Most_accurate(main).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t6MPjfT5NrKQ"
   },
   "source": [
    "<div align=\"center\">\n",
    "\n",
    "  <a href=\"https://ultralytics.com/yolov5\" target=\"_blank\">\n",
    "    <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov5/v70/splash.png\"></a>\n",
    "\n",
    "\n",
    "<br>\n",
    "  <a href=\"https://bit.ly/yolov5-paperspace-notebook\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"></a>\n",
    "  <a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
    "  <a href=\"https://www.kaggle.com/ultralytics/yolov5\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
    "<br>\n",
    "\n",
    "This <a href=\"https://github.com/ultralytics/yolov5\">YOLOv5</a> 🚀 notebook by <a href=\"https://ultralytics.com\">Ultralytics</a> presents simple train, validate and predict examples to help start your AI adventure.<br>We hope that the resources in this notebook will help you get the most out of YOLOv5. Please browse the YOLOv5 <a href=\"https://docs.ultralytics.com/yolov5\">Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/yolov5\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7mGmQbAO5pQb"
   },
   "source": [
    "# Setup\n",
    "\n",
    "Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wbvMlHd_QwMG",
    "outputId": "69b8cc6d-de75-4919-a8cf-63e75bfcb7df"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 v7.0-247-g3f02fde Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 27.0/78.2 GB disk)\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/ultralytics/yolov5  # clone\n",
    "%cd yolov5\n",
    "%pip install -qr requirements.txt comet_ml  # install\n",
    "\n",
    "import torch\n",
    "import utils\n",
    "display = utils.notebook_init()  # checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "NlqCDT7lFDmG",
    "outputId": "c3307ef1-9f9c-45e7-d30e-49a58ffe2271"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[?25hRequirement already satisfied: urllib3>=1.26.6 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.0.7)\n",
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      "  Downloading python_magic-0.4.27-py2.py3-none-any.whl (13 kB)\n",
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      "Requirement already satisfied: scipy<2.0.0,>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from supervision->roboflow) (1.11.4)\n",
      "Installing collected packages: python-magic, python-dotenv, pyparsing, opencv-python-headless, idna, cycler, chardet, certifi, supervision, roboflow\n",
      "  Attempting uninstall: pyparsing\n",
      "    Found existing installation: pyparsing 3.1.1\n",
      "    Uninstalling pyparsing-3.1.1:\n",
      "      Successfully uninstalled pyparsing-3.1.1\n",
      "  Attempting uninstall: opencv-python-headless\n",
      "    Found existing installation: opencv-python-headless 4.8.1.78\n",
      "    Uninstalling opencv-python-headless-4.8.1.78:\n",
      "      Successfully uninstalled opencv-python-headless-4.8.1.78\n",
      "  Attempting uninstall: idna\n",
      "    Found existing installation: idna 3.6\n",
      "    Uninstalling idna-3.6:\n",
      "      Successfully uninstalled idna-3.6\n",
      "  Attempting uninstall: cycler\n",
      "    Found existing installation: cycler 0.12.1\n",
      "    Uninstalling cycler-0.12.1:\n",
      "      Successfully uninstalled cycler-0.12.1\n",
      "  Attempting uninstall: chardet\n",
      "    Found existing installation: chardet 5.2.0\n",
      "    Uninstalling chardet-5.2.0:\n",
      "      Successfully uninstalled chardet-5.2.0\n",
      "  Attempting uninstall: certifi\n",
      "    Found existing installation: certifi 2023.11.17\n",
      "    Uninstalling certifi-2023.11.17:\n",
      "      Successfully uninstalled certifi-2023.11.17\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "lida 0.0.10 requires fastapi, which is not installed.\n",
      "lida 0.0.10 requires kaleido, which is not installed.\n",
      "lida 0.0.10 requires python-multipart, which is not installed.\n",
      "lida 0.0.10 requires uvicorn, which is not installed.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed certifi-2023.7.22 chardet-4.0.0 cycler-0.10.0 idna-2.10 opencv-python-headless-4.8.0.74 pyparsing-2.4.7 python-dotenv-1.0.0 python-magic-0.4.27 roboflow-1.1.11 supervision-0.16.0\n"
     ]
    },
    {
     "data": {
      "application/vnd.colab-display-data+json": {
       "pip_warning": {
        "packages": [
         "certifi",
         "chardet",
         "cv2",
         "cycler",
         "idna",
         "pyparsing"
        ]
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading Roboflow workspace...\n",
      "loading Roboflow project...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading Dataset Version Zip in Speclearn-(ALL-Radar)-6 to yolov5pytorch:: 100%|██████████| 190385/190385 [00:05<00:00, 35980.66it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Extracting Dataset Version Zip to Speclearn-(ALL-Radar)-6 in yolov5pytorch:: 100%|██████████| 5322/5322 [00:01<00:00, 5106.07it/s]\n"
     ]
    }
   ],
   "source": [
    "!pip install roboflow\n",
    "\n",
    "from roboflow import Roboflow\n",
    "rf = Roboflow(api_key=\"BFtzywDAwu2V97Di0vv9\")\n",
    "project = rf.workspace(\"spectram\").project(\"speclearn-all-radar\")\n",
    "dataset = project.version(6).download(\"yolov5\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NTIWVH6fFOw4",
    "outputId": "afb783ed-65e8-4517-96a9-2868ab20ab99"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-12-04 02:43:09.513808: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2023-12-04 02:43:09.513889: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2023-12-04 02:43:09.513953: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=/content/yolov5/Speclearn-(ALL-Radar)-6/data.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
      "YOLOv5 🚀 v7.0-247-g3f02fde Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "COMET WARNING: Comet credentials have not been set. Comet will default to offline logging. Please set your credentials to enable online logging.\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Using '/content/yolov5/.cometml-runs' path as offline directory. Pass 'offline_directory' parameter into constructor or set the 'COMET_OFFLINE_DIRECTORY' environment variable to manually choose where to store offline experiment archives.\n",
      "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...\n",
      "100%|██████████| 14.1M/14.1M [00:00<00:00, 200MB/s]\n",
      "\n",
      "Overriding model.yaml nc=80 with nc=4\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     24273  models.yolo.Detect                      [4, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "Model summary: 214 layers, 7030417 parameters, 7030417 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 343/349 items from yolov5s.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/yolov5/Speclearn-(ALL-Radar)-6/train/labels... 2337 images, 0 backgrounds, 0 corrupt: 100%|██████████| 2337/2337 [00:01<00:00, 1873.74it/s]\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/yolov5/Speclearn-(ALL-Radar)-6/train/labels.cache\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (2.7GB ram): 100%|██████████| 2337/2337 [00:14<00:00, 156.07it/s]\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/yolov5/Speclearn-(ALL-Radar)-6/valid/labels... 210 images, 0 backgrounds, 0 corrupt: 100%|██████████| 210/210 [00:00<00:00, 650.40it/s]\n",
      "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/yolov5/Speclearn-(ALL-Radar)-6/valid/labels.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.2GB ram): 100%|██████████| 210/210 [00:02<00:00, 86.64it/s]\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m2.15 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/exp/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 2 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99       3.5G    0.07011    0.04078    0.03544          6        640: 100%|██████████| 147/147 [00:46<00:00,  3.15it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:04<00:00,  1.75it/s]\n",
      "                   all        210        495      0.374      0.288      0.154     0.0615\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      4.33G    0.05009    0.03304    0.01903          5        640: 100%|██████████| 147/147 [00:41<00:00,  3.57it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.74it/s]\n",
      "                   all        210        495      0.764      0.408      0.378      0.235\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      4.33G    0.04743    0.02886     0.0128          7        640: 100%|██████████| 147/147 [00:38<00:00,  3.79it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.65it/s]\n",
      "                   all        210        495      0.409      0.317      0.204      0.117\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      4.33G    0.04307     0.0263    0.01028          4        640: 100%|██████████| 147/147 [00:40<00:00,  3.63it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.23it/s]\n",
      "                   all        210        495      0.777      0.581      0.592       0.35\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      4.33G    0.03949    0.02529   0.009832          8        640: 100%|██████████| 147/147 [00:40<00:00,  3.61it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.10it/s]\n",
      "                   all        210        495      0.917       0.67      0.692      0.413\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      4.33G    0.03689    0.02403   0.008739         10        640: 100%|██████████| 147/147 [00:38<00:00,  3.80it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.38it/s]\n",
      "                   all        210        495      0.739      0.507       0.52       0.31\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      4.33G    0.03613    0.02379   0.008271          6        640: 100%|██████████| 147/147 [00:39<00:00,  3.76it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.13it/s]\n",
      "                   all        210        495      0.795      0.593      0.583      0.334\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      4.33G    0.03487    0.02352   0.008115          6        640: 100%|██████████| 147/147 [00:41<00:00,  3.58it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.02it/s]\n",
      "                   all        210        495      0.961      0.703      0.729      0.487\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      4.33G    0.03395    0.02294    0.00742          8        640: 100%|██████████| 147/147 [00:39<00:00,  3.74it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.07it/s]\n",
      "                   all        210        495      0.925      0.708      0.704      0.495\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      4.33G     0.0335    0.02229   0.006711          7        640: 100%|██████████| 147/147 [00:38<00:00,  3.77it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.99it/s]\n",
      "                   all        210        495      0.961      0.688      0.718      0.476\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      4.33G    0.03256    0.02165   0.006885          4        640: 100%|██████████| 147/147 [00:41<00:00,  3.57it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.09it/s]\n",
      "                   all        210        495      0.945      0.695      0.709      0.494\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      4.33G    0.03224    0.02175   0.006407          5        640: 100%|██████████| 147/147 [00:40<00:00,  3.65it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.19it/s]\n",
      "                   all        210        495      0.933      0.706       0.72      0.503\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      4.33G    0.03154     0.0212   0.006237          6        640: 100%|██████████| 147/147 [00:39<00:00,  3.74it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.53it/s]\n",
      "                   all        210        495      0.968      0.717      0.741      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      4.33G    0.03132    0.02124    0.00617          3        640: 100%|██████████| 147/147 [00:39<00:00,  3.72it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.12it/s]\n",
      "                   all        210        495       0.98      0.714      0.734      0.517\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      4.33G    0.03114    0.02089   0.006707         11        640: 100%|██████████| 147/147 [00:40<00:00,  3.60it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.98it/s]\n",
      "                   all        210        495      0.973       0.72      0.734      0.529\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      4.33G    0.03087    0.02099   0.006443          7        640: 100%|██████████| 147/147 [00:38<00:00,  3.79it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.30it/s]\n",
      "                   all        210        495       0.94      0.693       0.71      0.493\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      4.33G    0.03088    0.02116   0.006242          4        640: 100%|██████████| 147/147 [00:39<00:00,  3.75it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.08it/s]\n",
      "                   all        210        495      0.982      0.724      0.734      0.511\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      4.33G    0.03075    0.02097   0.005697          5        640: 100%|██████████| 147/147 [00:40<00:00,  3.62it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.06it/s]\n",
      "                   all        210        495      0.973      0.731       0.74       0.52\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      4.33G    0.03022    0.02079   0.006728          2        640: 100%|██████████| 147/147 [00:38<00:00,  3.77it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.17it/s]\n",
      "                   all        210        495      0.973      0.721      0.738      0.514\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      4.33G    0.03006    0.02093   0.006075          9        640: 100%|██████████| 147/147 [00:39<00:00,  3.75it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.12it/s]\n",
      "                   all        210        495      0.974      0.723      0.736      0.522\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      4.33G    0.03087     0.0203   0.005985          4        640: 100%|██████████| 147/147 [00:40<00:00,  3.63it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.17it/s]\n",
      "                   all        210        495      0.976       0.72       0.74      0.522\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      4.33G    0.03045    0.02063   0.005613          7        640: 100%|██████████| 147/147 [00:39<00:00,  3.75it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.13it/s]\n",
      "                   all        210        495      0.972      0.719       0.74      0.527\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      4.33G    0.02949    0.01999   0.006549          3        640: 100%|██████████| 147/147 [00:38<00:00,  3.80it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.12it/s]\n",
      "                   all        210        495      0.986       0.73      0.741       0.52\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/99      4.33G    0.02965    0.01987   0.005337          7        640: 100%|██████████| 147/147 [00:40<00:00,  3.59it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.06it/s]\n",
      "                   all        210        495      0.979      0.731      0.742      0.528\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      4.33G    0.02843    0.01996   0.005711          4        640: 100%|██████████| 147/147 [00:39<00:00,  3.72it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  1.97it/s]\n",
      "                   all        210        495      0.968      0.717      0.736      0.532\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      4.33G    0.02943    0.02077   0.005154          9        640: 100%|██████████| 147/147 [00:38<00:00,  3.80it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.54it/s]\n",
      "                   all        210        495      0.981      0.727      0.739       0.54\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      4.33G    0.02886    0.01989   0.005446          6        640: 100%|██████████| 147/147 [00:39<00:00,  3.68it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.12it/s]\n",
      "                   all        210        495      0.976      0.719      0.736      0.526\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      4.33G     0.0293    0.01974   0.006045          2        640: 100%|██████████| 147/147 [00:41<00:00,  3.56it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.65it/s]\n",
      "                   all        210        495      0.983      0.732      0.739      0.527\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      4.33G    0.02891    0.02014   0.005785          8        640: 100%|██████████| 147/147 [00:38<00:00,  3.79it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.25it/s]\n",
      "                   all        210        495      0.976      0.734      0.748      0.539\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      4.33G    0.02867    0.01968   0.005984          2        640: 100%|██████████| 147/147 [00:39<00:00,  3.77it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.08it/s]\n",
      "                   all        210        495       0.98      0.733      0.743      0.532\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      4.33G    0.02818    0.01941    0.00518          2        640: 100%|██████████| 147/147 [00:40<00:00,  3.63it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.83it/s]\n",
      "                   all        210        495      0.989       0.73      0.741      0.542\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      4.33G    0.02914     0.0192   0.005614          2        640: 100%|██████████| 147/147 [00:39<00:00,  3.76it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.27it/s]\n",
      "                   all        210        495       0.98      0.727       0.74      0.539\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      4.33G    0.02732    0.01933   0.004931          4        640: 100%|██████████| 147/147 [00:39<00:00,  3.76it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.17it/s]\n",
      "                   all        210        495      0.977       0.73      0.735      0.532\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      4.33G    0.02804     0.0192   0.005381          3        640: 100%|██████████| 147/147 [00:40<00:00,  3.64it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.24it/s]\n",
      "                   all        210        495      0.985      0.734      0.739      0.541\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      4.33G    0.02681    0.01932   0.005183          4        640: 100%|██████████| 147/147 [00:40<00:00,  3.67it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.23it/s]\n",
      "                   all        210        495      0.982      0.733       0.74      0.538\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      4.33G     0.0276    0.01898   0.004884          2        640: 100%|██████████| 147/147 [00:39<00:00,  3.68it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.07it/s]\n",
      "                   all        210        495      0.982      0.737       0.74      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      4.33G    0.02645    0.01947   0.005533         10        640: 100%|██████████| 147/147 [00:42<00:00,  3.48it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.17it/s]\n",
      "                   all        210        495      0.975      0.734      0.739      0.543\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/99      4.33G    0.02797    0.01916    0.00491          5        640: 100%|██████████| 147/147 [00:41<00:00,  3.51it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.16it/s]\n",
      "                   all        210        495      0.979      0.734      0.739      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      4.33G    0.02686    0.01878   0.005106          3        640: 100%|██████████| 147/147 [00:39<00:00,  3.70it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.00it/s]\n",
      "                   all        210        495      0.988      0.726       0.74      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      4.33G    0.02675     0.0193   0.005461          4        640: 100%|██████████| 147/147 [00:39<00:00,  3.75it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.13it/s]\n",
      "                   all        210        495      0.983      0.737       0.74      0.545\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/99      4.33G    0.02704     0.0194    0.00496          8        640: 100%|██████████| 147/147 [00:40<00:00,  3.60it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.12it/s]\n",
      "                   all        210        495      0.986      0.734       0.74      0.549\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/99      4.33G    0.02644    0.01911   0.004775          4        640: 100%|██████████| 147/147 [00:39<00:00,  3.69it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  1.91it/s]\n",
      "                   all        210        495      0.974      0.723      0.737      0.539\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/99      4.33G    0.02674    0.01875    0.00546          2        640: 100%|██████████| 147/147 [00:39<00:00,  3.72it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.30it/s]\n",
      "                   all        210        495      0.981      0.732      0.738      0.543\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/99      4.33G    0.02664    0.01908    0.00493          7        640: 100%|██████████| 147/147 [00:40<00:00,  3.63it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.13it/s]\n",
      "                   all        210        495      0.982      0.734      0.738      0.549\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/99      4.33G    0.02612    0.01894   0.004911          8        640: 100%|██████████| 147/147 [00:40<00:00,  3.60it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.05it/s]\n",
      "                   all        210        495      0.985      0.734      0.739      0.546\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/99      4.33G    0.02575    0.01827   0.004758          4        640: 100%|██████████| 147/147 [00:39<00:00,  3.69it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.10it/s]\n",
      "                   all        210        495       0.99      0.732      0.741      0.544\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/99      4.33G    0.02687     0.0184   0.004968          6        640: 100%|██████████| 147/147 [00:38<00:00,  3.78it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.07it/s]\n",
      "                   all        210        495      0.984      0.726      0.739      0.543\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/99      4.33G    0.02632    0.01879   0.004775          8        640: 100%|██████████| 147/147 [00:41<00:00,  3.54it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.10it/s]\n",
      "                   all        210        495      0.981      0.731      0.739      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/99      4.33G    0.02638     0.0187   0.004685          2        640: 100%|██████████| 147/147 [00:39<00:00,  3.76it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  1.97it/s]\n",
      "                   all        210        495      0.981      0.728      0.741      0.546\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/99      4.33G    0.02718    0.01863   0.004851          4        640: 100%|██████████| 147/147 [00:39<00:00,  3.77it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.10it/s]\n",
      "                   all        210        495      0.979      0.727       0.74      0.545\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/99      4.33G     0.0259    0.01816   0.004717          4        640: 100%|██████████| 147/147 [00:41<00:00,  3.56it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.38it/s]\n",
      "                   all        210        495      0.986      0.731      0.739      0.548\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/99      4.33G    0.02596    0.01891   0.004898          6        640: 100%|██████████| 147/147 [00:41<00:00,  3.55it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.71it/s]\n",
      "                   all        210        495       0.98      0.728      0.738      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/99      4.33G    0.02617     0.0186   0.005054         10        640: 100%|██████████| 147/147 [00:39<00:00,  3.76it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.49it/s]\n",
      "                   all        210        495      0.984       0.73      0.739      0.542\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/99      4.33G    0.02627     0.0184   0.004686          4        640: 100%|██████████| 147/147 [00:39<00:00,  3.68it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.03it/s]\n",
      "                   all        210        495      0.984      0.733      0.739      0.548\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/99      4.33G    0.02617    0.01878   0.004738          8        640: 100%|██████████| 147/147 [00:41<00:00,  3.58it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.01it/s]\n",
      "                   all        210        495       0.98      0.732      0.742      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/99      4.33G    0.02631    0.01834   0.004373         11        640: 100%|██████████| 147/147 [00:40<00:00,  3.63it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.22it/s]\n",
      "                   all        210        495      0.985      0.735      0.739      0.546\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/99      4.33G    0.02554    0.01823   0.004607          3        640: 100%|██████████| 147/147 [00:38<00:00,  3.82it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.32it/s]\n",
      "                   all        210        495      0.977      0.727      0.738      0.546\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/99      4.33G    0.02574    0.01802   0.004614          2        640: 100%|██████████| 147/147 [00:37<00:00,  3.91it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.35it/s]\n",
      "                   all        210        495       0.98      0.733      0.738       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/99      4.33G    0.02552    0.01812   0.004728         10        640: 100%|██████████| 147/147 [00:38<00:00,  3.81it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.72it/s]\n",
      "                   all        210        495      0.986      0.731       0.74      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/99      4.33G    0.02589    0.01838   0.004752          8        640: 100%|██████████| 147/147 [00:39<00:00,  3.74it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.55it/s]\n",
      "                   all        210        495      0.988      0.724      0.742      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      60/99      4.33G    0.02525    0.01836   0.004599          7        640: 100%|██████████| 147/147 [00:37<00:00,  3.88it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.80it/s]\n",
      "                   all        210        495      0.983      0.734       0.74      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      61/99      4.33G    0.02515    0.01806   0.004536          5        640: 100%|██████████| 147/147 [00:38<00:00,  3.81it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.69it/s]\n",
      "                   all        210        495      0.988      0.732      0.741       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      62/99      4.33G    0.02544    0.01821   0.004499          3        640: 100%|██████████| 147/147 [00:38<00:00,  3.83it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.15it/s]\n",
      "                   all        210        495      0.988      0.731      0.742      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      63/99      4.33G    0.02479    0.01805   0.004406          9        640: 100%|██████████| 147/147 [00:39<00:00,  3.71it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.20it/s]\n",
      "                   all        210        495      0.984      0.726       0.74      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      64/99      4.33G     0.0256    0.01773   0.004467          4        640: 100%|██████████| 147/147 [00:39<00:00,  3.74it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.10it/s]\n",
      "                   all        210        495      0.986      0.725      0.739       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      65/99      4.33G     0.0253    0.01813   0.004254          7        640: 100%|██████████| 147/147 [00:39<00:00,  3.68it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.01it/s]\n",
      "                   all        210        495      0.986      0.733       0.74      0.548\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      66/99      4.33G    0.02397    0.01759   0.004182          8        640: 100%|██████████| 147/147 [00:39<00:00,  3.71it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.92it/s]\n",
      "                   all        210        495      0.988      0.731      0.739      0.549\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      67/99      4.33G    0.02493    0.01766   0.004033          2        640: 100%|██████████| 147/147 [00:40<00:00,  3.63it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.01it/s]\n",
      "                   all        210        495      0.985      0.732      0.739      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      68/99      4.33G    0.02477    0.01769   0.004605          4        640: 100%|██████████| 147/147 [00:40<00:00,  3.61it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.95it/s]\n",
      "                   all        210        495      0.987       0.73      0.741      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      69/99      4.33G    0.02507    0.01773   0.004591          6        640: 100%|██████████| 147/147 [00:40<00:00,  3.65it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.12it/s]\n",
      "                   all        210        495      0.985      0.731       0.74      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      70/99      4.33G    0.02456    0.01799    0.00414          3        640: 100%|██████████| 147/147 [00:40<00:00,  3.64it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.99it/s]\n",
      "                   all        210        495      0.979      0.727      0.738      0.549\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      71/99      4.33G    0.02469    0.01757   0.004111          6        640: 100%|██████████| 147/147 [00:40<00:00,  3.66it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.05it/s]\n",
      "                   all        210        495      0.983      0.737      0.742      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      72/99      4.33G    0.02385      0.018   0.004164         10        640: 100%|██████████| 147/147 [00:40<00:00,  3.62it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.08it/s]\n",
      "                   all        210        495      0.981      0.731      0.742      0.556\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      73/99      4.33G    0.02414    0.01771    0.00412          9        640: 100%|██████████| 147/147 [00:40<00:00,  3.65it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.95it/s]\n",
      "                   all        210        495      0.986      0.731      0.741      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      74/99      4.33G    0.02436    0.01749   0.004955         10        640: 100%|██████████| 147/147 [00:40<00:00,  3.62it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.90it/s]\n",
      "                   all        210        495      0.983      0.727       0.74      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      75/99      4.33G    0.02458    0.01752   0.004001          3        640: 100%|██████████| 147/147 [00:39<00:00,  3.73it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.52it/s]\n",
      "                   all        210        495      0.986      0.732      0.739      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      76/99      4.33G     0.0243    0.01769   0.004228         10        640: 100%|██████████| 147/147 [00:40<00:00,  3.67it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.57it/s]\n",
      "                   all        210        495      0.984       0.72       0.74      0.545\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      77/99      4.33G    0.02508    0.01748   0.004604          2        640: 100%|██████████| 147/147 [00:39<00:00,  3.73it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.14it/s]\n",
      "                   all        210        495       0.98      0.725      0.737      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      78/99      4.33G    0.02319    0.01759   0.004305          5        640: 100%|██████████| 147/147 [00:39<00:00,  3.72it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.38it/s]\n",
      "                   all        210        495      0.983      0.732      0.739      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      79/99      4.33G    0.02299    0.01776   0.004323          4        640: 100%|██████████| 147/147 [00:38<00:00,  3.80it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.24it/s]\n",
      "                   all        210        495      0.976      0.725      0.736      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      80/99      4.33G    0.02356    0.01754   0.004228          2        640: 100%|██████████| 147/147 [00:39<00:00,  3.72it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.12it/s]\n",
      "                   all        210        495      0.977      0.731      0.737      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      81/99      4.33G    0.02374    0.01743   0.004356          8        640: 100%|██████████| 147/147 [00:39<00:00,  3.72it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.27it/s]\n",
      "                   all        210        495      0.981       0.73      0.741      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      82/99      4.33G    0.02343    0.01727   0.004432          5        640: 100%|██████████| 147/147 [00:38<00:00,  3.80it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.22it/s]\n",
      "                   all        210        495      0.985      0.726      0.739      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      83/99      4.33G    0.02343    0.01723   0.004204          7        640: 100%|██████████| 147/147 [00:38<00:00,  3.78it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.27it/s]\n",
      "                   all        210        495      0.984      0.736      0.739      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      84/99      4.33G    0.02417    0.01718   0.004089          6        640: 100%|██████████| 147/147 [00:39<00:00,  3.75it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.14it/s]\n",
      "                   all        210        495      0.986      0.729      0.737      0.548\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      85/99      4.33G    0.02391    0.01712   0.004193          7        640: 100%|██████████| 147/147 [00:38<00:00,  3.83it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:03<00:00,  2.08it/s]\n",
      "                   all        210        495      0.984      0.729      0.737       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      86/99      4.33G    0.02271    0.01699   0.003893          3        640: 100%|██████████| 147/147 [00:38<00:00,  3.84it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.44it/s]\n",
      "                   all        210        495      0.984      0.725      0.736      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      87/99      4.33G    0.02398    0.01754    0.00411          5        640: 100%|██████████| 147/147 [00:38<00:00,  3.78it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.69it/s]\n",
      "                   all        210        495      0.984      0.727      0.737      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      88/99      4.33G    0.02339     0.0173    0.00419          9        640: 100%|██████████| 147/147 [00:39<00:00,  3.70it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.85it/s]\n",
      "                   all        210        495      0.984       0.73      0.737      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      89/99      4.33G    0.02335    0.01723   0.004125          9        640: 100%|██████████| 147/147 [00:40<00:00,  3.65it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.53it/s]\n",
      "                   all        210        495      0.988      0.726      0.737      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      90/99      4.33G    0.02275    0.01715   0.003892          3        640: 100%|██████████| 147/147 [00:39<00:00,  3.74it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.89it/s]\n",
      "                   all        210        495      0.985       0.73      0.737      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      91/99      4.33G    0.02265    0.01736   0.004224          4        640: 100%|██████████| 147/147 [00:40<00:00,  3.67it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.73it/s]\n",
      "                   all        210        495      0.986      0.731      0.737      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      92/99      4.33G    0.02291     0.0173   0.004032          9        640: 100%|██████████| 147/147 [00:39<00:00,  3.76it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.10it/s]\n",
      "                   all        210        495      0.987       0.73      0.737      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      93/99      4.33G    0.02324    0.01675   0.003871          9        640: 100%|██████████| 147/147 [00:40<00:00,  3.67it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.06it/s]\n",
      "                   all        210        495      0.986      0.728      0.738      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      94/99      4.33G    0.02318    0.01732    0.00404          6        640: 100%|██████████| 147/147 [00:40<00:00,  3.65it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.92it/s]\n",
      "                   all        210        495      0.984      0.734      0.738      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      95/99      4.33G    0.02383    0.01711   0.004033         10        640: 100%|██████████| 147/147 [00:40<00:00,  3.65it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.05it/s]\n",
      "                   all        210        495      0.983      0.733      0.738      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      96/99      4.33G    0.02324    0.01707   0.003914          4        640: 100%|██████████| 147/147 [00:39<00:00,  3.70it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.09it/s]\n",
      "                   all        210        495      0.983      0.731      0.738      0.556\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      97/99      4.33G    0.02238    0.01716   0.004113          3        640: 100%|██████████| 147/147 [00:40<00:00,  3.65it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.08it/s]\n",
      "                   all        210        495      0.984      0.731      0.737      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      98/99      4.33G    0.02369    0.01698   0.004051          4        640: 100%|██████████| 147/147 [00:40<00:00,  3.66it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  2.99it/s]\n",
      "                   all        210        495      0.982      0.733      0.738      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      99/99      4.33G    0.02342    0.01672    0.00419          6        640: 100%|██████████| 147/147 [00:40<00:00,  3.62it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:02<00:00,  3.09it/s]\n",
      "                   all        210        495      0.981      0.736      0.738      0.554\n",
      "\n",
      "100 epochs completed in 1.194 hours.\n",
      "Optimizer stripped from runs/train/exp/weights/last.pt, 14.5MB\n",
      "Optimizer stripped from runs/train/exp/weights/best.pt, 14.5MB\n",
      "\n",
      "Validating runs/train/exp/weights/best.pt...\n",
      "Fusing layers... \n",
      "Model summary: 157 layers, 7020913 parameters, 0 gradients, 15.8 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 7/7 [00:05<00:00,  1.23it/s]\n",
      "                   all        210        495       0.98      0.732      0.742      0.555\n",
      "                     -        210          1          1          0    0.00655    0.00524\n",
      "                    5G        210        134      0.991      0.985      0.984      0.843\n",
      "                   LTE        210        150      0.981          1       0.99      0.863\n",
      "                 Radar        210        210      0.947      0.943      0.986       0.51\n",
      "Results saved to \u001b[1mruns/train/exp\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml OfflineExperiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : [OfflineExperiment will get URL after upload]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [1470]                : (0.029812591150403023, 3.1377036571502686)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [200]      : (0.1536856325846207, 0.7476249967705677)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [200] : (0.061460983945385045, 0.5558362799279235)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [200]    : (0.3738235080268033, 0.9897779839874481)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [200]       : (0.28796019900497516, 0.7369137913405315)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [200]       : (0.022376956418156624, 0.0701136365532875)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [200]       : (0.0038708585780113935, 0.03543846681714058)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [200]       : (0.01671653799712658, 0.04078484699130058)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [200]         : (0.017175419256091118, 0.04885638877749443)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [200]         : (0.0019939143676310778, 0.01948852650821209)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [200]         : (0.0058095455169677734, 0.01681593433022499)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [200]                : (0.0002980000000000002, 0.07020408163265307)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [200]                : (0.0002980000000000002, 0.009779773242630387)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [200]                : (0.0002980000000000002, 0.009779773242630387)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     offline_experiment          : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bbox_interval       : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cfg                 : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.025000000000000005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset               : 13 (1.23 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix    : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata        : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images              : 6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph         : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages         : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still saving offline stats to messages file before program termination (may take up to 120 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Starting saving the offline archive\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m To upload this offline experiment, run:\n",
      "    comet upload /content/yolov5/.cometml-runs/9bbb0f8ec3624a56aa9f032ba63bdd98.zip\n"
     ]
    }
   ],
   "source": [
    "!python train.py --img 640 --batch 16 --epochs 100 --data /content/yolov5/Speclearn-\\(ALL-Radar\\)-6/data.yaml --weights yolov5s.pt --cache"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4JnkELT0cIJg"
   },
   "source": [
    "# 1. Detect\n",
    "\n",
    "`detect.py` runs YOLOv5 inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/detect`. Example inference sources are:\n",
    "\n",
    "```shell\n",
    "python detect.py --source 0  # webcam\n",
    "                          img.jpg  # image\n",
    "                          vid.mp4  # video\n",
    "                          screen  # screenshot\n",
    "                          path/  # directory\n",
    "                         'path/*.jpg'  # glob\n",
    "                         'https://youtu.be/LNwODJXcvt4'  # YouTube\n",
    "                         'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zR9ZbuQCH7FX",
    "outputId": "9afed774-9402-4d9a-e1d4-8752f9fc5044"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['/content/yolov5/runs/train/exp/weights/best.pt'], source=/content/yolov5/Speclearn-(ALL-Radar)-6/test/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.05, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_csv=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n",
      "YOLOv5 🚀 v7.0-247-g3f02fde Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
      "\n",
      "Fusing layers... \n",
      "Model summary: 157 layers, 7020913 parameters, 0 gradients, 15.8 GFLOPs\n",
      "image 1/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC1_frame_100_jpg.rf.95ab949756a6329c1b37479638c07f45.jpg: 640x640 1 LTE, 1 Radar, 11.5ms\n",
      "image 2/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC1_frame_112_jpg.rf.a4cb6c0935490cf1a2d5c7dff257478c.jpg: 640x640 1 LTE, 1 Radar, 11.6ms\n",
      "image 3/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC1_frame_11_jpg.rf.19eefdc94d88604516c64fcc7df444d3.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 11.5ms\n",
      "image 4/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC1_frame_168_jpg.rf.8a8947d384d39670626d3820d3104b03.jpg: 640x640 1 LTE, 2 Radars, 11.5ms\n",
      "image 5/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC1_frame_179_jpg.rf.898e40e1c3451465fe5ceb50e48006b8.jpg: 640x640 1 5G, 2 Radars, 11.5ms\n",
      "image 6/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC1_frame_189_jpg.rf.95cd8a418dbb37ad703bfab55c54c128.jpg: 640x640 1 5G, 2 Radars, 11.5ms\n",
      "image 7/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC1_frame_194_jpg.rf.8c7791b27fcbb46838e24696ec0b54fe.jpg: 640x640 1 5G, 1 Radar, 11.5ms\n",
      "image 8/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC1_frame_195_jpg.rf.54147a7692361d38646d5e33c6680b66.jpg: 640x640 1 5G, 1 Radar, 11.5ms\n",
      "image 9/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC1_frame_199_jpg.rf.4e0b25876d40a6ca9bdc8224df7b02f5.jpg: 640x640 1 5G, 1 Radar, 11.5ms\n",
      "image 10/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC1_frame_49_jpg.rf.5229a3f7895e957f572276b29dcbe7e1.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 11.5ms\n",
      "image 11/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC1_frame_51_jpg.rf.bd9f16fa18760994f3b38c91fcb3c169.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 11.5ms\n",
      "image 12/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC1_frame_69_jpg.rf.911b8b1805b896322fe6940aa3e9a6c9.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 12.9ms\n",
      "image 13/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC1_frame_72_jpg.rf.85d6652bd932e25649ece3c60232886b.jpg: 640x640 1 5G, 1 LTE, 2 Radars, 11.5ms\n",
      "image 14/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC1_frame_83_jpg.rf.701a752b1631e969e52ea53a6ed1d880.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 11.5ms\n",
      "image 15/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC2_frame_111_jpg.rf.d7885351767dc2437f21b4cfc53c027b.jpg: 640x640 1 LTE, 2 Radars, 11.5ms\n",
      "image 16/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC2_frame_127_jpg.rf.92b16fe91cd8a19bcfe2f59d23ef1291.jpg: 640x640 1 LTE, 1 Radar, 11.5ms\n",
      "image 17/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC2_frame_128_jpg.rf.7fb5684183ddc5905ee91cf5c984fb8a.jpg: 640x640 1 LTE, 1 Radar, 11.5ms\n",
      "image 18/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC2_frame_137_jpg.rf.5bb157393714469ea8a198feb5088f40.jpg: 640x640 1 LTE, 1 Radar, 11.5ms\n",
      "image 19/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC2_frame_141_jpg.rf.26cc9726c584ad05f6cb2eca8b5d67ba.jpg: 640x640 1 LTE, 1 Radar, 11.5ms\n",
      "image 20/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC2_frame_149_jpg.rf.60cf577ca729823f3d82eb8f8d29bd1f.jpg: 640x640 1 LTE, 1 Radar, 11.5ms\n",
      "image 21/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC2_frame_14_jpg.rf.9c897162deb857f1e0e584f6c50f6789.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 11.5ms\n",
      "image 22/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC2_frame_16_jpg.rf.6df140507c7f814ddb51758002842ea0.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 11.5ms\n",
      "image 23/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC2_frame_170_jpg.rf.f1af02623c132568a3c5c3df16b28367.jpg: 640x640 1 LTE, 1 Radar, 11.5ms\n",
      "image 24/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC2_frame_172_jpg.rf.cae219d613d42624cf1bb8cf193dc990.jpg: 640x640 1 5G, 1 Radar, 11.5ms\n",
      "image 25/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC2_frame_208_jpg.rf.7d544c51e9272b1a80ce8918e5398d0b.jpg: 640x640 1 5G, 1 Radar, 11.5ms\n",
      "image 26/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC2_frame_221_jpg.rf.472e1f120975c34cd1fc25d226eb5296.jpg: 640x640 1 5G, 1 Radar, 11.5ms\n",
      "image 27/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC2_frame_227_jpg.rf.d884e15c9a6830f493f10fbabaa01820.jpg: 640x640 1 5G, 1 Radar, 11.5ms\n",
      "image 28/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC2_frame_233_jpg.rf.d5caa994b74675ab5e86034c9384e562.jpg: 640x640 1 5G, 1 Radar, 11.5ms\n",
      "image 29/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC2_frame_25_jpg.rf.c7d7a90d05a7f0d1685cd552e909c582.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 11.5ms\n",
      "image 30/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC2_frame_33_jpg.rf.cbe88f13c9adae54d1cc10b27e521562.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 11.5ms\n",
      "image 31/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC2_frame_42_jpg.rf.e1c4a52482e39493ed97121179e86f43.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 11.5ms\n",
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      "image 33/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC2_frame_91_jpg.rf.8d130e08276eabff8c2b41c7123f2c52.jpg: 640x640 1 LTE, 1 Radar, 11.5ms\n",
      "image 34/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC2_frame_99_jpg.rf.84ac5cabff2fd3e8c413961630721335.jpg: 640x640 1 LTE, 1 Radar, 11.5ms\n",
      "image 35/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC3_frame_103_jpg.rf.9e4756cf8f611476c4500a08fd6ce686.jpg: 640x640 1 LTE, 1 Radar, 11.5ms\n",
      "image 36/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC3_frame_125_jpg.rf.cdfa92c09b7dac5cdec0e92c8f054949.jpg: 640x640 1 LTE, 1 Radar, 11.5ms\n",
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      "image 39/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC3_frame_150_jpg.rf.bd04bf68603577ed165db99beb568261.jpg: 640x640 1 LTE, 1 Radar, 11.5ms\n",
      "image 40/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC3_frame_153_jpg.rf.e62e2d71b054c76e5c13f3291a10f24d.jpg: 640x640 1 LTE, 1 Radar, 11.6ms\n",
      "image 41/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC3_frame_175_jpg.rf.ad209a1b7f1d2936b1e07cddea7402a0.jpg: 640x640 1 5G, 1 Radar, 11.6ms\n",
      "image 42/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC3_frame_18_jpg.rf.b8b1f57109335e7aed2ff86394575099.jpg: 640x640 2 5Gs, 1 LTE, 2 Radars, 11.5ms\n",
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      "image 69/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC4_frame_1_jpg.rf.170b32b539117180558860284c4f96a3.jpg: 640x640 1 5G, 1 LTE, 2 Radars, 11.0ms\n",
      "image 70/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC4_frame_210_jpg.rf.4818a4d0b9e3f5b0d3c878ffdf7af057.jpg: 640x640 1 5G, 1 Radar, 11.0ms\n",
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      "image 72/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC4_frame_237_jpg.rf.f55ad69dceeb3fc1183c39354138e5c0.jpg: 640x640 1 5G, 1 Radar, 11.0ms\n",
      "image 73/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC4_frame_249_jpg.rf.56f703050d5ec92030d3fa7adbe149cc.jpg: 640x640 1 5G, 1 Radar, 11.0ms\n",
      "image 74/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC4_frame_43_jpg.rf.bd19c12ab2d26928f6fd7ed0ee82ae01.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 11.0ms\n",
      "image 75/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC4_frame_53_jpg.rf.9dbf1ac5f4b528439ae70d5c4dfe6d94.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 11.0ms\n",
      "image 76/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC4_frame_79_jpg.rf.4bdb0b825da9092606de3cf65026c633.jpg: 640x640 1 5G, 1 LTE, 2 Radars, 11.0ms\n",
      "image 77/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC4_frame_84_jpg.rf.ddf728a32725f52555f263676108bc8c.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 11.0ms\n",
      "image 78/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC4_frame_86_jpg.rf.bec16f23dfa920888b77b4f66f743a43.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 10.8ms\n",
      "image 79/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC4_frame_9_jpg.rf.c4138bc83025fc41c81892f69ed69e29.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 10.8ms\n",
      "image 80/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_114_jpg.rf.9a59c03395c27f9b41adcd3e1051e619.jpg: 640x640 1 LTE, 1 Radar, 10.8ms\n",
      "image 81/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_124_jpg.rf.2874eae82321628a33f5629ce88a5a7d.jpg: 640x640 1 LTE, 1 Radar, 10.8ms\n",
      "image 82/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_136_jpg.rf.a149b1d38d28af6ec384f615ca8f8eea.jpg: 640x640 1 LTE, 1 Radar, 10.8ms\n",
      "image 83/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_139_jpg.rf.57b449201b705ff265d866661ebe03af.jpg: 640x640 1 LTE, 1 Radar, 10.8ms\n",
      "image 84/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_151_jpg.rf.580f845b5d8b47dbac7c3375067cec32.jpg: 640x640 1 LTE, 1 Radar, 10.8ms\n",
      "image 85/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_16_jpg.rf.d6361bf860f2ca0468f9d07b530dbe87.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 10.8ms\n",
      "image 86/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_178_jpg.rf.e30beeea9d09e3cc2c3c9ec7aec175a0.jpg: 640x640 1 5G, 1 Radar, 10.8ms\n",
      "image 87/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_180_jpg.rf.492a7772387150a686a0f15712a2eb9a.jpg: 640x640 1 5G, 1 Radar, 10.8ms\n",
      "image 88/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_193_jpg.rf.b28776e1cfc793487db94103515b1790.jpg: 640x640 1 5G, 1 Radar, 10.8ms\n",
      "image 89/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_211_jpg.rf.28f8859e9de99494af2d643fb0f74cd1.jpg: 640x640 1 5G, 2 Radars, 10.8ms\n",
      "image 90/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_212_jpg.rf.698a2221d5f66e944976ae7a03d33876.jpg: 640x640 1 5G, 1 Radar, 10.8ms\n",
      "image 91/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_213_jpg.rf.832da485359ef32be223c873de8a16c3.jpg: 640x640 1 5G, 1 Radar, 10.8ms\n",
      "image 92/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_235_jpg.rf.fa6b505fc7762c4760a8855f2ce53f0d.jpg: 640x640 1 5G, 1 Radar, 10.8ms\n",
      "image 93/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_242_jpg.rf.6abe155fdd00ab0f3265e386e68ba733.jpg: 640x640 1 5G, 1 Radar, 10.8ms\n",
      "image 94/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_250_jpg.rf.d1099cc664e126b38d0a8fbbbb85497c.jpg: 640x640 1 5G, 1 Radar, 10.8ms\n",
      "image 95/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_29_jpg.rf.7fec3804400ac9478ec9c696912c914b.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 10.8ms\n",
      "image 96/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_36_jpg.rf.46426b90c0cd82cb0f4967803b0398ab.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 10.8ms\n",
      "image 97/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_3_jpg.rf.ec9a41242448d5fde141b9e0d520b8a2.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 10.8ms\n",
      "image 98/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_41_jpg.rf.124facdc621d50486a6d2028ed0a6efe.jpg: 640x640 1 5G, 1 LTE, 2 Radars, 10.9ms\n",
      "image 99/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_44_jpg.rf.bd3b40a3eb61c16483ce89c01982bac7.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 10.8ms\n",
      "image 100/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_54_jpg.rf.a5cd073ae0d8d5a348864f08c8758361.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 10.8ms\n",
      "image 101/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_59_jpg.rf.4d59079e5530635402e8d35c91a905a4.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 10.9ms\n",
      "image 102/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_65_jpg.rf.ad051c913255c7cd94944fd49c91fcbd.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 10.8ms\n",
      "image 103/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_66_jpg.rf.b574d7475b9e5caa9f104c443b9df31c.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 10.8ms\n",
      "image 104/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_74_jpg.rf.fa79e2c0d42b59c4a4357ff06bc4a655.jpg: 640x640 1 5G, 1 LTE, 1 Radar, 10.8ms\n",
      "image 105/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_78_jpg.rf.5036b64ad7e76bd4a321c4120370150f.jpg: 640x640 1 5G, 1 LTE, 2 Radars, 10.9ms\n",
      "image 106/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_93_jpg.rf.974b012dd5d374dd8c5064b6447562f8.jpg: 640x640 1 LTE, 2 Radars, 10.8ms\n",
      "image 107/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_97_jpg.rf.8dd0f9d731936b1113608175a80900fc.jpg: 640x640 1 LTE, 1 Radar, 10.8ms\n",
      "image 108/108 /content/yolov5/Speclearn-(ALL-Radar)-6/test/images/Signals_ESC5_frame_99_jpg.rf.bb98232d0b403bf367ebbe43c5149b7d.jpg: 640x640 1 LTE, 1 Radar, 10.8ms\n",
      "Speed: 0.5ms pre-process, 11.2ms inference, 1.9ms NMS per image at shape (1, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/detect/exp\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!python detect.py --weights /content/yolov5/runs/train/exp/weights/best.pt --img 640 --conf 0.05 --source /content/yolov5/Speclearn-\\(ALL-Radar\\)-6/test/images\n",
    "# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hkAzDWJ7cWTr"
   },
   "source": [
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\n",
    "<img align=\"left\" src=\"https://user-images.githubusercontent.com/26833433/127574988-6a558aa1-d268-44b9-bf6b-62d4c605cc72.jpg\" width=\"600\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0eq1SMWl6Sfn"
   },
   "source": [
    "# 2. Validate\n",
    "Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WQPtK1QYVaD_",
    "outputId": "cf7d52f0-281c-4c96-a488-79f5908f8426"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 780M/780M [00:12<00:00, 66.6MB/s]\n"
     ]
    }
   ],
   "source": [
    "# Download COCO val\n",
    "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')  # download (780M - 5000 images)\n",
    "!unzip -q tmp.zip -d ../datasets && rm tmp.zip  # unzip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "X58w8JLpMnjH",
    "outputId": "3e234e05-ee8b-4ad1-b1a4-f6a55d5e4f3d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False\n",
      "YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:02<00:00, 2024.59it/s]\n",
      "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 157/157 [01:25<00:00,  1.84it/s]\n",
      "                   all       5000      36335      0.671      0.519      0.566      0.371\n",
      "Speed: 0.1ms pre-process, 3.1ms inference, 2.3ms NMS per image at shape (32, 3, 640, 640)\n",
      "\n",
      "Evaluating pycocotools mAP... saving runs/val/exp/yolov5s_predictions.json...\n",
      "loading annotations into memory...\n",
      "Done (t=0.43s)\n",
      "creating index...\n",
      "index created!\n",
      "Loading and preparing results...\n",
      "DONE (t=5.32s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=78.89s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=14.51s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.374\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.572\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.402\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.211\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.423\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.489\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.311\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.516\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.566\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.378\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.625\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.722\n",
      "Results saved to \u001b[1mruns/val/exp\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Validate YOLOv5s on COCO val\n",
    "!python val.py --weights yolov5s.pt --data coco.yaml --img 640 --half"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZY2VXXXu74w5"
   },
   "source": [
    "# 3. Train\n",
    "\n",
    "<p align=\"\"><a href=\"https://bit.ly/ultralytics_hub\"><img width=\"1000\" src=\"https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png\"/></a></p>\n",
    "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n",
    "<br><br>\n",
    "\n",
    "Train a YOLOv5s model on the [COCO128](https://www.kaggle.com/ultralytics/coco128) dataset with `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`.\n",
    "\n",
    "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n",
    "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n",
    "- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\n",
    "- **Training Results** are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc.\n",
    "<br>\n",
    "\n",
    "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n",
    "\n",
    "## Label a dataset on Roboflow (optional)\n",
    "\n",
    "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "i3oKtE4g-aNn"
   },
   "outputs": [],
   "source": [
    "#@title Select YOLOv5 🚀 logger {run: 'auto'}\n",
    "logger = 'Comet' #@param ['Comet', 'ClearML', 'TensorBoard']\n",
    "\n",
    "if logger == 'Comet':\n",
    "  %pip install -q comet_ml\n",
    "  import comet_ml; comet_ml.init()\n",
    "elif logger == 'ClearML':\n",
    "  %pip install -q clearml\n",
    "  import clearml; clearml.browser_login()\n",
    "elif logger == 'TensorBoard':\n",
    "  %load_ext tensorboard\n",
    "  %tensorboard --logdir runs/train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1NcFxRcFdJ_O",
    "outputId": "bbeeea2b-04fc-4185-aa64-258690495b5a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-04-09 14:11:38.063605: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-04-09 14:11:39.026661: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
      "YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n",
      "\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\n",
      "Dataset not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']\n",
      "Downloading https://ultralytics.com/assets/coco128.zip to coco128.zip...\n",
      "100% 6.66M/6.66M [00:00<00:00, 75.6MB/s]\n",
      "Dataset download success ✅ (0.6s), saved to \u001b[1m/content/datasets\u001b[0m\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1    229245  models.yolo.Detect                      [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "Model summary: 214 layers, 7235389 parameters, 7235389 gradients, 16.6 GFLOPs\n",
      "\n",
      "Transferred 349/349 items from yolov5s.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1709.36it/s]\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco128/labels/train2017.cache\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 264.35it/s]\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
      "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:01<00:00, 107.05it/s]\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.27 anchors/target, 0.994 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/exp/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 2 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp\u001b[0m\n",
      "Starting training for 3 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        0/2      3.91G    0.04618    0.07209    0.01703        232        640: 100% 8/8 [00:09<00:00,  1.17s/it]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 4/4 [00:01<00:00,  2.01it/s]\n",
      "                   all        128        929      0.667      0.602       0.68       0.45\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        1/2      4.76G    0.04622    0.06891    0.01817        201        640: 100% 8/8 [00:02<00:00,  3.78it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 4/4 [00:01<00:00,  2.16it/s]\n",
      "                   all        128        929      0.709      0.645      0.722      0.478\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "        2/2      4.76G     0.0436     0.0647    0.01698        227        640: 100% 8/8 [00:01<00:00,  4.19it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 4/4 [00:01<00:00,  2.95it/s]\n",
      "                   all        128        929      0.761      0.647      0.735       0.49\n",
      "\n",
      "3 epochs completed in 0.006 hours.\n",
      "Optimizer stripped from runs/train/exp/weights/last.pt, 14.8MB\n",
      "Optimizer stripped from runs/train/exp/weights/best.pt, 14.8MB\n",
      "\n",
      "Validating runs/train/exp/weights/best.pt...\n",
      "Fusing layers... \n",
      "Model summary: 157 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 4/4 [00:06<00:00,  1.56s/it]\n",
      "                   all        128        929      0.759      0.646      0.734       0.49\n",
      "                person        128        254      0.857      0.706      0.805      0.525\n",
      "               bicycle        128          6      0.773      0.577      0.725      0.414\n",
      "                   car        128         46      0.664      0.435      0.551       0.24\n",
      "            motorcycle        128          5      0.587        0.8      0.837      0.635\n",
      "              airplane        128          6          1      0.989      0.995      0.715\n",
      "                   bus        128          7      0.635      0.714      0.753      0.651\n",
      "                 train        128          3      0.686      0.333       0.72      0.504\n",
      "                 truck        128         12      0.604      0.333      0.472      0.259\n",
      "                  boat        128          6      0.938      0.333      0.449      0.177\n",
      "         traffic light        128         14      0.778      0.255      0.401      0.217\n",
      "             stop sign        128          2      0.826          1      0.995      0.895\n",
      "                 bench        128          9      0.711      0.556      0.661      0.313\n",
      "                  bird        128         16      0.962          1      0.995      0.642\n",
      "                   cat        128          4      0.868          1      0.995      0.754\n",
      "                   dog        128          9          1      0.652      0.899      0.651\n",
      "                 horse        128          2      0.853          1      0.995      0.622\n",
      "              elephant        128         17      0.909      0.882      0.934      0.698\n",
      "                  bear        128          1      0.696          1      0.995      0.995\n",
      "                 zebra        128          4      0.855          1      0.995      0.905\n",
      "               giraffe        128          9      0.788      0.828      0.912      0.701\n",
      "              backpack        128          6      0.835        0.5      0.738      0.311\n",
      "              umbrella        128         18      0.785      0.814      0.859       0.48\n",
      "               handbag        128         19      0.759      0.263      0.366      0.205\n",
      "                   tie        128          7      0.983      0.714       0.77      0.492\n",
      "              suitcase        128          4      0.656          1      0.945      0.631\n",
      "               frisbee        128          5      0.721        0.8      0.759      0.724\n",
      "                  skis        128          1      0.737          1      0.995        0.3\n",
      "             snowboard        128          7      0.829      0.696       0.83      0.537\n",
      "           sports ball        128          6      0.637      0.667      0.602      0.311\n",
      "                  kite        128         10      0.636        0.6      0.599      0.226\n",
      "          baseball bat        128          4      0.501       0.25      0.468      0.205\n",
      "        baseball glove        128          7      0.483      0.429      0.465      0.292\n",
      "            skateboard        128          5      0.932        0.6      0.687      0.493\n",
      "         tennis racket        128          7       0.77      0.429      0.547      0.332\n",
      "                bottle        128         18      0.577      0.379      0.554      0.276\n",
      "            wine glass        128         16      0.704      0.875       0.89       0.51\n",
      "                   cup        128         36      0.841      0.667      0.837      0.533\n",
      "                  fork        128          6      0.992      0.333       0.45      0.315\n",
      "                 knife        128         16      0.768      0.688      0.695      0.403\n",
      "                 spoon        128         22      0.838       0.47      0.639      0.384\n",
      "                  bowl        128         28      0.764       0.58      0.716      0.513\n",
      "                banana        128          1      0.902          1      0.995      0.301\n",
      "              sandwich        128          2          1          0      0.359      0.326\n",
      "                orange        128          4      0.722       0.75      0.912      0.581\n",
      "              broccoli        128         11      0.547      0.364      0.432      0.317\n",
      "                carrot        128         24      0.619      0.625      0.724      0.495\n",
      "               hot dog        128          2      0.409          1      0.828      0.762\n",
      "                 pizza        128          5      0.833      0.995      0.962      0.727\n",
      "                 donut        128         14      0.631          1       0.96      0.839\n",
      "                  cake        128          4       0.87          1      0.995       0.83\n",
      "                 chair        128         35      0.583        0.6      0.608      0.317\n",
      "                 couch        128          6      0.907      0.667      0.815      0.544\n",
      "          potted plant        128         14      0.739      0.786      0.823       0.48\n",
      "                   bed        128          3      0.985      0.333       0.83      0.441\n",
      "          dining table        128         13      0.821      0.357      0.578      0.342\n",
      "                toilet        128          2          1      0.988      0.995      0.846\n",
      "                    tv        128          2       0.57          1      0.995      0.796\n",
      "                laptop        128          3          1          0      0.593      0.312\n",
      "                 mouse        128          2          1          0      0.089     0.0445\n",
      "                remote        128          8          1      0.624      0.634      0.538\n",
      "            cell phone        128          8      0.622      0.417      0.421      0.187\n",
      "             microwave        128          3      0.711          1      0.995      0.766\n",
      "                  oven        128          5      0.329        0.4       0.43      0.282\n",
      "                  sink        128          6      0.437      0.333      0.338      0.265\n",
      "          refrigerator        128          5      0.567        0.8      0.799      0.536\n",
      "                  book        128         29      0.597      0.257      0.349      0.154\n",
      "                 clock        128          9      0.765      0.889      0.932      0.736\n",
      "                  vase        128          2       0.33          1      0.995      0.895\n",
      "              scissors        128          1          1          0      0.497     0.0498\n",
      "            teddy bear        128         21      0.856      0.569      0.841      0.547\n",
      "            toothbrush        128          5        0.8          1      0.928      0.574\n",
      "Results saved to \u001b[1mruns/train/exp\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Train YOLOv5s on COCO128 for 3 epochs\n",
    "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "15glLzbQx5u0"
   },
   "source": [
    "# 4. Visualize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nWOsI5wJR1o3"
   },
   "source": [
    "## Comet Logging and Visualization 🌟 NEW\n",
    "\n",
    "[Comet](https://www.comet.com/site/lp/yolov5-with-comet/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!\n",
    "\n",
    "Getting started is easy:\n",
    "```shell\n",
    "pip install comet_ml  # 1. install\n",
    "export COMET_API_KEY=<Your API Key>  # 2. paste API key\n",
    "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt  # 3. train\n",
    "```\n",
    "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n",
    "\n",
    "<a href=\"https://bit.ly/yolov5-readme-comet2\">\n",
    "<img alt=\"Comet Dashboard\" src=\"https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png\" width=\"1280\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lay2WsTjNJzP"
   },
   "source": [
    "## ClearML Logging and Automation 🌟 NEW\n",
    "\n",
    "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n",
    "\n",
    "- `pip install clearml`\n",
    "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n",
    "\n",
    "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n",
    "\n",
    "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\n",
    "\n",
    "<a href=\"https://cutt.ly/yolov5-notebook-clearml\">\n",
    "<img alt=\"ClearML Experiment Management UI\" src=\"https://github.com/thepycoder/clearml_screenshots/raw/main/scalars.jpg\" width=\"1280\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-WPvRbS5Swl6"
   },
   "source": [
    "## Local Logging\n",
    "\n",
    "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n",
    "\n",
    "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices.\n",
    "\n",
    "<img alt=\"Local logging results\" src=\"https://user-images.githubusercontent.com/26833433/183222430-e1abd1b7-782c-4cde-b04d-ad52926bf818.jpg\" width=\"1280\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zelyeqbyt3GD"
   },
   "source": [
    "# Environments\n",
    "\n",
    "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n",
    "\n",
    "- **Notebooks** with free GPU: <a href=\"https://bit.ly/yolov5-paperspace-notebook\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"></a> <a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a> <a href=\"https://www.kaggle.com/ultralytics/yolov5\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
    "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n",
    "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n",
    "- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) <a href=\"https://hub.docker.com/r/ultralytics/yolov5\"><img src=\"https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker\" alt=\"Docker Pulls\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6Qu7Iesl0p54"
   },
   "source": [
    "# Status\n",
    "\n",
    "![YOLOv5 CI](https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg)\n",
    "\n",
    "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IEijrePND_2I"
   },
   "source": [
    "# Appendix\n",
    "\n",
    "Additional content below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GMusP4OAxFu6"
   },
   "outputs": [],
   "source": [
    "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
    "import torch\n",
    "\n",
    "model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True, trust_repo=True)  # or yolov5n - yolov5x6 or custom\n",
    "im = 'https://ultralytics.com/images/zidane.jpg'  # file, Path, PIL.Image, OpenCV, nparray, list\n",
    "results = model(im)  # inference\n",
    "results.print()  # or .show(), .save(), .crop(), .pandas(), etc."
   ]
  }
 ],
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