_last.weights will be saved to the build\\darknet\\x64\\backup\\ for each 100 iterations)\n",
        "#(file yolo-obj_xxxx.weights will be saved to the build\\darknet\\x64\\backup\\ for each 1000 iterations)\n",
        "#After training is complete - get result yolo-obj_final.weights from path build\\darknet\\x64\\bac\n",
        "!ls backup\n",
        "#if it is empty you haven't trained for long enough yet, you need to train for at least 100 iterations"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "custom-yolov4-detector_1000.weights   custom-yolov4-detector_last.weights\n",
            "custom-yolov4-detector_final.weights\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-_E3O5Mf4Mf"
      },
      "source": [
        "#coco.names is hardcoded somewhere in the detector\n",
        "%cp data/obj.names data/coco.names"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NjKzw2TvZrOQ",
        "outputId": "7a02ba57-2b64-4fe2-df08-8c379a47b6c3"
      },
      "source": [
        "\n",
        "#/test has images that we can test our detector on\n",
        "test_images = [f for f in os.listdir('test') if f.endswith('.jpg')]\n",
        "import random\n",
        "img_path = \"test/\" + random.choice(test_images);\n",
        "\n",
        "#test out our detector!\n",
        "!./darknet detect cfg/custom-yolov4-detector.cfg backup/custom-yolov4-detector_final.weights {img_path} -dont-show\n",
        "imShow('/content/darknet/predictions.jpg')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " CUDA-version: 11000 (11020), cuDNN: 7.6.5, GPU count: 1  \n",
            " OpenCV version: 3.2.0\n",
            " compute_capability = 750, cudnn_half = 0 \n",
            "net.optimized_memory = 0 \n",
            "mini_batch = 1, batch = 24, time_steps = 1, train = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 conv     32       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  32 0.299 BF\n",
            "   1 conv     64       3 x 3/ 2    416 x 416 x  32 ->  208 x 208 x  64 1.595 BF\n",
            "   2 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n",
            "   3 route  1 \t\t                           ->  208 x 208 x  64 \n",
            "   4 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n",
            "   5 conv     32       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  32 0.177 BF\n",
            "   6 conv     64       3 x 3/ 1    208 x 208 x  32 ->  208 x 208 x  64 1.595 BF\n",
            "   7 Shortcut Layer: 4,  wt = 0, wn = 0, outputs: 208 x 208 x  64 0.003 BF\n",
            "   8 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n",
            "   9 route  8 2 \t                           ->  208 x 208 x 128 \n",
            "  10 conv     64       1 x 1/ 1    208 x 208 x 128 ->  208 x 208 x  64 0.709 BF\n",
            "  11 conv    128       3 x 3/ 2    208 x 208 x  64 ->  104 x 104 x 128 1.595 BF\n",
            "  12 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF\n",
            "  13 route  11 \t\t                           ->  104 x 104 x 128 \n",
            "  14 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF\n",
            "  15 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n",
            "  16 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF\n",
            "  17 Shortcut Layer: 14,  wt = 0, wn = 0, outputs: 104 x 104 x  64 0.001 BF\n",
            "  18 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n",
            "  19 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF\n",
            "  20 Shortcut Layer: 17,  wt = 0, wn = 0, outputs: 104 x 104 x  64 0.001 BF\n",
            "  21 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n",
            "  22 route  21 12 \t                           ->  104 x 104 x 128 \n",
            "  23 conv    128       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x 128 0.354 BF\n",
            "  24 conv    256       3 x 3/ 2    104 x 104 x 128 ->   52 x  52 x 256 1.595 BF\n",
            "  25 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            "  26 route  24 \t\t                           ->   52 x  52 x 256 \n",
            "  27 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            "  28 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  29 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  31 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  32 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  34 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  35 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  37 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  38 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  39 Shortcut Layer: 36,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  40 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  41 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  42 Shortcut Layer: 39,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  43 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  44 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  45 Shortcut Layer: 42,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  46 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  47 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  48 Shortcut Layer: 45,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  49 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  50 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  51 Shortcut Layer: 48,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  52 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  53 route  52 25 \t                           ->   52 x  52 x 256 \n",
            "  54 conv    256       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 256 0.354 BF\n",
            "  55 conv    512       3 x 3/ 2     52 x  52 x 256 ->   26 x  26 x 512 1.595 BF\n",
            "  56 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            "  57 route  55 \t\t                           ->   26 x  26 x 512 \n",
            "  58 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            "  59 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  60 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  62 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  63 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  64 Shortcut Layer: 61,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  65 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  66 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  67 Shortcut Layer: 64,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  68 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  69 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  70 Shortcut Layer: 67,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  71 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  72 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  73 Shortcut Layer: 70,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  74 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  75 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  76 Shortcut Layer: 73,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  77 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  78 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  79 Shortcut Layer: 76,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  80 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  81 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  82 Shortcut Layer: 79,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  83 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  84 route  83 56 \t                           ->   26 x  26 x 512 \n",
            "  85 conv    512       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 512 0.354 BF\n",
            "  86 conv   1024       3 x 3/ 2     26 x  26 x 512 ->   13 x  13 x1024 1.595 BF\n",
            "  87 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            "  88 route  86 \t\t                           ->   13 x  13 x1024 \n",
            "  89 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            "  90 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n",
            "  91 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n",
            "  92 Shortcut Layer: 89,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n",
            "  93 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n",
            "  94 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n",
            "  95 Shortcut Layer: 92,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n",
            "  96 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n",
            "  97 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n",
            "  98 Shortcut Layer: 95,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n",
            "  99 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n",
            " 100 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n",
            " 101 Shortcut Layer: 98,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n",
            " 102 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n",
            " 103 route  102 87 \t                           ->   13 x  13 x1024 \n",
            " 104 conv   1024       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x1024 0.354 BF\n",
            " 105 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 106 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            " 107 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 108 max                5x 5/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.002 BF\n",
            " 109 route  107 \t\t                           ->   13 x  13 x 512 \n",
            " 110 max                9x 9/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.007 BF\n",
            " 111 route  107 \t\t                           ->   13 x  13 x 512 \n",
            " 112 max               13x13/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.015 BF\n",
            " 113 route  112 110 108 107 \t                   ->   13 x  13 x2048 \n",
            " 114 conv    512       1 x 1/ 1     13 x  13 x2048 ->   13 x  13 x 512 0.354 BF\n",
            " 115 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            " 116 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 117 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF\n",
            " 118 upsample                 2x    13 x  13 x 256 ->   26 x  26 x 256\n",
            " 119 route  85 \t\t                           ->   26 x  26 x 512 \n",
            " 120 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 121 route  120 118 \t                           ->   26 x  26 x 512 \n",
            " 122 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 123 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n",
            " 124 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 125 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n",
            " 126 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 127 conv    128       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 128 0.044 BF\n",
            " 128 upsample                 2x    26 x  26 x 128 ->   52 x  52 x 128\n",
            " 129 route  54 \t\t                           ->   52 x  52 x 256 \n",
            " 130 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            " 131 route  130 128 \t                           ->   52 x  52 x 256 \n",
            " 132 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            " 133 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n",
            " 134 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            " 135 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n",
            " 136 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            " 137 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n",
            " 138 conv     42       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x  42 0.058 BF\n",
            " 139 yolo\n",
            "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.20\n",
            "nms_kind: greedynms (1), beta = 0.600000 \n",
            " 140 route  136 \t\t                           ->   52 x  52 x 128 \n",
            " 141 conv    256       3 x 3/ 2     52 x  52 x 128 ->   26 x  26 x 256 0.399 BF\n",
            " 142 route  141 126 \t                           ->   26 x  26 x 512 \n",
            " 143 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 144 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n",
            " 145 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 146 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n",
            " 147 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 148 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n",
            " 149 conv     42       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x  42 0.029 BF\n",
            " 150 yolo\n",
            "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.10\n",
            "nms_kind: greedynms (1), beta = 0.600000 \n",
            " 151 route  147 \t\t                           ->   26 x  26 x 256 \n",
            " 152 conv    512       3 x 3/ 2     26 x  26 x 256 ->   13 x  13 x 512 0.399 BF\n",
            " 153 route  152 116 \t                           ->   13 x  13 x1024 \n",
            " 154 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 155 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            " 156 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 157 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            " 158 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 159 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            " 160 conv     42       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x  42 0.015 BF\n",
            " 161 yolo\n",
            "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.05\n",
            "nms_kind: greedynms (1), beta = 0.600000 \n",
            "Total BFLOPS 59.621 \n",
            "avg_outputs = 490830 \n",
            " Allocate additional workspace_size = 52.43 MB \n",
            "Loading weights from backup/custom-yolov4-detector_final.weights...\n",
            " seen 64, trained: 48 K-images (0 Kilo-batches_64) \n",
            "Done! Loaded 162 layers from weights-file \n",
            "test/IMG_9398_jpg.rf.3ac8e72bd333bfcab91110ef3ce0cb35.jpg: Predicted in 43.941000 milli-seconds.\n",
            "Unable to init server: Could not connect: Connection refused\n",
            "\n",
            "(predictions:2337): Gtk-\u001b[1;33mWARNING\u001b[0m **: \u001b[34m05:58:25.617\u001b[0m: cannot open display: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}