NNDL_HW3_Q3.ipynb

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      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt"
      ],
      "metadata": {
        "id": "vFRfLpT1w-Ao"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision"
      ],
      "metadata": {
        "id": "shRxGzNHIZJM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install -r /content/drive/MyDrive/Q3/requirements.txt"
      ],
      "metadata": {
        "id": "j9bjOPnfH0D0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! git clone https://github.com/meituan/YOLOv6"
      ],
      "metadata": {
        "id": "FwYgp4zGJdEc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd YOLOv6"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5YhNSnH_JhkV",
        "outputId": "b5e15bdb-3ad8-4c0b-dbfa-4d267ee95447"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/YOLOv6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6s.pt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cvR6H8GBMjJe",
        "outputId": "be0550ad-faad-4990-e057-92f7fc753cd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-12-07 10:43:49--  https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6s.pt\n",
            "Resolving github.com (github.com)... 20.205.243.166\n",
            "Connecting to github.com (github.com)|20.205.243.166|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/501076075/cb6de918-8c19-473d-ac07-06850e97307a?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20221207%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20221207T104349Z&X-Amz-Expires=300&X-Amz-Signature=3b58f16f80eaf8cd7f91207d6a42ce3173f2618434bb3accf46fde3ff0ec1d07&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=501076075&response-content-disposition=attachment%3B%20filename%3Dyolov6s.pt&response-content-type=application%2Foctet-stream [following]\n",
            "--2022-12-07 10:43:49--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/501076075/cb6de918-8c19-473d-ac07-06850e97307a?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20221207%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20221207T104349Z&X-Amz-Expires=300&X-Amz-Signature=3b58f16f80eaf8cd7f91207d6a42ce3173f2618434bb3accf46fde3ff0ec1d07&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=501076075&response-content-disposition=attachment%3B%20filename%3Dyolov6s.pt&response-content-type=application%2Foctet-stream\n",
            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.111.133, ...\n",
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            "Length: 38101272 (36M) [application/octet-stream]\n",
            "Saving to: ‘yolov6s.pt’\n",
            "\n",
            "yolov6s.pt          100%[===================>]  36.34M  8.74MB/s    in 4.2s    \n",
            "\n",
            "2022-12-07 10:43:55 (8.74 MB/s) - ‘yolov6s.pt’ saved [38101272/38101272]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! mkdir weights\n",
        "! mv yolov6s.pt weights/"
      ],
      "metadata": {
        "id": "btfR5FFBL21P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.is_available()\n",
        "torch.cuda.get_device_name(0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "MLGIng7DKtl1",
        "outputId": "fa188c75-842c-4da3-f7cf-cb0ac188ba93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Tesla T4'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python tools/train.py --batch 32 --conf configs/yolov6s_finetune.py --data-path /content/drive/MyDrive/Q3/data.yaml --device 0 --epochs 50 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a1OIHjeIJyRY",
        "outputId": "f88c135c-697c-4a73-f608-16693bab00ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using 1 GPU for training... \n",
            "training args are: Namespace(batch_size=32, calib=False, check_images=False, check_labels=False, conf_file='configs/yolov6s_finetune.py', data_path='/content/drive/MyDrive/Q3/data.yaml', device='0', dist_url='env://', distill=False, distill_feat=False, epochs=50, eval_final_only=False, eval_interval=20, gpu_count=0, heavy_eval_range=50, img_size=640, local_rank=-1, name='exp', output_dir='./runs/train', quant=False, rank=-1, resume=False, save_ckpt_on_last_n_epoch=-1, save_dir='runs/train/exp', stop_aug_last_n_epoch=15, teacher_model_path=None, temperature=20, workers=8, world_size=1, write_trainbatch_tb=False)\n",
            "\n",
            "Train: Final numbers of valid images: 606/ labels: 606. \n",
            "5.7s for dataset initialization.\n",
            "Convert to COCO format\n",
            "100% 58/58 [00:00<00:00, 28755.28it/s]\n",
            "Convert to COCO format finished. Resutls saved in /content/drive/MyDrive/Q3/annotations/instances_valid.json\n",
            "Val: Final numbers of valid images: 58/ labels: 58. \n",
            "2.0s for dataset initialization.\n",
            "Loading state_dict from weights/yolov6s.pt for fine-tuning...\n",
            "Model: Model(\n",
            "  (backbone): EfficientRep(\n",
            "    (stem): RepVGGBlock(\n",
            "      (nonlinearity): ReLU(inplace=True)\n",
            "      (se): Identity()\n",
            "      (rbr_dense): Sequential(\n",
            "        (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (rbr_1x1): Sequential(\n",
            "        (conv): Conv2d(3, 32, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (ERBlock_2): Sequential(\n",
            "      (0): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (1): RepBlock(\n",
            "        (conv1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (block): Sequential(\n",
            "          (0): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (ERBlock_3): Sequential(\n",
            "      (0): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (1): RepBlock(\n",
            "        (conv1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (block): Sequential(\n",
            "          (0): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (1): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (2): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (ERBlock_4): Sequential(\n",
            "      (0): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (1): RepBlock(\n",
            "        (conv1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (block): Sequential(\n",
            "          (0): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (1): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (2): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (3): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (4): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (ERBlock_5): Sequential(\n",
            "      (0): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (1): RepBlock(\n",
            "        (conv1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (block): Sequential(\n",
            "          (0): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (2): SimSPPF(\n",
            "        (cv1): SimConv(\n",
            "          (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (act): ReLU(inplace=True)\n",
            "        )\n",
            "        (cv2): SimConv(\n",
            "          (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (act): ReLU(inplace=True)\n",
            "        )\n",
            "        (m): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (neck): RepPANNeck(\n",
            "    (Rep_p4): RepBlock(\n",
            "      (conv1): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (block): Sequential(\n",
            "        (0): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (2): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (Rep_p3): RepBlock(\n",
            "      (conv1): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (block): Sequential(\n",
            "        (0): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (2): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (Rep_n3): RepBlock(\n",
            "      (conv1): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (block): Sequential(\n",
            "        (0): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (2): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (Rep_n4): RepBlock(\n",
            "      (conv1): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (block): Sequential(\n",
            "        (0): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (2): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (reduce_layer0): SimConv(\n",
            "      (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "      (act): ReLU(inplace=True)\n",
            "    )\n",
            "    (upsample0): Transpose(\n",
            "      (upsample_transpose): ConvTranspose2d(128, 128, kernel_size=(2, 2), stride=(2, 2))\n",
            "    )\n",
            "    (reduce_layer1): SimConv(\n",
            "      (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "      (act): ReLU(inplace=True)\n",
            "    )\n",
            "    (upsample1): Transpose(\n",
            "      (upsample_transpose): ConvTranspose2d(64, 64, kernel_size=(2, 2), stride=(2, 2))\n",
            "    )\n",
            "    (downsample2): SimConv(\n",
            "      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "      (act): ReLU(inplace=True)\n",
            "    )\n",
            "    (downsample1): SimConv(\n",
            "      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "      (act): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (detect): Detect(\n",
            "    (proj_conv): Conv2d(17, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "    (stems): ModuleList(\n",
            "      (0): Conv(\n",
            "        (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "      (1): Conv(\n",
            "        (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "      (2): Conv(\n",
            "        (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (cls_convs): ModuleList(\n",
            "      (0): Conv(\n",
            "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "      (1): Conv(\n",
            "        (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "      (2): Conv(\n",
            "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (reg_convs): ModuleList(\n",
            "      (0): Conv(\n",
            "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "      (1): Conv(\n",
            "        (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "      (2): Conv(\n",
            "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (cls_preds): ModuleList(\n",
            "      (0): Conv2d(64, 13, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): Conv2d(128, 13, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): Conv2d(256, 13, kernel_size=(1, 1), stride=(1, 1))\n",
            "    )\n",
            "    (reg_preds): ModuleList(\n",
            "      (0): Conv2d(64, 4, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): Conv2d(256, 4, kernel_size=(1, 1), stride=(1, 1))\n",
            "    )\n",
            "  )\n",
            ")\n",
            "Training start...\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "  0% 0/19 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2894.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
            "      0/49    0.7981         0     3.149: 100% 19/19 [00:42<00:00,  2.26s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:00<00:00,  1.01it/s]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Epoch: 0 | mAP@0.5: 0.0 | mAP@0.50:0.95: 0.0\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "      1/49     0.716         0     3.167: 100% 19/19 [00:20<00:00,  1.08s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:01<00:00,  1.02s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Epoch: 1 | mAP@0.5: 0.0 | mAP@0.50:0.95: 0.0\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "      2/49    0.6567         0     3.077: 100% 19/19 [00:21<00:00,  1.11s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:01<00:00,  1.76s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.02s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.53s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.10s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.046\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.080\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.045\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.134\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.056\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.016\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.086\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.100\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.307\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.097\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 2 | mAP@0.5: 0.07992962179714624 | mAP@0.50:0.95: 0.046395030204780634\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "      3/49    0.5985         0     2.862: 100% 19/19 [00:21<00:00,  1.11s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.25s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.30s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.46s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.08s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.067\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.105\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.083\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.182\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.089\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.021\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.105\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.119\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.396\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.109\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 3 | mAP@0.5: 0.10534340057148495 | mAP@0.50:0.95: 0.06721592776305232\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "      4/49    0.4649         0     2.119: 100% 19/19 [00:21<00:00,  1.16s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:04<00:00,  4.13s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.20s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.50s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.11s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.086\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.119\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.111\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.224\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.108\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.044\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.188\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.212\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.575\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.207\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 4 | mAP@0.5: 0.119279208408144 | mAP@0.50:0.95: 0.08610867826814224\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "      5/49    0.4507         0     1.879: 100% 19/19 [00:19<00:00,  1.05s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:04<00:00,  4.64s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.20s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.58s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.14s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.105\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.149\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.135\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.244\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.132\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.079\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.344\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.423\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.656\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.421\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 5 | mAP@0.5: 0.14865444931821895 | mAP@0.50:0.95: 0.10499518409871571\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "      6/49    0.4508         0     1.648: 100% 19/19 [00:21<00:00,  1.12s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:04<00:00,  4.25s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.21s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.55s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.17s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.130\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.187\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.167\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.305\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.155\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.117\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.475\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.596\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.736\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.592\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 6 | mAP@0.5: 0.18726351659697024 | mAP@0.50:0.95: 0.13003646933136273\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "      7/49    0.4492         0     1.421: 100% 19/19 [00:19<00:00,  1.04s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:04<00:00,  4.48s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.20s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.56s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.17s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.168\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.245\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.210\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.354\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.175\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.181\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.589\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.702\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.737\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.698\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 7 | mAP@0.5: 0.2452265167608306 | mAP@0.50:0.95: 0.16825000159354198\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "      8/49    0.4523         0     1.295: 100% 19/19 [00:20<00:00,  1.08s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:04<00:00,  4.06s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.05s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.56s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.19s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.200\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.294\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.258\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.356\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.201\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.233\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.650\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.721\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.742\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.716\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 8 | mAP@0.5: 0.29354860980567543 | mAP@0.50:0.95: 0.20006061836379604\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "      9/49    0.4475         0     1.225: 100% 19/19 [00:20<00:00,  1.08s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:04<00:00,  4.03s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.21s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.54s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.17s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.237\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.343\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.304\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.370\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.238\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.249\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.674\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.730\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.756\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.723\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 9 | mAP@0.5: 0.34346476580173585 | mAP@0.50:0.95: 0.23695769449464912\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     10/49    0.4361         0     1.187: 100% 19/19 [00:20<00:00,  1.08s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.35s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.21s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.57s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.16s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.266\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.384\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.343\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.376\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.264\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.280\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.691\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.734\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.756\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.726\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 10 | mAP@0.5: 0.38405448978161244 | mAP@0.50:0.95: 0.26646300363947106\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     11/49    0.4382         0     1.165: 100% 19/19 [00:20<00:00,  1.09s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.79s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.06s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.55s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.17s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.297\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.438\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.373\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.409\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.291\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.292\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.699\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.724\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.747\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.719\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 11 | mAP@0.5: 0.437590539053995 | mAP@0.50:0.95: 0.29669018335456065\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     12/49    0.4319         0     1.138: 100% 19/19 [00:20<00:00,  1.07s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.79s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.30s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.64s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.17s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.348\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.496\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.450\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.441\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.344\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.341\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.722\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.739\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.748\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.733\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 12 | mAP@0.5: 0.4962857516405744 | mAP@0.50:0.95: 0.3477564084803597\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     13/49    0.4247         0     1.112: 100% 19/19 [00:20<00:00,  1.09s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.51s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.26s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.59s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.16s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.375\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.537\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.472\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.525\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.368\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.343\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.730\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.740\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.744\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.735\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 13 | mAP@0.5: 0.5372171231060061 | mAP@0.50:0.95: 0.3747093124608965\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     14/49    0.4175         0     1.081: 100% 19/19 [00:20<00:00,  1.07s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.60s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.09s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.89s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.16s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.417\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.582\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.529\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.525\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.415\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.380\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.742\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.749\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.760\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.742\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 14 | mAP@0.5: 0.5821261875515676 | mAP@0.50:0.95: 0.4167350565869843\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     15/49     0.416         0     1.058: 100% 19/19 [00:21<00:00,  1.11s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.37s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.21s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.56s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.17s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.440\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.618\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.555\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.592\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.439\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.387\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.742\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.746\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.761\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.740\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 15 | mAP@0.5: 0.6183950865602685 | mAP@0.50:0.95: 0.44046787621998706\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     16/49    0.4063         0     1.047: 100% 19/19 [00:20<00:00,  1.10s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.41s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.29s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.63s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.17s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.461\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.647\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.594\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.584\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.460\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.400\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.744\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.748\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.762\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.740\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 16 | mAP@0.5: 0.6469085869418867 | mAP@0.50:0.95: 0.46077546911794676\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     17/49    0.4071         0     1.017: 100% 19/19 [00:20<00:00,  1.08s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.40s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.09s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.91s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.17s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.483\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.669\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.616\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.700\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.484\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.405\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.748\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.753\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.759\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.748\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 17 | mAP@0.5: 0.6691216881033312 | mAP@0.50:0.95: 0.4828157058479231\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     18/49    0.4005         0    0.9992: 100% 19/19 [00:20<00:00,  1.06s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.61s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.20s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.55s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.18s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.500\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.691\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.642\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.694\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.500\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.408\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.753\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.758\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.735\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.755\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 18 | mAP@0.5: 0.6911349959265215 | mAP@0.50:0.95: 0.5002421160679182\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     19/49    0.3909         0    0.9612: 100% 19/19 [00:20<00:00,  1.08s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.33s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.44s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.64s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.16s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.511\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.716\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.647\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.701\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.510\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.419\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.743\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.748\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.761\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.742\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 19 | mAP@0.5: 0.7156794533585532 | mAP@0.50:0.95: 0.5106684768643291\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     20/49    0.4013         0    0.9469: 100% 19/19 [00:20<00:00,  1.09s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.37s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.09s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.62s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.16s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.508\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.717\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.656\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.681\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.507\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.423\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.748\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.751\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.720\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.748\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 20 | mAP@0.5: 0.7169737800587159 | mAP@0.50:0.95: 0.5082278260148007\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     21/49    0.3898         0    0.9222: 100% 19/19 [00:20<00:00,  1.09s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.28s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.30s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.63s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.16s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.528\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.738\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.669\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.695\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.528\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.445\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.752\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.757\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.739\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.752\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 21 | mAP@0.5: 0.7381075840614166 | mAP@0.50:0.95: 0.5284386509096469\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     22/49    0.3927         0    0.9116: 100% 19/19 [00:20<00:00,  1.05s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.10s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.31s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.79s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.16s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.537\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.752\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.697\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.693\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.536\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.464\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.751\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.756\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.739\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.752\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 22 | mAP@0.5: 0.7522236565200929 | mAP@0.50:0.95: 0.5367984989813366\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     23/49    0.3954         0    0.9033: 100% 19/19 [00:20<00:00,  1.07s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.38s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.31s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.67s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.19s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.551\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.764\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.724\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.703\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.549\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.496\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.761\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.765\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.725\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.763\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 23 | mAP@0.5: 0.7641057103068287 | mAP@0.50:0.95: 0.5505466969834225\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     24/49    0.3867         0    0.8799: 100% 19/19 [00:20<00:00,  1.06s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.12s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.29s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.76s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.17s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.587\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.799\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.758\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.698\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.587\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.525\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.770\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.774\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.720\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.774\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 24 | mAP@0.5: 0.7987246027651732 | mAP@0.50:0.95: 0.5869986200110783\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     25/49    0.3973         0    0.8754: 100% 19/19 [00:20<00:00,  1.07s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.55s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.09s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.63s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.16s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.593\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.808\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.772\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.702\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.593\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.534\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.772\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.777\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.751\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.774\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 25 | mAP@0.5: 0.8077109267611564 | mAP@0.50:0.95: 0.5933989224751562\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     26/49    0.3863         0    0.8499: 100% 19/19 [00:19<00:00,  1.04s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.43s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.30s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.66s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.17s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.590\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.829\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.736\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.700\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.589\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.537\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.759\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.763\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.723\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.762\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 26 | mAP@0.5: 0.8289604881906493 | mAP@0.50:0.95: 0.5897100465087234\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     27/49    0.3924         0    0.8505: 100% 19/19 [00:20<00:00,  1.07s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.49s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.28s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.73s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.16s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.619\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.852\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.799\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.698\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.619\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.552\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.766\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.770\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.720\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.769\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 27 | mAP@0.5: 0.852476009877169 | mAP@0.50:0.95: 0.6190949625418795\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     28/49    0.3871         0     0.843: 100% 19/19 [00:19<00:00,  1.04s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.75s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.29s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.65s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.15s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.639\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.891\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.819\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.729\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.636\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.574\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.761\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.765\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.761\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.760\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 28 | mAP@0.5: 0.890794251784015 | mAP@0.50:0.95: 0.6387309924512912\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     29/49    0.3827         0    0.8311: 100% 19/19 [00:20<00:00,  1.08s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.35s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.45s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.79s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.17s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.660\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.903\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.845\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.710\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.659\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.580\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.770\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.774\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.739\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.770\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 29 | mAP@0.5: 0.9029321021953792 | mAP@0.50:0.95: 0.660182040041713\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     30/49    0.3846         0    0.8165: 100% 19/19 [00:20<00:00,  1.06s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.41s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.31s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.80s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.19s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.678\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.924\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.876\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.702\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.677\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.583\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.772\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.776\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.722\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.775\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 30 | mAP@0.5: 0.9238855858640954 | mAP@0.50:0.95: 0.6780472303537959\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     31/49    0.3815         0    0.8082: 100% 19/19 [00:20<00:00,  1.09s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.23s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.19s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.56s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.17s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.672\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.921\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.855\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.701\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.670\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.586\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.769\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.774\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.725\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.771\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 31 | mAP@0.5: 0.9209843326932109 | mAP@0.50:0.95: 0.6721197970209178\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     32/49    0.3816         0    0.8098: 100% 19/19 [00:20<00:00,  1.08s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.65s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.11s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.62s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.16s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.692\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.939\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.891\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.700\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.691\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.590\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.775\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.779\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.723\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.779\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 32 | mAP@0.5: 0.9385553668415754 | mAP@0.50:0.95: 0.6917149679743082\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     33/49    0.3741         0    0.8006: 100% 19/19 [00:20<00:00,  1.07s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:03<00:00,  3.16s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.33s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.74s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.18s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.687\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.943\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.885\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.705\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.686\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.585\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.768\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.772\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.737\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.768\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 33 | mAP@0.5: 0.9425180135518731 | mAP@0.50:0.95: 0.6869102364258822\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     34/49    0.3764         0    0.7887: 100% 19/19 [00:19<00:00,  1.05s/it]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:02<00:00,  2.96s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.33s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=1.06s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.17s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.680\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.953\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.870\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.741\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.677\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.588\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.756\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.762\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.779\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.756\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 34 | mAP@0.5: 0.9533406422115718 | mAP@0.50:0.95: 0.67964710996678\n",
            "Train: Final numbers of valid images: 606/ labels: 606. \n",
            "0.2s for dataset initialization.\n",
            "Convert to COCO format\n",
            "100% 58/58 [00:00<00:00, 15821.39it/s]\n",
            "Convert to COCO format finished. Resutls saved in /content/drive/MyDrive/Q3/annotations/instances_valid.json\n",
            "Val: Final numbers of valid images: 58/ labels: 58. \n",
            "0.1s for dataset initialization.\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     35/49    0.3662         0    0.7491: 100% 19/19 [00:15<00:00,  1.23it/s]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:02<00:00,  2.22s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.06s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.62s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.18s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.685\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.948\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.864\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.660\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.686\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.587\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.761\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.764\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.678\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.766\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 35 | mAP@0.5: 0.9478111368826833 | mAP@0.50:0.95: 0.6848866719459904\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     36/49      0.37         0    0.7436: 100% 19/19 [00:13<00:00,  1.42it/s]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:01<00:00,  1.99s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.20s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.58s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.18s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.701\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.960\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.901\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.698\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.700\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.596\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.770\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.773\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.723\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.771\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 36 | mAP@0.5: 0.959872869338912 | mAP@0.50:0.95: 0.7007332310639212\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     37/49    0.3541         0    0.7287: 100% 19/19 [00:13<00:00,  1.42it/s]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:02<00:00,  2.10s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.05s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.71s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.19s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.691\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.966\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.861\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.664\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.691\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.581\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.761\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.761\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.683\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.762\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 37 | mAP@0.5: 0.966035529968123 | mAP@0.50:0.95: 0.6908856814800401\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     38/49    0.3536         0    0.7111: 100% 19/19 [00:13<00:00,  1.42it/s]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:01<00:00,  1.98s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.21s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.59s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.18s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.713\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.969\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.914\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.712\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.712\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.599\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.775\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.777\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.744\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.772\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 38 | mAP@0.5: 0.96866061489721 | mAP@0.50:0.95: 0.7133391542083829\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     39/49    0.3469         0    0.7049: 100% 19/19 [00:13<00:00,  1.42it/s]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:01<00:00,  2.00s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.20s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.59s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.18s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.715\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.968\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.900\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.714\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.713\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.599\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.779\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.781\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.745\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.776\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 39 | mAP@0.5: 0.9680119955494847 | mAP@0.50:0.95: 0.714761259181537\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     40/49    0.3361         0    0.7022: 100% 19/19 [00:13<00:00,  1.42it/s]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:01<00:00,  1.92s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.20s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.58s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.17s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.715\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.967\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.910\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.712\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.714\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.598\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.781\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.783\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.739\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.780\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 40 | mAP@0.5: 0.9670865729189765 | mAP@0.50:0.95: 0.715153116972204\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     41/49    0.3423         0    0.7016: 100% 19/19 [00:13<00:00,  1.42it/s]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:01<00:00,  1.95s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.20s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.57s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.18s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.720\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.968\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.931\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.702\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.720\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.600\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.784\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.785\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.728\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.783\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 41 | mAP@0.5: 0.9679448321392167 | mAP@0.50:0.95: 0.7197979758997255\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     42/49    0.3397         0    0.6892: 100% 19/19 [00:13<00:00,  1.42it/s]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:02<00:00,  2.11s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.05s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.59s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.17s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.728\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.969\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.928\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.680\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.728\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.603\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.788\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.789\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.699\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.788\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 42 | mAP@0.5: 0.9691656292826573 | mAP@0.50:0.95: 0.728099936509184\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     43/49    0.3405         0     0.688: 100% 19/19 [00:13<00:00,  1.42it/s]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:01<00:00,  1.77s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.20s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.54s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.15s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.706\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.969\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.890\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.719\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.704\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.593\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.771\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.773\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.756\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.768\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 43 | mAP@0.5: 0.9686788622579181 | mAP@0.50:0.95: 0.7057185480005284\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     44/49    0.3333         0    0.6811: 100% 19/19 [00:13<00:00,  1.42it/s]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:01<00:00,  1.80s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.19s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.55s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.17s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.716\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.970\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.905\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.713\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.716\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.603\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.780\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.783\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.743\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.779\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 44 | mAP@0.5: 0.9703779068179543 | mAP@0.50:0.95: 0.7161996873159081\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     45/49    0.3322         0    0.6818: 100% 19/19 [00:13<00:00,  1.41it/s]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:01<00:00,  1.81s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.19s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.59s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.16s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.721\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.968\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.903\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.717\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.720\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.609\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.783\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.785\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.744\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.782\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 45 | mAP@0.5: 0.9684856159909507 | mAP@0.50:0.95: 0.72078482637847\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     46/49     0.328         0    0.6671: 100% 19/19 [00:13<00:00,  1.42it/s]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:01<00:00,  1.94s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.04s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.55s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.16s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.723\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.970\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.926\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.705\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.724\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.611\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.787\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.789\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.729\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.787\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 46 | mAP@0.5: 0.9699678652697298 | mAP@0.50:0.95: 0.7233278706816682\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     47/49    0.3315         0    0.6678: 100% 19/19 [00:13<00:00,  1.41it/s]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:01<00:00,  1.98s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.04s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.56s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.17s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.720\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.970\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.917\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.678\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.721\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.609\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.785\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.788\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.705\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.785\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 47 | mAP@0.5: 0.9698029303059509 | mAP@0.50:0.95: 0.7203598693731095\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     48/49    0.3294         0    0.6723: 100% 19/19 [00:13<00:00,  1.42it/s]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:01<00:00,  1.85s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.20s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.56s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.18s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.725\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.971\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.915\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.681\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.725\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.611\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.788\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.790\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.707\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.788\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 48 | mAP@0.5: 0.970714264833415 | mAP@0.50:0.95: 0.7253672307902543\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "     49/49    0.3254         0     0.657: 100% 19/19 [00:13<00:00,  1.42it/s]\n",
            "Inferencing model in train datasets.: 100% 1/1 [00:01<00:00,  1.82s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.19s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.55s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.16s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.727\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.970\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.911\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.681\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.728\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.611\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.791\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.794\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.706\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.792\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
            "Results saved to runs/train/exp\n",
            "Epoch: 49 | mAP@0.5: 0.9704187526035853 | mAP@0.50:0.95: 0.7273013753282828\n",
            "\n",
            "Training completed in 0.347 hours.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python tools/infer.py --weights runs/train/exp/weights/best_ckpt.pt --source /content/drive/MyDrive/Q3/images/test/ --yaml /content/drive/MyDrive/Q3/data.yaml --device 0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0zAGGZdUOQ4d",
        "outputId": "333fd067-7849-4701-89ff-5e8e9f7df239"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Namespace(agnostic_nms=False, classes=None, conf_thres=0.4, device='0', half=False, hide_conf=False, hide_labels=False, img_size=[640, 640], iou_thres=0.45, max_det=1000, name='exp', not_save_img=False, project='runs/inference', save_dir=None, save_txt=False, source='/content/drive/MyDrive/Q3/images/test/', view_img=False, weights='runs/train/exp/weights/best_ckpt.pt', yaml='/content/drive/MyDrive/Q3/data.yaml')\n",
            "Save directory already existed\n",
            "Loading checkpoint from runs/train/exp/weights/best_ckpt.pt\n",
            "\n",
            "Fusing model...\n",
            "Switch model to deploy modality.\n",
            "/usr/local/lib/python3.8/dist-packages/torch/functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2894.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
            "100% 29/29 [00:16<00:00,  1.74it/s]\n",
            "Results saved to runs/inference/exp\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import Image\n",
        "Image(filename=\"/content/YOLOv6/runs/inference/exp/cfc306bf86176b92ffc1afbb98d7896f_jpg.rf.4423638c782bb0d9751f1a14072fdbb4.jpg\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "id": "QeE-HmpsMsjs",
        "outputId": "30e4523c-d703-4d0a-dd61-1e50076d1d0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    }
  ]
}