Pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e9ba9954-3366-4810-9b5e-430f3694b460",
   "metadata": {},
   "source": [
    "# YOLOR installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d703b9cc-b645-4dd3-8f55-4f1ebc309067",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cloning into 'yolor'...\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "git clone https://github.com/WongKinYiu/yolor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2be4a8a5-ce78-474b-8f66-e512e837adee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " team_4.csv     team_4.md    team_4_similarity_classifier.ipynb   test_team.csv\r\n",
      " team_4.ipynb   team_4.mp4  'Team 4 v2.mov'                       \u001b[0m\u001b[01;34myolor\u001b[0m/\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e7406e9b-d50d-4bb4-9f0c-285c757e3a3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jovyan/activities_data/hi__paris_2022_hackathon/final_challenge/group_00086_shared_workspace/submissions/yolor\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.4/60.4 MB\u001b[0m \u001b[31m33.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25h\u001b[33m  WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError(\"HTTPSConnectionPool(host='files.pythonhosted.org', port=443): Read timed out. (read timeout=15)\")': /packages/05/2d/bb4611cf053eaa679f6086b78cff2776ff1d51a15fe5e063cdcbfc6b5577/torch-1.7.0-cp38-cp38-manylinux1_x86_64.whl\u001b[0m\u001b[33m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m776.8/776.8 MB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.8/12.8 MB\u001b[0m \u001b[31m67.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m39.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for pycocotools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
     ]
    }
   ],
   "source": [
    "# pip install required packages\n",
    "%cd yolor\n",
    "!pip install -qr requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8035c1e2-1645-4457-8bb1-809b68f7a7db",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cloning into 'mish-cuda'...\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "# install mish-cuda if you want to use mish activation\n",
    "# https://github.com/thomasbrandon/mish-cuda\n",
    "# https://github.com/JunnYu/mish-cuda\n",
    "git clone https://github.com/JunnYu/mish-cuda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "07bb9293-433f-4565-9f1c-ac0486c91e37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'\n",
      "running build\n",
      "running build_py\n",
      "creating build\n",
      "creating build/lib.linux-x86_64-3.8\n",
      "creating build/lib.linux-x86_64-3.8/mish_cuda\n",
      "copying src/mish_cuda/__init__.py -> build/lib.linux-x86_64-3.8/mish_cuda\n",
      "running egg_info\n",
      "creating src/mish_cuda.egg-info\n",
      "writing src/mish_cuda.egg-info/PKG-INFO\n",
      "writing dependency_links to src/mish_cuda.egg-info/dependency_links.txt\n",
      "writing requirements to src/mish_cuda.egg-info/requires.txt\n",
      "writing top-level names to src/mish_cuda.egg-info/top_level.txt\n",
      "writing manifest file 'src/mish_cuda.egg-info/SOURCES.txt'\n",
      "reading manifest file 'src/mish_cuda.egg-info/SOURCES.txt'\n",
      "adding license file 'LICENSE'\n",
      "writing manifest file 'src/mish_cuda.egg-info/SOURCES.txt'\n",
      "running build_ext\n",
      "building 'mish_cuda._C' extension\n",
      "creating build/temp.linux-x86_64-3.8\n",
      "creating build/temp.linux-x86_64-3.8/csrc\n",
      "creating build/temp.linux-x86_64-3.8/csrc/cpu\n",
      "creating build/temp.linux-x86_64-3.8/csrc/cuda\n",
      "gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.8/site-packages/torch/include -I/opt/conda/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.8/site-packages/torch/include/TH -I/opt/conda/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.8 -c csrc/cpu/mish_cpu.cpp -o build/temp.linux-x86_64-3.8/csrc/cpu/mish_cpu.o -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14\n",
      "gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.8/site-packages/torch/include -I/opt/conda/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.8/site-packages/torch/include/TH -I/opt/conda/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.8 -c csrc/cuda/mish_cuda.cpp -o build/temp.linux-x86_64-3.8/csrc/cuda/mish_cuda.o -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++\n",
      "In file included from /opt/conda/lib/python3.8/site-packages/torch/include/ATen/Parallel.h:149,\n",
      "                 from /opt/conda/lib/python3.8/site-packages/torch/include/ATen/CPUApplyUtils.h:3,\n",
      "                 from csrc/cpu/mish_cpu.cpp:3:\n",
      "/opt/conda/lib/python3.8/site-packages/torch/include/ATen/ParallelOpenMP.h:84: warning: ignoring #pragma omp parallel [-Wunknown-pragmas]\n",
      "   84 | #pragma omp parallel for if ((end - begin) >= grain_size)\n",
      "      | \n",
      "cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++\n",
      "In file included from /opt/conda/lib/python3.8/site-packages/torch/include/ATen/Parallel.h:149,\n",
      "                 from /opt/conda/lib/python3.8/site-packages/torch/include/torch/csrc/api/include/torch/utils.h:3,\n",
      "                 from /opt/conda/lib/python3.8/site-packages/torch/include/torch/csrc/api/include/torch/nn/cloneable.h:5,\n",
      "                 from /opt/conda/lib/python3.8/site-packages/torch/include/torch/csrc/api/include/torch/nn.h:3,\n",
      "                 from /opt/conda/lib/python3.8/site-packages/torch/include/torch/csrc/api/include/torch/all.h:12,\n",
      "                 from /opt/conda/lib/python3.8/site-packages/torch/include/torch/extension.h:4,\n",
      "                 from csrc/cuda/mish_cuda.cpp:2:\n",
      "/opt/conda/lib/python3.8/site-packages/torch/include/ATen/ParallelOpenMP.h:84: warning: ignoring #pragma omp parallel [-Wunknown-pragmas]\n",
      "   84 | #pragma omp parallel for if ((end - begin) >= grain_size)\n",
      "      | \n",
      "/opt/conda/lib/python3.8/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at  /pytorch/c10/cuda/CUDAFunctions.cpp:100.)\n",
      "  return torch._C._cuda_getDeviceCount() > 0\n",
      "/opt/conda/lib/python3.8/distutils/extension.py:131: UserWarning: Unknown Extension options: 'headers'\n",
      "  warnings.warn(msg)\n",
      "/opt/conda/lib/python3.8/site-packages/torch/utils/cpp_extension.py:339: UserWarning: Attempted to use ninja as the BuildExtension backend but we could not find ninja.. Falling back to using the slow distutils backend.\n",
      "  warnings.warn(msg.format('we could not find ninja.'))\n",
      "Traceback (most recent call last):\n",
      "  File \"setup.py\", line 13, in <module>\n",
      "    setup(name='mish_cuda',\n",
      "  File \"/opt/conda/lib/python3.8/site-packages/setuptools/__init__.py\", line 153, in setup\n",
      "    return distutils.core.setup(**attrs)\n",
      "  File \"/opt/conda/lib/python3.8/distutils/core.py\", line 148, in setup\n",
      "    dist.run_commands()\n",
      "  File \"/opt/conda/lib/python3.8/distutils/dist.py\", line 966, in run_commands\n",
      "    self.run_command(cmd)\n",
      "  File \"/opt/conda/lib/python3.8/distutils/dist.py\", line 985, in run_command\n",
      "    cmd_obj.run()\n",
      "  File \"/opt/conda/lib/python3.8/distutils/command/build.py\", line 135, in run\n",
      "    self.run_command(cmd_name)\n",
      "  File \"/opt/conda/lib/python3.8/distutils/cmd.py\", line 313, in run_command\n",
      "    self.distribution.run_command(command)\n",
      "  File \"/opt/conda/lib/python3.8/distutils/dist.py\", line 985, in run_command\n",
      "    cmd_obj.run()\n",
      "  File \"/opt/conda/lib/python3.8/site-packages/setuptools/command/build_ext.py\", line 79, in run\n",
      "    _build_ext.run(self)\n",
      "  File \"/opt/conda/lib/python3.8/site-packages/Cython/Distutils/old_build_ext.py\", line 186, in run\n",
      "    _build_ext.build_ext.run(self)\n",
      "  File \"/opt/conda/lib/python3.8/distutils/command/build_ext.py\", line 340, in run\n",
      "    self.build_extensions()\n",
      "  File \"/opt/conda/lib/python3.8/site-packages/torch/utils/cpp_extension.py\", line 653, in build_extensions\n",
      "    build_ext.build_extensions(self)\n",
      "  File \"/opt/conda/lib/python3.8/site-packages/Cython/Distutils/old_build_ext.py\", line 195, in build_extensions\n",
      "    _build_ext.build_ext.build_extensions(self)\n",
      "  File \"/opt/conda/lib/python3.8/distutils/command/build_ext.py\", line 449, in build_extensions\n",
      "    self._build_extensions_serial()\n",
      "  File \"/opt/conda/lib/python3.8/distutils/command/build_ext.py\", line 474, in _build_extensions_serial\n",
      "    self.build_extension(ext)\n",
      "  File \"/opt/conda/lib/python3.8/site-packages/setuptools/command/build_ext.py\", line 202, in build_extension\n",
      "    _build_ext.build_extension(self, ext)\n",
      "  File \"/opt/conda/lib/python3.8/distutils/command/build_ext.py\", line 528, in build_extension\n",
      "    objects = self.compiler.compile(sources,\n",
      "  File \"/opt/conda/lib/python3.8/distutils/ccompiler.py\", line 574, in compile\n",
      "    self._compile(obj, src, ext, cc_args, extra_postargs, pp_opts)\n",
      "  File \"/opt/conda/lib/python3.8/site-packages/torch/utils/cpp_extension.py\", line 400, in unix_wrap_single_compile\n",
      "    cflags = unix_cuda_flags(cflags)\n",
      "  File \"/opt/conda/lib/python3.8/site-packages/torch/utils/cpp_extension.py\", line 377, in unix_cuda_flags\n",
      "    cflags + _get_cuda_arch_flags(cflags) +\n",
      "  File \"/opt/conda/lib/python3.8/site-packages/torch/utils/cpp_extension.py\", line 1407, in _get_cuda_arch_flags\n",
      "    capability = torch.cuda.get_device_capability()\n",
      "  File \"/opt/conda/lib/python3.8/site-packages/torch/cuda/__init__.py\", line 291, in get_device_capability\n",
      "    prop = get_device_properties(device)\n",
      "  File \"/opt/conda/lib/python3.8/site-packages/torch/cuda/__init__.py\", line 296, in get_device_properties\n",
      "    _lazy_init()  # will define _get_device_properties\n",
      "  File \"/opt/conda/lib/python3.8/site-packages/torch/cuda/__init__.py\", line 172, in _lazy_init\n",
      "    torch._C._cuda_init()\n",
      "RuntimeError: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "cd mish-cuda\n",
    "python setup.py build install\n",
    "cd ..\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b2c56ee2-7b74-4487-982c-baee7d8e8412",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cloning into 'pytorch_wavelets'...\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "# install pytorch_wavelets if you want to use dwt down-sampling module\n",
    "# https://github.com/fbcotter/pytorch_wavelets\n",
    "git clone https://github.com/fbcotter/pytorch_wavelets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "84080aea-f6b2-42ea-b96f-40f5a09fd9b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing /home/jovyan/activities_data/hi__paris_2022_hackathon/final_challenge/group_00086_shared_workspace/submissions/yolor/pytorch_wavelets\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.8/site-packages (from pytorch-wavelets==1.3.0) (1.19.5)\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.8/site-packages (from pytorch-wavelets==1.3.0) (1.16.0)\n",
      "Requirement already satisfied: torch in /opt/conda/lib/python3.8/site-packages (from pytorch-wavelets==1.3.0) (1.7.0)\n",
      "Requirement already satisfied: dataclasses in /opt/conda/lib/python3.8/site-packages (from torch->pytorch-wavelets==1.3.0) (0.6)\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.8/site-packages (from torch->pytorch-wavelets==1.3.0) (4.1.1)\n",
      "Requirement already satisfied: future in /opt/conda/lib/python3.8/site-packages (from torch->pytorch-wavelets==1.3.0) (0.18.2)\n",
      "Building wheels for collected packages: pytorch-wavelets\n",
      "  Building wheel for pytorch-wavelets (setup.py): started\n",
      "  Building wheel for pytorch-wavelets (setup.py): finished with status 'done'\n",
      "  Created wheel for pytorch-wavelets: filename=pytorch_wavelets-1.3.0-py3-none-any.whl size=54869 sha256=775e8f3f4eb019875fdd174c6270530aa10284510e92456b641af7b11f8e6691\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-1tb4twis/wheels/02/61/e9/0b3a361b5b2fbe471cbfcac62431a1a1d1657dd403ce28f88b\n",
      "Successfully built pytorch-wavelets\n",
      "Installing collected packages: pytorch-wavelets\n",
      "Successfully installed pytorch-wavelets-1.3.0\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "cd pytorch_wavelets\n",
    "pip install .\n",
    "cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5b31d767-dbb2-4617-b107-eafdcbde0c31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Errno 2] No such file or directory: 'yolor'\n",
      "/home/jovyan/activities_data/hi__paris_2022_hackathon/final_challenge/group_00086_shared_workspace/submissions/yolor\n"
     ]
    }
   ],
   "source": [
    "%cd yolor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d5beda9f-125a-4c90-b325-bc8160b5e11c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Namespace(agnostic_nms=False, augment=False, cfg='cfg/yolor_p6.cfg', classes=None, conf_thres=0.25, device='0', img_size=1280, iou_thres=0.5, names='data/coco.names', output='inference/output', save_txt=False, source='/home/jovyan/activities_data/hi__paris_2022_hackathon/final_challenge/datasets/datasets_test/test', update=False, view_img=False, weights=['yolor_p6.pt'])\r\n",
      "/opt/conda/lib/python3.8/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at  /pytorch/c10/cuda/CUDAFunctions.cpp:100.)\r\n",
      "  return torch._C._cuda_getDeviceCount() > 0\r\n",
      "Traceback (most recent call last):\r\n",
      "  File \"detect.py\", line 186, in <module>\r\n",
      "    detect()\r\n",
      "  File \"detect.py\", line 36, in detect\r\n",
      "    device = select_device(opt.device)\r\n",
      "  File \"/home/jovyan/activities_data/hi__paris_2022_hackathon/final_challenge/group_00086_shared_workspace/submissions/yolor/utils/torch_utils.py\", line 47, in select_device\r\n",
      "    assert torch.cuda.is_available(), 'CUDA unavailable, invalid device %s requested' % device  # check availablity\r\n",
      "AssertionError: CUDA unavailable, invalid device 0 requested\r\n"
     ]
    }
   ],
   "source": [
    "!python3 detect.py --cfg cfg/yolor_p6.cfg --weights yolor_p6.pt --source '/home/jovyan/activities_data/hi__paris_2022_hackathon/final_challenge/datasets/datasets_test/test' --conf 0.25 --img-size 1280 --device 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d00504c4-b8de-4605-aa1a-a1e12f63e4be",
   "metadata": {},
   "source": [
    "# ML_decoder installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1487582e-37f8-4bda-8a67-f8d4aef70845",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cloning into 'ml_decoder'...\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "git clone https://github.com/alibaba-miil/ml_decoder\n",
    "cd ml_decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6589b9f7-d768-4242-b3fd-f4bb4f6b67c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[01;34mcfg\u001b[0m/       \u001b[01;34mfigure\u001b[0m/     \u001b[01;34mml_decoder\u001b[0m/        requirements.txt  tune.py\r\n",
      "\u001b[01;34mdarknet\u001b[0m/   \u001b[01;34minference\u001b[0m/  \u001b[01;34mmodels\u001b[0m/            \u001b[01;34mscripts\u001b[0m/          \u001b[01;34mutils\u001b[0m/\r\n",
      "\u001b[01;34mdata\u001b[0m/      LICENSE     \u001b[01;34mpytorch_wavelets\u001b[0m/  test.py\r\n",
      "detect.py  \u001b[01;34mmish-cuda\u001b[0m/  README.md          train.py\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "475b1340-cc4c-4867-b08d-1ed31705e6a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jovyan/activities_data/hi__paris_2022_hackathon/final_challenge/group_00086_shared_workspace/submissions/yolor/ml_decoder\n",
      "Requirement already satisfied: torch>=1.7 in /opt/conda/lib/python3.8/site-packages (from -r requirements.txt (line 1)) (1.7.0)\n",
      "Requirement already satisfied: torchvision>=0.5.0 in /opt/conda/lib/python3.8/site-packages (from -r requirements.txt (line 2)) (0.8.1)\n",
      "Collecting inplace-abn\n",
      "  Downloading inplace-abn-1.1.0.tar.gz (137 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m137.3/137.3 KB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "Requirement already satisfied: pycocotools in /opt/conda/lib/python3.8/site-packages (from -r requirements.txt (line 4)) (2.0.0)\n",
      "Collecting randaugment\n",
      "  Downloading randaugment-1.0.2-py3-none-any.whl (6.8 kB)\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.8/site-packages (from torch>=1.7->-r requirements.txt (line 1)) (4.1.1)\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.8/site-packages (from torch>=1.7->-r requirements.txt (line 1)) (1.19.5)\n",
      "Requirement already satisfied: future in /opt/conda/lib/python3.8/site-packages (from torch>=1.7->-r requirements.txt (line 1)) (0.18.2)\n",
      "Requirement already satisfied: dataclasses in /opt/conda/lib/python3.8/site-packages (from torch>=1.7->-r requirements.txt (line 1)) (0.6)\n",
      "Requirement already satisfied: pillow>=4.1.1 in /opt/conda/lib/python3.8/site-packages (from torchvision>=0.5.0->-r requirements.txt (line 2)) (8.4.0)\n",
      "Building wheels for collected packages: inplace-abn\n",
      "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
      "  \n",
      "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py bdist_wheel\u001b[0m did not run successfully.\n",
      "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
      "  \u001b[31m╰─>\u001b[0m See above for output.\n",
      "  \n",
      "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
      "  Building wheel for inplace-abn (setup.py) ... \u001b[?25lerror\n",
      "\u001b[31m  ERROR: Failed building wheel for inplace-abn\u001b[0m\u001b[31m\n",
      "\u001b[0m\u001b[?25h  Running setup.py clean for inplace-abn\n",
      "Failed to build inplace-abn\n",
      "Installing collected packages: randaugment, inplace-abn\n",
      "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
      "  \n",
      "  \u001b[31m×\u001b[0m \u001b[32mRunning setup.py install for inplace-abn\u001b[0m did not run successfully.\n",
      "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
      "  \u001b[31m╰─>\u001b[0m See above for output.\n",
      "  \n",
      "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
      "  Running setup.py install for inplace-abn ... \u001b[?25l\u001b[?25herror\n",
      "\u001b[1;31merror\u001b[0m: \u001b[1mlegacy-install-failure\u001b[0m\n",
      "\n",
      "\u001b[31m×\u001b[0m Encountered error while trying to install package.\n",
      "\u001b[31m╰─>\u001b[0m inplace-abn\n",
      "\n",
      "\u001b[1;35mnote\u001b[0m: This is an issue with the package mentioned above, not pip.\n",
      "\u001b[1;36mhint\u001b[0m: See above for output from the failure.\n",
      "/home/jovyan/activities_data/hi__paris_2022_hackathon/final_challenge/group_00086_shared_workspace/submissions/yolor\n"
     ]
    }
   ],
   "source": [
    "%cd ml_decoder\n",
    "!pip install -r requirements.txt\n",
    "%cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c00782d7-ab0e-4e94-84be-f656efccebfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting inplace-abn==1.0.11\n",
      "  Downloading inplace-abn-1.0.11.tar.gz (136 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m136.8/136.8 KB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "Building wheels for collected packages: inplace-abn\n",
      "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
      "  \n",
      "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py bdist_wheel\u001b[0m did not run successfully.\n",
      "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
      "  \u001b[31m╰─>\u001b[0m See above for output.\n",
      "  \n",
      "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
      "  Building wheel for inplace-abn (setup.py) ... \u001b[?25lerror\n",
      "\u001b[31m  ERROR: Failed building wheel for inplace-abn\u001b[0m\u001b[31m\n",
      "\u001b[0m\u001b[?25h  Running setup.py clean for inplace-abn\n",
      "Failed to build inplace-abn\n",
      "Installing collected packages: inplace-abn\n",
      "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
      "  \n",
      "  \u001b[31m×\u001b[0m \u001b[32mRunning setup.py install for inplace-abn\u001b[0m did not run successfully.\n",
      "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
      "  \u001b[31m╰─>\u001b[0m See above for output.\n",
      "  \n",
      "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
      "  Running setup.py install for inplace-abn ... \u001b[?25l\u001b[?25herror\n",
      "\u001b[1;31merror\u001b[0m: \u001b[1mlegacy-install-failure\u001b[0m\n",
      "\n",
      "\u001b[31m×\u001b[0m Encountered error while trying to install package.\n",
      "\u001b[31m╰─>\u001b[0m inplace-abn\n",
      "\n",
      "\u001b[1;35mnote\u001b[0m: This is an issue with the package mentioned above, not pip.\n",
      "\u001b[1;36mhint\u001b[0m: See above for output from the failure.\n"
     ]
    }
   ],
   "source": [
    "!pip install inplace-abn==1.0.11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "43f040da-164c-4f67-802a-97e2b51163c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Errno 2] No such file or directory: 'yolor'\n",
      "/home/jovyan/activities_data/hi__paris_2022_hackathon/final_challenge/group_00086_shared_workspace/submissions/yolor\n",
      "Namespace(agnostic_nms=False, augment=False, cfg='cfg/yolor_p6.cfg', classes=None, conf_thres=0.45, device='0', img_size=1280, iou_thres=0.5, names='data/coco.names', output='inference/test2', save_txt=False, source='/home/jovyan/activities_data/hi__paris_2022_hackathon/final_challenge/datasets/datasets_test/test', update=False, view_img=False, weights=['yolor_p6.pt'])\n",
      "/opt/conda/lib/python3.8/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at  /pytorch/c10/cuda/CUDAFunctions.cpp:100.)\n",
      "  return torch._C._cuda_getDeviceCount() > 0\n",
      "Traceback (most recent call last):\n",
      "  File \"detect.py\", line 186, in <module>\n",
      "    detect()\n",
      "  File \"detect.py\", line 36, in detect\n",
      "    device = select_device(opt.device)\n",
      "  File \"/home/jovyan/activities_data/hi__paris_2022_hackathon/final_challenge/group_00086_shared_workspace/submissions/yolor/utils/torch_utils.py\", line 47, in select_device\n",
      "    assert torch.cuda.is_available(), 'CUDA unavailable, invalid device %s requested' % device  # check availablity\n",
      "AssertionError: CUDA unavailable, invalid device 0 requested\n",
      "/home/jovyan/activities_data/hi__paris_2022_hackathon/final_challenge/group_00086_shared_workspace/submissions\n"
     ]
    }
   ],
   "source": [
    "#doing the detections\n",
    "%cd yolor\n",
    "!python3 detect.py --cfg cfg/yolor_p6.cfg --weights yolor_p6.pt --source '/home/jovyan/activities_data/hi__paris_2022_hackathon/final_challenge/datasets/datasets_test/test' --conf 0.45 --img-size 1280 --device 0 --output inference/test2\n",
    "%cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "076c823b-80f5-4c05-8ca4-2d6e014ca1dc",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Expected object or value",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_3575/416725282.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mpred_boxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'yolor/inference/test2/results.json'\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      5\u001b[0m \u001b[0mpred_boxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\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      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    197\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    297\u001b[0m                 )\n\u001b[1;32m    298\u001b[0m                 \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFutureWarning\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 299\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/pandas/io/json/_json.py\u001b[0m in \u001b[0;36mread_json\u001b[0;34m(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, lines, chunksize, compression, nrows, storage_options)\u001b[0m\n\u001b[1;32m    561\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 563\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\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[0m\u001b[1;32m    564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/pandas/io/json/_json.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    692\u001b[0m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_combine_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_lines\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    693\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 694\u001b[0;31m             \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\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    695\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\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    696\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/pandas/io/json/_json.py\u001b[0m in \u001b[0;36m_get_object_parser\u001b[0;34m(self, json)\u001b[0m\n\u001b[1;32m    714\u001b[0m         \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    715\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"frame\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 716\u001b[0;31m             \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFrameParser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\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[0m\u001b[1;32m    717\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"series\"\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/pandas/io/json/_json.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    829\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    830\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 831\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse_no_numpy\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[0m\u001b[1;32m    832\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    833\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/pandas/io/json/_json.py\u001b[0m in \u001b[0;36m_parse_no_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1077\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0morient\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1078\u001b[0m             self.obj = DataFrame(\n\u001b[0;32m-> 1079\u001b[0;31m                 \u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprecise_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprecise_float\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1080\u001b[0m             )\n\u001b[1;32m   1081\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0morient\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"split\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Expected object or value"
     ]
    }
   ],
   "source": [
    "#detecting the models\n",
    "import pandas as pd\n",
    "\n",
    "pred_boxes = pd.read_json('yolor/inference/test2/results.json')\n",
    "pred_boxes.head()\n",
    "\n",
    "#pred_boxes = pd.read_csv('datasets/datasets_train/train_annotation/_annotation.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "31c39f1b-b781-46a7-b40e-d3b3cdc9a4a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jovyan/activities_data/hi__paris_2022_hackathon/final_challenge/ml_decoder\n"
     ]
    }
   ],
   "source": [
    "%cd ml_decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6841ae2c-e292-4c3d-9104-2e1dee14a7df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference code on a single image\n",
      "creating model tresnet_l...\n",
      "done\n",
      "done\n",
      "\n",
      "loading image and doing inference...\n",
      "10236585.jpg ['AM_General_Hummer_SUV_2000']\n",
      "10284606.jpg ['Dodge_Durango_SUV_2007']\n",
      "10330960.jpg ['Jeep_Grand_Cherokee_SUV_2012' 'Jeep_Compass_SUV_2012']\n",
      "1087676.jpg ['Honda_Accord_Coupe_2012']\n",
      "10890044.jpg ['Infiniti_QX56_SUV_2011']\n",
      "1102508.jpg ['Audi_S5_Coupe_2012' 'Audi_A5_Coupe_2012']\n",
      "11252080.jpg ['Dodge_Caliber_Wagon_2007' 'Dodge_Caliber_Wagon_2012']\n",
      "11718488785.jpg ['Volvo_C30_Hatchback_2012']\n",
      "11828073.jpg ['Cadillac_SRX_SUV_2012']\n",
      "12396802.jpg ['Hyundai_Elantra_Sedan_2007']\n",
      "12530979.jpg ['Jeep_Liberty_SUV_2012']\n",
      "12668515.jpg ['Chevrolet_Malibu_Sedan_2007']\n",
      "12690737.jpg ['Hyundai_Genesis_Sedan_2012']\n",
      "12701714.jpg ['Chevrolet_Malibu_Sedan_2007' 'Chevrolet_Impala_Sedan_2007']\n",
      "13015113.jpg ['BMW_ActiveHybrid_5_Sedan_2012']\n",
      "13027277.jpg ['Ford_Mustang_Convertible_2007']\n",
      "13120897.jpg ['Audi_TT_RS_Coupe_2012' 'Audi_TT_Hatchback_2011' 'Audi_TTS_Coupe_2012']\n",
      "13367391.jpg ['Ford_F-150_Regular_Cab_2007']\n",
      "1344173.jpg ['HUMMER_H3T_Crew_Cab_2010']\n",
      "13647605.jpg ['Maybach_Landaulet_Convertible_2012']\n",
      "13908523.jpg ['Fisker_Karma_Sedan_2012']\n",
      "13974480745.jpg ['Ford_F-150_Regular_Cab_2007' 'Dodge_Ram_Pickup_3500_Quad_Cab_2009'\n",
      " 'Ford_Ranger_SuperCab_2011']\n",
      "14034307428.jpg ['HUMMER_H2_SUT_Crew_Cab_2009' 'AM_General_Hummer_SUV_2000']\n",
      "14265783.jpg ['Ford_Freestar_Minivan_2007']\n",
      "14335186.jpg ['Chevrolet_Camaro_Convertible_2012']\n",
      "14472176731.jpg ['Spyker_C8_Convertible_2009' 'Hyundai_Veloster_Hatchback_2012']\n",
      "14824499.jpg ['Eagle_Talon_Hatchback_1998']\n",
      "14856829.jpg ['Hyundai_Veracruz_SUV_2012']\n",
      "14887948.jpg ['Ford_E-Series_Wagon_Van_2012']\n",
      "1492490.jpg ['Ford_Focus_Sedan_2007']\n",
      "15253084438.jpg []\n",
      "error 15755972.jpg\n",
      "15797052.jpg ['Volvo_C30_Hatchback_2012']\n",
      "15931783.jpg ['FIAT_500_Convertible_2012' 'FIAT_500_Abarth_2012']\n",
      "15976603.jpg ['Volvo_XC90_SUV_2007']\n",
      "1607467.jpg ['Cadillac_SRX_SUV_2012']\n",
      "16112108.jpg ['Ford_Mustang_Convertible_2007']\n",
      "16607966867.jpg []\n",
      "16843658.jpg ['Scion_xD_Hatchback_2012']\n",
      "16999606.jpg ['Dodge_Durango_SUV_2007']\n",
      "17073306.jpg ['Honda_Odyssey_Minivan_2012' 'Honda_Odyssey_Minivan_2007']\n",
      "17141891.jpg ['Lamborghini_Gallardo_LP_570-4_Superleggera_2012']\n",
      "17210969.jpg ['Mercedes-Benz_Sprinter_Van_2012' 'Dodge_Sprinter_Cargo_Van_2009']\n",
      "173334.jpg ['Chevrolet_Express_Van_2007' 'GMC_Savana_Van_2012'\n",
      " 'Chevrolet_Express_Cargo_Van_2007']\n",
      "17695244.jpg ['Chevrolet_Silverado_1500_Classic_Extended_Cab_2007'\n",
      " 'Chevrolet_Avalanche_Crew_Cab_2012']\n",
      "17802078.jpg ['Lamborghini_Reventon_Coupe_2008']\n",
      "18187605.jpg ['Hyundai_Sonata_Hybrid_Sedan_2012']\n",
      "18406656.jpg ['Dodge_Charger_Sedan_2012']\n",
      "1852263.jpg ['Honda_Odyssey_Minivan_2012' 'Honda_Odyssey_Minivan_2007']\n",
      "18533815.jpg ['Nissan_Leaf_Hatchback_2012']\n",
      "18767599.jpg ['Dodge_Caliber_Wagon_2012' 'Dodge_Caliber_Wagon_2007']\n",
      "18790797.jpg ['Lamborghini_Reventon_Coupe_2008']\n",
      "18871609.jpg ['Mercedes-Benz_C-Class_Sedan_2012']\n",
      "19049851.jpg ['Ford_F-450_Super_Duty_Crew_Cab_2012']\n",
      "19200464.jpg ['Suzuki_SX4_Sedan_2012' 'Suzuki_SX4_Hatchback_2012'\n",
      " 'Suzuki_Aerio_Sedan_2007']\n",
      "19226795804.jpg ['Volkswagen_Golf_Hatchback_1991' 'Bentley_Arnage_Sedan_2009']\n",
      "19401604.jpg ['Ford_E-Series_Wagon_Van_2012']\n",
      "19725231.jpg ['Acura_ZDX_Hatchback_2012']\n",
      "19924727.jpg ['Toyota_Sequoia_SUV_2012']\n",
      "19930731.jpg ['Chevrolet_Sonic_Sedan_2012' 'Ford_Fiesta_Sedan_2012']\n",
      "19998840.jpg ['Audi_S4_Sedan_2007']\n",
      "20156761.jpg ['Eagle_Talon_Hatchback_1998']\n",
      "20165513592.jpg ['Mercedes-Benz_C-Class_Sedan_2012']\n",
      "20253335.jpg ['Nissan_Leaf_Hatchback_2012']\n",
      "20405556.jpg ['Volvo_240_Sedan_1993' 'Audi_V8_Sedan_1994']\n",
      "20558679.jpg ['Spyker_C8_Convertible_2009']\n",
      "20599930.jpg ['Hyundai_Genesis_Sedan_2012']\n",
      "20777894.jpg ['Dodge_Caliber_Wagon_2007' 'Dodge_Caliber_Wagon_2012']\n",
      "21188432.jpg []\n",
      "21359630.jpg ['BMW_3_Series_Wagon_2012' 'BMW_3_Series_Sedan_2012']\n",
      "21481258.jpg ['Chevrolet_HHR_SS_2010']\n",
      "2151809.jpg ['Mitsubishi_Lancer_Sedan_2012']\n",
      "21608766.jpg ['Lamborghini_Aventador_Coupe_2012']\n",
      "21660071.jpg ['Suzuki_SX4_Hatchback_2012']\n",
      "21770151.jpg ['Volvo_C30_Hatchback_2012']\n",
      "2180516114.jpg ['Volvo_240_Sedan_1993' 'Rolls-Royce_Phantom_Sedan_2012']\n",
      "21854514.jpg ['Scion_xD_Hatchback_2012']\n",
      "21926743.jpg ['Lamborghini_Reventon_Coupe_2008']\n",
      "2199320.jpg ['Toyota_Sequoia_SUV_2012']\n",
      "22639627.jpg ['Chevrolet_Silverado_1500_Extended_Cab_2012'\n",
      " 'Chevrolet_Silverado_1500_Hybrid_Crew_Cab_2012']\n",
      "22766866.jpg ['Audi_TT_RS_Coupe_2012' 'Volvo_C30_Hatchback_2012']\n",
      "22940362.jpg ['Ford_F-450_Super_Duty_Crew_Cab_2012']\n",
      "23378471.jpg ['Audi_TT_RS_Coupe_2012' 'Audi_TT_Hatchback_2011' 'Audi_TTS_Coupe_2012']\n",
      "23506996.jpg ['Suzuki_Kizashi_Sedan_2012']\n",
      "23729169.jpg ['Audi_TT_RS_Coupe_2012' 'Audi_TT_Hatchback_2011']\n",
      "23738114.jpg ['Honda_Accord_Sedan_2012' 'Honda_Accord_Coupe_2012']\n",
      "23748356.jpg ['Rolls-Royce_Phantom_Sedan_2012' 'Rolls-Royce_Ghost_Sedan_2012']\n",
      "23751568.jpg ['Ford_Expedition_EL_SUV_2009']\n",
      "23841269.jpg ['Mercedes-Benz_Sprinter_Van_2012']\n",
      "23931794.jpg ['Rolls-Royce_Ghost_Sedan_2012' 'Rolls-Royce_Phantom_Sedan_2012']\n",
      "23974597.jpg ['Ford_Fiesta_Sedan_2012']\n",
      "24117005.jpg ['Audi_TTS_Coupe_2012' 'Audi_TT_Hatchback_2011']\n",
      "24128766.jpg ['Buick_Rainier_SUV_2007']\n",
      "2442850439.jpg ['Dodge_Challenger_SRT8_2011']\n",
      "24686218.jpg ['Bentley_Continental_GT_Coupe_2012' 'Bentley_Continental_GT_Coupe_2007']\n",
      "24754528.jpg ['Chevrolet_Silverado_1500_Classic_Extended_Cab_2007']\n",
      "24775488.jpg ['Audi_S5_Coupe_2012' 'Audi_A5_Coupe_2012']\n",
      "25048036.jpg ['BMW_1_Series_Convertible_2012' 'BMW_1_Series_Coupe_2012']\n",
      "2507219215.jpg []\n",
      "25118658.jpg ['Lamborghini_Aventador_Coupe_2012']\n",
      "25603258.jpg ['Lamborghini_Gallardo_LP_570-4_Superleggera_2012']\n",
      "25961786.jpg ['Suzuki_SX4_Sedan_2012']\n",
      "26299855.jpg ['Chrysler_PT_Cruiser_Convertible_2008']\n",
      "26665171.jpg ['Volvo_XC90_SUV_2007']\n",
      "26863111.jpg ['Toyota_Camry_Sedan_2012']\n",
      "2749197.jpg ['Geo_Metro_Convertible_1993' 'Maybach_Landaulet_Convertible_2012']\n",
      "27845805.jpg ['Hyundai_Azera_Sedan_2012']\n",
      "27929781.jpg ['Toyota_Corolla_Sedan_2012']\n",
      "28144215.jpg ['Dodge_Magnum_Wagon_2008']\n",
      "28167579.jpg ['Infiniti_G_Coupe_IPL_2012']\n",
      "28239984.jpg ['Cadillac_SRX_SUV_2012']\n",
      "28372128.jpg ['Aston_Martin_V8_Vantage_Coupe_2012']\n",
      "28575158.jpg ['Lamborghini_Reventon_Coupe_2008']\n",
      "2864688.jpg ['Ford_F-150_Regular_Cab_2007']\n",
      "29021389.jpg ['Ford_Mustang_Convertible_2007']\n",
      "29048747.jpg ['Hyundai_Sonata_Hybrid_Sedan_2012']\n",
      "29249883.jpg ['BMW_ActiveHybrid_5_Sedan_2012']\n",
      "29304529.jpg ['Ferrari_458_Italia_Coupe_2012' 'Ferrari_458_Italia_Convertible_2012']\n",
      "29379255.jpg ['Ford_F-450_Super_Duty_Crew_Cab_2012']\n",
      "29412160.jpg ['Lamborghini_Gallardo_LP_570-4_Superleggera_2012']\n",
      "29443963.jpg ['BMW_ActiveHybrid_5_Sedan_2012']\n",
      "29733406.jpg ['Aston_Martin_Virage_Convertible_2012' 'Aston_Martin_Virage_Coupe_2012']\n",
      "29755162.jpg ['Lamborghini_Aventador_Coupe_2012']\n",
      "29763191.jpg ['Chevrolet_Silverado_1500_Classic_Extended_Cab_2007']\n",
      "29784716.jpg ['Dodge_Magnum_Wagon_2008']\n",
      "30044921.jpg ['Audi_V8_Sedan_1994' 'Audi_100_Sedan_1994']\n",
      "30124023.jpg ['Buick_Regal_GS_2012']\n",
      "30343117.jpg ['Chevrolet_Camaro_Convertible_2012']\n",
      "30585947.jpg ['Buick_Rainier_SUV_2007']\n",
      "30763744.jpg ['Cadillac_SRX_SUV_2012']\n",
      "30860806.jpg ['Chevrolet_HHR_SS_2010']\n",
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      "31194593.jpg ['Hyundai_Veracruz_SUV_2012']\n",
      "31456469.jpg ['Maybach_Landaulet_Convertible_2012']\n",
      "31567577.jpg ['BMW_1_Series_Convertible_2012' 'BMW_1_Series_Coupe_2012']\n",
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      "31706139.jpg ['Aston_Martin_V8_Vantage_Convertible_2012']\n",
      "31973610.jpg ['Lamborghini_Reventon_Coupe_2008']\n",
      "3200148.jpg []\n",
      "32850640.jpg ['Audi_TT_Hatchback_2011' 'Audi_TTS_Coupe_2012']\n",
      "33366422.jpg ['Chevrolet_HHR_SS_2010']\n",
      "33445267.jpg ['Mercedes-Benz_C-Class_Sedan_2012']\n",
      "33491581.jpg ['Dodge_Charger_Sedan_2012']\n",
      "33619615.jpg ['Ford_Fiesta_Sedan_2012']\n",
      "33703356.jpg ['Ford_Freestar_Minivan_2007']\n",
      "33711690.jpg ['Volkswagen_Beetle_Hatchback_2012']\n",
      "33724064.jpg ['Volkswagen_Beetle_Hatchback_2012']\n",
      "33936568.jpg ['Hyundai_Genesis_Sedan_2012']\n",
      "344879.jpg ['Suzuki_Aerio_Sedan_2007']\n",
      "34555915.jpg ['Chrysler_Crossfire_Convertible_2008']\n",
      "34569893.jpg []\n",
      "34600036.jpg ['Dodge_Caliber_Wagon_2012' 'Dodge_Caliber_Wagon_2007']\n",
      "34826803.jpg ['Chrysler_300_SRT-8_2010']\n",
      "35060332.jpg ['Ford_Fiesta_Sedan_2012']\n",
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      "35486583.jpg ['BMW_ActiveHybrid_5_Sedan_2012']\n",
      "35529240.jpg ['Bentley_Continental_GT_Coupe_2007' 'Bentley_Continental_GT_Coupe_2012']\n",
      "35661431.jpg ['Lamborghini_Aventador_Coupe_2012' 'Ferrari_458_Italia_Coupe_2012'\n",
      " 'Ferrari_458_Italia_Convertible_2012']\n",
      "35896044.jpg ['Land_Rover_LR2_SUV_2012']\n",
      "35963964.jpg ['Audi_V8_Sedan_1994' 'Audi_100_Sedan_1994']\n",
      "35971509.jpg ['Lamborghini_Aventador_Coupe_2012' 'Ferrari_458_Italia_Coupe_2012']\n",
      "35974020.jpg ['Geo_Metro_Convertible_1993']\n",
      "36118470.jpg ['Suzuki_SX4_Sedan_2012' 'Suzuki_SX4_Hatchback_2012']\n",
      "36173467.jpg ['Land_Rover_LR2_SUV_2012']\n",
      "36189098.jpg ['Hyundai_Veracruz_SUV_2012']\n",
      "36404717.jpg ['Honda_Accord_Coupe_2012']\n",
      "3641134.jpg []\n",
      "36819413.jpg ['Suzuki_Kizashi_Sedan_2012']\n",
      "36877656.jpg ['Hyundai_Veracruz_SUV_2012']\n",
      "37109184.jpg ['Tesla_Model_S_Sedan_2012']\n",
      "3756971.jpg ['GMC_Acadia_SUV_2012']\n",
      "37776012.jpg ['Ford_E-Series_Wagon_Van_2012']\n",
      "37848302.jpg ['Dodge_Magnum_Wagon_2008']\n",
      "37874474.jpg ['Hyundai_Genesis_Sedan_2012']\n",
      "37930450.jpg ['BMW_M5_Sedan_2010']\n",
      "38352138.jpg ['Acura_ZDX_Hatchback_2012']\n",
      "38395052.jpg ['Audi_S5_Coupe_2012' 'Audi_A5_Coupe_2012' 'Audi_S4_Sedan_2012']\n",
      "3846801.jpg ['Scion_xD_Hatchback_2012']\n",
      "38552399.jpg ['Audi_V8_Sedan_1994' 'Audi_100_Sedan_1994']\n",
      "38559539.jpg ['Volkswagen_Beetle_Hatchback_2012']\n",
      "38793995.jpg ['Lamborghini_Reventon_Coupe_2008']\n",
      "38896103.jpg ['Infiniti_QX56_SUV_2011']\n",
      "39114049.jpg ['Toyota_Corolla_Sedan_2012' 'Toyota_Camry_Sedan_2012']\n",
      "39183876.jpg ['Ford_Mustang_Convertible_2007']\n",
      "39277216.jpg ['AM_General_Hummer_SUV_2000']\n",
      "3931406.jpg ['Fisker_Karma_Sedan_2012']\n",
      "39430116.jpg ['BMW_1_Series_Convertible_2012' 'BMW_1_Series_Coupe_2012']\n",
      "40472278.jpg ['Audi_A5_Coupe_2012' 'Audi_S5_Coupe_2012']\n",
      "40595189.jpg ['Honda_Accord_Coupe_2012']\n",
      "40822312.jpg ['Jeep_Grand_Cherokee_SUV_2012' 'Jeep_Compass_SUV_2012']\n",
      "41042966.jpg ['Chrysler_300_SRT-8_2010']\n",
      "41216987.jpg ['Mitsubishi_Lancer_Sedan_2012']\n",
      "41276182.jpg ['GMC_Acadia_SUV_2012']\n",
      "41299035.jpg ['Infiniti_QX56_SUV_2011']\n",
      "41465378.jpg ['Chevrolet_Cobalt_SS_2010']\n",
      "41710520.jpg ['Eagle_Talon_Hatchback_1998']\n",
      "4176145.jpg ['Chevrolet_Silverado_1500_Extended_Cab_2012'\n",
      " 'GMC_Canyon_Extended_Cab_2012']\n",
      "42159542.jpg ['Chrysler_Crossfire_Convertible_2008']\n",
      "42215087.jpg ['Suzuki_Aerio_Sedan_2007']\n",
      "42242131.jpg ['Fisker_Karma_Sedan_2012']\n",
      "42312153.jpg ['Dodge_Magnum_Wagon_2008']\n",
      "42411253.jpg ['Ford_Freestar_Minivan_2007']\n",
      "42493269.jpg ['Volvo_C30_Hatchback_2012']\n",
      "42537318.jpg ['Ford_GT_Coupe_2006']\n",
      "43179701.jpg ['Volvo_C30_Hatchback_2012']\n",
      "43346549.jpg ['Ford_GT_Coupe_2006']\n",
      "43349339.jpg ['Mitsubishi_Lancer_Sedan_2012']\n",
      "43448361.jpg ['Hyundai_Genesis_Sedan_2012']\n",
      "43727600.jpg ['Chevrolet_HHR_SS_2010']\n",
      "43923032.jpg ['Toyota_Corolla_Sedan_2012' 'Toyota_Camry_Sedan_2012']\n",
      "44219774.jpg ['Lamborghini_Aventador_Coupe_2012']\n",
      "4426058.jpg ['Chevrolet_Silverado_1500_Classic_Extended_Cab_2007']\n",
      "4438665.jpg ['Infiniti_QX56_SUV_2011']\n",
      "44751804.jpg ['FIAT_500_Convertible_2012']\n",
      "44899498.jpg ['Suzuki_SX4_Sedan_2012']\n",
      "44997615.jpg ['Acura_TL_Type-S_2008']\n",
      "45582499.jpg ['Volvo_240_Sedan_1993']\n",
      "45737355.jpg ['Chrysler_Crossfire_Convertible_2008']\n",
      "4576576.jpg ['BMW_1_Series_Coupe_2012']\n",
      "45782553.jpg ['Audi_V8_Sedan_1994' 'Audi_100_Sedan_1994']\n",
      "45794006.jpg ['Spyker_C8_Convertible_2009' 'Spyker_C8_Coupe_2009']\n",
      "45855965.jpg ['Mercedes-Benz_Sprinter_Van_2012']\n",
      "46082306.jpg ['Aston_Martin_Virage_Coupe_2012']\n",
      "46096500.jpg ['BMW_1_Series_Convertible_2012' 'BMW_1_Series_Coupe_2012']\n",
      "46112687.jpg ['Rolls-Royce_Phantom_Sedan_2012' 'Rolls-Royce_Ghost_Sedan_2012']\n",
      "461302.jpg ['Geo_Metro_Convertible_1993']\n",
      "46345050.jpg ['Honda_Accord_Coupe_2012']\n",
      "46584890.jpg ['Ford_Ranger_SuperCab_2011']\n",
      "46689910.jpg ['Bentley_Continental_GT_Coupe_2007' 'Bentley_Continental_GT_Coupe_2012']\n",
      "46812731.jpg ['Rolls-Royce_Phantom_Drophead_Coupe_Convertible_2012'\n",
      " 'Rolls-Royce_Ghost_Sedan_2012']\n",
      "46842458.jpg ['GMC_Yukon_Hybrid_SUV_2012']\n",
      "47094896.jpg ['Audi_100_Wagon_1994']\n",
      "47330332.jpg ['Chrysler_300_SRT-8_2010' 'Dodge_Magnum_Wagon_2008']\n",
      "47709806.jpg ['Ferrari_458_Italia_Coupe_2012' 'Ferrari_458_Italia_Convertible_2012']\n",
      "4774009.jpg []\n",
      "47810113.jpg ['Buick_Regal_GS_2012']\n",
      "47909357.jpg ['Mitsubishi_Lancer_Sedan_2012']\n",
      "47961154.jpg ['Dodge_Sprinter_Cargo_Van_2009' 'Mercedes-Benz_Sprinter_Van_2012']\n",
      "4821202.jpg ['HUMMER_H3T_Crew_Cab_2010' 'HUMMER_H2_SUT_Crew_Cab_2009']\n",
      "4831124494.jpg []\n",
      "4836403887.jpg ['Lamborghini_Reventon_Coupe_2008']\n",
      "48699923.jpg ['Chevrolet_Silverado_1500_Extended_Cab_2012'\n",
      " 'Chevrolet_Silverado_1500_Regular_Cab_2012'\n",
      " 'Chevrolet_Silverado_1500_Hybrid_Crew_Cab_2012']\n",
      "48755647.jpg ['Toyota_Sequoia_SUV_2012' 'Toyota_4Runner_SUV_2012']\n",
      "48792922.jpg ['BMW_1_Series_Convertible_2012' 'BMW_1_Series_Coupe_2012']\n",
      "49000347.jpg []\n",
      "49055988.jpg ['Honda_Odyssey_Minivan_2012']\n",
      "49214204.jpg ['Chevrolet_Express_Van_2007' 'Chevrolet_Express_Cargo_Van_2007'\n",
      " 'GMC_Savana_Van_2012']\n",
      "49372989.jpg ['Volvo_C30_Hatchback_2012']\n",
      "49439691.jpg ['Aston_Martin_Virage_Convertible_2012']\n",
      "49509647.jpg ['Geo_Metro_Convertible_1993']\n",
      "49515214.jpg ['Rolls-Royce_Phantom_Drophead_Coupe_Convertible_2012'\n",
      " 'Rolls-Royce_Phantom_Sedan_2012' 'Rolls-Royce_Ghost_Sedan_2012']\n",
      "49619396.jpg ['Mitsubishi_Lancer_Sedan_2012']\n",
      "49719659.jpg ['Ford_Mustang_Convertible_2007']\n",
      "5004804.jpg ['Chevrolet_Malibu_Sedan_2007']\n",
      "50102930.jpg ['Ford_Focus_Sedan_2007']\n",
      "50390354.jpg ['Chevrolet_Corvette_ZR1_2012'\n",
      " 'Chevrolet_Corvette_Ron_Fellows_Edition_Z06_2007']\n",
      "50486455.jpg ['BMW_3_Series_Wagon_2012']\n",
      "50514338.jpg ['Volvo_240_Sedan_1993']\n",
      "50726008.jpg ['Cadillac_SRX_SUV_2012']\n",
      "50821929.jpg ['Chevrolet_TrailBlazer_SS_2009']\n",
      "50856761.jpg ['HUMMER_H2_SUT_Crew_Cab_2009' 'HUMMER_H3T_Crew_Cab_2010'\n",
      " 'AM_General_Hummer_SUV_2000']\n",
      "50922386.jpg ['Dodge_Charger_Sedan_2012']\n",
      "50941958.jpg ['Hyundai_Veracruz_SUV_2012']\n",
      "50952017.jpg ['Chevrolet_Camaro_Convertible_2012']\n",
      "5102825.jpg ['Maybach_Landaulet_Convertible_2012' 'Rolls-Royce_Phantom_Sedan_2012']\n",
      "5116251.jpg ['Infiniti_G_Coupe_IPL_2012']\n",
      "51338964.jpg ['Infiniti_QX56_SUV_2011']\n",
      "51398032.jpg ['Dodge_Charger_Sedan_2012']\n",
      "51452420.jpg ['Jeep_Liberty_SUV_2012']\n",
      "51668696.jpg ['Suzuki_Kizashi_Sedan_2012']\n",
      "51735800.jpg ['Dodge_Magnum_Wagon_2008']\n",
      "52035363.jpg ['Toyota_Sequoia_SUV_2012']\n",
      "52038279.jpg ['Chevrolet_Camaro_Convertible_2012']\n",
      "52123293.jpg ['Suzuki_Kizashi_Sedan_2012']\n",
      "52204045.jpg ['Scion_xD_Hatchback_2012']\n",
      "5279135.jpg ['Jeep_Grand_Cherokee_SUV_2012' 'Jeep_Compass_SUV_2012']\n",
      "52813347.jpg ['Rolls-Royce_Phantom_Drophead_Coupe_Convertible_2012'\n",
      " 'Rolls-Royce_Ghost_Sedan_2012' 'Rolls-Royce_Phantom_Sedan_2012']\n",
      "52999436.jpg []\n",
      "53150366.jpg ['Suzuki_SX4_Sedan_2012' 'Suzuki_Aerio_Sedan_2007'\n",
      " 'Suzuki_SX4_Hatchback_2012']\n",
      "5318870.jpg ['Toyota_Sequoia_SUV_2012']\n",
      "53367668.jpg ['Lamborghini_Aventador_Coupe_2012']\n",
      "5354234336.jpg ['Land_Rover_Range_Rover_SUV_2012']\n",
      "53642961.jpg ['BMW_X6_SUV_2012' 'BMW_X5_SUV_2007']\n",
      "53732848.jpg ['Chevrolet_Cobalt_SS_2010']\n",
      "53754528.jpg ['Buick_Rainier_SUV_2007']\n",
      "54115233.jpg ['Mercedes-Benz_C-Class_Sedan_2012' 'Mercedes-Benz_SL-Class_Coupe_2009']\n",
      "54232560.jpg ['Chevrolet_Malibu_Sedan_2007' 'Chevrolet_Impala_Sedan_2007']\n",
      "54283002.jpg ['Buick_Regal_GS_2012']\n",
      "5457985.jpg ['Suzuki_SX4_Sedan_2012' 'Suzuki_SX4_Hatchback_2012']\n",
      "error 54598045.jpg\n",
      "54968971.jpg ['Dodge_Caliber_Wagon_2012' 'Dodge_Caliber_Wagon_2007']\n",
      "55262017.jpg ['Volkswagen_Beetle_Hatchback_2012']\n",
      "55459888.jpg ['Jeep_Grand_Cherokee_SUV_2012']\n",
      "55467743.jpg []\n",
      "55775555.jpg ['Lamborghini_Aventador_Coupe_2012'\n",
      " 'Lamborghini_Gallardo_LP_570-4_Superleggera_2012']\n",
      "557876.jpg ['Volvo_C30_Hatchback_2012']\n",
      "55989707.jpg ['Ford_Expedition_EL_SUV_2009']\n",
      "56033546.jpg ['FIAT_500_Convertible_2012' 'FIAT_500_Abarth_2012']\n",
      "5626930214.jpg ['Ford_F-450_Super_Duty_Crew_Cab_2012'\n",
      " 'Chevrolet_Silverado_1500_Classic_Extended_Cab_2007'\n",
      " 'Dodge_Ram_Pickup_3500_Crew_Cab_2010']\n",
      "56356037.jpg ['Buick_Regal_GS_2012']\n",
      "56699146.jpg ['Lamborghini_Gallardo_LP_570-4_Superleggera_2012']\n",
      "56720158.jpg ['Chevrolet_Silverado_1500_Classic_Extended_Cab_2007']\n",
      "5684352.jpg ['Hyundai_Genesis_Sedan_2012']\n",
      "56894076.jpg ['Toyota_Camry_Sedan_2012' 'Toyota_Corolla_Sedan_2012']\n",
      "5690192117.jpg ['smart_fortwo_Convertible_2012']\n",
      "56999005.jpg ['Audi_S4_Sedan_2007']\n",
      "57050121.jpg ['Jeep_Wrangler_SUV_2012']\n",
      "57311619.jpg ['Aston_Martin_Virage_Coupe_2012' 'Aston_Martin_V8_Vantage_Coupe_2012']\n",
      "57330017.jpg ['Chevrolet_Corvette_ZR1_2012']\n",
      "57511866.jpg ['Honda_Accord_Coupe_2012']\n",
      "57544768.jpg ['Jaguar_XK_XKR_2012']\n",
      "57885338.jpg ['Maybach_Landaulet_Convertible_2012' 'Lincoln_Town_Car_Sedan_2011']\n",
      "57976968.jpg ['Rolls-Royce_Ghost_Sedan_2012' 'Rolls-Royce_Phantom_Sedan_2012']\n",
      "58498627.jpg ['Honda_Accord_Coupe_2012']\n",
      "58859340.jpg ['FIAT_500_Convertible_2012' 'FIAT_500_Abarth_2012']\n",
      "5920863.jpg ['Aston_Martin_Virage_Convertible_2012' 'Aston_Martin_Virage_Coupe_2012']\n",
      "59408446.jpg ['Fisker_Karma_Sedan_2012']\n",
      "59437905.jpg ['Chevrolet_Corvette_ZR1_2012']\n",
      "59454113.jpg ['Toyota_Corolla_Sedan_2012']\n",
      "59484488.jpg ['Jaguar_XK_XKR_2012']\n",
      "59825291.jpg ['BMW_3_Series_Wagon_2012']\n",
      "60172833.jpg ['Audi_100_Sedan_1994' 'Audi_V8_Sedan_1994' 'Audi_100_Wagon_1994']\n",
      "60185055.jpg ['Chevrolet_Impala_Sedan_2007' 'Chevrolet_Cobalt_SS_2010']\n",
      "60273414.jpg ['Bentley_Continental_GT_Coupe_2012' 'Bentley_Continental_GT_Coupe_2007']\n",
      "60456537.jpg ['Dodge_Durango_SUV_2007']\n",
      "60581317.jpg ['Chevrolet_Express_Van_2007' 'GMC_Savana_Van_2012'\n",
      " 'Chevrolet_Express_Cargo_Van_2007']\n",
      "6087324064.jpg ['Jeep_Wrangler_SUV_2012' 'AM_General_Hummer_SUV_2000']\n",
      "61225953.jpg ['Lamborghini_Gallardo_LP_570-4_Superleggera_2012']\n",
      "6156116.jpg ['Suzuki_Aerio_Sedan_2007']\n",
      "61823471.jpg ['Mitsubishi_Lancer_Sedan_2012' 'Audi_TT_RS_Coupe_2012']\n",
      "6189237.jpg ['Jeep_Wrangler_SUV_2012']\n",
      "6196447.jpg ['Chevrolet_TrailBlazer_SS_2009']\n",
      "62032006.jpg []\n",
      "62173877.jpg ['Porsche_Panamera_Sedan_2012']\n",
      "62290102.jpg ['Honda_Accord_Coupe_2012']\n",
      "62317382.jpg ['Ferrari_458_Italia_Convertible_2012' 'Ferrari_458_Italia_Coupe_2012']\n",
      "6242648.jpg ['Bentley_Continental_GT_Coupe_2012' 'Bentley_Continental_GT_Coupe_2007']\n",
      "62447120.jpg ['Hyundai_Genesis_Sedan_2012']\n",
      "62489679.jpg ['Chevrolet_TrailBlazer_SS_2009']\n",
      "62871224.jpg ['Hyundai_Sonata_Sedan_2012']\n",
      "6297222.jpg ['Eagle_Talon_Hatchback_1998']\n",
      "63165230.jpg ['BMW_1_Series_Convertible_2012' 'BMW_1_Series_Coupe_2012']\n",
      "63515321.jpg ['Ford_Expedition_EL_SUV_2009']\n",
      "63738119.jpg ['Jaguar_XK_XKR_2012']\n",
      "64166051.jpg ['Chevrolet_Silverado_1500_Extended_Cab_2012'\n",
      " 'GMC_Canyon_Extended_Cab_2012']\n",
      "64192383.jpg ['Ford_Ranger_SuperCab_2011']\n",
      "64251477.jpg ['Chevrolet_Corvette_ZR1_2012']\n",
      "64333922.jpg ['BMW_1_Series_Coupe_2012' 'BMW_1_Series_Convertible_2012']\n",
      "64499952.jpg ['Audi_TT_RS_Coupe_2012']\n",
      "64809720.jpg ['Porsche_Panamera_Sedan_2012']\n",
      "64951356.jpg ['Nissan_Leaf_Hatchback_2012']\n",
      "65272037.jpg ['Hyundai_Sonata_Sedan_2012']\n",
      "65336012.jpg ['BMW_1_Series_Coupe_2012']\n",
      "65404306.jpg ['Ford_Fiesta_Sedan_2012']\n",
      "65484611.jpg ['Infiniti_QX56_SUV_2011']\n",
      "65587126.jpg ['Ford_Freestar_Minivan_2007' 'Dodge_Caravan_Minivan_1997']\n",
      "65667313.jpg ['Ford_F-150_Regular_Cab_2007']\n",
      "66045962.jpg ['Scion_xD_Hatchback_2012']\n",
      "66079777.jpg ['Hyundai_Genesis_Sedan_2012']\n",
      "66219438.jpg ['FIAT_500_Convertible_2012']\n",
      "6635506.jpg ['Fisker_Karma_Sedan_2012']\n",
      "6650222.jpg ['Chevrolet_Camaro_Convertible_2012']\n",
      "66859425.jpg ['Volkswagen_Beetle_Hatchback_2012']\n",
      "66921763.jpg ['Suzuki_SX4_Sedan_2012']\n",
      "67618989.jpg ['Chevrolet_Silverado_1500_Extended_Cab_2012']\n",
      "67674109.jpg ['Fisker_Karma_Sedan_2012']\n",
      "67694009.jpg ['Spyker_C8_Convertible_2009' 'Spyker_C8_Coupe_2009']\n",
      "68256916.jpg ['Suzuki_Kizashi_Sedan_2012']\n",
      "68314277.jpg []\n",
      "6858047610.jpg []\n",
      "68643880.jpg ['Hyundai_Sonata_Sedan_2012' 'Honda_Accord_Sedan_2012']\n",
      "69207450.jpg ['Fisker_Karma_Sedan_2012']\n",
      "69223101.jpg ['FIAT_500_Convertible_2012']\n",
      "69639229.jpg ['GMC_Yukon_Hybrid_SUV_2012' 'Chevrolet_Tahoe_Hybrid_SUV_2012']\n",
      "69975288.jpg ['Aston_Martin_V8_Vantage_Coupe_2012'\n",
      " 'Aston_Martin_V8_Vantage_Convertible_2012']\n",
      "70174709.jpg ['Honda_Accord_Sedan_2012']\n",
      "70325609.jpg ['Rolls-Royce_Phantom_Sedan_2012' 'Rolls-Royce_Ghost_Sedan_2012'\n",
      " 'Rolls-Royce_Phantom_Drophead_Coupe_Convertible_2012']\n",
      "70478866.jpg ['Ford_GT_Coupe_2006']\n",
      "70745147.jpg ['Chrysler_PT_Cruiser_Convertible_2008']\n",
      "70860392.jpg ['Dodge_Caliber_Wagon_2012' 'Dodge_Caliber_Wagon_2007']\n",
      "70863871.jpg ['BMW_1_Series_Coupe_2012']\n",
      "71036277.jpg ['Volvo_C30_Hatchback_2012']\n",
      "71272194.jpg ['Rolls-Royce_Phantom_Sedan_2012']\n",
      "71389861.jpg ['AM_General_Hummer_SUV_2000' 'HUMMER_H2_SUT_Crew_Cab_2009']\n",
      "71456444.jpg ['Lamborghini_Aventador_Coupe_2012' 'Ferrari_458_Italia_Coupe_2012']\n",
      "7152533.jpg ['Acura_TL_Type-S_2008']\n",
      "71551154.jpg ['Infiniti_QX56_SUV_2011']\n",
      "71756325.jpg ['BMW_X6_SUV_2012' 'BMW_X5_SUV_2007']\n",
      "71865623.jpg ['Suzuki_Aerio_Sedan_2007']\n",
      "71895405.jpg ['Ford_E-Series_Wagon_Van_2012']\n",
      "7206587.jpg ['Spyker_C8_Coupe_2009' 'Spyker_C8_Convertible_2009']\n",
      "72199314.jpg ['Dodge_Charger_Sedan_2012']\n",
      "72431766.jpg ['Acura_ZDX_Hatchback_2012']\n",
      "7267931.jpg ['Chrysler_PT_Cruiser_Convertible_2008']\n",
      "72692422.jpg ['Toyota_Corolla_Sedan_2012']\n",
      "7280341.jpg []\n",
      "73075421.jpg ['Chevrolet_Silverado_1500_Hybrid_Crew_Cab_2012']\n",
      "73307865.jpg ['Chevrolet_Express_Cargo_Van_2007' 'Chevrolet_Express_Van_2007'\n",
      " 'GMC_Savana_Van_2012']\n",
      "73398349.jpg ['Porsche_Panamera_Sedan_2012']\n",
      "73537501.jpg ['Dodge_Durango_SUV_2007']\n",
      "73879915.jpg ['Jeep_Liberty_SUV_2012']\n",
      "73933254.jpg ['Suzuki_Aerio_Sedan_2007']\n",
      "74103170.jpg ['Bentley_Continental_GT_Coupe_2012' 'Bentley_Continental_GT_Coupe_2007']\n",
      "74285667.jpg ['Ford_Freestar_Minivan_2007']\n",
      "74343479.jpg ['Hyundai_Veracruz_SUV_2012']\n",
      "74451028.jpg ['Rolls-Royce_Phantom_Drophead_Coupe_Convertible_2012'\n",
      " 'Rolls-Royce_Phantom_Sedan_2012']\n",
      "74879521.jpg ['Ford_Focus_Sedan_2007']\n",
      "7492849.jpg ['Buick_Regal_GS_2012']\n",
      "74947621.jpg ['Jeep_Liberty_SUV_2012']\n",
      "74961161.jpg ['Audi_100_Sedan_1994' 'Audi_V8_Sedan_1994' 'Audi_100_Wagon_1994']\n",
      "7514213.jpg ['Eagle_Talon_Hatchback_1998']\n",
      "75148467.jpg ['Aston_Martin_V8_Vantage_Coupe_2012'\n",
      " 'Aston_Martin_V8_Vantage_Convertible_2012']\n",
      "75483353.jpg ['Toyota_Camry_Sedan_2012' 'Toyota_Corolla_Sedan_2012'\n",
      " 'Acura_TSX_Sedan_2012']\n",
      "75654049.jpg ['Ford_Freestar_Minivan_2007']\n",
      "7605436.jpg ['Hyundai_Azera_Sedan_2012']\n",
      "76188745.jpg ['Mercedes-Benz_300-Class_Convertible_1993']\n",
      "76189733.jpg ['Geo_Metro_Convertible_1993']\n",
      "76222490.jpg ['Audi_V8_Sedan_1994' 'Audi_100_Sedan_1994' 'Audi_100_Wagon_1994']\n",
      "76360354.jpg ['Hyundai_Azera_Sedan_2012' 'Mercedes-Benz_E-Class_Sedan_2012']\n",
      "76433914.jpg ['Infiniti_QX56_SUV_2011']\n",
      "7644001.jpg ['FIAT_500_Convertible_2012' 'FIAT_500_Abarth_2012']\n",
      "76494333.jpg ['Chevrolet_Corvette_ZR1_2012']\n",
      "76519938.jpg ['Lamborghini_Aventador_Coupe_2012']\n",
      "76578588.jpg ['Maybach_Landaulet_Convertible_2012']\n",
      "77060569.jpg ['GMC_Yukon_Hybrid_SUV_2012']\n",
      "77393109.jpg ['Aston_Martin_Virage_Coupe_2012' 'Aston_Martin_V8_Vantage_Coupe_2012']\n",
      "774098.jpg ['Volkswagen_Beetle_Hatchback_2012']\n",
      "77733310.jpg ['BMW_3_Series_Wagon_2012' 'BMW_3_Series_Sedan_2012']\n",
      "77973924.jpg ['Chevrolet_Cobalt_SS_2010']\n",
      "78711559.jpg ['Volkswagen_Beetle_Hatchback_2012']\n",
      "78980716.jpg ['Suzuki_Aerio_Sedan_2007' 'Ford_Focus_Sedan_2007']\n",
      "79097877.jpg ['Scion_xD_Hatchback_2012']\n",
      "79321410.jpg ['Rolls-Royce_Phantom_Sedan_2012' 'Rolls-Royce_Ghost_Sedan_2012']\n",
      "79329539.jpg ['Hyundai_Sonata_Sedan_2012']\n",
      "79408412.jpg ['BMW_X6_SUV_2012']\n",
      "79740569.jpg ['Volvo_240_Sedan_1993']\n",
      "7974952.jpg ['Land_Rover_LR2_SUV_2012']\n",
      "79884877.jpg ['Jeep_Liberty_SUV_2012']\n",
      "80061005.jpg ['Mercedes-Benz_C-Class_Sedan_2012' 'Mercedes-Benz_SL-Class_Coupe_2009']\n",
      "80284602.jpg ['Ferrari_458_Italia_Coupe_2012' 'Ferrari_458_Italia_Convertible_2012']\n",
      "80335377.jpg ['Nissan_Leaf_Hatchback_2012']\n",
      "80360584.jpg ['Audi_TT_RS_Coupe_2012' 'Audi_TT_Hatchback_2011' 'Audi_TTS_Coupe_2012']\n",
      "80425426.jpg ['Chevrolet_Corvette_ZR1_2012']\n",
      "80730203.jpg ['Volvo_C30_Hatchback_2012']\n",
      "80768625.jpg ['Aston_Martin_V8_Vantage_Coupe_2012' 'Aston_Martin_Virage_Coupe_2012']\n",
      "8120647.jpg ['FIAT_500_Convertible_2012']\n",
      "81404446.jpg ['Infiniti_QX56_SUV_2011']\n",
      "81820671.jpg ['Audi_V8_Sedan_1994' 'Audi_100_Sedan_1994' 'Audi_100_Wagon_1994']\n",
      "82141581.jpg ['Chrysler_300_SRT-8_2010']\n",
      "82234757.jpg ['BMW_3_Series_Sedan_2012' 'BMW_3_Series_Wagon_2012' 'BMW_M3_Coupe_2012']\n",
      "82290359.jpg ['Aston_Martin_Virage_Coupe_2012' 'Aston_Martin_V8_Vantage_Coupe_2012']\n",
      "82359436.jpg ['Lamborghini_Aventador_Coupe_2012']\n",
      "82735264.jpg ['Rolls-Royce_Phantom_Sedan_2012' 'Rolls-Royce_Ghost_Sedan_2012']\n",
      "82766281.jpg ['Audi_S5_Convertible_2012' 'Audi_RS_4_Convertible_2008']\n",
      "82814801.jpg ['Ford_E-Series_Wagon_Van_2012']\n",
      "82981701.jpg ['Ford_GT_Coupe_2006']\n",
      "83094413.jpg ['Suzuki_SX4_Sedan_2012' 'Suzuki_Aerio_Sedan_2007']\n",
      "83517864.jpg ['Acura_ZDX_Hatchback_2012']\n",
      "83641751.jpg ['Jaguar_XK_XKR_2012']\n",
      "83879213.jpg ['Chevrolet_Cobalt_SS_2010']\n",
      "83924300.jpg ['Volvo_XC90_SUV_2007']\n",
      "84029999.jpg ['Spyker_C8_Convertible_2009' 'Spyker_C8_Coupe_2009']\n",
      "84036491.jpg ['Audi_100_Sedan_1994' 'Audi_V8_Sedan_1994' 'Audi_100_Wagon_1994']\n",
      "84109337.jpg ['BMW_1_Series_Coupe_2012']\n",
      "84239890.jpg ['Lamborghini_Reventon_Coupe_2008']\n",
      "84432701.jpg ['Audi_TT_Hatchback_2011' 'Audi_TTS_Coupe_2012' 'Audi_TT_RS_Coupe_2012']\n",
      "84457502.jpg ['Suzuki_Kizashi_Sedan_2012']\n",
      "84790613.jpg ['Ferrari_458_Italia_Coupe_2012' 'Ferrari_458_Italia_Convertible_2012']\n",
      "84991909.jpg ['Nissan_240SX_Coupe_1998' 'Eagle_Talon_Hatchback_1998']\n",
      "8518534.jpg ['Ferrari_458_Italia_Convertible_2012' 'Ferrari_458_Italia_Coupe_2012']\n",
      "85211668.jpg ['Audi_S5_Convertible_2012']\n",
      "85318643.jpg ['Nissan_Leaf_Hatchback_2012']\n",
      "85427036.jpg ['Volvo_XC90_SUV_2007']\n",
      "85435846.jpg ['Audi_S5_Convertible_2012' 'Audi_RS_4_Convertible_2008']\n",
      "85569640.jpg ['Hyundai_Sonata_Hybrid_Sedan_2012']\n",
      "85668165.jpg ['BMW_3_Series_Wagon_2012' 'BMW_3_Series_Sedan_2012']\n",
      "85710645.jpg ['Lamborghini_Aventador_Coupe_2012']\n",
      "85854523.jpg ['Chevrolet_Silverado_1500_Extended_Cab_2012'\n",
      " 'GMC_Canyon_Extended_Cab_2012']\n",
      "86195834.jpg ['Lamborghini_Gallardo_LP_570-4_Superleggera_2012']\n",
      "86258149.jpg ['Ford_F-450_Super_Duty_Crew_Cab_2012']\n",
      "86336175.jpg ['Chevrolet_Corvette_ZR1_2012' 'FIAT_500_Abarth_2012']\n",
      "86752923.jpg ['Chevrolet_Corvette_ZR1_2012' 'Chevrolet_Corvette_Convertible_2012']\n",
      "87007048.jpg ['BMW_1_Series_Convertible_2012']\n",
      "87061916.jpg ['BMW_M5_Sedan_2010']\n",
      "87393015.jpg ['Suzuki_SX4_Sedan_2012' 'Suzuki_SX4_Hatchback_2012']\n",
      "8779616.jpg ['Rolls-Royce_Ghost_Sedan_2012' 'Rolls-Royce_Phantom_Sedan_2012']\n",
      "87802628.jpg ['Scion_xD_Hatchback_2012']\n",
      "88204358.jpg ['Honda_Odyssey_Minivan_2012']\n",
      "88222077.jpg ['Hyundai_Sonata_Hybrid_Sedan_2012']\n",
      "88517171.jpg ['Chevrolet_Malibu_Sedan_2007']\n",
      "88601208.jpg ['BMW_M5_Sedan_2010']\n",
      "88950593.jpg ['Audi_S4_Sedan_2007']\n",
      "89084602.jpg []\n",
      "8927100.jpg ['Aston_Martin_V8_Vantage_Convertible_2012']\n",
      "89582550.jpg []\n",
      "8982990.jpg ['BMW_3_Series_Sedan_2012' 'BMW_M3_Coupe_2012']\n",
      "89862599.jpg ['Ferrari_458_Italia_Coupe_2012' 'Ferrari_458_Italia_Convertible_2012']\n",
      "89918941.jpg ['Hyundai_Sonata_Sedan_2012']\n",
      "89936561.jpg ['FIAT_500_Convertible_2012']\n",
      "90024174.jpg ['Hyundai_Veracruz_SUV_2012']\n",
      "90027835.jpg ['Ford_Focus_Sedan_2007']\n",
      "90060863.jpg ['Volvo_XC90_SUV_2007']\n",
      "90174853.jpg ['Dodge_Magnum_Wagon_2008']\n",
      "90211090.jpg ['Honda_Accord_Coupe_2012']\n",
      "9024296.jpg ['BMW_ActiveHybrid_5_Sedan_2012']\n",
      "90248169.jpg ['Toyota_Corolla_Sedan_2012']\n",
      "9027153.jpg ['Ford_E-Series_Wagon_Van_2012']\n",
      "90303489.jpg ['Ford_F-150_Regular_Cab_2007']\n",
      "90643396.jpg ['Jeep_Liberty_SUV_2012']\n",
      "90892421.jpg ['BMW_1_Series_Convertible_2012' 'BMW_1_Series_Coupe_2012']\n",
      "91034470.jpg ['Chrysler_Crossfire_Convertible_2008']\n",
      "91128741.jpg ['Lamborghini_Gallardo_LP_570-4_Superleggera_2012'\n",
      " 'Lamborghini_Aventador_Coupe_2012']\n",
      "92223672.jpg ['Land_Rover_LR2_SUV_2012']\n",
      "92399140.jpg ['Ford_E-Series_Wagon_Van_2012']\n",
      "92424069.jpg ['Mercedes-Benz_Sprinter_Van_2012']\n",
      "92544664.jpg ['Honda_Odyssey_Minivan_2012']\n",
      "92746059.jpg ['Hyundai_Veracruz_SUV_2012']\n",
      "92803200.jpg ['Chevrolet_Impala_Sedan_2007']\n",
      "92859967.jpg ['Jaguar_XK_XKR_2012']\n",
      "92903003.jpg ['Ford_Mustang_Convertible_2007']\n",
      "92927028.jpg ['AM_General_Hummer_SUV_2000']\n",
      "92931749.jpg ['Audi_S4_Sedan_2012']\n",
      "9340708.jpg ['Nissan_Leaf_Hatchback_2012']\n",
      "93544155.jpg ['Hyundai_Azera_Sedan_2012']\n",
      "94467518.jpg ['Jaguar_XK_XKR_2012']\n",
      "94851961.jpg ['Jaguar_XK_XKR_2012']\n",
      "94887122.jpg ['Audi_S5_Convertible_2012']\n",
      "94997195.jpg ['Chrysler_300_SRT-8_2010']\n",
      "95506766.jpg ['BMW_M5_Sedan_2010']\n",
      "95516609.jpg ['Audi_100_Sedan_1994' 'Audi_V8_Sedan_1994' 'Audi_100_Wagon_1994']\n",
      "95975728.jpg ['Audi_100_Wagon_1994']\n",
      "96121127.jpg ['Audi_S4_Sedan_2007']\n",
      "9619312.jpg ['Acura_TL_Type-S_2008']\n",
      "96222322.jpg ['Lamborghini_Reventon_Coupe_2008' 'Audi_TT_RS_Coupe_2012'\n",
      " 'Lamborghini_Aventador_Coupe_2012']\n",
      "96285890.jpg ['Hyundai_Genesis_Sedan_2012']\n",
      "96503634.jpg ['Chrysler_PT_Cruiser_Convertible_2008']\n",
      "96516543.jpg ['Bentley_Continental_GT_Coupe_2012']\n",
      "9683771.jpg ['Audi_TTS_Coupe_2012' 'Audi_TT_Hatchback_2011']\n",
      "96853822.jpg ['Lamborghini_Aventador_Coupe_2012']\n",
      "97083435.jpg ['Volvo_XC90_SUV_2007']\n",
      "97154633.jpg ['Ferrari_458_Italia_Coupe_2012' 'Ferrari_458_Italia_Convertible_2012'\n",
      " 'Ferrari_FF_Coupe_2012']\n",
      "97247847.jpg ['Ford_Expedition_EL_SUV_2009']\n",
      "9737363.jpg ['Aston_Martin_Virage_Coupe_2012' 'Aston_Martin_V8_Vantage_Coupe_2012']\n",
      "97791385.jpg ['HUMMER_H3T_Crew_Cab_2010' 'HUMMER_H2_SUT_Crew_Cab_2009'\n",
      " 'AM_General_Hummer_SUV_2000']\n",
      "9797073.jpg ['Ford_Ranger_SuperCab_2011']\n",
      "98062944.jpg ['Acura_ZDX_Hatchback_2012']\n",
      "98143208.jpg ['Dodge_Charger_Sedan_2012']\n",
      "98186923.jpg ['Jeep_Grand_Cherokee_SUV_2012' 'Jeep_Compass_SUV_2012']\n",
      "98220062.jpg ['Buick_Rainier_SUV_2007']\n",
      "983702.jpg ['Audi_S5_Convertible_2012' 'Audi_S4_Sedan_2012'\n",
      " 'Audi_RS_4_Convertible_2008']\n",
      "98527197.jpg ['Dodge_Caliber_Wagon_2012' 'Dodge_Caliber_Wagon_2007']\n",
      "98745574.jpg ['Volvo_240_Sedan_1993']\n",
      "98954938.jpg ['Hyundai_Veracruz_SUV_2012']\n",
      "99007521.jpg ['Mercedes-Benz_Sprinter_Van_2012' 'Dodge_Sprinter_Cargo_Van_2009']\n",
      "99073539.jpg ['Chrysler_300_SRT-8_2010']\n",
      "99271857.jpg ['Spyker_C8_Convertible_2009' 'Spyker_C8_Coupe_2009']\n",
      "99289575.jpg ['Bentley_Continental_GT_Coupe_2007' 'Bentley_Continental_GT_Coupe_2012']\n",
      "99448327.jpg ['Chevrolet_TrailBlazer_SS_2009']\n",
      "99911939.jpg ['Infiniti_G_Coupe_IPL_2012']\n",
      "final done\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import argparse\n",
    "import time\n",
    "\n",
    "import torch\n",
    "import torch.nn.parallel\n",
    "import torch.optim\n",
    "import torch.utils.data.distributed\n",
    "\n",
    "from src_files.helper_functions.bn_fusion import fuse_bn_recursively\n",
    "from src_files.models import create_model\n",
    "import matplotlib\n",
    "\n",
    "from src_files.models.tresnet.tresnet import InplacABN_to_ABN\n",
    "\n",
    "#matplotlib.use('TkAgg')\n",
    "#matplotlib.use('TkAgg')\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "from types import SimpleNamespace\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "pred_cars = pred_boxes[(pred_boxes[\"class\"] == 2) | (pred_boxes[\"class\"] == 7)]\n",
    "\n",
    "\n",
    "image_path = '../datasets/datasets_test/test/'\n",
    "\n",
    "d_args = {'num_classes': 196, 'model_path': 'tresnet_l_stanford_card_96.41.pth', 'pic_path': '../datasets/datasets_train/train/15007024230.jpg', 'model_name': 'tresnet_l', 'image_size': 384, 'th': 0.95, 'top_k': 20, 'use_ml_decoder': 1, 'num_of_groups': -1, 'decoder_embedding': 768, 'zsl': 0}\n",
    "args = SimpleNamespace(**d_args)\n",
    "\n",
    "results = []\n",
    "model_names = []\n",
    "\n",
    "print('Inference code on a single image')\n",
    "\n",
    "\n",
    "# Setup model\n",
    "print('creating model {}...'.format(args.model_name))\n",
    "model = create_model(args, load_head=True).cuda()\n",
    "state = torch.load(args.model_path, map_location='cpu')\n",
    "model.load_state_dict(state['model'], strict=True)\n",
    "########### eliminate BN for faster inference ###########\n",
    "model = model.cpu()\n",
    "model = InplacABN_to_ABN(model)\n",
    "model = fuse_bn_recursively(model)\n",
    "model = model.cuda().half().eval()\n",
    "#######################################################\n",
    "print('done')\n",
    "\n",
    "\n",
    "classes_list = np.array(list(state['idx_to_class']))\n",
    "print('done\\n')\n",
    "\n",
    "# doing inference\n",
    "print('loading image and doing inference...')\n",
    "\n",
    "\n",
    "for index, row in pred_cars.iterrows():\n",
    "    try:\n",
    "        im = Image.open(image_path+row['im_name'])\n",
    "        plt.imshow(im)\n",
    "        im_resize = im.resize((args.image_size, args.image_size))\n",
    "        np_img = np.array(im_resize, dtype=np.uint8)\n",
    "        try:\n",
    "            tensor_img = torch.from_numpy(np_img).permute(2, 0, 1).float() / 255.0  # HWC to CHW\n",
    "        except:\n",
    "            print(row['im_name'],'failed')\n",
    "            continue\n",
    "        tensor_batch = torch.unsqueeze(tensor_img, 0).cuda().half() # float16 inference\n",
    "        output = torch.squeeze(torch.sigmoid(model(tensor_batch)))\n",
    "        np_output = output.cpu().detach().numpy()\n",
    "\n",
    "\n",
    "        ## Top-k predictions\n",
    "        # detected_classes = classes_list[np_output > args.th]\n",
    "        idx_sort = np.argsort(-np_output)\n",
    "        detected_classes = np.array(classes_list)[idx_sort][: args.top_k]\n",
    "        scores = np_output[idx_sort][: args.top_k]\n",
    "        idx_th = scores > args.th\n",
    "        detected_classes = detected_classes[idx_th]\n",
    "\n",
    "        model_name = {'im_name': row['im_name'], 'pred_models': detected_classes}\n",
    "        model_names.append(model_name)\n",
    "        print(row['im_name'],detected_classes)\n",
    "    except Error as e:\n",
    "        model_name = {'im_name': row['im_name'], 'pred_models': None}\n",
    "        model_names.append(model_name)\n",
    "        print(\"error\",row['im_name'])\n",
    "        \n",
    "\n",
    "print('final done\\n')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3ca4ea05-7f82-4150-9158-9dfdd65f04b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = pd.DataFrame(model_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "48e7ec79-6465-4d21-be31-a7fc49528a33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>im_name</th>\n",
       "      <th>pred_models</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10236585.jpg</td>\n",
       "      <td>[AM_General_Hummer_SUV_2000]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10284606.jpg</td>\n",
       "      <td>[Dodge_Durango_SUV_2007]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10330960.jpg</td>\n",
       "      <td>[Jeep_Grand_Cherokee_SUV_2012, Jeep_Compass_SU...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1087676.jpg</td>\n",
       "      <td>[Honda_Accord_Coupe_2012]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10890044.jpg</td>\n",
       "      <td>[Infiniti_QX56_SUV_2011]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        im_name                                        pred_models\n",
       "0  10236585.jpg                       [AM_General_Hummer_SUV_2000]\n",
       "1  10284606.jpg                           [Dodge_Durango_SUV_2007]\n",
       "2  10330960.jpg  [Jeep_Grand_Cherokee_SUV_2012, Jeep_Compass_SU...\n",
       "3   1087676.jpg                          [Honda_Accord_Coupe_2012]\n",
       "4  10890044.jpg                           [Infiniti_QX56_SUV_2011]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d9264e74-a812-4ab9-9693-1caf2fef4c13",
   "metadata": {},
   "outputs": [],
   "source": [
    "#cars_models = pd.merge(pred_cars, models, on='im_name',  how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c429cd7f-1d05-4bc5-9742-b6b95a78d89f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>im_name</th>\n",
       "      <th>x_min</th>\n",
       "      <th>y_min</th>\n",
       "      <th>x_max</th>\n",
       "      <th>y_max</th>\n",
       "      <th>class</th>\n",
       "      <th>models</th>\n",
       "      <th>pred_models</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>107347968.jpg</td>\n",
       "      <td>40.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>1144.0</td>\n",
       "      <td>637.0</td>\n",
       "      <td>car</td>\n",
       "      <td>Audi S5 Convertible 2012</td>\n",
       "      <td>[Audi_S5_Convertible_2012, Audi_RS_4_Convertib...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91160576.jpg</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>car</td>\n",
       "      <td>Audi 100 Wagon 1994</td>\n",
       "      <td>[Audi_100_Sedan_1994, Audi_100_Wagon_1994, Aud...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>273809408.jpg</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>car</td>\n",
       "      <td>Cadillac SRX SUV 2012</td>\n",
       "      <td>[Cadillac_SRX_SUV_2012]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>579534848.jpg</td>\n",
       "      <td>14.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>car</td>\n",
       "      <td>Ford Freestar Minivan 2007</td>\n",
       "      <td>[Ford_Freestar_Minivan_2007]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>950599680.jpg</td>\n",
       "      <td>4.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>car</td>\n",
       "      <td>Rolls-Royce Phantom Sedan 2012</td>\n",
       "      <td>[Rolls-Royce_Phantom_Sedan_2012]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         im_name  x_min  y_min   x_max  y_max class  \\\n",
       "0  107347968.jpg   40.0  244.0  1144.0  637.0   car   \n",
       "1   91160576.jpg    4.0    9.0    98.0   76.0   car   \n",
       "2  273809408.jpg   16.0    4.0   288.0  191.0   car   \n",
       "3  579534848.jpg   14.0   23.0   255.0  178.0   car   \n",
       "4  950599680.jpg    4.0   70.0   321.0  215.0   car   \n",
       "\n",
       "                           models  \\\n",
       "0        Audi S5 Convertible 2012   \n",
       "1             Audi 100 Wagon 1994   \n",
       "2           Cadillac SRX SUV 2012   \n",
       "3      Ford Freestar Minivan 2007   \n",
       "4  Rolls-Royce Phantom Sedan 2012   \n",
       "\n",
       "                                         pred_models  \n",
       "0  [Audi_S5_Convertible_2012, Audi_RS_4_Convertib...  \n",
       "1  [Audi_100_Sedan_1994, Audi_100_Wagon_1994, Aud...  \n",
       "2                            [Cadillac_SRX_SUV_2012]  \n",
       "3                       [Ford_Freestar_Minivan_2007]  \n",
       "4                   [Rolls-Royce_Phantom_Sedan_2012]  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cars_models.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "74ae1519-e28f-4934-a28a-6bf4146491d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COCO_annotation.ipynb          \u001b[0m\u001b[01;34mml_decoder\u001b[0m/       YOLOR.ipynb\n",
      "coco_dataset.json              ML_decoder.ipynb  \u001b[01;34myolov5\u001b[0m/\n",
      "\u001b[01;34mdatasets\u001b[0m/                      \u001b[01;34mml_decoder_old\u001b[0m/   YOLOV5_IOU.ipynb\n",
      "\u001b[01;34mdenoised\u001b[0m/                      tv_denoise.png    YOLOV5_results.ipynb\n",
      "denoise.ipynb                  unoised.png       yolov5s_results.csv\n",
      "Final_pipeline.ipynb           Untitled.ipynb    yolov5_train_val.ipynb\n",
      "\u001b[01;34mgroup_00086_shared_workspace\u001b[0m/  \u001b[01;34myolor\u001b[0m/\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f84547f7-0836-4cdb-89a9-fd2cf7d5867d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jovyan/activities_data/hi__paris_2022_hackathon/final_challenge\n"
     ]
    }
   ],
   "source": [
    "%cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aec1cc76-55c7-4bf0-b740-76315f3f1b31",
   "metadata": {},
   "outputs": [],
   "source": [
    "carbon = pd.read_csv('datasets/car_models_footprint.csv',sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "777f4330-e040-4d98-8027-aa437f9f9198",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>models</th>\n",
       "      <th>Brand</th>\n",
       "      <th>year</th>\n",
       "      <th>Average Urban Consumption</th>\n",
       "      <th>Average extra-urban consumption</th>\n",
       "      <th>Average mixed consumption</th>\n",
       "      <th>Average of CO2 (g per km)</th>\n",
       "      <th>Average CO type I</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ford Fiesta Sedan 2012</td>\n",
       "      <td>Ford</td>\n",
       "      <td>2012</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>3.600</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>0,421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FIAT 500 Convertible 2012</td>\n",
       "      <td>FIAT</td>\n",
       "      <td>2012</td>\n",
       "      <td>5.725000</td>\n",
       "      <td>4.175</td>\n",
       "      <td>4.775000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>0,318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chevrolet Corvette ZR1 2012</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>2012</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>8.900</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>293.000000</td>\n",
       "      <td>0,508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lamborghini Gallardo LP 570-4 Superleggera 2012</td>\n",
       "      <td>Lamborghini</td>\n",
       "      <td>2012</td>\n",
       "      <td>21.406667</td>\n",
       "      <td>9.860</td>\n",
       "      <td>14.086667</td>\n",
       "      <td>337.133333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Honda Accord Coupe 2012</td>\n",
       "      <td>Honda</td>\n",
       "      <td>2012</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>4.500</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>0,215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            models        Brand  year  \\\n",
       "0                           Ford Fiesta Sedan 2012         Ford  2012   \n",
       "1                        FIAT 500 Convertible 2012         FIAT  2012   \n",
       "2                      Chevrolet Corvette ZR1 2012    Chevrolet  2012   \n",
       "3  Lamborghini Gallardo LP 570-4 Superleggera 2012  Lamborghini  2012   \n",
       "4                          Honda Accord Coupe 2012        Honda  2012   \n",
       "\n",
       "   Average Urban Consumption  Average extra-urban consumption  \\\n",
       "0                   4.900000                            3.600   \n",
       "1                   5.725000                            4.175   \n",
       "2                  19.000000                            8.900   \n",
       "3                  21.406667                            9.860   \n",
       "4                   6.900000                            4.500   \n",
       "\n",
       "   Average mixed consumption  Average of CO2 (g per km) Average CO type I  \n",
       "0                   4.100000                 107.000000             0,421  \n",
       "1                   4.775000                 111.000000             0,318  \n",
       "2                  12.600000                 293.000000             0,508  \n",
       "3                  14.086667                 337.133333                 0  \n",
       "4                   5.400000                 141.000000             0,215  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carbon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ad9a373f-abaa-49d2-9a21-51e5e0ea331a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from difflib import SequenceMatcher\n",
    "import math \n",
    "def similar(a, b):\n",
    "    return SequenceMatcher(None, a, b).ratio()\n",
    "\n",
    "carbon_emission = []\n",
    "\n",
    "ref_models = carbon[\"models\"].values.tolist()\n",
    "\n",
    "for index, row in models.iterrows():\n",
    "    pred_models = row[\"pred_models\"]\n",
    "    best_pred = ''\n",
    "    best_score = 0\n",
    "    if type(pred_models) == float:\n",
    "        continue\n",
    "    if pred_models is None:\n",
    "        carbon_emission.append({'im_name': row['im_name'], 'e': 0})\n",
    "        continue\n",
    "    for model in pred_models:\n",
    "\n",
    "            \n",
    "        for ref in ref_models:\n",
    "            score = similar(ref,model.replace('_', ' '))             \n",
    "            if score > best_score:\n",
    "                      best_score = score\n",
    "                      best_pred = ref\n",
    "    carbon_km = carbon[carbon[\"models\"]==str(best_pred)][\"Average of CO2 (g per km)\"].values.tolist()\n",
    "    \n",
    "    if len(carbon_km) > 0:\n",
    "        carbon_emission.append({'im_name': row['im_name'], 'e': float(carbon_km[0])})\n",
    "    else:\n",
    "        carbon_emission.append({'im_name': row['im_name'], 'e': 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "47730a57-47c7-421f-be8e-c94438ef5359",
   "metadata": {},
   "outputs": [],
   "source": [
    "carbon_df = pd.DataFrame(carbon_emission)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1eb74a2d-f3db-493d-a1d3-313939a5efb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>im_name</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10236585.jpg</td>\n",
       "      <td>106.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10284606.jpg</td>\n",
       "      <td>232.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10330960.jpg</td>\n",
       "      <td>228.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1087676.jpg</td>\n",
       "      <td>141.000000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10890044.jpg</td>\n",
       "      <td>248.884615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        im_name           e\n",
       "0  10236585.jpg  106.000000\n",
       "1  10284606.jpg  232.629630\n",
       "2  10330960.jpg  228.400000\n",
       "3   1087676.jpg  141.000000\n",
       "4  10890044.jpg  248.884615"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carbon_df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "37ac8a66-48b1-494a-95c5-3ca8e7cd1eeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "cars_carbon = pd.merge(pred_cars, carbon_df, on='im_name',  how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "be6c7b3f-83e7-469d-9f81-a086912ee2fc",
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   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>10236585.jpg</td>\n",
       "      <td>16</td>\n",
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       "      <td>106.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10284606.jpg</td>\n",
       "      <td>63</td>\n",
       "      <td>342</td>\n",
       "      <td>973</td>\n",
       "      <td>793</td>\n",
       "      <td>7</td>\n",
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       "      <th>2</th>\n",
       "      <td>10330960.jpg</td>\n",
       "      <td>64</td>\n",
       "      <td>72</td>\n",
       "      <td>422</td>\n",
       "      <td>293</td>\n",
       "      <td>7</td>\n",
       "      <td>228.400000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1087676.jpg</td>\n",
       "      <td>41</td>\n",
       "      <td>196</td>\n",
       "      <td>855</td>\n",
       "      <td>564</td>\n",
       "      <td>2</td>\n",
       "      <td>141.000000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10890044.jpg</td>\n",
       "      <td>165</td>\n",
       "      <td>196</td>\n",
       "      <td>795</td>\n",
       "      <td>512</td>\n",
       "      <td>2</td>\n",
       "      <td>248.884615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        im_name  x_min  y_min  x_max  y_max  class           e\n",
       "0  10236585.jpg     16     41    243    160      7  106.000000\n",
       "1  10284606.jpg     63    342    973    793      7  232.629630\n",
       "2  10330960.jpg     64     72    422    293      7  228.400000\n",
       "3   1087676.jpg     41    196    855    564      2  141.000000\n",
       "4  10890044.jpg    165    196    795    512      2  248.884615"
      ]
     },
     "execution_count": 25,
     "metadata": {},
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   "execution_count": 36,
   "id": "e6b21337-4c7e-4c6c-ad8e-7e00438ece92",
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    {
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       "      <td>15253084438.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>134</td>\n",
       "      <td>1918</td>\n",
       "      <td>1051</td>\n",
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       "      <th>31</th>\n",
       "      <td>15755972.jpg</td>\n",
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       "      <td>196</td>\n",
       "      <td>814</td>\n",
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       "      <td>2</td>\n",
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       "      <th>37</th>\n",
       "      <td>16607966867.jpg</td>\n",
       "      <td>132</td>\n",
       "      <td>239</td>\n",
       "      <td>2005</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>21188432.jpg</td>\n",
       "      <td>43</td>\n",
       "      <td>39</td>\n",
       "      <td>217</td>\n",
       "      <td>119</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2507219215.jpg</td>\n",
       "      <td>144</td>\n",
       "      <td>250</td>\n",
       "      <td>2013</td>\n",
       "      <td>1442</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>3200148.jpg</td>\n",
       "      <td>33</td>\n",
       "      <td>217</td>\n",
       "      <td>574</td>\n",
       "      <td>390</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>34569893.jpg</td>\n",
       "      <td>106</td>\n",
       "      <td>426</td>\n",
       "      <td>572</td>\n",
       "      <td>590</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>167</th>\n",
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       "      <td>53</td>\n",
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       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>4774009.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>272</td>\n",
       "      <td>171</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>4831124494.jpg</td>\n",
       "      <td>255</td>\n",
       "      <td>239</td>\n",
       "      <td>1099</td>\n",
       "      <td>1997</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>49000347.jpg</td>\n",
       "      <td>72</td>\n",
       "      <td>144</td>\n",
       "      <td>841</td>\n",
       "      <td>550</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>52999436.jpg</td>\n",
       "      <td>29</td>\n",
       "      <td>41</td>\n",
       "      <td>251</td>\n",
       "      <td>165</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>54598045.jpg</td>\n",
       "      <td>60</td>\n",
       "      <td>121</td>\n",
       "      <td>415</td>\n",
       "      <td>285</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>55467743.jpg</td>\n",
       "      <td>48</td>\n",
       "      <td>230</td>\n",
       "      <td>961</td>\n",
       "      <td>745</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>62032006.jpg</td>\n",
       "      <td>14</td>\n",
       "      <td>35</td>\n",
       "      <td>269</td>\n",
       "      <td>135</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>68314277.jpg</td>\n",
       "      <td>57</td>\n",
       "      <td>127</td>\n",
       "      <td>545</td>\n",
       "      <td>364</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>6858047610.jpg</td>\n",
       "      <td>740</td>\n",
       "      <td>1055</td>\n",
       "      <td>3845</td>\n",
       "      <td>2416</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>396</th>\n",
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       "      <td>26</td>\n",
       "      <td>116</td>\n",
       "      <td>606</td>\n",
       "      <td>347</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>89084602.jpg</td>\n",
       "      <td>93</td>\n",
       "      <td>18</td>\n",
       "      <td>294</td>\n",
       "      <td>134</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>495</th>\n",
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       "      <td>17</td>\n",
       "      <td>86</td>\n",
       "      <td>497</td>\n",
       "      <td>315</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             im_name  x_min  y_min  x_max  y_max  class    e\n",
       "30   15253084438.jpg      0    134   1918   1051      2  0.0\n",
       "31      15755972.jpg    123    196    814    511      2  0.0\n",
       "37   16607966867.jpg    132    239   2005   1442      2  0.0\n",
       "68      21188432.jpg     43     39    217    119      2  0.0\n",
       "98    2507219215.jpg    144    250   2013   1442      7  0.0\n",
       "139      3200148.jpg     33    217    574    390      2  0.0\n",
       "151     34569893.jpg    106    426    572    590      2  0.0\n",
       "167      3641134.jpg    251     53   1815   1019      2  0.0\n",
       "235      4774009.jpg      0      9    272    171      2  0.0\n",
       "240   4831124494.jpg    255    239   1099   1997      2  0.0\n",
       "245     49000347.jpg     72    144    841    550      2  0.0\n",
       "278     52999436.jpg     29     41    251    165      7  0.0\n",
       "290     54598045.jpg     60    121    415    285      2  0.0\n",
       "294     55467743.jpg     48    230    961    745      7  0.0\n",
       "333     62032006.jpg     14     35    269    135      2  0.0\n",
       "369     68314277.jpg     57    127    545    364      2  0.0\n",
       "370   6858047610.jpg    740   1055   3845   2416      2  0.0\n",
       "396      7280341.jpg     26    116    606    347      2  0.0\n",
       "493     89084602.jpg     93     18    294    134      2  0.0\n",
       "495     89582550.jpg     17     86    497    315      2  0.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
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    }
   ],
   "source": [
    "cars_carbon[cars_carbon['e']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0cc04f1a-cdbf-4638-81bc-673010eda802",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = cars_carbon.loc[cars_carbon.e > 0, 'e'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "91ae3ea7-0cff-4314-889c-49d19d591584",
   "metadata": {},
   "outputs": [],
   "source": [
    "cars_carbon.loc[cars_carbon.e == 0, 'e'] = mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4c81d950-e222-4af9-a707-38b6a674d7da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: e, dtype: float64)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars_carbon.loc[cars_carbon.e == 0, 'e']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "17690793-ef8c-432c-a82f-08f7fe896769",
   "metadata": {},
   "outputs": [
    {
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       "      <th>3</th>\n",
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       "      <td>196</td>\n",
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       "      <td>196</td>\n",
       "      <td>795</td>\n",
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      "text/plain": [
       "        im_name  x_min  y_min  x_max  y_max  class           e\n",
       "0  10236585.jpg     16     41    243    160      7  106.000000\n",
       "1  10284606.jpg     63    342    973    793      7  232.629630\n",
       "2  10330960.jpg     64     72    422    293      7  228.400000\n",
       "3   1087676.jpg     41    196    855    564      2  141.000000\n",
       "4  10890044.jpg    165    196    795    512      2  248.884615"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "cars_carbon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9211f208-a90f-452e-93b0-cf0de61f03db",
   "metadata": {},
   "outputs": [],
   "source": [
    "cars_carbon.drop(['class'],axis=1).to_csv('final_results.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4277843a-3fb0-451e-b01f-e6bffe09b6aa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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