{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BN92a3OrYfVk",
        "outputId": "0d5a4658-358b-41cd-f37a-e00983cd0221"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting ultralytics\n",
            "  Downloading ultralytics-8.3.228-py3-none-any.whl.metadata (37 kB)\n",
            "Requirement already satisfied: numpy>=1.23.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (2.0.2)\n",
            "Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (3.10.0)\n",
            "Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (4.12.0.88)\n",
            "Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (11.3.0)\n",
            "Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (6.0.3)\n",
            "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (2.32.4)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (1.16.3)\n",
            "Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (2.8.0+cu126)\n",
            "Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (0.23.0+cu126)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.12/dist-packages (from ultralytics) (5.9.5)\n",
            "Requirement already satisfied: polars in /usr/local/lib/python3.12/dist-packages (from ultralytics) (1.31.0)\n",
            "Collecting ultralytics-thop>=2.0.18 (from ultralytics)\n",
            "  Downloading ultralytics_thop-2.0.18-py3-none-any.whl.metadata (14 kB)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.3.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.60.1)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.4.9)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (25.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.2.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (2.9.0.post0)\n",
            "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests>=2.23.0->ultralytics) (3.4.4)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests>=2.23.0->ultralytics) (3.11)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests>=2.23.0->ultralytics) (2.5.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests>=2.23.0->ultralytics) (2025.10.5)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (3.20.0)\n",
            "Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (4.15.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (75.2.0)\n",
            "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (1.13.3)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (3.5)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (3.1.6)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (2025.3.0)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (12.6.77)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (12.6.77)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.6.80 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (12.6.80)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (9.10.2.21)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.6.4.1 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (12.6.4.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.3.0.4 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (11.3.0.4)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.7.77 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (10.3.7.77)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.7.1.2 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (11.7.1.2)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.5.4.2 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (12.5.4.2)\n",
            "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (0.7.1)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.27.3 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (2.27.3)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (12.6.77)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12==12.6.85 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (12.6.85)\n",
            "Requirement already satisfied: nvidia-cufile-cu12==1.11.1.6 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (1.11.1.6)\n",
            "Requirement already satisfied: triton==3.4.0 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (3.4.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.17.0)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch>=1.8.0->ultralytics) (1.3.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (3.0.3)\n",
            "Downloading ultralytics-8.3.228-py3-none-any.whl (1.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m24.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading ultralytics_thop-2.0.18-py3-none-any.whl (28 kB)\n",
            "Installing collected packages: ultralytics-thop, ultralytics\n",
            "Successfully installed ultralytics-8.3.228 ultralytics-thop-2.0.18\n"
          ]
        }
      ],
      "source": [
        "pip install ultralytics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9AIavYDsPxVG"
      },
      "source": [
        "# II. DREPADEtect: Le Modèle proposé"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vdcl-87XTbSW"
      },
      "source": [
        "![Page_3_page-0001.jpg](data:image/jpeg;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AHyyoZTFP-QZ",
        "outputId": "d5f2e214-37a2-48e5-e951-93ff2d762ac8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/gdrive\n"
          ]
        }
      ],
      "source": [
        "%cd /content/gdrive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fpiUcj6uQBbY",
        "outputId": "6154befb-67b4-43fd-d315-db41080a00c5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/gdrive\n"
          ]
        }
      ],
      "source": [
        "!pwd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L28HZn3CQEEh",
        "outputId": "4528d967-d818-4d89-9569-5683958018f7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dossier créé : /content/drive/MyDrive/YOLOv8DREPACUSTOMALLDATA200\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "# Dossier de travail\n",
        "dossier_path = '/content/drive/MyDrive/YOLOv8DREPACUSTOMALLDATA400'\n",
        "\n",
        "# Creation du dossier\n",
        "os.makedirs(dossier_path, exist_ok=True)\n",
        "\n",
        "print(f\"Dossier créé : {dossier_path}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6rY3D9RQIKR",
        "outputId": "2bb44696-4784-46cd-83ba-1a6930951955"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/YOLOv8DREPACUSTOMALLDATA200\n"
          ]
        }
      ],
      "source": [
        "# Le chemin du dossier crée\n",
        "%cd /content/drive/MyDrive/YOLOv8DREPACUSTOMALLDATA400"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8s7KyTOccXrR",
        "outputId": "3921c8fb-22b0-4f62-f091-23acba9b400e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Ultralytics 8.3.71 🚀 Python-3.11.11 torch-2.5.1+cu124 CUDA:0 (Tesla T4, 15095MiB)\n",
            "Setup complete ✅ (8 CPUs, 51.0 GB RAM, 33.0/235.7 GB disk)\n"
          ]
        }
      ],
      "source": [
        "# Install YOLOv8\n",
        "%pip install ultralytics\n",
        "\n",
        "import ultralytics\n",
        "ultralytics.checks()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BdarsdTScfJq",
        "outputId": "bae9030f-6ea7-4d47-c02a-86bc7fd818f9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2025-02-04 21:57:09--  https://raw.githubusercontent.com/ultralytics/ultralytics/main/ultralytics/cfg/models/v8/yolov8.yaml\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1983 (1.9K) [text/plain]\n",
            "Saving to: ‘yolov8.yaml.4’\n",
            "\n",
            "yolov8.yaml.4       100%[===================>]   1.94K  --.-KB/s    in 0.001s  \n",
            "\n",
            "2025-02-04 21:57:10 (1.59 MB/s) - ‘yolov8.yaml.4’ saved [1983/1983]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Telechargement du modèle YOLOv8 standard\n",
        "!wget https://raw.githubusercontent.com/ultralytics/ultralytics/main/ultralytics/cfg/models/v8/yolov8.yaml"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ht56YziSQVUt"
      },
      "outputs": [],
      "source": [
        "# Creation et configuration de l'architure du modele DREPADetect optimisé\n",
        "!cp yolov8.yaml DREPADetect400.yaml"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gc4H24Z0wqTq"
      },
      "source": [
        "## 200"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cmAsO-zxt2l1",
        "outputId": "2ff40d7a-8378-4ed5-b26c-da953911a9d2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING ⚠️ 'label_smoothing' is deprecated and will be removed in in the future.\n",
            "Ultralytics 8.3.67 🚀 Python-3.11.11 torch-2.5.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=/content/drive/MyDrive/YOLOv8DREPACUSTOMALLDATA002/DREPADetect.yaml, data=/content/drive/MyDrive/yolo_detect/Modele_yolo/DrepaSeg.v1i.yolov8/data.yaml, epochs=200, time=None, patience=75, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=0, workers=4, project=None, name=YOLOv8DREPA_optimisation, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=True, 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=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.001, momentum=0.937, weight_decay=0.0005, warmup_epochs=3, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, 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, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/YOLOv8DREPA_optimisation\n",
            "Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n",
            "100% 755k/755k [00:00<00:00, 15.2MB/s]\n",
            "Overriding model.yaml nc=80 with nc=3\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1      1856  ultralytics.nn.modules.conv.Conv             [3, 64, 3, 2]                 \n",
            "  1                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n",
            "  2                  -1  3    279808  ultralytics.nn.modules.block.C2f             [128, 128, 3, True]           \n",
            "  3                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
            "  4                  -1  6   2101248  ultralytics.nn.modules.block.C2f             [256, 256, 6, True]           \n",
            "  5                  -1  1   1180672  ultralytics.nn.modules.conv.Conv             [256, 512, 3, 2]              \n",
            "  6                  -1  6   8396800  ultralytics.nn.modules.block.C2f             [512, 512, 6, True]           \n",
            "  7                  -1  1   1182208  ultralytics.nn.modules.block.SPPF            [512, 1024, 5]                \n",
            "  8                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            "  9             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 10                  -1  3   1378816  ultralytics.nn.modules.block.C2f             [1280, 256, 3]                \n",
            " 11                [10]  1   1370195  ultralytics.nn.modules.head.Detect           [3, [256]]                    \n",
            "DREPADetect summary: 204 layers, 16,261,011 parameters, 16,260,995 gradients, 119.1 GFLOPs\n",
            "\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/YOLOv8DREPA_optimisation', view at http://localhost:6006/\n",
            "Freezing layer 'model.11.dfl.conv.weight'\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n",
            "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...\n",
            "100% 5.35M/5.35M [00:00<00:00, 65.4MB/s]\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/drive/MyDrive/yolo_detect/Modele_yolo/DrepaSeg.v1i.yolov8/train/labels... 1005 images, 0 backgrounds, 0 corrupt: 100% 1005/1005 [01:00<00:00, 16.63it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/drive/MyDrive/yolo_detect/Modele_yolo/DrepaSeg.v1i.yolov8/train/labels.cache\n",
            "/usr/local/lib/python3.11/dist-packages/albumentations/__init__.py:24: UserWarning: A new version of Albumentations is available: 2.0.1 (you have 1.4.20). Upgrade using: pip install -U albumentations. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1.\n",
            "  check_for_updates()\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, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/MyDrive/yolo_detect/Modele_yolo/DrepaSeg.v1i.yolov8/valid/labels... 84 images, 0 backgrounds, 0 corrupt: 100% 84/84 [00:07<00:00, 11.41it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/drive/MyDrive/yolo_detect/Modele_yolo/DrepaSeg.v1i.yolov8/valid/labels.cache\n",
            "Plotting labels to runs/detect/YOLOv8DREPA_optimisation/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=0.001429, momentum=0.9) with parameter groups 54 weight(decay=0.0), 57 weight(decay=0.0005), 56 bias(decay=0.0)\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n",
            "Image sizes 640 train, 640 val\n",
            "Using 4 dataloader workers\n",
            "Logging results to \u001b[1mruns/detect/YOLOv8DREPA_optimisation\u001b[0m\n",
            "Starting training for 200 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      1/200      8.82G      3.466      2.767       3.76        382        640: 100% 63/63 [00:39<00:00,  1.61it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:02<00:00,  1.14it/s]\n",
            "                   all         84       2476     0.0073    0.00488    0.00386   0.000879\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      2/200       8.7G      2.108      1.786       2.51        659        640: 100% 63/63 [00:38<00:00,  1.62it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.91it/s]\n",
            "                   all         84       2476      0.203      0.529      0.309      0.138\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      3/200      8.56G       1.93      1.597      2.113        606        640: 100% 63/63 [00:39<00:00,  1.58it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.99it/s]\n",
            "                   all         84       2476      0.283      0.705      0.405      0.216\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      4/200      8.55G      1.849      1.548      1.983        571        640: 100% 63/63 [00:39<00:00,  1.60it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.89it/s]\n",
            "                   all         84       2476      0.375      0.741      0.471      0.256\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      5/200      8.65G      1.787      1.472      1.892        576        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.98it/s]\n",
            "                   all         84       2476       0.39       0.77       0.52      0.304\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      6/200      8.52G      1.727      1.406       1.81        503        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.99it/s]\n",
            "                   all         84       2476      0.456       0.77      0.566      0.276\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      7/200      8.78G      1.707      1.378      1.792        426        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.02it/s]\n",
            "                   all         84       2476      0.473      0.697      0.571      0.311\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      8/200      8.63G      1.687      1.354      1.767        494        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.04it/s]\n",
            "                   all         84       2476      0.529      0.773      0.642       0.38\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      9/200      8.65G      1.665      1.332      1.734        472        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.04it/s]\n",
            "                   all         84       2476      0.463      0.708       0.61      0.335\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     10/200      8.73G      1.652      1.291      1.718        633        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.01it/s]\n",
            "                   all         84       2476      0.487      0.756      0.683      0.394\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     11/200      8.63G      1.626      1.278      1.711        594        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.03it/s]\n",
            "                   all         84       2476      0.548      0.735      0.672      0.415\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     12/200      8.58G      1.618       1.25      1.696        655        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.04it/s]\n",
            "                   all         84       2476      0.558       0.76      0.696      0.436\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     13/200      8.67G      1.601      1.226      1.692        546        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.03it/s]\n",
            "                   all         84       2476      0.564      0.754      0.711       0.44\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     14/200       8.6G      1.578      1.211      1.668        431        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.00it/s]\n",
            "                   all         84       2476      0.543      0.748      0.696      0.402\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     15/200      8.77G      1.579      1.202      1.655        592        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.04it/s]\n",
            "                   all         84       2476      0.614      0.756      0.713      0.434\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     16/200      8.58G      1.562       1.18      1.657        640        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.02it/s]\n",
            "                   all         84       2476      0.635      0.784      0.745      0.454\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     17/200      8.67G       1.55       1.18      1.628        538        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.04it/s]\n",
            "                   all         84       2476      0.626      0.742      0.718      0.443\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     18/200      8.64G      1.543      1.143      1.633        524        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.606      0.805       0.75      0.472\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     19/200      8.74G       1.54      1.134      1.627        410        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476       0.64      0.745      0.724      0.447\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     20/200      8.59G      1.517      1.122      1.618        571        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.646      0.763      0.744      0.454\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     21/200      8.52G      1.521       1.11      1.605        701        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476       0.64      0.744      0.737      0.428\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     22/200      8.62G      1.519      1.111       1.61        720        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.646      0.776      0.745      0.465\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     23/200      8.84G      1.513       1.11      1.621        357        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.04it/s]\n",
            "                   all         84       2476      0.671      0.775      0.771      0.486\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     24/200      8.57G      1.486      1.079      1.592        577        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.03it/s]\n",
            "                   all         84       2476      0.641      0.804      0.777      0.486\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     25/200      8.63G      1.491      1.086      1.586        407        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.04it/s]\n",
            "                   all         84       2476      0.656      0.797      0.767       0.49\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     26/200      8.64G      1.518       1.08      1.607        535        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476      0.696       0.78      0.783      0.497\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     27/200      8.62G      1.502      1.066      1.586        540        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476      0.699      0.789      0.784      0.507\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     28/200      8.67G       1.46      1.052      1.577        704        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476       0.67      0.814      0.797      0.507\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     29/200      8.69G      1.459       1.04      1.565        528        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476       0.69      0.798      0.788       0.51\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     30/200      8.65G      1.451       1.05      1.567        664        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.673      0.788       0.78      0.492\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     31/200      8.77G      1.448      1.038      1.574        541        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476      0.694      0.802      0.796      0.515\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     32/200       8.6G      1.438       1.03       1.55        556        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476      0.703      0.797      0.784      0.469\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     33/200      8.64G      1.462      1.036      1.559        460        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.717      0.781      0.797      0.512\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     34/200      8.65G      1.424      1.012      1.536        591        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.05it/s]\n",
            "                   all         84       2476      0.689      0.778      0.787      0.513\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     35/200      8.82G      1.438      1.024      1.551        509        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.05it/s]\n",
            "                   all         84       2476      0.685      0.778      0.789      0.505\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     36/200      8.57G      1.417      1.013      1.537        388        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476       0.69      0.784      0.798      0.521\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     37/200      8.68G      1.425      1.019      1.552        537        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.685      0.785      0.791      0.496\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     38/200      8.68G      1.414      1.004      1.526        637        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476      0.666        0.8       0.79      0.511\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     39/200      8.88G      1.401     0.9935      1.534        638        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.716      0.804      0.806       0.53\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     40/200      8.67G       1.39     0.9761      1.504        594        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.701      0.812      0.808      0.523\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     41/200      8.72G      1.383     0.9694       1.51        657        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.718      0.812      0.817      0.534\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     42/200      8.76G      1.418      0.989      1.531        570        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476      0.683      0.779      0.792      0.496\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     43/200      8.77G      1.383     0.9762      1.523        715        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.679      0.806       0.79        0.5\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     44/200      8.61G      1.389     0.9661      1.505        598        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.05it/s]\n",
            "                   all         84       2476      0.708      0.804      0.807      0.533\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     45/200      8.71G      1.385     0.9676      1.512        559        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.726      0.782      0.809      0.533\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     46/200       8.7G      1.358     0.9495      1.482        418        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476       0.72      0.791      0.815      0.529\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     47/200      8.76G      1.369      0.955      1.498        579        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476       0.68      0.785      0.796      0.528\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     48/200      8.59G      1.363     0.9498      1.504        560        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.708      0.812      0.813      0.535\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     49/200      8.68G      1.352     0.9397      1.488        484        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.752      0.794      0.819      0.533\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     50/200      8.65G      1.361     0.9312      1.484        692        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476      0.723      0.797      0.809       0.51\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     51/200      8.78G      1.351     0.9358       1.49        524        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.725       0.82      0.822       0.55\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     52/200      8.71G      1.349     0.9309      1.487        560        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.707      0.806       0.81      0.527\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     53/200      8.67G      1.348     0.9297      1.489        626        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.695      0.799      0.804      0.532\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     54/200      8.62G      1.344     0.9219      1.485        354        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.698       0.82      0.813      0.533\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     55/200      8.86G      1.354     0.9178      1.485        876        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.05it/s]\n",
            "                   all         84       2476      0.721      0.784      0.818      0.543\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     56/200      8.67G      1.343     0.9154      1.482        467        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.698      0.841      0.821      0.541\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     57/200      8.64G       1.34     0.9157      1.475        480        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476      0.715      0.802      0.813      0.543\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     58/200      8.65G      1.314     0.8938      1.453        552        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.734      0.809      0.826      0.556\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     59/200      8.75G      1.335     0.8913      1.475        611        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476      0.726      0.818      0.824      0.542\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     60/200       8.6G      1.326     0.8953      1.464        595        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.735      0.826      0.825      0.552\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     61/200      8.63G      1.315     0.8837      1.462        627        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476       0.74      0.809      0.828      0.546\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     62/200      8.67G      1.328     0.9014      1.496        524        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476      0.729      0.804      0.828      0.548\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     63/200      8.67G      1.317     0.8842      1.461        553        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476      0.742      0.817      0.831      0.553\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     64/200      8.63G      1.307      0.881      1.463        518        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.04it/s]\n",
            "                   all         84       2476      0.733      0.812       0.83      0.548\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     65/200      8.65G      1.308     0.8882      1.463        526        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476      0.727      0.825      0.831      0.554\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     66/200      8.67G        1.3     0.8753      1.454        613        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476      0.748      0.827      0.833      0.548\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     67/200      8.78G      1.303     0.8687      1.457        502        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476      0.747      0.795      0.827      0.548\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     68/200       8.6G      1.291     0.8576      1.436        525        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.754      0.809      0.836      0.556\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     69/200      8.51G      1.296     0.8652      1.454        448        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.776      0.789      0.838      0.558\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     70/200      8.63G      1.292     0.8619       1.44        608        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476      0.743      0.816      0.823      0.554\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     71/200      8.81G      1.293     0.8567      1.434        629        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.736      0.815      0.836      0.558\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     72/200      8.59G      1.308     0.8593      1.455        575        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.764      0.801      0.832      0.557\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     73/200       8.6G       1.28     0.8493      1.437        499        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.05it/s]\n",
            "                   all         84       2476      0.742      0.826      0.837      0.555\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     74/200      8.53G      1.299     0.8459      1.449        600        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.745      0.814      0.836      0.558\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     75/200      8.81G      1.293     0.8419      1.434        469        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.776      0.782      0.841      0.567\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     76/200      8.66G      1.278     0.8254      1.429        697        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.747      0.827      0.844      0.565\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     77/200       8.7G      1.273     0.8376      1.442        588        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.729      0.822      0.846      0.568\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     78/200      8.71G      1.264     0.8355       1.44        606        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.759      0.798      0.833      0.546\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     79/200      8.81G      1.277     0.8347      1.447        441        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.734      0.833      0.847      0.567\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     80/200      8.59G      1.293     0.8394      1.441       1004        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476       0.72      0.821      0.831      0.561\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     81/200      8.62G      1.272     0.8288      1.437        499        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476       0.75      0.826      0.843      0.568\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     82/200      8.63G      1.256     0.8137       1.42        622        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.732      0.824      0.836      0.556\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     83/200      8.84G      1.258     0.8131      1.419        547        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.752      0.836      0.839      0.562\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     84/200      8.63G      1.261     0.8254      1.419        664        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476      0.768      0.814      0.845      0.557\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     85/200      8.62G      1.257     0.8045       1.42        489        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.773      0.826      0.848      0.567\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     86/200      8.66G      1.265     0.8052      1.438        803        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.765      0.811      0.844      0.569\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     87/200      8.82G      1.256     0.8035      1.428        550        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.759      0.831      0.848      0.565\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     88/200      8.67G      1.246     0.7951      1.411        675        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.779      0.817      0.841      0.567\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     89/200      8.71G      1.236     0.7955      1.412        587        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.757      0.833      0.845       0.57\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     90/200      8.51G      1.238     0.7837      1.418        449        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.772      0.858      0.861      0.577\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     91/200      8.75G      1.244      0.789      1.425        540        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.752      0.831      0.849      0.576\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     92/200      8.59G      1.232      0.781       1.42        606        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.781      0.831      0.857      0.575\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     93/200      8.64G      1.238     0.7806      1.407        465        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.786      0.836      0.856      0.574\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     94/200       8.7G      1.214     0.7676      1.394        453        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.794       0.81      0.852      0.575\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     95/200      8.71G      1.218     0.7646      1.393        664        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.11it/s]\n",
            "                   all         84       2476      0.789      0.834      0.864      0.587\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     96/200      8.63G      1.236      0.783      1.416        389        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.786      0.831      0.859      0.583\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     97/200      8.68G      1.216     0.7573      1.397        476        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.795      0.814       0.86      0.582\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     98/200      8.68G      1.212     0.7555       1.39        537        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.783      0.828      0.861      0.577\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     99/200      8.77G      1.218     0.7582      1.401        417        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.782      0.828      0.861      0.583\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    100/200      8.64G      1.211     0.7552      1.394        447        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.778      0.829      0.859       0.58\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    101/200      8.62G      1.213     0.7516      1.392        508        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.797      0.833      0.865      0.585\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    102/200       8.6G      1.209      0.746       1.39        577        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476      0.786      0.824       0.86      0.581\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    103/200      8.76G      1.211     0.7433      1.402        396        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.797      0.838      0.862      0.587\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    104/200      8.59G      1.192     0.7304      1.379        641        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.781      0.836       0.86      0.586\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    105/200      8.67G      1.197     0.7336      1.383        546        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.786      0.841      0.864      0.591\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    106/200      8.76G      1.193     0.7369      1.389        565        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.798      0.826      0.868      0.593\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    107/200       8.8G      1.186     0.7287      1.374        485        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.805       0.84      0.864      0.586\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    108/200      8.62G      1.188     0.7329      1.371        584        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476      0.778      0.854      0.867       0.59\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    109/200      8.53G      1.181     0.7191      1.382        536        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476        0.8      0.856       0.87      0.596\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    110/200      8.62G      1.186     0.7203       1.37        851        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.808      0.833       0.87      0.593\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    111/200      8.86G      1.177       0.71       1.37        643        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.11it/s]\n",
            "                   all         84       2476      0.796      0.843      0.865      0.592\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    112/200      8.37G       1.18     0.7113       1.37        642        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.11it/s]\n",
            "                   all         84       2476      0.813      0.854      0.873        0.6\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    113/200      8.73G      1.165     0.6985      1.355        661        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.806      0.863      0.878      0.605\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    114/200      8.65G      1.173     0.6948      1.354        655        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476      0.815      0.834      0.873      0.595\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    115/200      8.78G      1.173      0.694      1.354        672        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.11it/s]\n",
            "                   all         84       2476      0.795       0.85      0.873      0.601\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    116/200      8.63G      1.161     0.6923      1.359        534        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.808      0.846      0.877      0.601\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    117/200      8.67G      1.162     0.6926      1.363        494        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.832      0.851      0.884      0.606\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    118/200      8.69G      1.163     0.6925      1.354        562        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.831      0.849      0.874      0.602\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    119/200      8.85G      1.149     0.6851      1.357        445        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.825      0.848      0.883      0.605\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    120/200      8.66G      1.149     0.6811      1.349        608        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476       0.82      0.851      0.878       0.61\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    121/200      8.66G      1.147     0.6747       1.35        550        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.809      0.844      0.869        0.6\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    122/200      8.69G      1.148     0.6864       1.36        543        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.811      0.854      0.881      0.601\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    123/200      8.77G      1.149     0.6863      1.375        489        640: 100% 63/63 [00:39<00:00,  1.60it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476       0.81      0.866       0.88      0.606\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    124/200      8.59G      1.141     0.6687      1.343        587        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.818      0.873      0.886      0.615\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    125/200      8.65G      1.131     0.6604      1.336        396        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476       0.81      0.875      0.882       0.61\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    126/200      8.64G      1.123     0.6549      1.325        611        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.828      0.857      0.889      0.617\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    127/200      8.77G       1.12     0.6521      1.331        609        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.829      0.859      0.888      0.615\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    128/200      8.62G      1.113     0.6469      1.318        669        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.829      0.863      0.892      0.618\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    129/200      8.68G      1.123     0.6554       1.33        439        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.818      0.857      0.881      0.608\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    130/200      8.54G      1.117     0.6461      1.328        522        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.819      0.864      0.892      0.621\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    131/200      8.82G      1.113      0.643      1.317        562        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.829      0.847      0.882       0.61\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    132/200      8.59G      1.111      0.643      1.337        535        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.843       0.86      0.895      0.623\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    133/200      8.68G      1.098     0.6433      1.317        696        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.842      0.855      0.889      0.618\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    134/200      8.72G      1.104     0.6419      1.318        561        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476      0.817      0.889      0.896      0.624\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    135/200       8.8G      1.098     0.6336       1.32        404        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476       0.82      0.877      0.894       0.62\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    136/200      8.47G      1.105     0.6386      1.324        682        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.836      0.871      0.891       0.62\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    137/200      8.62G      1.094     0.6311      1.314        506        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.853      0.851      0.897      0.626\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    138/200      8.69G      1.091     0.6284      1.298        484        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
            "                   all         84       2476       0.85      0.868      0.896      0.627\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    139/200      8.74G      1.082     0.6232      1.308        535        640: 100% 63/63 [00:39<00:00,  1.60it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.836      0.871      0.897      0.627\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    140/200      8.62G      1.088     0.6215      1.299        451        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476      0.825      0.876      0.895      0.624\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    141/200      8.66G       1.08     0.6191      1.289        543        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.846      0.869      0.886       0.62\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    142/200      8.69G      1.088     0.6257      1.312        771        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476       0.84      0.867      0.892      0.623\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    143/200      8.86G       1.08      0.615      1.298        492        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.849       0.85      0.888      0.622\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    144/200      8.47G      1.073     0.6159      1.299        682        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.847      0.862      0.893      0.625\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    145/200      8.65G      1.066     0.6052       1.28        623        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.842      0.874      0.897       0.63\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    146/200      8.52G       1.06     0.6075      1.287        541        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.846      0.876      0.895      0.628\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    147/200      8.84G      1.059     0.6019      1.291        551        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.836      0.884      0.895      0.629\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    148/200      8.64G      1.061     0.6004      1.289        553        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.839      0.878      0.893      0.626\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    149/200      8.66G      1.062     0.6042      1.285        536        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.837      0.886      0.892      0.626\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    150/200      8.62G      1.052     0.5988      1.282        647        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.845      0.873      0.899      0.631\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    151/200      8.75G       1.05     0.5985      1.289        581        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.848       0.87      0.896      0.629\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    152/200      8.66G      1.054     0.5978      1.287        626        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.848      0.874      0.894      0.628\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    153/200      8.73G       1.04     0.5936      1.281        362        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.842      0.872      0.895      0.631\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    154/200      8.71G      1.048     0.5912      1.283        597        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.07it/s]\n",
            "                   all         84       2476       0.85      0.881      0.901      0.634\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    155/200      8.79G      1.039     0.5896      1.284        569        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.11it/s]\n",
            "                   all         84       2476      0.847      0.877      0.902      0.639\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    156/200      8.69G      1.044     0.5927      1.286        568        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.862      0.862      0.901      0.636\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    157/200      8.69G      1.038     0.5866      1.291        491        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.853      0.872      0.902      0.637\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    158/200      8.66G      1.035     0.5852       1.27        445        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.866      0.874      0.901      0.637\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    159/200      8.65G      1.028     0.5781      1.268        481        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.12it/s]\n",
            "                   all         84       2476      0.863      0.878      0.903      0.638\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    160/200      8.62G      1.029     0.5833      1.264        537        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476       0.86      0.874      0.902      0.637\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    161/200      8.72G      1.025     0.5842       1.27        489        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.869      0.865      0.905       0.64\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    162/200       8.6G      1.021     0.5765      1.267        653        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.853      0.885      0.904      0.638\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    163/200      8.84G      1.016     0.5735      1.265        539        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.862       0.87      0.902      0.636\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    164/200      8.59G      1.021     0.5748      1.278        563        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.866      0.879      0.905      0.641\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    165/200      8.64G      1.018     0.5732      1.268        633        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.863      0.879      0.902       0.64\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    166/200      8.78G      1.013     0.5705      1.259        649        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.854      0.877      0.902      0.642\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    167/200      8.73G      1.017     0.5711       1.26        712        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.854      0.877      0.903       0.64\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    168/200      8.66G      1.007     0.5692      1.249        486        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.862      0.879      0.903      0.641\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    169/200      8.69G      1.008     0.5665      1.244        567        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.861      0.871      0.903      0.641\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    170/200      8.49G      1.015     0.5703      1.273        613        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.11it/s]\n",
            "                   all         84       2476      0.853      0.881        0.9      0.636\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    171/200      8.84G      1.008     0.5659      1.252        451        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.868       0.88      0.904      0.642\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    172/200      8.55G     0.9972     0.5633      1.238        604        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.876      0.872      0.906      0.643\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    173/200      8.69G      1.005     0.5612      1.245        469        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.11it/s]\n",
            "                   all         84       2476      0.873      0.877      0.904      0.644\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    174/200      8.64G     0.9947     0.5583       1.25        484        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.12it/s]\n",
            "                   all         84       2476      0.866      0.884      0.905      0.644\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    175/200      8.84G      1.008     0.5676      1.272        520        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.859      0.885      0.903      0.642\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    176/200      8.64G      1.004     0.5642      1.254        538        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.865      0.876      0.902      0.639\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    177/200      8.71G     0.9969     0.5609      1.257        631        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.876      0.873      0.902      0.642\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    178/200       8.6G     0.9896     0.5534      1.244        531        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.11it/s]\n",
            "                   all         84       2476      0.875      0.871      0.903      0.643\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    179/200      8.75G     0.9999     0.5578      1.243        608        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.865      0.889        0.9      0.641\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    180/200      8.38G     0.9919     0.5508      1.235        465        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.863      0.884      0.902      0.644\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    181/200      8.63G     0.9984     0.5638      1.257        457        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.873      0.883      0.904      0.644\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    182/200      8.64G     0.9903     0.5564      1.244        459        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.876      0.876      0.903      0.646\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    183/200      8.72G     0.9927     0.5558      1.242        639        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.864      0.887      0.903      0.645\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    184/200      8.62G     0.9816     0.5536      1.244        567        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.12it/s]\n",
            "                   all         84       2476      0.874      0.878      0.904      0.644\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    185/200      8.69G     0.9905     0.5603      1.247        736        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.871      0.878      0.903      0.644\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    186/200      8.68G     0.9976      0.559      1.252        522        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.11it/s]\n",
            "                   all         84       2476      0.865      0.886      0.901      0.645\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    187/200      8.73G     0.9918     0.5579      1.258        524        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.864      0.886      0.903      0.645\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    188/200      8.65G     0.9907     0.5615      1.253        517        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.865      0.888      0.903      0.645\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    189/200      8.63G     0.9869     0.5503      1.232        633        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.11it/s]\n",
            "                   all         84       2476      0.864      0.884      0.903      0.645\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    190/200      8.61G     0.9933      0.557      1.255        484        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.11it/s]\n",
            "                   all         84       2476      0.859       0.89      0.902      0.646\n",
            "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, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    191/200      8.61G      1.026     0.5575      1.365        308        640: 100% 63/63 [00:39<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.845      0.884      0.897      0.636\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    192/200      8.45G      1.006     0.5351      1.349        357        640: 100% 63/63 [00:38<00:00,  1.62it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.856      0.873      0.896      0.633\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    193/200      8.52G       1.01     0.5328      1.352        388        640: 100% 63/63 [00:38<00:00,  1.62it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.08it/s]\n",
            "                   all         84       2476      0.853      0.878      0.897      0.636\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    194/200      8.48G     0.9989     0.5294      1.343        377        640: 100% 63/63 [00:38<00:00,  1.62it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.858      0.876      0.899      0.638\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    195/200      8.62G      1.012     0.5349      1.369        436        640: 100% 63/63 [00:38<00:00,  1.62it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476      0.852      0.881      0.899       0.64\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    196/200      8.43G     0.9941     0.5198      1.338        388        640: 100% 63/63 [00:38<00:00,  1.62it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476       0.86      0.873        0.9       0.64\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    197/200      8.49G      1.001     0.5258      1.343        310        640: 100% 63/63 [00:38<00:00,  1.62it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.10it/s]\n",
            "                   all         84       2476      0.861      0.874        0.9      0.641\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    198/200      8.52G     0.9932     0.5203      1.349        308        640: 100% 63/63 [00:38<00:00,  1.62it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476       0.86      0.876        0.9      0.641\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    199/200      8.64G     0.9929     0.5166      1.345        410        640: 100% 63/63 [00:38<00:00,  1.62it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.09it/s]\n",
            "                   all         84       2476       0.86      0.878      0.901      0.642\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    200/200      8.44G      1.001     0.5312      1.347        511        640: 100% 63/63 [00:38<00:00,  1.62it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.11it/s]\n",
            "                   all         84       2476       0.86      0.876        0.9      0.641\n",
            "\n",
            "200 epochs completed in 2.332 hours.\n",
            "Optimizer stripped from runs/detect/YOLOv8DREPA_optimisation/weights/last.pt, 32.8MB\n",
            "Optimizer stripped from runs/detect/YOLOv8DREPA_optimisation/weights/best.pt, 32.8MB\n",
            "\n",
            "Validating runs/detect/YOLOv8DREPA_optimisation/weights/best.pt...\n",
            "Ultralytics 8.3.67 🚀 Python-3.11.11 torch-2.5.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "DREPADetect summary (fused): 150 layers, 16,250,067 parameters, 0 gradients, 118.6 GFLOPs\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:02<00:00,  1.06it/s]\n",
            "                   all         84       2476      0.876      0.876      0.903      0.646\n",
            "                autres         84       1033       0.83      0.828      0.854      0.595\n",
            "           falciformes         83        463      0.909       0.88      0.921      0.653\n",
            "              normales         82        980       0.89       0.92      0.933       0.69\n",
            "Speed: 0.2ms preprocess, 12.9ms inference, 0.0ms loss, 1.3ms postprocess per image\n",
            "Results saved to \u001b[1mruns/detect/YOLOv8DREPA_optimisation\u001b[0m\n",
            "💡 Learn more at https://docs.ultralytics.com/modes/train\n"
          ]
        }
      ],
      "source": [
        "# Entraînement du modèle DREPADetect optimisé\n",
        "!yolo detect train \\\n",
        "    model=/content/drive/MyDrive/YOLOv8DREPACUSTOMALLDATA002/DREPADetect.yaml \\\n",
        "    data=/content/drive/MyDrive/yolo_detect/Modele_yolo/DrepaSeg.v1i.yolov8/data.yaml \\\n",
        "    workers=4 \\\n",
        "    batch=16 \\\n",
        "    device=0 \\\n",
        "    epochs=200 \\\n",
        "    patience=75 \\\n",
        "    imgsz=640 \\\n",
        "    lr0=0.01 \\\n",
        "    lrf=0.001 \\\n",
        "    warmup_epochs=3 \\\n",
        "    weight_decay=0.0005 \\\n",
        "    cos_lr=True \\\n",
        "    close_mosaic=10 \\\n",
        "    label_smoothing=0.1 \\\n",
        "    overlap_mask=True \\\n",
        "    mask_ratio=4 \\\n",
        "    name=YOLOv8DREPA_optimisation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vMy-gG2xybCi",
        "outputId": "1becc66d-7c25-400e-f56a-bdf6fa920975"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING ⚠️ 'label_smoothing' is deprecated and will be removed in in the future.\n",
            "Ultralytics 8.3.55 🚀 Python-3.10.12 torch-2.5.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=/content/gdrive/MyDrive/YOLOv8DREPACUSTOM/YOLOv8DREPACUSTOM.yaml, data=/content/gdrive/MyDrive/yolo_detect/Modele_yolo/DrepaSeg.v1i.yolov8/data.yaml, epochs=200, time=None, patience=75, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=0, workers=4, project=None, name=YOLOv8DREPA_optimisation, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=True, 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=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.001, momentum=0.937, weight_decay=0.0005, warmup_epochs=3, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, 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, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/YOLOv8DREPA_optimisation\n",
            "Overriding model.yaml nc=80 with nc=3\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1      1856  ultralytics.nn.modules.conv.Conv             [3, 64, 3, 2]                 \n",
            "  1                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n",
            "  2                  -1  3    279808  ultralytics.nn.modules.block.C2f             [128, 128, 3, True]           \n",
            "  3                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
            "  4                  -1  6   2101248  ultralytics.nn.modules.block.C2f             [256, 256, 6, True]           \n",
            "  5                  -1  1   1180672  ultralytics.nn.modules.conv.Conv             [256, 512, 3, 2]              \n",
            "  6                  -1  6   8396800  ultralytics.nn.modules.block.C2f             [512, 512, 6, True]           \n",
            "  7                  -1  1   1182208  ultralytics.nn.modules.block.SPPF            [512, 1024, 5]                \n",
            "  8                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            "  9             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 10                  -1  3   1378816  ultralytics.nn.modules.block.C2f             [1280, 256, 3]                \n",
            " 11                [10]  1   1370195  ultralytics.nn.modules.head.Detect           [3, [256]]                    \n",
            "YOLOv8DREPACUSTOM summary: 204 layers, 16,261,011 parameters, 16,260,995 gradients, 119.1 GFLOPs\n",
            "\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/YOLOv8DREPA_optimisation', view at http://localhost:6006/\n",
            "Freezing layer 'model.11.dfl.conv.weight'\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/gdrive/MyDrive/yolo_detect/Modele_yolo/DrepaSeg.v1i.yolov8/train/labels.cache... 1005 images, 0 backgrounds, 0 corrupt: 100% 1005/1005 [00:00<?, ?it/s]\n",
            "/usr/local/lib/python3.10/dist-packages/albumentations/__init__.py:24: UserWarning: A new version of Albumentations is available: 1.4.24 (you have 1.4.20). Upgrade using: pip install -U albumentations. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1.\n",
            "  check_for_updates()\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, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/gdrive/MyDrive/yolo_detect/Modele_yolo/DrepaSeg.v1i.yolov8/valid/labels.cache... 84 images, 0 backgrounds, 0 corrupt: 100% 84/84 [00:00<?, ?it/s]\n",
            "Plotting labels to runs/detect/YOLOv8DREPA_optimisation/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=0.001429, momentum=0.9) with parameter groups 54 weight(decay=0.0), 57 weight(decay=0.0005), 56 bias(decay=0.0)\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n",
            "Image sizes 640 train, 640 val\n",
            "Using 4 dataloader workers\n",
            "Logging results to \u001b[1mruns/detect/YOLOv8DREPA_optimisation\u001b[0m\n",
            "Starting training for 200 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      1/200      8.82G      3.466      2.767       3.76        382        640: 100% 63/63 [00:55<00:00,  1.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.14it/s]\n",
            "                   all         84       2476     0.0073    0.00488    0.00386   0.000879\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      2/200       8.7G      2.108      1.786       2.51        659        640: 100% 63/63 [00:43<00:00,  1.45it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.79it/s]\n",
            "                   all         84       2476      0.203      0.529      0.309      0.138\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      3/200      8.56G       1.93      1.597      2.113        606        640: 100% 63/63 [00:42<00:00,  1.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.88it/s]\n",
            "                   all         84       2476      0.283      0.705      0.405      0.216\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      4/200      8.55G      1.849      1.548      1.983        571        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.79it/s]\n",
            "                   all         84       2476      0.375      0.741      0.471      0.256\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      5/200      8.65G      1.787      1.472      1.892        576        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.88it/s]\n",
            "                   all         84       2476       0.39       0.77       0.52      0.304\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      6/200      8.52G      1.727      1.406       1.81        503        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.86it/s]\n",
            "                   all         84       2476      0.456       0.77      0.566      0.276\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      7/200      8.78G      1.707      1.378      1.792        426        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.89it/s]\n",
            "                   all         84       2476      0.473      0.697      0.571      0.311\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      8/200      8.63G      1.687      1.354      1.767        494        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.90it/s]\n",
            "                   all         84       2476      0.529      0.773      0.642       0.38\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      9/200      8.65G      1.665      1.332      1.734        472        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.91it/s]\n",
            "                   all         84       2476      0.463      0.708       0.61      0.335\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     10/200      8.73G      1.652      1.291      1.718        633        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.90it/s]\n",
            "                   all         84       2476      0.487      0.756      0.683      0.394\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     11/200      8.63G      1.626      1.278      1.711        594        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.92it/s]\n",
            "                   all         84       2476      0.548      0.735      0.672      0.415\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     12/200      8.58G      1.618       1.25      1.696        655        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.93it/s]\n",
            "                   all         84       2476      0.558       0.76      0.696      0.436\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     13/200      8.67G      1.601      1.226      1.692        546        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.90it/s]\n",
            "                   all         84       2476      0.564      0.754      0.711       0.44\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     14/200       8.6G      1.578      1.211      1.668        431        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.89it/s]\n",
            "                   all         84       2476      0.543      0.748      0.696      0.402\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     15/200      8.77G      1.579      1.202      1.655        592        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.614      0.756      0.713      0.434\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     16/200      8.58G      1.562       1.18      1.657        640        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.91it/s]\n",
            "                   all         84       2476      0.635      0.784      0.745      0.454\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     17/200      8.67G       1.55       1.18      1.628        538        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.626      0.742      0.718      0.443\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     18/200      8.63G      1.543      1.143      1.633        524        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.606      0.805       0.75      0.472\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     19/200      8.75G       1.54      1.134      1.627        410        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.93it/s]\n",
            "                   all         84       2476       0.64      0.745      0.724      0.447\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     20/200      8.59G      1.517      1.122      1.618        571        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.92it/s]\n",
            "                   all         84       2476      0.646      0.763      0.744      0.454\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     21/200      8.52G      1.521       1.11      1.605        701        640: 100% 63/63 [00:43<00:00,  1.46it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476       0.64      0.744      0.737      0.428\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     22/200      8.62G      1.519      1.111       1.61        720        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.93it/s]\n",
            "                   all         84       2476      0.646      0.776      0.745      0.465\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     23/200      8.83G      1.513       1.11      1.621        357        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.93it/s]\n",
            "                   all         84       2476      0.671      0.775      0.771      0.486\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     24/200      8.57G      1.486      1.079      1.592        577        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.92it/s]\n",
            "                   all         84       2476      0.641      0.804      0.777      0.486\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     25/200      8.63G      1.491      1.086      1.586        407        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.91it/s]\n",
            "                   all         84       2476      0.656      0.797      0.767       0.49\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     26/200      8.62G      1.518       1.08      1.607        535        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.696       0.78      0.783      0.497\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     27/200      8.62G      1.502      1.066      1.586        540        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.699      0.789      0.784      0.507\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     28/200      8.67G       1.46      1.052      1.577        704        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476       0.67      0.814      0.797      0.507\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     29/200      8.68G      1.459       1.04      1.565        528        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.93it/s]\n",
            "                   all         84       2476       0.69      0.798      0.788       0.51\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     30/200      8.64G      1.451       1.05      1.567        664        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.673      0.788       0.78      0.492\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     31/200      8.73G      1.448      1.038      1.574        541        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.694      0.802      0.796      0.515\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     32/200       8.6G      1.438       1.03       1.55        556        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.703      0.797      0.784      0.469\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     33/200      8.64G      1.462      1.036      1.559        460        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.717      0.781      0.797      0.512\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     34/200      8.63G      1.424      1.012      1.536        591        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.93it/s]\n",
            "                   all         84       2476      0.689      0.778      0.787      0.513\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     35/200       8.8G      1.438      1.024      1.551        509        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.685      0.778      0.789      0.505\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     36/200      8.58G      1.417      1.013      1.537        388        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476       0.69      0.784      0.798      0.521\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     37/200      8.69G      1.425      1.019      1.552        537        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.685      0.785      0.791      0.496\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     38/200      8.66G      1.414      1.004      1.526        637        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.666        0.8       0.79      0.511\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     39/200      8.88G      1.401     0.9935      1.534        638        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.716      0.804      0.806       0.53\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     40/200      8.67G       1.39     0.9761      1.504        594        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.701      0.812      0.808      0.523\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     41/200      8.71G      1.383     0.9694       1.51        657        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.718      0.812      0.817      0.534\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     42/200      8.77G      1.418      0.989      1.531        570        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.683      0.779      0.792      0.496\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     43/200      8.76G      1.383     0.9762      1.523        715        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.92it/s]\n",
            "                   all         84       2476      0.679      0.806       0.79        0.5\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     44/200      8.59G      1.389     0.9661      1.505        598        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.708      0.804      0.807      0.533\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     45/200      8.71G      1.385     0.9676      1.512        559        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.726      0.782      0.809      0.533\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     46/200       8.7G      1.358     0.9495      1.482        418        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476       0.72      0.791      0.815      0.529\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     47/200      8.77G      1.369      0.955      1.498        579        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476       0.68      0.785      0.796      0.528\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     48/200      8.59G      1.363     0.9498      1.504        560        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.708      0.812      0.813      0.535\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     49/200      8.68G      1.352     0.9397      1.488        484        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.93it/s]\n",
            "                   all         84       2476      0.752      0.794      0.819      0.533\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     50/200      8.65G      1.361     0.9312      1.484        692        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.92it/s]\n",
            "                   all         84       2476      0.723      0.797      0.809       0.51\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     51/200      8.76G      1.351     0.9358       1.49        524        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.725       0.82      0.822       0.55\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     52/200      8.69G      1.349     0.9309      1.487        560        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.707      0.806       0.81      0.527\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     53/200      8.68G      1.348     0.9297      1.489        626        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.93it/s]\n",
            "                   all         84       2476      0.695      0.799      0.804      0.532\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     54/200      8.61G      1.344     0.9219      1.485        354        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.698       0.82      0.813      0.533\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     55/200      8.88G      1.354     0.9178      1.485        876        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.721      0.784      0.818      0.543\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     56/200      8.69G      1.343     0.9154      1.482        467        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.698      0.841      0.821      0.541\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     57/200      8.64G       1.34     0.9157      1.475        480        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.715      0.802      0.813      0.543\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     58/200      8.67G      1.314     0.8938      1.453        552        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.734      0.809      0.826      0.556\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     59/200      8.76G      1.335     0.8913      1.475        611        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.726      0.818      0.824      0.542\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     60/200      8.62G      1.326     0.8953      1.464        595        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.735      0.826      0.825      0.552\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     61/200      8.63G      1.315     0.8837      1.462        627        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476       0.74      0.809      0.828      0.546\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     62/200      8.67G      1.328     0.9014      1.496        524        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.729      0.804      0.828      0.548\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     63/200      8.66G      1.317     0.8842      1.461        553        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.742      0.817      0.831      0.553\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     64/200      8.64G      1.307      0.881      1.463        518        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.93it/s]\n",
            "                   all         84       2476      0.733      0.812       0.83      0.548\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     65/200      8.65G      1.308     0.8882      1.463        526        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.727      0.825      0.831      0.554\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     66/200      8.67G        1.3     0.8753      1.454        613        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.748      0.827      0.833      0.548\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     67/200      8.75G      1.303     0.8687      1.457        502        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.747      0.795      0.827      0.548\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     68/200      8.56G      1.291     0.8576      1.436        525        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.754      0.809      0.836      0.556\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     69/200       8.5G      1.296     0.8652      1.454        448        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.776      0.789      0.838      0.558\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     70/200      8.63G      1.292     0.8619       1.44        608        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.743      0.816      0.823      0.554\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     71/200      8.78G      1.293     0.8567      1.434        629        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.736      0.815      0.836      0.558\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     72/200      8.58G      1.308     0.8593      1.455        575        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.764      0.801      0.832      0.557\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     73/200      8.61G       1.28     0.8493      1.437        499        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.742      0.826      0.837      0.555\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     74/200      8.52G      1.299     0.8459      1.449        600        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.745      0.814      0.836      0.558\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     75/200      8.82G      1.293     0.8419      1.434        469        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.776      0.782      0.841      0.567\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     76/200      8.66G      1.278     0.8254      1.429        697        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.747      0.827      0.844      0.565\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     77/200      8.69G      1.273     0.8376      1.442        588        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.729      0.822      0.846      0.568\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     78/200      8.69G      1.264     0.8355       1.44        606        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.759      0.798      0.833      0.546\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     79/200       8.8G      1.277     0.8347      1.447        441        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.93it/s]\n",
            "                   all         84       2476      0.734      0.833      0.847      0.567\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     80/200      8.58G      1.293     0.8394      1.441       1004        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476       0.72      0.821      0.831      0.561\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     81/200      8.62G      1.272     0.8288      1.437        499        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476       0.75      0.826      0.843      0.568\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     82/200      8.64G      1.256     0.8137       1.42        622        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.732      0.824      0.836      0.556\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     83/200      8.84G      1.258     0.8131      1.419        547        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.752      0.836      0.839      0.562\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     84/200      8.63G      1.261     0.8254      1.419        664        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.768      0.814      0.845      0.557\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     85/200      8.62G      1.257     0.8045       1.42        489        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.773      0.826      0.848      0.567\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     86/200      8.66G      1.265     0.8052      1.438        803        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.765      0.811      0.844      0.569\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     87/200      8.85G      1.256     0.8035      1.428        550        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.92it/s]\n",
            "                   all         84       2476      0.759      0.831      0.848      0.565\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     88/200      8.67G      1.246     0.7951      1.411        675        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.93it/s]\n",
            "                   all         84       2476      0.779      0.817      0.841      0.567\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     89/200       8.7G      1.236     0.7955      1.412        587        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.757      0.833      0.845       0.57\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     90/200      8.51G      1.238     0.7837      1.418        449        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.772      0.858      0.861      0.577\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     91/200      8.77G      1.244      0.789      1.425        540        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.752      0.831      0.849      0.576\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     92/200      8.59G      1.232      0.781       1.42        606        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.93it/s]\n",
            "                   all         84       2476      0.781      0.831      0.857      0.575\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     93/200      8.65G      1.238     0.7806      1.407        465        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.786      0.836      0.856      0.574\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     94/200      8.72G      1.214     0.7676      1.394        453        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.794       0.81      0.852      0.575\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     95/200      8.71G      1.218     0.7646      1.393        664        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.789      0.834      0.864      0.587\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     96/200      8.63G      1.236      0.783      1.416        389        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.786      0.831      0.859      0.583\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     97/200      8.68G      1.216     0.7573      1.397        476        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.93it/s]\n",
            "                   all         84       2476      0.795      0.814       0.86      0.582\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     98/200      8.68G      1.212     0.7555       1.39        537        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.783      0.828      0.861      0.577\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     99/200      8.78G      1.218     0.7582      1.401        417        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.782      0.828      0.861      0.583\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    100/200      8.64G      1.211     0.7552      1.394        447        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.778      0.829      0.859       0.58\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    101/200      8.62G      1.213     0.7516      1.392        508        640: 100% 63/63 [00:43<00:00,  1.46it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.797      0.833      0.865      0.585\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    102/200       8.6G      1.209      0.746       1.39        577        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.786      0.824       0.86      0.581\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    103/200      8.79G      1.211     0.7433      1.402        396        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.797      0.838      0.862      0.587\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    104/200      8.59G      1.192     0.7304      1.379        641        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.781      0.836       0.86      0.586\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    105/200      8.67G      1.197     0.7336      1.383        546        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.786      0.841      0.864      0.591\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    106/200      8.75G      1.193     0.7369      1.389        565        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.798      0.826      0.868      0.593\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    107/200      8.81G      1.186     0.7287      1.374        485        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.805       0.84      0.864      0.586\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    108/200      8.63G      1.188     0.7329      1.371        584        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.778      0.854      0.867       0.59\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    109/200      8.52G      1.181     0.7191      1.382        536        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476        0.8      0.856       0.87      0.596\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    110/200       8.6G      1.186     0.7203       1.37        851        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.808      0.833       0.87      0.593\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    111/200      8.84G      1.177       0.71       1.37        643        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.796      0.843      0.865      0.592\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    112/200      8.37G       1.18     0.7113       1.37        642        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.98it/s]\n",
            "                   all         84       2476      0.813      0.854      0.873        0.6\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    113/200      8.74G      1.165     0.6985      1.355        661        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.806      0.863      0.878      0.605\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    114/200      8.65G      1.173     0.6948      1.354        655        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.815      0.834      0.873      0.595\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    115/200      8.76G      1.173      0.694      1.354        672        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.98it/s]\n",
            "                   all         84       2476      0.795       0.85      0.873      0.601\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    116/200      8.64G      1.161     0.6923      1.359        534        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.808      0.846      0.877      0.601\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    117/200      8.67G      1.162     0.6926      1.363        494        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.832      0.851      0.884      0.606\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    118/200      8.71G      1.163     0.6925      1.354        562        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.831      0.849      0.874      0.602\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    119/200      8.83G      1.149     0.6851      1.357        445        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.825      0.848      0.883      0.605\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    120/200      8.66G      1.149     0.6811      1.349        608        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476       0.82      0.851      0.878       0.61\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    121/200      8.65G      1.147     0.6747       1.35        550        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.809      0.844      0.869        0.6\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    122/200      8.69G      1.148     0.6864       1.36        543        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.811      0.854      0.881      0.601\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    123/200      8.74G      1.149     0.6863      1.375        489        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476       0.81      0.866       0.88      0.606\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    124/200      8.58G      1.141     0.6687      1.343        587        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.98it/s]\n",
            "                   all         84       2476      0.818      0.873      0.886      0.615\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    125/200      8.65G      1.131     0.6604      1.336        396        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476       0.81      0.875      0.882       0.61\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    126/200      8.64G      1.123     0.6549      1.325        611        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.98it/s]\n",
            "                   all         84       2476      0.828      0.857      0.889      0.617\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    127/200      8.76G       1.12     0.6521      1.331        609        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.829      0.859      0.888      0.615\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    128/200      8.61G      1.113     0.6469      1.318        669        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.829      0.863      0.892      0.618\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    129/200      8.68G      1.123     0.6554       1.33        439        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.818      0.857      0.881      0.608\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    130/200      8.52G      1.117     0.6461      1.328        522        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.819      0.864      0.892      0.621\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    131/200       8.8G      1.113      0.643      1.317        562        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.98it/s]\n",
            "                   all         84       2476      0.829      0.847      0.882       0.61\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    132/200      8.59G      1.111      0.643      1.337        535        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.843       0.86      0.895      0.623\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    133/200      8.68G      1.098     0.6433      1.317        696        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.842      0.855      0.889      0.618\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    134/200      8.71G      1.104     0.6419      1.318        561        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.817      0.889      0.896      0.624\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    135/200      8.81G      1.098     0.6336       1.32        404        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476       0.82      0.877      0.894       0.62\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    136/200      8.48G      1.105     0.6386      1.324        682        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.836      0.871      0.891       0.62\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    137/200      8.62G      1.094     0.6311      1.314        506        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.853      0.851      0.897      0.626\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    138/200       8.7G      1.091     0.6284      1.298        484        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476       0.85      0.868      0.896      0.627\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    139/200      8.75G      1.082     0.6232      1.308        535        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.836      0.871      0.897      0.627\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    140/200      8.63G      1.088     0.6215      1.299        451        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.825      0.876      0.895      0.624\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    141/200      8.66G       1.08     0.6191      1.289        543        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.846      0.869      0.886       0.62\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    142/200      8.69G      1.088     0.6257      1.312        771        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476       0.84      0.867      0.892      0.623\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    143/200      8.85G       1.08      0.615      1.298        492        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.849       0.85      0.888      0.622\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    144/200      8.47G      1.073     0.6159      1.299        682        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.847      0.862      0.893      0.625\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    145/200      8.64G      1.066     0.6052       1.28        623        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.842      0.874      0.897       0.63\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    146/200      8.52G       1.06     0.6075      1.287        541        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.846      0.876      0.895      0.628\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    147/200      8.86G      1.059     0.6019      1.291        551        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.836      0.884      0.895      0.629\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    148/200      8.64G      1.061     0.6004      1.289        553        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.839      0.878      0.893      0.626\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    149/200      8.66G      1.062     0.6042      1.285        536        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.837      0.886      0.892      0.626\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    150/200      8.63G      1.052     0.5988      1.282        647        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.845      0.873      0.899      0.631\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    151/200      8.74G       1.05     0.5985      1.289        581        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.848       0.87      0.896      0.629\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    152/200      8.66G      1.054     0.5978      1.287        626        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.848      0.874      0.894      0.628\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    153/200      8.72G       1.04     0.5936      1.281        362        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.98it/s]\n",
            "                   all         84       2476      0.842      0.872      0.895      0.631\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    154/200      8.72G      1.048     0.5912      1.283        597        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476       0.85      0.881      0.901      0.634\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    155/200       8.8G      1.039     0.5896      1.284        569        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.847      0.877      0.902      0.639\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    156/200      8.67G      1.044     0.5927      1.286        568        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.862      0.862      0.901      0.636\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    157/200      8.69G      1.038     0.5866      1.291        491        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.93it/s]\n",
            "                   all         84       2476      0.853      0.872      0.902      0.637\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    158/200      8.66G      1.035     0.5852       1.27        445        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.866      0.874      0.901      0.637\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    159/200      8.66G      1.028     0.5781      1.268        481        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.863      0.878      0.903      0.638\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    160/200       8.6G      1.029     0.5833      1.264        537        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.98it/s]\n",
            "                   all         84       2476       0.86      0.874      0.902      0.637\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    161/200      8.72G      1.025     0.5842       1.27        489        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.98it/s]\n",
            "                   all         84       2476      0.869      0.865      0.905       0.64\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    162/200       8.6G      1.021     0.5765      1.267        653        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.853      0.885      0.904      0.638\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    163/200      8.84G      1.016     0.5735      1.265        539        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.98it/s]\n",
            "                   all         84       2476      0.862       0.87      0.902      0.636\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    164/200      8.59G      1.021     0.5748      1.278        563        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.866      0.879      0.905      0.641\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    165/200      8.64G      1.018     0.5732      1.268        633        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.98it/s]\n",
            "                   all         84       2476      0.863      0.879      0.902       0.64\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    166/200      8.77G      1.013     0.5705      1.259        649        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.854      0.877      0.902      0.642\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    167/200      8.75G      1.017     0.5711       1.26        712        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.98it/s]\n",
            "                   all         84       2476      0.854      0.877      0.903       0.64\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    168/200      8.64G      1.007     0.5692      1.249        486        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.862      0.879      0.903      0.641\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    169/200      8.69G      1.008     0.5665      1.244        567        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.861      0.871      0.903      0.641\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    170/200      8.51G      1.015     0.5703      1.273        613        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.853      0.881        0.9      0.636\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    171/200      8.85G      1.008     0.5659      1.252        451        640: 100% 63/63 [00:43<00:00,  1.46it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.868       0.88      0.904      0.642\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    172/200      8.56G     0.9972     0.5633      1.238        604        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.876      0.872      0.906      0.643\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    173/200      8.69G      1.005     0.5612      1.245        469        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.98it/s]\n",
            "                   all         84       2476      0.873      0.877      0.904      0.644\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    174/200      8.64G     0.9947     0.5583       1.25        484        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.866      0.884      0.905      0.644\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    175/200      8.84G      1.008     0.5676      1.272        520        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.93it/s]\n",
            "                   all         84       2476      0.859      0.885      0.903      0.642\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    176/200      8.64G      1.004     0.5642      1.254        538        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.865      0.876      0.902      0.639\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    177/200      8.69G     0.9969     0.5609      1.257        631        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.876      0.873      0.902      0.642\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    178/200       8.6G     0.9896     0.5534      1.244        531        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.875      0.871      0.903      0.643\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    179/200      8.77G     0.9999     0.5578      1.243        608        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.865      0.889        0.9      0.641\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    180/200       8.4G     0.9919     0.5508      1.235        465        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.863      0.884      0.902      0.644\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    181/200      8.63G     0.9984     0.5638      1.257        457        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.873      0.883      0.904      0.644\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    182/200      8.64G     0.9903     0.5564      1.244        459        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.876      0.876      0.903      0.646\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    183/200      8.72G     0.9927     0.5558      1.242        639        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.864      0.887      0.903      0.645\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    184/200      8.62G     0.9816     0.5536      1.244        567        640: 100% 63/63 [00:42<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.874      0.878      0.904      0.644\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    185/200      8.69G     0.9905     0.5603      1.247        736        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.871      0.878      0.903      0.644\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    186/200      8.68G     0.9976      0.559      1.252        522        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.865      0.886      0.901      0.645\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    187/200      8.76G     0.9918     0.5579      1.258        524        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.864      0.886      0.903      0.645\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    188/200      8.65G     0.9907     0.5615      1.253        517        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.865      0.888      0.903      0.645\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    189/200      8.62G     0.9869     0.5503      1.232        633        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.864      0.884      0.903      0.645\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    190/200      8.61G     0.9933      0.557      1.255        484        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.859       0.89      0.902      0.646\n",
            "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, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    191/200       8.6G      1.026     0.5575      1.365        308        640: 100% 63/63 [00:42<00:00,  1.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.94it/s]\n",
            "                   all         84       2476      0.845      0.884      0.897      0.636\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    192/200      8.45G      1.006     0.5351      1.349        357        640: 100% 63/63 [00:42<00:00,  1.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.97it/s]\n",
            "                   all         84       2476      0.856      0.873      0.896      0.633\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    193/200      8.51G       1.01     0.5328      1.352        388        640: 100% 63/63 [00:42<00:00,  1.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.853      0.878      0.897      0.636\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    194/200      8.48G     0.9989     0.5294      1.343        377        640: 100% 63/63 [00:42<00:00,  1.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.858      0.876      0.899      0.638\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    195/200      8.61G      1.012     0.5349      1.369        436        640: 100% 63/63 [00:42<00:00,  1.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.96it/s]\n",
            "                   all         84       2476      0.852      0.881      0.899       0.64\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    196/200      8.43G     0.9941     0.5198      1.338        388        640: 100% 63/63 [00:42<00:00,  1.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476       0.86      0.873        0.9       0.64\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    197/200      8.49G      1.001     0.5258      1.343        310        640: 100% 63/63 [00:42<00:00,  1.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476      0.861      0.874        0.9      0.641\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    198/200      8.52G     0.9932     0.5203      1.349        308        640: 100% 63/63 [00:42<00:00,  1.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476       0.86      0.876        0.9      0.641\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    199/200      8.63G     0.9929     0.5166      1.345        410        640: 100% 63/63 [00:42<00:00,  1.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.98it/s]\n",
            "                   all         84       2476       0.86      0.878      0.901      0.642\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    200/200      8.43G      1.001     0.5312      1.347        511        640: 100% 63/63 [00:42<00:00,  1.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.95it/s]\n",
            "                   all         84       2476       0.86      0.876        0.9      0.641\n",
            "\n",
            "200 epochs completed in 2.519 hours.\n",
            "Optimizer stripped from runs/detect/YOLOv8DREPA_optimisation/weights/last.pt, 32.8MB\n",
            "Optimizer stripped from runs/detect/YOLOv8DREPA_optimisation/weights/best.pt, 32.8MB\n",
            "\n",
            "Validating runs/detect/YOLOv8DREPA_optimisation/weights/best.pt...\n",
            "Ultralytics 8.3.55 🚀 Python-3.10.12 torch-2.5.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "YOLOv8DREPACUSTOM summary (fused): 150 layers, 16,250,067 parameters, 0 gradients, 118.6 GFLOPs\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:02<00:00,  1.04it/s]\n",
            "                   all         84       2476      0.876      0.876      0.903      0.646\n",
            "                autres         84       1033       0.83      0.828      0.854      0.595\n",
            "           falciformes         83        463      0.909       0.88      0.921      0.653\n",
            "              normales         82        980       0.89       0.92      0.933       0.69\n",
            "Speed: 0.2ms preprocess, 14.1ms inference, 0.0ms loss, 1.2ms postprocess per image\n",
            "Results saved to \u001b[1mruns/detect/YOLOv8DREPA_optimisation\u001b[0m\n",
            "💡 Learn more at https://docs.ultralytics.com/modes/train\n"
          ]
        }
      ],
      "source": [
        "# Entraînement du modèle DREPADetect optimisé\n",
        "!yolo detect train \\\n",
        "    model=/content/gdrive/MyDrive/YOLOv8DREPACUSTOM/YOLOv8DREPACUSTOM.yaml \\\n",
        "    data=/content/gdrive/MyDrive/yolo_detect/Modele_yolo/DrepaSeg.v1i.yolov8/data.yaml \\\n",
        "    workers=4 \\\n",
        "    batch=16 \\\n",
        "    device=0 \\\n",
        "    epochs=200 \\\n",
        "    patience=75 \\\n",
        "    imgsz=640 \\\n",
        "    lr0=0.01 \\\n",
        "    lrf=0.001 \\\n",
        "    warmup_epochs=3 \\\n",
        "    weight_decay=0.0005 \\\n",
        "    cos_lr=True \\\n",
        "    close_mosaic=10 \\\n",
        "    label_smoothing=0.1 \\\n",
        "    overlap_mask=True \\\n",
        "    mask_ratio=4 \\\n",
        "    name=YOLOv8DREPA_optimisation"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "W_IWXn8mwDWe",
        "mBeSrDbqwnxc",
        "KDr5gnGmfhNL",
        "1c9gk231Jjee"
      ],
      "gpuType": "T4",
      "machine_shape": "hm",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "02614ce4e88149129a07085807e3e465": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "0bd4dccd7cbc48348e23c91ab4a123f5": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "0cfb34a5b70c487da90d758ae6733fd0": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "10d7ae20c1054fc98d43d19414caf31e": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": "20px"
          }
        },
        "12aff4946a7d4ccd8b980ff5e61c5f19": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "19ed7f95805744fab828d2409cc5c8bc": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "256542fb6da44b90b905ed13f0816fa3": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_a729ef049e3244f1a2a3f52e184be0ea",
            "placeholder": "​",
            "style": "IPY_MODEL_bcea3c8775f64bfd860dc28a5e772512",
            "value": "  ...epadetecte_upload/best.pt: 100%"
          }
        },
        "32e3901f00004914b79f6b46b684012f": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_8f7a73992b974bdfb2e1c219bcac7702",
            "placeholder": "​",
            "style": "IPY_MODEL_ade3cd975427456aa8268d308835e036",
            "value": " 32.8MB / 32.8MB, 2.53MB/s  "
          }
        },
        "5ec8253ee25748c192a21c9dac4687b8": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "65c844e08dd641f8a0faf91882c0029d": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "6b0a5134785349cc844b49f7ebc8670d": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "6c1faea6d72a4b95b0ee63bd7fc742b2": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": "20px"
          }
        },
        "6c75889dbb3c4570a1a873f73c03d4cd": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_0cfb34a5b70c487da90d758ae6733fd0",
            "placeholder": "​",
            "style": "IPY_MODEL_6b0a5134785349cc844b49f7ebc8670d",
            "value": "New Data Upload               : 100%"
          }
        },
        "6f9d2be6dbfb4377b344e9c7e19de260": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "7a42b0e34f3945af847d1dfed87c692a": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "7e606667a7e0435e9488369c3b487144": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_256542fb6da44b90b905ed13f0816fa3",
              "IPY_MODEL_cbb3105bfbba4c65aca66a201059983e",
              "IPY_MODEL_f0aa339db04e4d0cb0f92c438a4bea38"
            ],
            "layout": "IPY_MODEL_c58a97c724844265aacd67c19ff336f3"
          }
        },
        "8131bacf423b4b2495f839caa8c85877": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "8f7a73992b974bdfb2e1c219bcac7702": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9f456dceea71424db65252d588e34801": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_a505bf788801474da5a11396b0440333",
            "placeholder": "​",
            "style": "IPY_MODEL_65c844e08dd641f8a0faf91882c0029d",
            "value": "Processing Files (1 / 1)      : 100%"
          }
        },
        "a1b8a09153cf484dbcbb99c65fb852f8": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_6c1faea6d72a4b95b0ee63bd7fc742b2",
            "max": 1,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_02614ce4e88149129a07085807e3e465",
            "value": 1
          }
        },
        "a3399f73011e4d6695a864fbedffb67a": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a505bf788801474da5a11396b0440333": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a729ef049e3244f1a2a3f52e184be0ea": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "ade3cd975427456aa8268d308835e036": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "b4c42b542f854d89bd4af304f3640536": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_10d7ae20c1054fc98d43d19414caf31e",
            "max": 1,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_19ed7f95805744fab828d2409cc5c8bc",
            "value": 1
          }
        },
        "bcea3c8775f64bfd860dc28a5e772512": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "c58a97c724844265aacd67c19ff336f3": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "cbb3105bfbba4c65aca66a201059983e": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_8131bacf423b4b2495f839caa8c85877",
            "max": 32765029,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_12aff4946a7d4ccd8b980ff5e61c5f19",
            "value": 32765029
          }
        },
        "db69f6f74efa45babcdac7794327a5e4": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_9f456dceea71424db65252d588e34801",
              "IPY_MODEL_b4c42b542f854d89bd4af304f3640536",
              "IPY_MODEL_32e3901f00004914b79f6b46b684012f"
            ],
            "layout": "IPY_MODEL_a3399f73011e4d6695a864fbedffb67a"
          }
        },
        "dcb9cd50dde548709ab6eb2204d455f6": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_6f9d2be6dbfb4377b344e9c7e19de260",
            "placeholder": "​",
            "style": "IPY_MODEL_e745f6e5d6ad44f18f00b4b4b4f19ccd",
            "value": " 32.8MB / 32.8MB, 2.53MB/s  "
          }
        },
        "ddbb0057c23f4528ae642399ef515098": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_6c75889dbb3c4570a1a873f73c03d4cd",
              "IPY_MODEL_a1b8a09153cf484dbcbb99c65fb852f8",
              "IPY_MODEL_dcb9cd50dde548709ab6eb2204d455f6"
            ],
            "layout": "IPY_MODEL_0bd4dccd7cbc48348e23c91ab4a123f5"
          }
        },
        "e745f6e5d6ad44f18f00b4b4b4f19ccd": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "f0aa339db04e4d0cb0f92c438a4bea38": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_5ec8253ee25748c192a21c9dac4687b8",
            "placeholder": "​",
            "style": "IPY_MODEL_7a42b0e34f3945af847d1dfed87c692a",
            "value": " 32.8MB / 32.8MB            "
          }
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}