pose_estimate.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# Roboflow 설치\n",
        "!pip install roboflow\n",
        "\n",
        "# YOLOv8 설치\n",
        "!pip install ultralytics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xdtav0WqPhD-",
        "outputId": "17e1e086-9774-47fb-d990-5ba68bb26a48"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting roboflow\n",
            "  Downloading roboflow-1.1.36-py3-none-any.whl (76 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.7/76.7 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from roboflow) (2024.7.4)\n",
            "Collecting chardet==4.0.0 (from roboflow)\n",
            "  Downloading chardet-4.0.0-py2.py3-none-any.whl (178 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m178.7/178.7 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: idna==3.7 in /usr/local/lib/python3.10/dist-packages (from roboflow) (3.7)\n",
            "Requirement already satisfied: cycler in /usr/local/lib/python3.10/dist-packages (from roboflow) (0.12.1)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.4.5)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from roboflow) (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.25.2)\n",
            "Requirement already satisfied: opencv-python-headless==4.10.0.84 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.10.0.84)\n",
            "Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from roboflow) (9.4.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.8.2)\n",
            "Collecting python-dotenv (from roboflow)\n",
            "  Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.31.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.16.0)\n",
            "Requirement already satisfied: urllib3>=1.26.6 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.0.7)\n",
            "Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.66.4)\n",
            "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (6.0.1)\n",
            "Collecting requests-toolbelt (from roboflow)\n",
            "  Downloading requests_toolbelt-1.0.0-py2.py3-none-any.whl (54 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.5/54.5 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting filetype (from roboflow)\n",
            "  Downloading filetype-1.2.0-py2.py3-none-any.whl (19 kB)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (1.2.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (4.53.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (24.1)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (3.1.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->roboflow) (3.3.2)\n",
            "Installing collected packages: filetype, python-dotenv, chardet, requests-toolbelt, roboflow\n",
            "  Attempting uninstall: chardet\n",
            "    Found existing installation: chardet 5.2.0\n",
            "    Uninstalling chardet-5.2.0:\n",
            "      Successfully uninstalled chardet-5.2.0\n",
            "Successfully installed chardet-4.0.0 filetype-1.2.0 python-dotenv-1.0.1 requests-toolbelt-1.0.0 roboflow-1.1.36\n",
            "Collecting ultralytics\n",
            "  Downloading ultralytics-8.2.58-py3-none-any.whl (802 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m802.7/802.7 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy<2.0.0,>=1.23.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.25.2)\n",
            "Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (3.7.1)\n",
            "Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.8.0.76)\n",
            "Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (9.4.0)\n",
            "Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (6.0.1)\n",
            "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.31.0)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.11.4)\n",
            "Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.3.0+cu121)\n",
            "Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.18.0+cu121)\n",
            "Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.66.4)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from ultralytics) (5.9.5)\n",
            "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.10/dist-packages (from ultralytics) (9.0.0)\n",
            "Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.0.3)\n",
            "Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.13.1)\n",
            "Collecting ultralytics-thop>=2.0.0 (from ultralytics)\n",
            "  Downloading ultralytics_thop-2.0.0-py3-none-any.whl (25 kB)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.53.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (24.1)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->ultralytics) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->ultralytics) (2024.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2024.7.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.15.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (1.13.0)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2023.6.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.20.5 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2.3.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.8.0->ultralytics)\n",
            "  Downloading nvidia_nvjitlink_cu12-12.5.82-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m48.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.5)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)\n",
            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, ultralytics-thop, ultralytics\n",
            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105 ultralytics-8.2.58 ultralytics-thop-2.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Roboflow API를 사용하여 데이터셋 다운로드\n",
        "from roboflow import Roboflow\n",
        "rf = Roboflow(api_key=\"S1EtPb2dI29rmxjT3Zr5\")\n",
        "project = rf.workspace(\"faizan-ali-bjbfp\").project(\"jump_fall_combined\")\n",
        "version = project.version(3)\n",
        "dataset = version.download(\"yolov8\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J0YlqgmhPbb6",
        "outputId": "23a638e8-4390-4af4-eff2-31202afaba5b"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n",
            "Dependency ultralytics==8.0.196 is required but found version=8.2.58, to fix: `pip install ultralytics==8.0.196`\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading Dataset Version Zip in Jump_Fall_Combined-3 to yolov8:: 100%|██████████| 114290/114290 [00:02<00:00, 38338.50it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "Extracting Dataset Version Zip to Jump_Fall_Combined-3 in yolov8:: 100%|██████████| 4605/4605 [00:01<00:00, 3567.71it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "icZvWc1mNemc",
        "outputId": "ae73b99f-4af9-4ee1-82f7-e673c8ec4f2b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'ultralytics.models.yolo.model.YOLO'>\n"
          ]
        }
      ],
      "source": [
        "# PyTorch 및 기타 종속성 설치\n",
        "import torch\n",
        "import torchvision\n",
        "import ultralytics\n",
        "from google.colab import files\n",
        "import os\n",
        "\n",
        "# YOLOv8 확인\n",
        "from ultralytics import YOLO\n",
        "print(YOLO)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 모델 생성\n",
        "model = YOLO('yolov8n-pose.pt')  # 프리트레인 모델 사용\n",
        "\n",
        "\n",
        "# 데이터 경로 설정\n",
        "data_path = dataset.location+\"/data.yaml\"\n",
        "\n",
        "\n",
        "# 학습\n",
        "model.train(data=data_path, epochs=50, imgsz=640, project='runs/my_project', name='pose_estimation')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A1vnX9N8NpfD",
        "outputId": "62d13345-c359-40bd-85e8-6d26b33cbda9"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-pose.pt to 'yolov8n-pose.pt'...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 6.52M/6.52M [00:00<00:00, 115MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ultralytics YOLOv8.2.58 🚀 Python-3.10.12 torch-2.3.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=pose, mode=train, model=yolov8n-pose.pt, data=/content/Jump_Fall_Combined-3/data.yaml, epochs=50, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=runs/my_project, name=pose_estimation, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/my_project/pose_estimation\n",
            "Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 755k/755k [00:00<00:00, 18.8MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overriding model.yaml kpt_shape=[17, 3] with kpt_shape=[12, 3]\n",
            "Overriding model.yaml nc=1 with nc=6\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 \n",
            "  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                \n",
            "  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             \n",
            "  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n",
            "  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             \n",
            "  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n",
            "  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           \n",
            "  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
            "  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           \n",
            "  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 \n",
            " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 \n",
            " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  \n",
            " 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n",
            " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 \n",
            " 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
            " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 \n",
            " 22        [15, 18, 21]  1    937054  ultralytics.nn.modules.head.Pose             [6, [12, 3], [64, 128, 256]]  \n",
            "YOLOv8n-pose summary: 250 layers, 3,196,590 parameters, 3,196,574 gradients, 8.9 GFLOPs\n",
            "\n",
            "Transferred 355/397 items from pretrained weights\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/my_project/pose_estimation', view at http://localhost:6006/\n",
            "Freezing layer 'model.22.dfl.conv.weight'\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
            "Downloading https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt to 'yolov8n.pt'...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 6.25M/6.25M [00:00<00:00, 105MB/s]\n",
            "Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/Jump_Fall_Combined-3/train/labels... 1954 images, 0 backgrounds, 0 corrupt: 100%|██████████| 1954/1954 [00:01<00:00, 1442.63it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/Jump_Fall_Combined-3/train/labels.cache\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/Jump_Fall_Combined-3/valid/labels... 344 images, 0 backgrounds, 0 corrupt: 100%|██████████| 344/344 [00:00<00:00, 619.60it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/Jump_Fall_Combined-3/valid/labels.cache\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Plotting labels to runs/my_project/pose_estimation/labels.jpg... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001, momentum=0.9) with parameter groups 63 weight(decay=0.0), 73 weight(decay=0.0005), 72 bias(decay=0.0)\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n",
            "Image sizes 640 train, 640 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1mruns/my_project/pose_estimation\u001b[0m\n",
            "Starting training for 50 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/123 [00:00<?, ?it/s]Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n",
            "       1/50      2.71G      1.313      8.657     0.6831      2.682      1.543         29        640:  99%|█████████▉| 122/123 [01:04<00:00,  2.60it/s]Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n",
            "       1/50      2.75G      1.314      8.656     0.6821      2.686      1.548          2        640: 100%|██████████| 123/123 [01:05<00:00,  1.87it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:07<00:00,  1.48it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.557      0.503       0.47      0.252      0.168    0.00417   4.18e-05   4.18e-06\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       2/50      2.44G      1.399      7.443     0.6367       2.16      1.574          5        640: 100%|██████████| 123/123 [00:51<00:00,  2.40it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:05<00:00,  2.00it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.233      0.443       0.29      0.146     0.0083     0.0204   0.000891   0.000131\n",
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       3/50      2.42G      1.481      7.266     0.6161      2.106      1.639          7        640: 100%|██████████| 123/123 [00:49<00:00,  2.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:05<00:00,  2.08it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.317      0.707      0.454      0.259      0.373     0.0673     0.0125    0.00135\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       4/50       2.4G      1.494      7.003     0.6046      1.979      1.629          2        640: 100%|██████████| 123/123 [00:51<00:00,  2.41it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.30it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346       0.37      0.621      0.457      0.249     0.0205     0.0508    0.00521   0.000644\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       5/50      2.41G      1.475      7.021     0.5899      1.915      1.608          4        640: 100%|██████████| 123/123 [00:54<00:00,  2.24it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:07<00:00,  1.54it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.486      0.597      0.502      0.265     0.0471     0.0939     0.0188    0.00231\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       6/50      2.41G      1.423      6.932     0.5707      1.875      1.572          2        640: 100%|██████████| 123/123 [00:50<00:00,  2.44it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:08<00:00,  1.28it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.573      0.519      0.494      0.289      0.212     0.0747     0.0258    0.00286\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       7/50      2.41G      1.435      6.731     0.5528      1.819      1.592          7        640: 100%|██████████| 123/123 [00:50<00:00,  2.44it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.31it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.575      0.569      0.557      0.314       0.22     0.0859     0.0197    0.00247\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       8/50       2.4G      1.403      6.677     0.5376      1.781      1.559          5        640: 100%|██████████| 123/123 [00:50<00:00,  2.46it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.48it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.617      0.566      0.602      0.325      0.213     0.0823     0.0189    0.00266\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       9/50      2.41G      1.409      6.504      0.525      1.746      1.552          5        640: 100%|██████████| 123/123 [00:54<00:00,  2.24it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:06<00:00,  1.71it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.612      0.598      0.611      0.357      0.243     0.0983     0.0355    0.00534\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      10/50      2.41G      1.376      6.537     0.5122      1.692      1.527          4        640: 100%|██████████| 123/123 [00:47<00:00,  2.57it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:06<00:00,  1.66it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.673      0.558      0.622      0.371      0.241      0.107     0.0364    0.00574\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      11/50      2.41G      1.364      6.458     0.5003       1.62      1.527          6        640: 100%|██████████| 123/123 [00:50<00:00,  2.43it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.52it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.726      0.527      0.607      0.372      0.257      0.102     0.0367    0.00686\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      12/50      2.39G      1.339      6.426     0.4916      1.599      1.509          6        640: 100%|██████████| 123/123 [00:56<00:00,  2.19it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:05<00:00,  2.04it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.656      0.553      0.689      0.433      0.264     0.0929     0.0567    0.00982\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      13/50      2.41G      1.351      6.359     0.4733      1.588      1.499          3        640: 100%|██████████| 123/123 [00:49<00:00,  2.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.49it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346       0.65      0.588      0.621      0.368      0.237      0.119     0.0362    0.00427\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      14/50      2.41G      1.328      6.285     0.4652      1.548        1.5          4        640: 100%|██████████| 123/123 [00:56<00:00,  2.17it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:07<00:00,  1.48it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.499      0.782      0.667      0.402     0.0778      0.141     0.0385    0.00528\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      15/50      2.41G       1.32      6.276     0.4688      1.528      1.477          7        640: 100%|██████████| 123/123 [00:51<00:00,  2.39it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:06<00:00,  1.77it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346       0.57      0.743      0.688       0.42     0.0868      0.123     0.0386    0.00536\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      16/50      2.39G      1.333      6.262      0.462      1.515      1.496          2        640: 100%|██████████| 123/123 [00:49<00:00,  2.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.41it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.498      0.758      0.631      0.404      0.261      0.126     0.0521    0.00841\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      17/50      2.41G      1.315      6.086      0.453      1.474      1.485          7        640: 100%|██████████| 123/123 [00:51<00:00,  2.38it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.55it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.489      0.799      0.663      0.428      0.101       0.15      0.058     0.0095\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      18/50      2.41G      1.278      6.051     0.4549      1.472      1.474          4        640: 100%|██████████| 123/123 [00:55<00:00,  2.20it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.48it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.476      0.751      0.682       0.43      0.123      0.156     0.0755     0.0114\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      19/50      2.41G      1.277      5.973     0.4459      1.453      1.454          4        640: 100%|██████████| 123/123 [00:54<00:00,  2.27it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:07<00:00,  1.57it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.558      0.763      0.696      0.455      0.329      0.174     0.0864     0.0171\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      20/50      2.39G      1.266      5.933     0.4447      1.449      1.456          4        640: 100%|██████████| 123/123 [00:49<00:00,  2.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.50it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.486      0.732      0.656      0.415     0.0971      0.154     0.0486    0.00948\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      21/50      2.41G      1.268      5.994     0.4461      1.401      1.456          3        640: 100%|██████████| 123/123 [00:50<00:00,  2.41it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.68it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.561      0.732      0.718      0.468      0.359      0.158     0.0983     0.0182\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      22/50      2.41G      1.265      5.887     0.4388       1.41      1.464          6        640: 100%|██████████| 123/123 [00:54<00:00,  2.25it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:05<00:00,  2.14it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346       0.66      0.747      0.773      0.494      0.223      0.196      0.128       0.02\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      23/50      2.41G      1.243      5.838     0.4321      1.349      1.429          4        640: 100%|██████████| 123/123 [00:49<00:00,  2.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.33it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.645      0.747      0.717      0.451      0.209      0.216     0.0989     0.0179\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      24/50      2.39G      1.244      5.786     0.4317      1.348       1.43          3        640: 100%|██████████| 123/123 [00:55<00:00,  2.23it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:05<00:00,  2.16it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.668      0.708      0.714      0.451      0.247      0.253      0.139     0.0276\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      25/50      2.41G      1.233      5.747     0.4369      1.321      1.428          6        640: 100%|██████████| 123/123 [00:49<00:00,  2.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.60it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.645      0.699      0.691       0.44      0.208      0.247      0.094     0.0172\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      26/50      2.41G      1.233      5.687     0.4323      1.365      1.439          5        640: 100%|██████████| 123/123 [00:50<00:00,  2.43it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.73it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.633      0.766      0.744       0.48      0.227      0.274      0.146      0.025\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      27/50      2.41G      1.206      5.678     0.4279      1.287      1.406          6        640: 100%|██████████| 123/123 [00:54<00:00,  2.25it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.59it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.599      0.758      0.739      0.482       0.29      0.258      0.171     0.0293\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      28/50      2.39G      1.207      5.609     0.4368      1.292      1.421          4        640: 100%|██████████| 123/123 [00:55<00:00,  2.23it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.60it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.633      0.794      0.769      0.505       0.21      0.242      0.126      0.023\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      29/50      2.41G      1.203      5.573     0.4302      1.257      1.417          3        640: 100%|██████████| 123/123 [00:51<00:00,  2.38it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.56it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.656      0.784      0.769      0.518      0.241      0.301      0.169     0.0275\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      30/50      2.41G      1.209      5.472     0.4251      1.272      1.414          5        640: 100%|██████████| 123/123 [00:52<00:00,  2.34it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.49it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346       0.72      0.689      0.763      0.497      0.315      0.296      0.192     0.0338\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      31/50      2.41G      1.181      5.508     0.4219      1.242      1.404          4        640: 100%|██████████| 123/123 [00:54<00:00,  2.25it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.68it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.625      0.708        0.7      0.465      0.291      0.297      0.152     0.0255\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      32/50      2.39G      1.165      5.547     0.4241       1.21      1.388          3        640: 100%|██████████| 123/123 [00:55<00:00,  2.20it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.66it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346       0.67      0.793      0.802      0.535      0.277      0.298      0.191     0.0367\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      33/50      2.41G       1.16      5.454     0.4258      1.188      1.391          4        640: 100%|██████████| 123/123 [00:51<00:00,  2.40it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.54it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346       0.62      0.784      0.723      0.486       0.33       0.26      0.146     0.0284\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      34/50       2.4G      1.172      5.414     0.4258      1.181      1.386          2        640: 100%|██████████| 123/123 [00:51<00:00,  2.39it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:05<00:00,  1.95it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.677      0.712      0.751      0.504      0.308      0.265      0.189     0.0356\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      35/50       2.4G      1.149      5.348     0.4187      1.203      1.379          2        640: 100%|██████████| 123/123 [00:50<00:00,  2.42it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:07<00:00,  1.46it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.733      0.776      0.806      0.535      0.307      0.328      0.201     0.0358\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      36/50      2.39G      1.133      5.306     0.4226      1.173      1.362          8        640: 100%|██████████| 123/123 [00:47<00:00,  2.60it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:08<00:00,  1.33it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.747      0.736      0.809      0.547      0.343      0.267      0.193     0.0355\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      37/50      2.41G       1.14      5.312     0.4151      1.136      1.365          8        640: 100%|██████████| 123/123 [00:51<00:00,  2.37it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:06<00:00,  1.72it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.663      0.757      0.747      0.504      0.348      0.301      0.185     0.0357\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      38/50      2.41G      1.119      5.317     0.4184      1.119      1.351          7        640: 100%|██████████| 123/123 [00:47<00:00,  2.58it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:07<00:00,  1.38it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.636      0.779      0.752      0.507      0.307      0.275      0.198     0.0414\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      39/50      2.41G      1.124      5.291      0.421       1.11      1.357          5        640: 100%|██████████| 123/123 [00:51<00:00,  2.37it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:07<00:00,  1.52it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.698      0.715      0.759       0.51      0.297      0.255       0.17     0.0349\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      40/50      2.39G      1.132      5.245     0.4172       1.13      1.353          4        640: 100%|██████████| 123/123 [00:47<00:00,  2.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:07<00:00,  1.47it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.663      0.771      0.761      0.515      0.283      0.344      0.194     0.0381\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Closing dataloader mosaic\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      41/50      2.54G      1.027      5.289     0.2641     0.7877        1.4          2        640: 100%|██████████| 123/123 [00:53<00:00,  2.28it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:05<00:00,  2.11it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346       0.67      0.761      0.789      0.525      0.307      0.301      0.188     0.0377\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      42/50      2.41G      1.017      5.212     0.2529     0.7204      1.391          2        640: 100%|██████████| 123/123 [00:45<00:00,  2.68it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:08<00:00,  1.34it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.712      0.799      0.799       0.54      0.312      0.335       0.21     0.0454\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      43/50      2.41G     0.9868       5.15     0.2559     0.6948      1.362          2        640: 100%|██████████| 123/123 [00:45<00:00,  2.73it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:05<00:00,  2.03it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.717       0.79      0.802      0.537      0.337      0.374       0.23     0.0484\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      44/50      2.39G     0.9815      5.103     0.2539     0.6815      1.346          2        640: 100%|██████████| 123/123 [00:53<00:00,  2.28it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.62it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.699      0.753      0.768       0.52      0.287      0.331      0.178     0.0388\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      45/50       2.4G     0.9688       5.08     0.2457     0.6653      1.339          2        640: 100%|██████████| 123/123 [00:51<00:00,  2.39it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.64it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.717      0.772      0.808       0.54      0.304       0.35      0.215      0.046\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      46/50       2.4G     0.9722      5.008     0.2454     0.6613       1.33          2        640: 100%|██████████| 123/123 [00:50<00:00,  2.46it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:05<00:00,  2.17it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.695       0.78      0.784      0.531      0.324      0.303        0.2     0.0446\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      47/50       2.4G     0.9386      4.991     0.2462     0.6379      1.318          2        640: 100%|██████████| 123/123 [00:46<00:00,  2.62it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:06<00:00,  1.69it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346       0.76       0.75      0.804      0.546      0.352      0.351      0.238     0.0515\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      48/50      2.39G     0.9276      4.943     0.2456     0.6155      1.312          2        640: 100%|██████████| 123/123 [00:49<00:00,  2.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:07<00:00,  1.57it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.778      0.742      0.802      0.553       0.36      0.336      0.237     0.0509\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      49/50       2.4G     0.9295      4.927     0.2484      0.613      1.319          2        640: 100%|██████████| 123/123 [00:46<00:00,  2.66it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:05<00:00,  2.13it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.725      0.765      0.792      0.539      0.336      0.378      0.238     0.0525\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss  pose_loss  kobj_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      50/50       2.4G     0.9054      4.892     0.2514     0.6007      1.302          2        640: 100%|██████████| 123/123 [00:49<00:00,  2.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.67it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.757      0.759      0.802      0.548      0.352      0.364      0.238     0.0533\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "50 epochs completed in 0.810 hours.\n",
            "Optimizer stripped from runs/my_project/pose_estimation/weights/last.pt, 6.6MB\n",
            "Optimizer stripped from runs/my_project/pose_estimation/weights/best.pt, 6.6MB\n",
            "\n",
            "Validating runs/my_project/pose_estimation/weights/best.pt...\n",
            "Ultralytics YOLOv8.2.58 🚀 Python-3.10.12 torch-2.3.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "YOLOv8n-pose summary (fused): 187 layers, 3,191,174 parameters, 0 gradients, 8.8 GFLOPs\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Pose(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:12<00:00,  1.10s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        344        346      0.778      0.742      0.802      0.553       0.36      0.336      0.237     0.0511\n",
            "                  fall         57         57      0.885      0.825      0.887      0.602       0.19      0.169     0.0657     0.0118\n",
            "                  jump        104        104      0.905      0.894      0.958      0.682       0.18      0.173     0.0618    0.00913\n",
            "                s_wall         47         48      0.669      0.625      0.638      0.411      0.426      0.375      0.291     0.0721\n",
            "               sitting         40         40      0.887      0.975      0.969      0.709       0.62      0.675      0.495      0.125\n",
            "              standing         52         53      0.746      0.499      0.694      0.496      0.286       0.17       0.14     0.0234\n",
            "               walking         44         44      0.574      0.636      0.666      0.421       0.46      0.455       0.37     0.0656\n",
            "Speed: 0.3ms preprocess, 3.8ms inference, 0.0ms loss, 6.4ms postprocess per image\n",
            "Results saved to \u001b[1mruns/my_project/pose_estimation\u001b[0m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ultralytics.utils.metrics.PoseMetrics object with attributes:\n",
              "\n",
              "ap_class_index: array([0, 1, 2, 3, 4, 5])\n",
              "box: ultralytics.utils.metrics.Metric object\n",
              "confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7ec71daf75e0>\n",
              "curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)', 'Precision-Recall(P)', 'F1-Confidence(P)', 'Precision-Confidence(P)', 'Recall-Confidence(P)']\n",
              "curves_results: [[array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
              "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
              "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
              "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
              "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
              "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
              "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
              "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
              "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
              "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
              "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
              "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
              "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
              "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
              "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
              "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
              "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
              "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
              "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
              "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
              "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
              "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
              "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
              "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
              "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
              "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
              "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
              "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
              "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
              "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
              "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
              "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
              "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
              "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
              "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
              "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
              "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
              "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
              "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
              "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
              "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
              "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[          1,           1,           1, ...,      0.2253,      0.2253,           0],\n",
              "       [          1,           1,           1, ...,     0.22511,     0.22511,           0],\n",
              "       [    0.85714,     0.85714,     0.85714, ...,     0.23762,     0.23762,           0],\n",
              "       [          1,           1,           1, ...,     0.54054,     0.54054,           0],\n",
              "       [          1,           1,           1, ...,     0.27604,     0.27604,           0],\n",
              "       [          1,           1,           1, ...,   0.0068003,   0.0034002,           0]]), 'Recall', 'Precision'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
              "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
              "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
              "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
              "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
              "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
              "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
              "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
              "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
              "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
              "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
              "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
              "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
              "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
              "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
              "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
              "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
              "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
              "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
              "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
              "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
              "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
              "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
              "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
              "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
              "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
              "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
              "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
              "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
              "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
              "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
              "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
              "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
              "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
              "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
              "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
              "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
              "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
              "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
              "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
              "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
              "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[    0.17896,     0.17896,     0.25981, ...,           0,           0,           0],\n",
              "       [    0.31902,     0.31902,      0.4552, ...,           0,           0,           0],\n",
              "       [    0.17712,     0.17712,     0.24118, ...,           0,           0,           0],\n",
              "       [    0.28269,     0.28269,     0.37688, ...,           0,           0,           0],\n",
              "       [    0.32416,     0.32416,     0.39064, ...,           0,           0,           0],\n",
              "       [    0.14309,     0.14309,     0.19525, ...,           0,           0,           0]]), 'Confidence', 'F1'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
              "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
              "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
              "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
              "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
              "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
              "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
              "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
              "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
              "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
              "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
              "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
              "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
              "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
              "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
              "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
              "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
              "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
              "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
              "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
              "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
              "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
              "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
              "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
              "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
              "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
              "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
              "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
              "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
              "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
              "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
              "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
              "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
              "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
              "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
              "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
              "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
              "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
              "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
              "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
              "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
              "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[   0.098276,    0.098276,      0.1493, ...,           1,           1,           1],\n",
              "       [    0.18978,     0.18978,     0.29551, ...,           1,           1,           1],\n",
              "       [   0.097166,    0.097166,     0.13712, ...,           1,           1,           1],\n",
              "       [    0.16461,     0.16461,     0.23219, ...,           1,           1,           1],\n",
              "       [    0.19343,     0.19343,     0.24273, ...,           1,           1,           1],\n",
              "       [   0.077199,    0.077199,     0.10875, ...,           1,           1,           1]]), 'Confidence', 'Precision'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
              "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
              "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
              "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
              "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
              "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
              "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
              "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
              "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
              "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
              "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
              "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
              "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
              "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
              "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
              "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
              "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
              "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
              "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
              "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
              "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
              "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
              "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
              "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
              "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
              "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
              "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
              "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
              "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
              "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
              "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
              "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
              "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
              "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
              "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
              "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
              "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
              "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
              "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
              "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
              "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
              "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[          1,           1,           1, ...,           0,           0,           0],\n",
              "       [          1,           1,     0.99038, ...,           0,           0,           0],\n",
              "       [          1,           1,           1, ...,           0,           0,           0],\n",
              "       [          1,           1,           1, ...,           0,           0,           0],\n",
              "       [          1,           1,           1, ...,           0,           0,           0],\n",
              "       [    0.97727,     0.97727,     0.95455, ...,           0,           0,           0]]), 'Confidence', 'Recall'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
              "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
              "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
              "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
              "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
              "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
              "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
              "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
              "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
              "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
              "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
              "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
              "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
              "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
              "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
              "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
              "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
              "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
              "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
              "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
              "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
              "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
              "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
              "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
              "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
              "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
              "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
              "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
              "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
              "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
              "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
              "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
              "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
              "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
              "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
              "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
              "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
              "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
              "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
              "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
              "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
              "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[          1,           1,           1, ...,   5.813e-05,  2.9065e-05,           0],\n",
              "       [    0.24324,     0.24324,     0.24324, ...,  0.00010194,  5.0968e-05,           0],\n",
              "       [    0.71429,     0.71429,     0.71429, ...,  0.00017896,  8.9482e-05,           0],\n",
              "       [    0.73684,     0.73684,     0.73684, ...,  0.00068445,  0.00034222,           0],\n",
              "       [          1,           1,           1, ...,  0.00019916,  9.9578e-05,           0],\n",
              "       [          1,           1,           1, ...,  0.00030575,  0.00015288,           0]]), 'Recall', 'Precision'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
              "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
              "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
              "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
              "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
              "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
              "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
              "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
              "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
              "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
              "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
              "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
              "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
              "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
              "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
              "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
              "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
              "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
              "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
              "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
              "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
              "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
              "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
              "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
              "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
              "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
              "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
              "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
              "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
              "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
              "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
              "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
              "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
              "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
              "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
              "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
              "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
              "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
              "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
              "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
              "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
              "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[   0.040816,    0.040816,    0.059254, ...,           0,           0,           0],\n",
              "       [   0.067485,    0.067485,    0.097228, ...,           0,           0,           0],\n",
              "       [   0.084871,    0.084871,     0.11556, ...,           0,           0,           0],\n",
              "       [    0.19081,     0.19081,     0.25439, ...,           0,           0,           0],\n",
              "       [    0.11009,     0.11009,     0.13267, ...,           0,           0,           0],\n",
              "       [   0.096506,    0.096506,     0.13482, ...,           0,           0,           0]]), 'Confidence', 'F1'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
              "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
              "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
              "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
              "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
              "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
              "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
              "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
              "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
              "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
              "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
              "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
              "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
              "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
              "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
              "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
              "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
              "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
              "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
              "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
              "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
              "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
              "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
              "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
              "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
              "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
              "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
              "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
              "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
              "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
              "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
              "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
              "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
              "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
              "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
              "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
              "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
              "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
              "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
              "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
              "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
              "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[   0.022414,    0.022414,     0.03405, ...,           1,           1,           1],\n",
              "       [   0.040146,    0.040146,    0.063119, ...,           1,           1,           1],\n",
              "       [   0.046559,    0.046559,    0.065706, ...,           1,           1,           1],\n",
              "       [    0.11111,     0.11111,     0.15673, ...,           1,           1,           1],\n",
              "       [   0.065693,    0.065693,    0.082437, ...,           1,           1,           1],\n",
              "       [   0.052065,    0.052065,    0.075087, ...,           1,           1,           1]]), 'Confidence', 'Precision'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
              "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
              "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
              "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
              "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
              "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
              "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
              "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
              "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
              "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
              "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
              "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
              "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
              "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
              "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
              "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
              "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
              "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
              "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
              "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
              "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
              "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
              "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
              "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
              "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
              "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
              "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
              "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
              "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
              "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
              "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
              "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
              "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
              "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
              "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
              "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
              "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
              "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
              "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
              "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
              "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
              "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[    0.22807,     0.22807,     0.22807, ...,           0,           0,           0],\n",
              "       [    0.21154,     0.21154,     0.21154, ...,           0,           0,           0],\n",
              "       [    0.47917,     0.47917,     0.47917, ...,           0,           0,           0],\n",
              "       [      0.675,       0.675,       0.675, ...,           0,           0,           0],\n",
              "       [    0.33962,     0.33962,     0.33962, ...,           0,           0,           0],\n",
              "       [    0.65909,     0.65909,     0.65909, ...,           0,           0,           0]]), 'Confidence', 'Recall']]\n",
              "fitness: 0.6478785243098442\n",
              "keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)', 'metrics/precision(P)', 'metrics/recall(P)', 'metrics/mAP50(P)', 'metrics/mAP50-95(P)']\n",
              "maps: array([    0.61352,     0.69101,      0.4829,     0.83329,     0.51931,     0.48638])\n",
              "names: {0: 'fall', 1: 'jump', 2: 's_wall', 3: 'sitting', 4: 'standing', 5: 'walking'}\n",
              "plot: True\n",
              "pose: ultralytics.utils.metrics.Metric object\n",
              "results_dict: {'metrics/precision(B)': 0.7777090391447846, 'metrics/recall(B)': 0.7423672098237204, 'metrics/mAP50(B)': 0.8019269829017913, 'metrics/mAP50-95(B)': 0.5532656359082161, 'metrics/precision(P)': 0.3602752246003433, 'metrics/recall(P)': 0.33612506391387553, 'metrics/mAP50(P)': 0.23724640208147782, 'metrics/mAP50-95(P)': 0.051135681660136496, 'fitness': 0.6478785243098442}\n",
              "save_dir: PosixPath('runs/my_project/pose_estimation')\n",
              "seg: ultralytics.utils.metrics.Metric object\n",
              "speed: {'preprocess': 0.33369175223417064, 'inference': 3.792903451032417, 'loss': 0.001474868419558503, 'postprocess': 6.375775087711422}\n",
              "task: 'pose'"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 모델 가중치 경로 설정\n",
        "weights_dir = 'runs/my_project/pose_estimation/weights'\n",
        "weights_file = os.path.join(weights_dir, 'best.pt')"
      ],
      "metadata": {
        "id": "v2ir6OqyOKZn"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# 테스트 이미지 경로\n",
        "test_image_path = '/content/example.jpg'\n",
        "\n",
        "# 추론\n",
        "results = model(test_image_path)\n",
        "\n",
        "# 결과 시각화\n",
        "for result in results:\n",
        "    result.show()"
      ],
      "metadata": {
        "id": "--9cPAkQNuLU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6df1c9a3-e038-4628-a712-45fc0093f671"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "image 1/1 /content/example.jpg: 448x640 2 falls, 27.1ms\n",
            "Speed: 4.8ms preprocess, 27.1ms inference, 2.5ms postprocess per image at shape (1, 3, 448, 640)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 추론\n",
        "results = model(test_image_path)\n",
        "\n",
        "# OpenCV를 사용하여 이미지를 불러오기\n",
        "image = cv2.imread(test_image_path)\n",
        "\n",
        "# 결과를 이미지 위에 그리기\n",
        "for result in results:\n",
        "    # 각 객체에 대한 정보 추출\n",
        "    for box in result.boxes:\n",
        "        x1, y1, x2, y2 = map(int, box.xyxy[0])\n",
        "        label = result.names[box.cls[0].item()]\n",
        "        confidence = box.conf[0].item()\n",
        "\n",
        "        # 박스 그리기\n",
        "        cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)\n",
        "\n",
        "        # 레이블과 신뢰도 그리기\n",
        "        cv2.putText(image, f'{label} {confidence:.2f}', (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2)\n",
        "\n",
        "# OpenCV에서 이미지를 BGR에서 RGB로 변환\n",
        "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "# 이미지를 표시\n",
        "plt.imshow(image)\n",
        "plt.axis('off')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "yTOrF5Y_iq-D",
        "outputId": "2f5717e8-d054-414f-f9fe-10d803293e84"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "image 1/1 /content/example.jpg: 448x640 2 falls, 57.7ms\n",
            "Speed: 10.3ms preprocess, 57.7ms inference, 10.2ms postprocess per image at shape (1, 3, 448, 640)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}