“YOLOv5_Tutorial”的副本.ipynb

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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/wmh02240/3D-convolutional-speaker-recognition-pytorch/blob/master/%E2%80%9CYOLOv5_Tutorial%E2%80%9D%E7%9A%84%E5%89%AF%E6%9C%AC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HvhYZrIZCEyo"
      },
      "source": [
        "<img src=\"https://user-images.githubusercontent.com/26833433/98702494-b71c4e80-237a-11eb-87ed-17fcd6b3f066.jpg\">\n",
        "\n",
        "This notebook was written by Ultralytics LLC, and is freely available for redistribution under the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/). \n",
        "For more information please visit https://github.com/ultralytics/yolov5 and https://www.ultralytics.com."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7mGmQbAO5pQb"
      },
      "source": [
        "# Setup\n",
        "\n",
        "Clone repo, install dependencies and check PyTorch and GPU."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wbvMlHd_QwMG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15938900-b2a7-443c-89f2-833550281e57"
      },
      "source": [
        "!git clone https://github.com/ultralytics/yolov5  # clone repo\n",
        "%cd yolov5\n",
        "%pip install -qr requirements.txt  # install dependencies\n",
        "\n",
        "import torch\n",
        "from IPython.display import Image, clear_output  # to display images\n",
        "\n",
        "clear_output()\n",
        "print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Setup complete. Using torch 1.7.0+cu101 _CudaDeviceProperties(name='Tesla T4', major=7, minor=5, total_memory=15079MB, multi_processor_count=40)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4JnkELT0cIJg"
      },
      "source": [
        "# 1. Inference\n",
        "\n",
        "`detect.py` runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zR9ZbuQCH7FX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "outputId": "050149b8-8750-4fd2-ded6-112b8ef42a41"
      },
      "source": [
        "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images/\n",
        "Image(filename='runs/detect/exp/zidane.jpg', width=600)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov5s.pt'])\n",
            "Using torch 1.7.0+cu101 CUDA:0 (Tesla T4, 15079MB)\n",
            "\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5s.pt to yolov5s.pt...\n",
            "100% 14.5M/14.5M [00:00<00:00, 85.2MB/s]\n",
            "\n",
            "Fusing layers... \n",
            "Model Summary: 232 layers, 7459581 parameters, 0 gradients, 17.5 GFLOPS\n",
            "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 buss, 1 skateboards, Done. (0.035s)\n",
            "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.011s)\n",
            "Results saved to runs/detect/exp\n",
            "Done. (0.243s)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/jpeg": {
              "width": 600
            }
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4qbaa3iEcrcE"
      },
      "source": [
        "Results are saved to `runs/detect`. A full list of available inference sources:\n",
        "<img src=\"https://user-images.githubusercontent.com/26833433/98274798-2b7a7a80-1f94-11eb-91a4-70c73593e26b.jpg\" width=\"900\"> "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0eq1SMWl6Sfn"
      },
      "source": [
        "# 2. Test\n",
        "Test a model on [COCO](https://cocodataset.org/#home) val or test-dev dataset to evaluate trained accuracy. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag. Note that `pycocotools` metrics may be 1-2% better than the equivalent repo metrics, as is visible below, due to slight differences in mAP computation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eyTZYGgRjnMc"
      },
      "source": [
        "## COCO val2017\n",
        "Download [COCO val 2017](https://github.com/ultralytics/yolov5/blob/74b34872fdf41941cddcf243951cdb090fbac17b/data/coco.yaml#L14) dataset (1GB - 5000 images), and test model accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQPtK1QYVaD_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "02ac0588602847eea00a0205f87bcce2",
            "c472ea49806447a68b5a9221a4ddae85",
            "091fdf499bd44a80af7281d16da4aa93",
            "c79f69c959de4427ba102a87a9f46d80",
            "c42ae5af74a0491187827d0a1fc259bb",
            "5a90f72d3a2d46cb9ad915daa3ead8b4",
            "2a7ed6611da34662b10e37fd4f4e4438",
            "fead0160658445bf9e966daa4481cad0"
          ]
        },
        "outputId": "780d8f5f-766e-4b99-e370-11f9b884c27a"
      },
      "source": [
        "# Download COCO val2017\n",
        "torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017val.zip', 'tmp.zip')\n",
        "!unzip -q tmp.zip -d ../ && rm tmp.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "02ac0588602847eea00a0205f87bcce2",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=819257867.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X58w8JLpMnjH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "013935a5-ba81-4810-b723-0cb01cf7bc79"
      },
      "source": [
        "# Run YOLOv5x on COCO val2017\n",
        "!python test.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', exist_ok=False, img_size=640, iou_thres=0.65, name='exp', project='runs/test', save_conf=False, save_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n",
            "Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n",
            "\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5x.pt to yolov5x.pt...\n",
            "100% 170M/170M [00:05<00:00, 32.6MB/s]\n",
            "\n",
            "Fusing layers... \n",
            "Model Summary: 484 layers, 88922205 parameters, 0 gradients\n",
            "Scanning labels ../coco/labels/val2017.cache (4952 found, 0 missing, 48 empty, 0 duplicate, for 5000 images): 5000it [00:00, 14785.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 157/157 [01:30<00:00,  1.74it/s]\n",
            "                 all       5e+03    3.63e+04       0.409       0.754       0.672       0.484\n",
            "Speed: 5.9/2.1/7.9 ms inference/NMS/total per 640x640 image at batch-size 32\n",
            "\n",
            "Evaluating pycocotools mAP... saving runs/test/exp/yolov5x_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=4.67s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=92.11s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=13.24s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.492\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.676\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.534\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.318\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.541\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.633\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.376\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.617\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.670\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.493\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.723\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.812\n",
            "Results saved to runs/test/exp\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rc_KbFk0juX2"
      },
      "source": [
        "## COCO test-dev2017\n",
        "Download [COCO test2017](https://github.com/ultralytics/yolov5/blob/74b34872fdf41941cddcf243951cdb090fbac17b/data/coco.yaml#L15) dataset (7GB - 40,000 images), to test model accuracy on test-dev set (20,000 images). Results are saved to a `*.json` file which can be submitted to the evaluation server at https://competitions.codalab.org/competitions/20794."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0AJnSeCIHyJ"
      },
      "source": [
        "# Download COCO test-dev2017\n",
        "torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels.zip', 'tmp.zip')\n",
        "!unzip -q tmp.zip -d ../ && rm tmp.zip  # unzip labels\n",
        "!f=\"test2017.zip\" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f  # 7GB,  41k images\n",
        "%mv ./test2017 ./coco/images && mv ./coco ../  # move images to /coco and move /coco next to /yolov5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "29GJXAP_lPrt"
      },
      "source": [
        "# Run YOLOv5s on COCO test-dev2017 using --task test\n",
        "!python test.py --weights yolov5s.pt --data coco.yaml --task test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VUOiNLtMP5aG"
      },
      "source": [
        "# 3. Train\n",
        "\n",
        "Download [COCO128](https://www.kaggle.com/ultralytics/coco128), a small 128-image tutorial dataset, start tensorboard and train YOLOv5s from a pretrained checkpoint for 3 epochs (note actual training is typically much longer, around **300-1000 epochs**, depending on your dataset)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Knxi2ncxWffW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "d4c4313d180c46019fbc59cd292bbab3",
            "c82ab54b47db4fd9a887e1da6a09f012",
            "51cf064898e64074bf6ad75b3bd38aeb",
            "b956db8dc1a1416895e7a10e19dfa2e1",
            "4c87c95df3e8435eb514a45310049cb9",
            "a385e72894224f6599a9699d36c67d2a",
            "8645d59f1a5047d8adad48d5dd9a33be",
            "94be18a25e844d659ed9a95c5c8445b8"
          ]
        },
        "outputId": "a49b37d9-61ec-4b0f-8701-fdfbe9274eb2"
      },
      "source": [
        "# Download COCO128\n",
        "torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip', 'tmp.zip')\n",
        "!unzip -q tmp.zip -d ../ && rm tmp.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d4c4313d180c46019fbc59cd292bbab3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=22091032.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_pOkGLv1dMqh"
      },
      "source": [
        "Train a YOLOv5s model on [COCO128](https://www.kaggle.com/ultralytics/coco128) with `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and **COCO, COCO128, and VOC datasets are downloaded automatically** on first use.\n",
        "\n",
        "All training results are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bOy5KI2ncnWd"
      },
      "source": [
        "# Tensorboard (optional)\n",
        "%load_ext tensorboard\n",
        "%tensorboard --logdir runs/train"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fLAV42oNb7M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3f34ff3-a46a-4705-d7b7-2070f0c1068b"
      },
      "source": [
        "# Weights & Biases (optional)\n",
        "%pip install -q wandb  \n",
        "!wandb login  # use 'wandb disabled' or 'wandb enabled' to disable or enable"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 1.8MB 23.1MB/s \n",
            "\u001b[K     |████████████████████████████████| 133kB 61.1MB/s \n",
            "\u001b[K     |████████████████████████████████| 102kB 13.8MB/s \n",
            "\u001b[K     |████████████████████████████████| 81kB 13.0MB/s \n",
            "\u001b[K     |████████████████████████████████| 163kB 51.5MB/s \n",
            "\u001b[K     |████████████████████████████████| 71kB 11.8MB/s \n",
            "\u001b[?25h  Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NcFxRcFdJ_O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68e06519-a71b-44cf-da74-f0fe6684797a"
      },
      "source": [
        "# Train YOLOv5s on COCO128 for 3 epochs\n",
        "!python train.py --img 640 --batch 16 --epochs 300 --data coco128.yaml --weights yolov5s.pt --nosave --cache"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using torch 1.7.0+cu101 CUDA:0 (Tesla T4, 15079MB)\n",
            "\n",
            "Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', epochs=300, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, log_artifacts=False, log_imgs=16, multi_scale=False, name='exp', noautoanchor=False, nosave=True, notest=False, project='runs/train', rect=False, resume=False, save_dir='runs/train/exp2', single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5s.pt', workers=8, world_size=1)\n",
            "Start Tensorboard with \"tensorboard --logdir runs/train\", view at http://localhost:6006/\n",
            "2020-12-24 01:42:20.368064: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n",
            "Hyperparameters {'lr0': 0.01, 'lrf': 0.2, '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}\n",
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1      3520  models.common.Focus                     [3, 32, 3]                    \n",
            "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
            "  2                -1  1     19904  models.common.BottleneckCSP             [64, 64, 1]                   \n",
            "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
            "  4                -1  1    161152  models.common.BottleneckCSP             [128, 128, 3]                 \n",
            "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
            "  6                -1  1    641792  models.common.BottleneckCSP             [256, 256, 3]                 \n",
            "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
            "  8                -1  1    656896  models.common.SPP                       [512, 512, [5, 9, 13]]        \n",
            "  9                -1  1   1248768  models.common.BottleneckCSP             [512, 512, 1, False]          \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    378624  models.common.BottleneckCSP             [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     95104  models.common.BottleneckCSP             [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    313088  models.common.BottleneckCSP             [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   1248768  models.common.BottleneckCSP             [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: 283 layers, 7468157 parameters, 7468157 gradients, 17.5 GFLOPS\n",
            "\n",
            "Transferred 370/370 items from yolov5s.pt\n",
            "Optimizer groups: 62 .bias, 70 conv.weight, 59 other\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mwmh02240\u001b[0m (use `wandb login --relogin` to force relogin)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.10.12\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mexp2\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/wmh02240/YOLOv5\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/wmh02240/YOLOv5/runs/29or7gxf\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in /content/yolov5/wandb/run-20201224_014226-29or7gxf\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run `wandb offline` to turn off syncing.\n",
            "\n",
            "Scanning '../coco128/labels/train2017.cache' for images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100%|██████████| 128/128 [00:00<00:00, 412026.79it/s]\n",
            "Caching images (0.1GB): 100%|██████████| 128/128 [00:00<00:00, 207.51it/s]\n",
            "Scanning '../coco128/labels/train2017.cache' for images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100%|██████████| 128/128 [00:00<00:00, 110308.39it/s]\n",
            "Caching images (0.1GB): 100%|██████████| 128/128 [00:00<00:00, 166.08it/s]\n",
            "Plotting labels... \n",
            "NumExpr defaulting to 2 threads.\n",
            "Images sizes do not match. This will causes images to be display incorrectly in the UI.\n",
            "\n",
            "Analyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n",
            "Image sizes 640 train, 640 test\n",
            "Using 2 dataloader workers\n",
            "Logging results to runs/train/exp2\n",
            "Starting training for 300 epochs...\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "     0/299     5.28G   0.04202   0.06746   0.01503    0.1245       194       640: 100%|██████████| 8/8 [00:05<00:00,  1.38it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:07<00:00,  1.11it/s]\n",
            "                 all         128         929       0.406       0.762       0.701       0.458\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "     1/299     4.51G   0.04461   0.05872   0.01689    0.1202       142       640: 100%|██████████| 8/8 [00:02<00:00,  3.45it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.38it/s]\n",
            "                 all         128         929       0.389       0.764         0.7       0.462\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "     2/299     4.51G   0.04442   0.06537   0.01661    0.1264       149       640: 100%|██████████| 8/8 [00:02<00:00,  3.51it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.92it/s]\n",
            "                 all         128         929       0.382       0.766       0.701       0.461\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "     3/299     4.51G   0.04243   0.06085   0.01549    0.1188       124       640: 100%|██████████| 8/8 [00:02<00:00,  3.42it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.45it/s]\n",
            "                 all         128         929       0.388       0.772       0.706       0.467\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "     4/299     4.51G   0.04317   0.06171    0.0163    0.1212       158       640: 100%|██████████| 8/8 [00:02<00:00,  3.52it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.51it/s]\n",
            "                 all         128         929       0.386       0.775       0.713       0.475\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "     5/299     4.51G   0.04082   0.05516   0.01562    0.1116       180       640: 100%|██████████| 8/8 [00:02<00:00,  3.52it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.84it/s]\n",
            "                 all         128         929       0.386        0.78       0.715       0.478\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "     6/299     4.51G   0.04281   0.06605   0.01554    0.1244       198       640: 100%|██████████| 8/8 [00:02<00:00,  3.38it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.02it/s]\n",
            "                 all         128         929       0.388       0.788       0.722       0.479\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "     7/299     4.51G   0.04451    0.0632   0.01582    0.1235       207       640: 100%|██████████| 8/8 [00:02<00:00,  3.46it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.98it/s]\n",
            "                 all         128         929        0.39        0.79       0.737       0.483\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "     8/299     4.51G   0.04151   0.06035   0.01586    0.1177       240       640: 100%|██████████| 8/8 [00:02<00:00,  3.45it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.89it/s]\n",
            "                 all         128         929       0.399       0.792       0.735        0.49\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "     9/299     4.51G   0.04094   0.05241   0.01516    0.1085       217       640: 100%|██████████| 8/8 [00:02<00:00,  3.32it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.72it/s]\n",
            "                 all         128         929       0.407       0.804       0.754       0.499\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    10/299     4.51G   0.04087   0.06141   0.01478    0.1171       183       640: 100%|██████████| 8/8 [00:02<00:00,  3.44it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.58it/s]\n",
            "                 all         128         929       0.409       0.806        0.76       0.511\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    11/299     4.51G    0.0407    0.0577   0.01351    0.1119       196       640: 100%|██████████| 8/8 [00:02<00:00,  3.41it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.73it/s]\n",
            "                 all         128         929       0.407       0.805       0.763       0.513\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    12/299     4.51G   0.04009   0.06096   0.01385    0.1149       160       640: 100%|██████████| 8/8 [00:02<00:00,  3.47it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.90it/s]\n",
            "                 all         128         929       0.404       0.802        0.77       0.523\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    13/299     4.51G   0.04219   0.06279   0.01252    0.1175       219       640: 100%|██████████| 8/8 [00:02<00:00,  3.50it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.45it/s]\n",
            "                 all         128         929       0.405       0.801       0.767       0.526\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    14/299     4.51G   0.04011   0.05593   0.01314    0.1092       169       640: 100%|██████████| 8/8 [00:02<00:00,  3.38it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.42it/s]\n",
            "                 all         128         929       0.413       0.818       0.776       0.531\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    15/299     4.51G   0.03887   0.05537    0.0131    0.1074       151       640: 100%|██████████| 8/8 [00:02<00:00,  3.37it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.44it/s]\n",
            "                 all         128         929       0.418       0.821       0.778       0.536\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    16/299     4.51G   0.04079    0.0614   0.01276     0.115       143       640: 100%|██████████| 8/8 [00:02<00:00,  3.48it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.78it/s]\n",
            "                 all         128         929       0.425       0.821       0.781       0.538\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    17/299     4.51G   0.03936   0.06053   0.01312     0.113       171       640: 100%|██████████| 8/8 [00:02<00:00,  3.29it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.05it/s]\n",
            "                 all         128         929       0.426       0.826       0.788       0.545\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    18/299     4.51G     0.042   0.05756   0.01272    0.1123       195       640: 100%|██████████| 8/8 [00:02<00:00,  3.36it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.52it/s]\n",
            "                 all         128         929       0.435       0.831       0.791       0.546\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    19/299     4.51G   0.03849   0.05201   0.01124    0.1017       185       640: 100%|██████████| 8/8 [00:02<00:00,  3.37it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.06it/s]\n",
            "                 all         128         929       0.452       0.833         0.8       0.557\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    20/299     4.51G    0.0401   0.05783   0.01051    0.1084       206       640: 100%|██████████| 8/8 [00:02<00:00,  3.41it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.79it/s]\n",
            "                 all         128         929       0.454       0.837       0.801       0.555\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    21/299     4.51G   0.03926   0.05422   0.01006    0.1035       193       640: 100%|██████████| 8/8 [00:02<00:00,  3.60it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.96it/s]\n",
            "                 all         128         929       0.452       0.842       0.808       0.564\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    22/299     4.51G   0.03856   0.05334  0.009826    0.1017       204       640: 100%|██████████| 8/8 [00:02<00:00,  3.65it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.05it/s]\n",
            "                 all         128         929       0.454       0.845       0.819       0.573\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    23/299     4.51G   0.03924   0.05734   0.01103    0.1076       228       640: 100%|██████████| 8/8 [00:02<00:00,  3.73it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.80it/s]\n",
            "                 all         128         929        0.46       0.851       0.821       0.576\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    24/299     4.51G   0.03965   0.05446   0.01025    0.1044       231       640: 100%|██████████| 8/8 [00:02<00:00,  3.52it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.94it/s]\n",
            "                 all         128         929       0.472       0.854       0.824       0.574\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    25/299     4.51G   0.03934   0.05247   0.01141    0.1032       109       640: 100%|██████████| 8/8 [00:02<00:00,  3.57it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.50it/s]\n",
            "                 all         128         929       0.483       0.861       0.832       0.573\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    26/299     4.51G    0.0387   0.05631   0.01074    0.1058       274       640: 100%|██████████| 8/8 [00:02<00:00,  3.51it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.85it/s]\n",
            "                 all         128         929       0.485       0.866       0.839        0.57\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    27/299     4.51G   0.03847   0.05475   0.01001    0.1032       244       640: 100%|██████████| 8/8 [00:02<00:00,  3.57it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.00it/s]\n",
            "                 all         128         929       0.479       0.874       0.841       0.586\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    28/299     4.51G   0.03927   0.05517   0.01056     0.105       186       640: 100%|██████████| 8/8 [00:02<00:00,  3.58it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.98it/s]\n",
            "                 all         128         929       0.485       0.882       0.853       0.589\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    29/299     4.51G   0.03895   0.05116   0.01065    0.1008       159       640: 100%|██████████| 8/8 [00:02<00:00,  3.54it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.07it/s]\n",
            "                 all         128         929       0.503       0.878       0.852       0.599\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    30/299     4.51G   0.03927   0.05366  0.009123     0.102       259       640: 100%|██████████| 8/8 [00:02<00:00,  3.68it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.56it/s]\n",
            "                 all         128         929       0.506       0.893       0.861       0.598\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    31/299     4.51G   0.03765   0.05329   0.01046    0.1014       229       640: 100%|██████████| 8/8 [00:02<00:00,  3.59it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.54it/s]\n",
            "                 all         128         929       0.511       0.899       0.865       0.601\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    32/299     4.51G   0.03975   0.05379  0.009845    0.1034       172       640: 100%|██████████| 8/8 [00:02<00:00,  3.65it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.57it/s]\n",
            "                 all         128         929        0.51       0.896       0.862       0.602\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    33/299     4.51G   0.03836    0.0547  0.008149    0.1012       230       640: 100%|██████████| 8/8 [00:02<00:00,  3.66it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.04it/s]\n",
            "                 all         128         929       0.494         0.9       0.863       0.603\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    34/299     4.51G    0.0393   0.04943  0.008914   0.09764       202       640: 100%|██████████| 8/8 [00:02<00:00,  3.54it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.58it/s]\n",
            "                 all         128         929       0.499       0.891       0.863       0.593\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    35/299     4.51G    0.0389   0.04923  0.009209   0.09734       184       640: 100%|██████████| 8/8 [00:02<00:00,  3.60it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.92it/s]\n",
            "                 all         128         929       0.505       0.895       0.866       0.596\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    36/299     4.51G   0.03935   0.05207  0.009257    0.1007       228       640: 100%|██████████| 8/8 [00:02<00:00,  3.61it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.96it/s]\n",
            "                 all         128         929       0.511       0.904       0.872       0.595\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    37/299     4.51G   0.03953   0.05374  0.008865    0.1021       193       640: 100%|██████████| 8/8 [00:02<00:00,  3.56it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.04it/s]\n",
            "                 all         128         929       0.497       0.903       0.879       0.607\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    38/299     4.51G   0.03971   0.05189  0.008344   0.09994       188       640: 100%|██████████| 8/8 [00:02<00:00,  3.56it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.02it/s]\n",
            "                 all         128         929       0.516       0.899       0.876       0.608\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    39/299     4.51G   0.04018    0.0516  0.008423    0.1002       228       640: 100%|██████████| 8/8 [00:02<00:00,  3.64it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.31it/s]\n",
            "                 all         128         929       0.524       0.904       0.877       0.604\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    40/299     4.51G   0.04002   0.04849  0.008706   0.09722       157       640: 100%|██████████| 8/8 [00:02<00:00,  3.52it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.96it/s]\n",
            "                 all         128         929       0.535       0.911       0.879       0.587\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    41/299     4.51G   0.03836   0.04773  0.008223   0.09431       236       640: 100%|██████████| 8/8 [00:02<00:00,  3.55it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.02it/s]\n",
            "                 all         128         929       0.531       0.908       0.878       0.584\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    42/299     4.51G   0.03912   0.04899  0.008306   0.09641       214       640: 100%|██████████| 8/8 [00:02<00:00,  3.59it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.05it/s]\n",
            "                 all         128         929       0.502       0.898       0.857       0.585\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    43/299     4.51G   0.03843   0.04814   0.00932   0.09589       163       640: 100%|██████████| 8/8 [00:02<00:00,  3.56it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.89it/s]\n",
            "                 all         128         929       0.518       0.909       0.879       0.603\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    44/299     4.51G   0.03747   0.04461  0.008516   0.09059       190       640: 100%|██████████| 8/8 [00:02<00:00,  3.63it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.10it/s]\n",
            "                 all         128         929       0.533       0.911        0.89       0.608\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    45/299     4.51G   0.03741   0.04892  0.007595   0.09393       183       640: 100%|██████████| 8/8 [00:02<00:00,  3.64it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.02it/s]\n",
            "                 all         128         929       0.523       0.916       0.887       0.607\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    46/299     4.51G   0.03853   0.04605  0.009104   0.09369       168       640: 100%|██████████| 8/8 [00:02<00:00,  3.63it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.11it/s]\n",
            "                 all         128         929       0.519       0.926       0.888       0.599\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    47/299     4.51G   0.04027    0.0476  0.008138     0.096       211       640: 100%|██████████| 8/8 [00:02<00:00,  3.62it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.05it/s]\n",
            "                 all         128         929        0.53       0.922       0.897       0.635\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    48/299     4.51G    0.0381   0.04394  0.007156    0.0892       195       640: 100%|██████████| 8/8 [00:02<00:00,  3.59it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.92it/s]\n",
            "                 all         128         929       0.536       0.911       0.897       0.612\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    49/299     4.51G   0.03764   0.04562  0.008211   0.09147       153       640: 100%|██████████| 8/8 [00:02<00:00,  3.57it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.43it/s]\n",
            "                 all         128         929       0.534       0.911       0.897       0.615\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    50/299     4.51G   0.03829   0.04705  0.007221   0.09256       194       640: 100%|██████████| 8/8 [00:02<00:00,  3.59it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.53it/s]\n",
            "                 all         128         929       0.536        0.91       0.896       0.625\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    51/299     4.51G   0.03835    0.0472  0.007916   0.09347       188       640: 100%|██████████| 8/8 [00:02<00:00,  3.53it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.77it/s]\n",
            "                 all         128         929       0.545       0.926       0.896       0.619\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    52/299     4.51G   0.03791   0.05061  0.007168   0.09569       164       640: 100%|██████████| 8/8 [00:02<00:00,  3.64it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.16it/s]\n",
            "                 all         128         929       0.535       0.933       0.904       0.643\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    53/299     4.51G   0.03885   0.04747  0.007753   0.09407       147       640: 100%|██████████| 8/8 [00:02<00:00,  3.59it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.52it/s]\n",
            "                 all         128         929       0.528       0.934       0.905       0.581\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    54/299     4.51G   0.03794   0.04798  0.007189   0.09311       175       640: 100%|██████████| 8/8 [00:02<00:00,  3.60it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.05it/s]\n",
            "                 all         128         929       0.557       0.928       0.906       0.638\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    55/299     4.51G   0.03736   0.04461  0.007716   0.08968       153       640: 100%|██████████| 8/8 [00:02<00:00,  3.63it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.11it/s]\n",
            "                 all         128         929       0.546       0.934       0.906       0.658\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    56/299     4.51G   0.03626   0.04559  0.008161      0.09       198       640: 100%|██████████| 8/8 [00:02<00:00,  3.60it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.43it/s]\n",
            "                 all         128         929       0.544       0.937       0.916       0.644\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    57/299     4.51G   0.03735    0.0459  0.007384   0.09064       160       640: 100%|██████████| 8/8 [00:02<00:00,  3.55it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.04it/s]\n",
            "                 all         128         929       0.551       0.944        0.92       0.657\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    58/299     4.51G   0.03617   0.04845  0.006516   0.09113       221       640: 100%|██████████| 8/8 [00:02<00:00,  3.61it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.71it/s]\n",
            "                 all         128         929       0.524        0.94       0.892        0.61\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    59/299     4.51G   0.03944   0.04789  0.006919   0.09425       202       640: 100%|██████████| 8/8 [00:02<00:00,  3.49it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.54it/s]\n",
            "                 all         128         929       0.568       0.943        0.91       0.658\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    60/299     4.51G   0.03729   0.04767   0.00746   0.09243       243       640: 100%|██████████| 8/8 [00:02<00:00,  3.49it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.79it/s]\n",
            "                 all         128         929        0.58       0.937       0.913       0.651\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    61/299     4.51G   0.03585   0.04217  0.006081    0.0841       300       640: 100%|██████████| 8/8 [00:02<00:00,  3.50it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.00it/s]\n",
            "                 all         128         929       0.563       0.943       0.914       0.655\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    62/299     4.51G   0.03755   0.04752  0.007292   0.09237       208       640: 100%|██████████| 8/8 [00:02<00:00,  3.48it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.42it/s]\n",
            "                 all         128         929       0.555       0.944        0.92       0.642\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    63/299     4.51G   0.03826   0.04567  0.006796   0.09073       155       640: 100%|██████████| 8/8 [00:02<00:00,  3.62it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.61it/s]\n",
            "                 all         128         929       0.478       0.943       0.879       0.566\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    64/299     4.51G   0.04127   0.04521  0.007087   0.09357       189       640: 100%|██████████| 8/8 [00:02<00:00,  3.54it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.05it/s]\n",
            "                 all         128         929       0.511       0.938       0.892       0.571\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    65/299     4.51G   0.04396   0.04798   0.00672   0.09866       182       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.99it/s]\n",
            "                 all         128         929       0.539       0.934       0.895       0.612\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    66/299     4.51G    0.0391   0.04312  0.006244   0.08847       172       640: 100%|██████████| 8/8 [00:02<00:00,  3.65it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.66it/s]\n",
            "                 all         128         929       0.544       0.941       0.917       0.621\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    67/299     4.51G   0.03839   0.04971  0.006845   0.09495       212       640: 100%|██████████| 8/8 [00:02<00:00,  3.65it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.89it/s]\n",
            "                 all         128         929       0.544       0.942       0.911       0.632\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    68/299     4.51G   0.04179    0.0486  0.007504    0.0979       208       640: 100%|██████████| 8/8 [00:02<00:00,  3.65it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.55it/s]\n",
            "                 all         128         929       0.567        0.94       0.913        0.63\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    69/299     4.51G   0.04233   0.04464  0.007972   0.09494       301       640: 100%|██████████| 8/8 [00:02<00:00,  3.60it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.49it/s]\n",
            "                 all         128         929        0.57       0.935       0.909       0.624\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    70/299     4.51G    0.0386   0.04397  0.006573   0.08914       203       640: 100%|██████████| 8/8 [00:02<00:00,  3.53it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.01it/s]\n",
            "                 all         128         929       0.571       0.943       0.911        0.65\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    71/299     4.51G   0.04072   0.04246  0.007616   0.09079       213       640: 100%|██████████| 8/8 [00:02<00:00,  3.68it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.07it/s]\n",
            "                 all         128         929       0.569       0.933       0.903        0.62\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    72/299     4.51G   0.04183   0.04838  0.006864   0.09708       200       640: 100%|██████████| 8/8 [00:02<00:00,  3.62it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.77it/s]\n",
            "                 all         128         929       0.561       0.935        0.91       0.634\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    73/299     4.51G   0.03861   0.04384  0.008029   0.09047       192       640: 100%|██████████| 8/8 [00:02<00:00,  3.58it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.00it/s]\n",
            "                 all         128         929       0.563       0.949        0.92       0.656\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    74/299     4.51G   0.03819   0.04643  0.006946   0.09156       179       640: 100%|██████████| 8/8 [00:02<00:00,  3.66it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.99it/s]\n",
            "                 all         128         929        0.56       0.948       0.922       0.656\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    75/299     4.51G   0.03963   0.04266  0.007328   0.08962       186       640: 100%|██████████| 8/8 [00:02<00:00,  3.54it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.64it/s]\n",
            "                 all         128         929       0.565       0.946       0.915       0.633\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    76/299     4.51G   0.03894   0.04731  0.006743   0.09299       193       640: 100%|██████████| 8/8 [00:02<00:00,  3.48it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.48it/s]\n",
            "                 all         128         929       0.599       0.941       0.921       0.668\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    77/299     4.51G   0.03804   0.04738  0.007294   0.09271       249       640: 100%|██████████| 8/8 [00:02<00:00,  3.69it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.62it/s]\n",
            "                 all         128         929       0.607       0.943       0.924       0.654\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    78/299     4.51G   0.03899   0.04187  0.007389   0.08826       235       640: 100%|██████████| 8/8 [00:02<00:00,  3.56it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.71it/s]\n",
            "                 all         128         929       0.579       0.938       0.911       0.642\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    79/299     4.51G   0.03777   0.04645  0.007631   0.09185       269       640: 100%|██████████| 8/8 [00:02<00:00,  3.63it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.74it/s]\n",
            "                 all         128         929       0.575       0.948       0.918       0.644\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    80/299     4.51G   0.03959   0.04315  0.006332   0.08908       176       640: 100%|██████████| 8/8 [00:02<00:00,  3.67it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.76it/s]\n",
            "                 all         128         929        0.58       0.951       0.924       0.646\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    81/299     4.51G   0.03983    0.0435  0.006844   0.09017       227       640: 100%|██████████| 8/8 [00:02<00:00,  3.52it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.66it/s]\n",
            "                 all         128         929       0.569       0.951       0.931       0.608\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    82/299     4.51G   0.03858   0.04306   0.00667   0.08831       180       640: 100%|██████████| 8/8 [00:02<00:00,  3.69it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.75it/s]\n",
            "                 all         128         929       0.564       0.948       0.931       0.672\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    83/299     4.51G   0.04005   0.04437  0.006941   0.09137       311       640: 100%|██████████| 8/8 [00:02<00:00,  3.68it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.62it/s]\n",
            "                 all         128         929       0.557       0.953       0.921       0.644\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    84/299     4.51G   0.03979   0.04483  0.007325   0.09194       214       640: 100%|██████████| 8/8 [00:02<00:00,  3.61it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.54it/s]\n",
            "                 all         128         929       0.565       0.944       0.925       0.618\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    85/299     4.51G   0.04063   0.04705  0.007539   0.09522       180       640: 100%|██████████| 8/8 [00:02<00:00,  3.58it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.13it/s]\n",
            "                 all         128         929       0.554       0.953        0.92       0.627\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    86/299     4.51G   0.03804   0.04527  0.007424   0.09073       186       640: 100%|██████████| 8/8 [00:02<00:00,  3.60it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.64it/s]\n",
            "                 all         128         929       0.563       0.947       0.921        0.62\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    87/299     4.51G   0.03884   0.03973  0.006802   0.08538       152       640: 100%|██████████| 8/8 [00:02<00:00,  3.69it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.11it/s]\n",
            "                 all         128         929       0.578       0.951        0.92       0.634\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    88/299     4.51G    0.0379   0.04128  0.006187   0.08537       146       640: 100%|██████████| 8/8 [00:02<00:00,  3.58it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.96it/s]\n",
            "                 all         128         929       0.581       0.951        0.92       0.642\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    89/299     4.51G    0.0405   0.04386   0.00792   0.09229       199       640: 100%|██████████| 8/8 [00:02<00:00,  3.76it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.98it/s]\n",
            "                 all         128         929       0.572       0.949       0.921       0.645\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    90/299     4.51G   0.03879   0.03669  0.007655   0.08313       206       640: 100%|██████████| 8/8 [00:02<00:00,  3.65it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.04it/s]\n",
            "                 all         128         929       0.607       0.951       0.921       0.641\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    91/299     4.51G   0.03761   0.04201   0.00734   0.08695       194       640: 100%|██████████| 8/8 [00:02<00:00,  3.58it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.66it/s]\n",
            "                 all         128         929       0.585       0.957       0.933       0.622\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    92/299     4.51G   0.04233   0.04811  0.006805   0.09725       190       640: 100%|██████████| 8/8 [00:02<00:00,  3.68it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.68it/s]\n",
            "                 all         128         929       0.578       0.959       0.926       0.594\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    93/299     4.51G   0.04197   0.04518  0.006345    0.0935       166       640: 100%|██████████| 8/8 [00:02<00:00,  3.67it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.04it/s]\n",
            "                 all         128         929        0.57       0.951       0.922       0.609\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    94/299     4.51G   0.03848   0.04266  0.006792   0.08792       131       640: 100%|██████████| 8/8 [00:02<00:00,  3.65it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.99it/s]\n",
            "                 all         128         929       0.582       0.952       0.934       0.647\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    95/299     4.51G    0.0374    0.0439  0.006997    0.0883       160       640: 100%|██████████| 8/8 [00:02<00:00,  3.76it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.08it/s]\n",
            "                 all         128         929       0.584       0.946       0.934       0.669\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    96/299     4.51G   0.03898   0.04287   0.00648   0.08833       247       640: 100%|██████████| 8/8 [00:02<00:00,  3.66it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.07it/s]\n",
            "                 all         128         929       0.572       0.949       0.936       0.672\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    97/299     4.51G   0.03589   0.04254  0.006292   0.08472       187       640: 100%|██████████| 8/8 [00:02<00:00,  3.63it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.64it/s]\n",
            "                 all         128         929       0.594        0.96       0.934        0.66\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    98/299     4.51G   0.03715   0.04191   0.00609   0.08515       202       640: 100%|██████████| 8/8 [00:02<00:00,  3.68it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.12it/s]\n",
            "                 all         128         929       0.604       0.962       0.945       0.645\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "    99/299     4.51G   0.03716   0.04409  0.007036   0.08829       198       640: 100%|██████████| 8/8 [00:02<00:00,  3.66it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.58it/s]\n",
            "                 all         128         929       0.586       0.953       0.931       0.619\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   100/299     4.51G   0.03694   0.03972  0.007532   0.08419       136       640: 100%|██████████| 8/8 [00:02<00:00,  3.60it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.14it/s]\n",
            "                 all         128         929       0.596       0.955       0.939       0.645\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   101/299     4.51G   0.03924   0.04086  0.006579   0.08668       189       640: 100%|██████████| 8/8 [00:02<00:00,  3.75it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.76it/s]\n",
            "                 all         128         929       0.569       0.952        0.93       0.599\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   102/299     4.51G   0.04071   0.04406   0.00597   0.09074       248       640: 100%|██████████| 8/8 [00:02<00:00,  3.58it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.51it/s]\n",
            "                 all         128         929       0.539       0.948       0.909       0.598\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   103/299     4.51G   0.03775   0.04146  0.005905   0.08511       177       640: 100%|██████████| 8/8 [00:02<00:00,  3.62it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.84it/s]\n",
            "                 all         128         929       0.566       0.955       0.932       0.647\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   104/299     4.51G   0.04562   0.04593  0.006448     0.098       285       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.50it/s]\n",
            "                 all         128         929       0.523       0.934        0.89       0.465\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   105/299     4.51G   0.05182    0.0476   0.00727    0.1067       187       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.00it/s]\n",
            "                 all         128         929       0.526       0.954       0.911       0.622\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   106/299     4.51G   0.04681   0.04146  0.006851   0.09512       229       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.91it/s]\n",
            "                 all         128         929       0.548       0.959       0.919       0.609\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   107/299     4.51G   0.04058   0.04638  0.007345    0.0943       181       640: 100%|██████████| 8/8 [00:02<00:00,  3.74it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.64it/s]\n",
            "                 all         128         929        0.56       0.949       0.921       0.607\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   108/299     4.51G   0.04127   0.04627  0.008114   0.09565       127       640: 100%|██████████| 8/8 [00:02<00:00,  3.67it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.78it/s]\n",
            "                 all         128         929       0.574       0.952       0.926       0.658\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   109/299     4.51G   0.03875   0.03969  0.008305   0.08674       145       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.94it/s]\n",
            "                 all         128         929       0.568       0.944       0.925       0.659\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   110/299     4.51G   0.03866   0.04133  0.007664   0.08766       165       640: 100%|██████████| 8/8 [00:02<00:00,  3.74it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.47it/s]\n",
            "                 all         128         929       0.578       0.951       0.934       0.654\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   111/299     4.51G    0.0396   0.03946  0.006785   0.08585       170       640: 100%|██████████| 8/8 [00:02<00:00,  3.67it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.88it/s]\n",
            "                 all         128         929       0.573       0.939       0.919        0.65\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   112/299     4.51G    0.0404    0.0393  0.007011   0.08671       242       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.92it/s]\n",
            "                 all         128         929       0.551       0.946       0.921       0.646\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   113/299     4.51G    0.0421   0.03927  0.007402   0.08877       215       640: 100%|██████████| 8/8 [00:02<00:00,  3.73it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.76it/s]\n",
            "                 all         128         929       0.554       0.935        0.91       0.639\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   114/299     4.51G    0.0433   0.04378  0.007639   0.09473       197       640: 100%|██████████| 8/8 [00:02<00:00,  3.76it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.92it/s]\n",
            "                 all         128         929       0.559       0.951       0.925       0.644\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   115/299     4.51G   0.04014    0.0386  0.007629   0.08638       147       640: 100%|██████████| 8/8 [00:02<00:00,  3.74it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.43it/s]\n",
            "                 all         128         929       0.562       0.948       0.925       0.645\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   116/299     4.51G   0.04013   0.04406  0.006727   0.09092       182       640: 100%|██████████| 8/8 [00:02<00:00,  3.57it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.58it/s]\n",
            "                 all         128         929       0.534       0.957       0.929       0.618\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   117/299     4.51G   0.04259   0.04111  0.006836   0.09053       184       640: 100%|██████████| 8/8 [00:02<00:00,  3.74it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.94it/s]\n",
            "                 all         128         929       0.527       0.939       0.914       0.612\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   118/299     4.51G   0.04138    0.0412  0.007461   0.09003       213       640: 100%|██████████| 8/8 [00:02<00:00,  3.66it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.79it/s]\n",
            "                 all         128         929       0.548       0.952       0.923       0.619\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   119/299     4.51G   0.04246   0.04485  0.007955   0.09526       113       640: 100%|██████████| 8/8 [00:02<00:00,  3.70it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.96it/s]\n",
            "                 all         128         929       0.547        0.94       0.914       0.638\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   120/299     4.51G    0.0396   0.04617  0.006913   0.09268       174       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.91it/s]\n",
            "                 all         128         929       0.552       0.952       0.927       0.634\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   121/299     4.51G   0.03866   0.04072  0.006928   0.08632       281       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.42it/s]\n",
            "                 all         128         929       0.551       0.955       0.937       0.653\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   122/299     4.51G    0.0379   0.03934  0.006829   0.08407       179       640: 100%|██████████| 8/8 [00:02<00:00,  3.67it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.43it/s]\n",
            "                 all         128         929       0.548       0.966       0.941       0.646\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   123/299     4.51G   0.04017   0.04344  0.007094   0.09071       307       640: 100%|██████████| 8/8 [00:02<00:00,  3.73it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.01it/s]\n",
            "                 all         128         929        0.56        0.96       0.932       0.641\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   124/299     4.51G   0.04121   0.03926  0.005952   0.08642       231       640: 100%|██████████| 8/8 [00:02<00:00,  3.77it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.25it/s]\n",
            "                 all         128         929       0.522       0.958       0.926       0.615\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   125/299     4.51G   0.04073    0.0397  0.006342   0.08677       154       640: 100%|██████████| 8/8 [00:02<00:00,  3.61it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.41it/s]\n",
            "                 all         128         929       0.524       0.953        0.91       0.601\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   126/299     4.51G   0.04291   0.03863  0.006759    0.0883       240       640: 100%|██████████| 8/8 [00:02<00:00,  3.77it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.98it/s]\n",
            "                 all         128         929       0.512       0.962       0.908       0.602\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   127/299     4.51G    0.0411   0.04211  0.007147   0.09035       179       640: 100%|██████████| 8/8 [00:02<00:00,  3.73it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.89it/s]\n",
            "                 all         128         929       0.507       0.964       0.918       0.605\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   128/299     4.51G   0.04072   0.03993  0.006928   0.08757       222       640: 100%|██████████| 8/8 [00:02<00:00,  3.73it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.92it/s]\n",
            "                 all         128         929       0.523       0.964        0.93       0.608\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   129/299     4.51G   0.04029   0.04298  0.006964   0.09023       197       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.85it/s]\n",
            "                 all         128         929        0.56        0.96       0.933       0.635\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   130/299     4.51G   0.04249   0.04195  0.006694   0.09114       228       640: 100%|██████████| 8/8 [00:02<00:00,  3.64it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.95it/s]\n",
            "                 all         128         929       0.561        0.97       0.944        0.61\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   131/299     4.51G    0.0451   0.04606  0.007153   0.09831       307       640: 100%|██████████| 8/8 [00:02<00:00,  3.76it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.96it/s]\n",
            "                 all         128         929       0.557       0.957        0.93       0.634\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   132/299     4.51G   0.04097   0.03739  0.006431   0.08479       168       640: 100%|██████████| 8/8 [00:02<00:00,  3.75it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.55it/s]\n",
            "                 all         128         929       0.524       0.969       0.926       0.608\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   133/299     4.51G   0.04154   0.04543  0.007063   0.09404       211       640: 100%|██████████| 8/8 [00:02<00:00,  3.59it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.93it/s]\n",
            "                 all         128         929       0.556       0.963       0.931       0.632\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   134/299     4.51G   0.03892   0.04257  0.007474   0.08896       178       640: 100%|██████████| 8/8 [00:02<00:00,  3.73it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.73it/s]\n",
            "                 all         128         929       0.572       0.961       0.938       0.637\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   135/299     4.51G    0.0386   0.04102  0.006859   0.08648       180       640: 100%|██████████| 8/8 [00:02<00:00,  3.70it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.96it/s]\n",
            "                 all         128         929       0.586        0.96       0.941       0.649\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   136/299     4.51G   0.03716   0.04059  0.006848    0.0846       230       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.39it/s]\n",
            "                 all         128         929       0.583        0.96       0.939       0.647\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   137/299     4.51G   0.03896   0.04161  0.007015   0.08758       244       640: 100%|██████████| 8/8 [00:02<00:00,  3.68it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.55it/s]\n",
            "                 all         128         929        0.59       0.958       0.943       0.652\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   138/299     4.51G     0.039     0.044  0.006991      0.09       236       640: 100%|██████████| 8/8 [00:02<00:00,  3.73it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.43it/s]\n",
            "                 all         128         929       0.605       0.966       0.949       0.652\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   139/299     4.51G   0.03935   0.04135  0.006408    0.0871       141       640: 100%|██████████| 8/8 [00:02<00:00,  3.74it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.99it/s]\n",
            "                 all         128         929       0.597       0.957       0.932       0.626\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   140/299     4.51G   0.04556   0.04723  0.006093   0.09889       257       640: 100%|██████████| 8/8 [00:02<00:00,  3.67it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.77it/s]\n",
            "                 all         128         929       0.566       0.965       0.936       0.633\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   141/299     4.51G   0.04075   0.03774   0.00629   0.08478       122       640: 100%|██████████| 8/8 [00:02<00:00,  3.74it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.63it/s]\n",
            "                 all         128         929       0.581       0.957       0.937       0.646\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   142/299     4.51G   0.03955   0.03947  0.007171   0.08619       162       640: 100%|██████████| 8/8 [00:02<00:00,  3.65it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.51it/s]\n",
            "                 all         128         929       0.566       0.954       0.933       0.648\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   143/299     4.51G   0.03652    0.0397  0.006779     0.083       204       640: 100%|██████████| 8/8 [00:02<00:00,  3.70it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.93it/s]\n",
            "                 all         128         929        0.59       0.954       0.943       0.668\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   144/299     4.51G   0.03611   0.04203  0.006829   0.08498       237       640: 100%|██████████| 8/8 [00:02<00:00,  3.65it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.86it/s]\n",
            "                 all         128         929       0.594       0.957       0.946       0.674\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   145/299     4.51G   0.03672   0.03637   0.00704   0.08013       176       640: 100%|██████████| 8/8 [00:02<00:00,  3.69it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.03it/s]\n",
            "                 all         128         929       0.608       0.954       0.945       0.662\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   146/299     4.51G   0.03785   0.04209  0.006252   0.08619       151       640: 100%|██████████| 8/8 [00:02<00:00,  3.75it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.13it/s]\n",
            "                 all         128         929        0.61       0.963       0.951       0.652\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   147/299     4.51G   0.04099   0.04219  0.007206   0.09039       238       640: 100%|██████████| 8/8 [00:02<00:00,  3.62it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.01it/s]\n",
            "                 all         128         929       0.588       0.959       0.942       0.656\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   148/299     4.51G   0.04009   0.03934  0.006525   0.08595       172       640: 100%|██████████| 8/8 [00:02<00:00,  3.69it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.04it/s]\n",
            "                 all         128         929       0.563       0.968       0.941       0.617\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   149/299     4.51G   0.03918    0.0432  0.006212    0.0886       203       640: 100%|██████████| 8/8 [00:02<00:00,  3.70it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.30it/s]\n",
            "                 all         128         929       0.574       0.963       0.944       0.647\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   150/299     4.51G   0.03776   0.04033  0.007127   0.08522       238       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.88it/s]\n",
            "                 all         128         929        0.59       0.964       0.935       0.651\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   151/299     4.51G   0.03732   0.04243  0.006536   0.08628       208       640: 100%|██████████| 8/8 [00:02<00:00,  3.63it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.25it/s]\n",
            "                 all         128         929       0.603       0.965       0.937        0.67\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   152/299     4.51G   0.03793   0.03921  0.006314   0.08345       166       640: 100%|██████████| 8/8 [00:02<00:00,  3.73it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.99it/s]\n",
            "                 all         128         929       0.584        0.97       0.946       0.642\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   153/299     4.51G   0.03994   0.04304  0.006246   0.08922       254       640: 100%|██████████| 8/8 [00:02<00:00,  3.74it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.92it/s]\n",
            "                 all         128         929       0.609       0.958       0.933       0.661\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   154/299     4.51G   0.03941   0.03762  0.006782   0.08382       141       640: 100%|██████████| 8/8 [00:02<00:00,  3.76it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.97it/s]\n",
            "                 all         128         929       0.619       0.964       0.947       0.635\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   155/299     4.51G   0.03974   0.04136  0.006836   0.08794       231       640: 100%|██████████| 8/8 [00:02<00:00,  3.77it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.98it/s]\n",
            "                 all         128         929       0.613       0.963       0.941       0.658\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   156/299     4.51G   0.03794   0.03799  0.005777   0.08171       153       640: 100%|██████████| 8/8 [00:02<00:00,  3.65it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.49it/s]\n",
            "                 all         128         929       0.598       0.974       0.949       0.674\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   157/299     4.51G   0.03661   0.04028  0.006564   0.08345       282       640: 100%|██████████| 8/8 [00:02<00:00,  3.60it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.94it/s]\n",
            "                 all         128         929       0.589       0.967       0.947       0.677\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   158/299     4.51G   0.03586   0.03854  0.006575   0.08098       148       640: 100%|██████████| 8/8 [00:02<00:00,  3.66it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.47it/s]\n",
            "                 all         128         929       0.576       0.967       0.951       0.685\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   159/299     4.51G   0.03685   0.04081  0.006685   0.08435       213       640: 100%|██████████| 8/8 [00:02<00:00,  3.78it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.78it/s]\n",
            "                 all         128         929        0.58       0.973        0.95       0.675\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   160/299     4.51G   0.03564   0.04178  0.006325   0.08375       164       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.88it/s]\n",
            "                 all         128         929       0.591       0.972       0.948       0.678\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   161/299     4.51G   0.03495   0.03997  0.006402   0.08133       229       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.95it/s]\n",
            "                 all         128         929       0.605       0.961       0.943         0.7\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   162/299     4.51G   0.03663   0.04294  0.006558   0.08613       213       640: 100%|██████████| 8/8 [00:02<00:00,  3.73it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.22it/s]\n",
            "                 all         128         929       0.592       0.968       0.947       0.682\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   163/299     4.51G   0.03601   0.03652  0.005863   0.07839       159       640: 100%|██████████| 8/8 [00:02<00:00,  3.69it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.30it/s]\n",
            "                 all         128         929       0.619       0.955        0.94        0.69\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   164/299     4.51G   0.03703   0.03534  0.006769   0.07913       214       640: 100%|██████████| 8/8 [00:02<00:00,  3.67it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.22it/s]\n",
            "                 all         128         929       0.592       0.965       0.944       0.671\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   165/299     4.51G   0.03748   0.04416  0.006077   0.08772       227       640: 100%|██████████| 8/8 [00:02<00:00,  3.63it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.17it/s]\n",
            "                 all         128         929       0.593        0.96       0.942       0.693\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   166/299     4.51G   0.03591   0.03797  0.005793   0.07967       200       640: 100%|██████████| 8/8 [00:02<00:00,  3.68it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.93it/s]\n",
            "                 all         128         929        0.58       0.962       0.943       0.672\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   167/299     4.51G   0.03747    0.0421  0.005895   0.08546       242       640: 100%|██████████| 8/8 [00:02<00:00,  3.59it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.71it/s]\n",
            "                 all         128         929         0.6       0.964       0.948       0.688\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   168/299     4.51G   0.03623   0.03931  0.006026   0.08156       217       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.93it/s]\n",
            "                 all         128         929       0.611       0.968       0.953       0.691\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   169/299     4.51G   0.03614   0.03711  0.006119   0.07937       187       640: 100%|██████████| 8/8 [00:02<00:00,  3.70it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.87it/s]\n",
            "                 all         128         929       0.622       0.965        0.95        0.69\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   170/299     4.51G   0.03824   0.04433  0.006144   0.08872       276       640: 100%|██████████| 8/8 [00:02<00:00,  3.62it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.85it/s]\n",
            "                 all         128         929       0.616       0.974       0.953       0.683\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   171/299     4.51G   0.03742   0.04163  0.006213   0.08526       155       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.34it/s]\n",
            "                 all         128         929       0.607       0.974       0.954       0.688\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   172/299     4.51G    0.0338   0.03837  0.006305   0.07848       166       640: 100%|██████████| 8/8 [00:02<00:00,  3.66it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.91it/s]\n",
            "                 all         128         929       0.621       0.976        0.96       0.704\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   173/299     4.51G   0.03521   0.03696   0.00646   0.07863       193       640: 100%|██████████| 8/8 [00:02<00:00,  3.68it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.24it/s]\n",
            "                 all         128         929       0.623       0.971       0.956       0.715\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   174/299     4.51G   0.03253   0.03938  0.005575   0.07749       130       640: 100%|██████████| 8/8 [00:02<00:00,  3.68it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.97it/s]\n",
            "                 all         128         929       0.621       0.972       0.962       0.722\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   175/299     4.51G   0.03334   0.04032  0.005889   0.07955       166       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.99it/s]\n",
            "                 all         128         929        0.62       0.972       0.959       0.707\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   176/299     4.51G   0.03407   0.04169  0.005908   0.08167       251       640: 100%|██████████| 8/8 [00:02<00:00,  3.73it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.40it/s]\n",
            "                 all         128         929       0.624       0.965       0.956       0.703\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   177/299     4.51G   0.03366   0.04055  0.005562   0.07977       210       640: 100%|██████████| 8/8 [00:02<00:00,  3.73it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.89it/s]\n",
            "                 all         128         929       0.619       0.968       0.953       0.697\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   178/299     4.51G    0.0361   0.03958   0.00631     0.082       193       640: 100%|██████████| 8/8 [00:02<00:00,  3.67it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.71it/s]\n",
            "                 all         128         929       0.632       0.969       0.956       0.706\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   179/299     4.51G   0.03408   0.04007  0.006229   0.08038       223       640: 100%|██████████| 8/8 [00:02<00:00,  3.68it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.22it/s]\n",
            "                 all         128         929       0.628       0.965       0.956       0.717\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   180/299     4.51G    0.0335   0.04064   0.00572   0.07986       175       640: 100%|██████████| 8/8 [00:02<00:00,  3.66it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.42it/s]\n",
            "                 all         128         929       0.622       0.968       0.958       0.702\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   181/299     4.51G   0.03555   0.04053  0.005538   0.08163       209       640: 100%|██████████| 8/8 [00:02<00:00,  3.63it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.06it/s]\n",
            "                 all         128         929       0.622       0.969       0.959       0.716\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   182/299     4.51G   0.03497   0.03681  0.005527   0.07731       176       640: 100%|██████████| 8/8 [00:02<00:00,  3.76it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.99it/s]\n",
            "                 all         128         929       0.629       0.971       0.958       0.721\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   183/299     4.51G   0.03353   0.03881  0.005337   0.07768       221       640: 100%|██████████| 8/8 [00:02<00:00,  3.74it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.72it/s]\n",
            "                 all         128         929       0.633       0.971       0.961       0.723\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   184/299     4.51G   0.03347   0.03925  0.006189    0.0789       209       640: 100%|██████████| 8/8 [00:02<00:00,  3.59it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.86it/s]\n",
            "                 all         128         929       0.641       0.971       0.963       0.728\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   185/299     4.51G   0.03469   0.03929  0.005337   0.07932       192       640: 100%|██████████| 8/8 [00:02<00:00,  3.66it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.07it/s]\n",
            "                 all         128         929       0.641        0.97       0.961       0.723\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   186/299     4.51G   0.03293   0.03437  0.005475   0.07278       164       640: 100%|██████████| 8/8 [00:02<00:00,  3.64it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.52it/s]\n",
            "                 all         128         929       0.646       0.971       0.961       0.731\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   187/299     4.51G   0.03454   0.03509  0.005558   0.07518       187       640: 100%|██████████| 8/8 [00:02<00:00,  3.68it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.43it/s]\n",
            "                 all         128         929       0.647       0.974       0.961       0.719\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   188/299     4.51G   0.03565   0.03933  0.005305   0.08028       216       640: 100%|██████████| 8/8 [00:02<00:00,  3.74it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.01it/s]\n",
            "                 all         128         929       0.649       0.973       0.963        0.73\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   189/299     4.51G   0.03251   0.03565  0.005548   0.07371       170       640: 100%|██████████| 8/8 [00:02<00:00,  3.70it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.70it/s]\n",
            "                 all         128         929       0.643        0.97       0.959       0.722\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   190/299     4.51G   0.03395   0.03586  0.006091    0.0759       118       640: 100%|██████████| 8/8 [00:02<00:00,  3.64it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.86it/s]\n",
            "                 all         128         929       0.644       0.971       0.959       0.726\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   191/299     4.51G    0.0315   0.03413  0.006374   0.07201       242       640: 100%|██████████| 8/8 [00:02<00:00,  3.70it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.02it/s]\n",
            "                 all         128         929       0.639       0.972       0.956       0.721\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   192/299     4.51G   0.03196   0.03698  0.005562   0.07451       199       640: 100%|██████████| 8/8 [00:02<00:00,  3.67it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.69it/s]\n",
            "                 all         128         929       0.648       0.973       0.958       0.729\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   193/299     4.51G   0.03372   0.04051   0.00521   0.07944       239       640: 100%|██████████| 8/8 [00:02<00:00,  3.37it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.07it/s]\n",
            "                 all         128         929       0.637       0.973       0.958       0.728\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   194/299     4.51G   0.03312     0.035  0.005341   0.07346       228       640: 100%|██████████| 8/8 [00:02<00:00,  3.63it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.35it/s]\n",
            "                 all         128         929       0.633       0.974        0.96       0.723\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   195/299     4.51G   0.03374   0.04146  0.005489   0.08069       199       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.68it/s]\n",
            "                 all         128         929       0.628       0.972       0.959       0.708\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   196/299     4.51G   0.03455   0.03627  0.005195   0.07602       247       640: 100%|██████████| 8/8 [00:02<00:00,  3.69it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.71it/s]\n",
            "                 all         128         929        0.64       0.972       0.961        0.72\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   197/299     4.51G   0.03425   0.03787   0.00545   0.07756       185       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.66it/s]\n",
            "                 all         128         929       0.642       0.973       0.963       0.723\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   198/299     4.51G   0.03508   0.03695  0.005364    0.0774       229       640: 100%|██████████| 8/8 [00:02<00:00,  3.74it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.57it/s]\n",
            "                 all         128         929       0.638       0.973       0.961       0.707\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   199/299     4.51G   0.03481   0.03451  0.005198   0.07453       223       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.95it/s]\n",
            "                 all         128         929       0.631       0.971       0.962       0.711\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   200/299     4.51G   0.03367   0.03775  0.005249   0.07668       197       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.75it/s]\n",
            "                 all         128         929       0.631       0.975       0.961       0.724\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   201/299     4.51G   0.03261   0.03798  0.005831   0.07642       179       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.50it/s]\n",
            "                 all         128         929       0.624        0.98       0.962       0.719\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   202/299     4.51G   0.03312   0.03611  0.005326   0.07456       191       640: 100%|██████████| 8/8 [00:02<00:00,  3.64it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.03it/s]\n",
            "                 all         128         929       0.635       0.979       0.963       0.734\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   203/299     4.51G   0.03326    0.0381  0.005058   0.07642       227       640: 100%|██████████| 8/8 [00:02<00:00,  3.68it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.02it/s]\n",
            "                 all         128         929       0.634       0.979       0.961       0.728\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   204/299     4.51G   0.03227   0.03906  0.005308   0.07664       215       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.13it/s]\n",
            "                 all         128         929       0.639       0.977       0.961       0.735\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   205/299     4.51G   0.03387   0.03616  0.005567    0.0756       221       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.78it/s]\n",
            "                 all         128         929       0.633       0.974        0.96       0.726\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   206/299     4.51G   0.03206   0.03737   0.00552   0.07495       228       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.95it/s]\n",
            "                 all         128         929       0.644        0.97       0.954       0.736\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   207/299     4.51G   0.03346   0.03469  0.005644   0.07379       220       640: 100%|██████████| 8/8 [00:02<00:00,  3.62it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.17it/s]\n",
            "                 all         128         929       0.642       0.967       0.955       0.741\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   208/299     4.51G   0.03051   0.03434  0.005189   0.07003       199       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.10it/s]\n",
            "                 all         128         929       0.636       0.967       0.954       0.754\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   209/299     4.51G    0.0319   0.03555   0.00528   0.07273       209       640: 100%|██████████| 8/8 [00:02<00:00,  3.74it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.05it/s]\n",
            "                 all         128         929       0.642       0.971       0.959       0.746\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   210/299     4.51G   0.03169   0.03576  0.005343    0.0728       211       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.08it/s]\n",
            "                 all         128         929       0.633       0.969       0.956       0.755\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   211/299     4.51G   0.03273   0.03516  0.005482   0.07337       212       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.08it/s]\n",
            "                 all         128         929       0.637        0.97       0.959       0.742\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   212/299     4.51G   0.03015   0.03492  0.005701   0.07077       180       640: 100%|██████████| 8/8 [00:02<00:00,  3.75it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.38it/s]\n",
            "                 all         128         929       0.634       0.969       0.958       0.748\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   213/299     4.51G   0.03172   0.03893  0.005502   0.07616       202       640: 100%|██████████| 8/8 [00:02<00:00,  3.64it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.64it/s]\n",
            "                 all         128         929       0.629       0.967       0.957       0.748\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   214/299     4.51G   0.03245   0.03817  0.005593   0.07621       258       640: 100%|██████████| 8/8 [00:02<00:00,  3.67it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.57it/s]\n",
            "                 all         128         929       0.639       0.971       0.962       0.751\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   215/299     4.51G   0.03107   0.03839  0.005347   0.07481       179       640: 100%|██████████| 8/8 [00:02<00:00,  3.64it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.96it/s]\n",
            "                 all         128         929       0.643       0.973       0.964       0.754\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   216/299     4.51G   0.02919   0.03638  0.005063   0.07063       207       640: 100%|██████████| 8/8 [00:02<00:00,  3.60it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.21it/s]\n",
            "                 all         128         929       0.648       0.971        0.96        0.76\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   217/299     4.51G   0.02879   0.03451  0.004615   0.06791       219       640: 100%|██████████| 8/8 [00:02<00:00,  3.66it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.94it/s]\n",
            "                 all         128         929        0.65       0.968        0.96       0.757\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   218/299     4.51G   0.03139   0.03597  0.004977   0.07234       223       640: 100%|██████████| 8/8 [00:02<00:00,  3.55it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.94it/s]\n",
            "                 all         128         929       0.646       0.965       0.959       0.751\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   219/299     4.51G   0.03111   0.03371  0.005268   0.07009       201       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.54it/s]\n",
            "                 all         128         929       0.647       0.972       0.963       0.751\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   220/299     4.51G   0.03239    0.0449  0.005265   0.08256       239       640: 100%|██████████| 8/8 [00:02<00:00,  3.65it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.18it/s]\n",
            "                 all         128         929       0.648       0.973       0.964        0.76\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   221/299     4.51G   0.03137   0.03835  0.005028   0.07474       186       640: 100%|██████████| 8/8 [00:02<00:00,  3.70it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.81it/s]\n",
            "                 all         128         929       0.639       0.973       0.964       0.759\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   222/299     4.51G   0.03199   0.03495   0.00511   0.07206       209       640: 100%|██████████| 8/8 [00:02<00:00,  3.65it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.14it/s]\n",
            "                 all         128         929       0.645       0.974       0.963       0.761\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   223/299     4.51G   0.03178   0.03504  0.005176     0.072       211       640: 100%|██████████| 8/8 [00:02<00:00,  3.63it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.63it/s]\n",
            "                 all         128         929       0.643       0.974       0.962       0.762\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   224/299     4.51G   0.03001   0.03442  0.005022   0.06945       163       640: 100%|██████████| 8/8 [00:02<00:00,  3.69it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.73it/s]\n",
            "                 all         128         929       0.644       0.973       0.961        0.76\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   225/299     4.51G   0.03201   0.03735  0.005494   0.07485       213       640: 100%|██████████| 8/8 [00:02<00:00,  3.68it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.99it/s]\n",
            "                 all         128         929       0.639       0.976       0.961       0.746\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   226/299     4.51G   0.02983   0.03224   0.00505   0.06713       192       640: 100%|██████████| 8/8 [00:02<00:00,  3.70it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.72it/s]\n",
            "                 all         128         929       0.651       0.973       0.964        0.77\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   227/299     4.51G   0.02789   0.03239  0.004914   0.06519       181       640: 100%|██████████| 8/8 [00:02<00:00,  3.59it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.08it/s]\n",
            "                 all         128         929       0.653       0.971       0.962       0.766\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   228/299     4.51G   0.03107   0.03831  0.005703   0.07508       161       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.13it/s]\n",
            "                 all         128         929       0.659        0.97       0.963       0.767\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   229/299     4.51G   0.02949   0.03693  0.005284    0.0717       178       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.30it/s]\n",
            "                 all         128         929       0.658       0.971       0.964       0.775\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   230/299     4.51G    0.0291   0.03297  0.004843   0.06691       123       640: 100%|██████████| 8/8 [00:02<00:00,  3.77it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.12it/s]\n",
            "                 all         128         929       0.659       0.971       0.963       0.765\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   231/299     4.51G   0.03058   0.03584  0.004689   0.07111       203       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.59it/s]\n",
            "                 all         128         929       0.661       0.968       0.961       0.768\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   232/299     4.51G   0.02995   0.03422  0.004893   0.06905       174       640: 100%|██████████| 8/8 [00:02<00:00,  3.67it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.93it/s]\n",
            "                 all         128         929       0.661       0.973       0.963        0.76\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   233/299     4.51G   0.02985   0.03687  0.005002   0.07173       166       640: 100%|██████████| 8/8 [00:02<00:00,  3.73it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.81it/s]\n",
            "                 all         128         929       0.658       0.972       0.962        0.76\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   234/299     4.51G   0.03104   0.03328  0.005455   0.06977       189       640: 100%|██████████| 8/8 [00:02<00:00,  3.62it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.90it/s]\n",
            "                 all         128         929        0.66       0.975       0.967       0.761\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   235/299     4.51G   0.03124   0.03853  0.005478   0.07526       168       640: 100%|██████████| 8/8 [00:02<00:00,  3.63it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.09it/s]\n",
            "                 all         128         929       0.662       0.973       0.966       0.767\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   236/299     4.51G   0.02891   0.03442  0.004511   0.06784       177       640: 100%|██████████| 8/8 [00:02<00:00,  3.70it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.13it/s]\n",
            "                 all         128         929       0.652       0.974       0.968       0.768\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   237/299     4.51G   0.02935   0.03211  0.004776   0.06624       121       640: 100%|██████████| 8/8 [00:02<00:00,  3.63it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.06it/s]\n",
            "                 all         128         929       0.643        0.97       0.963       0.773\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   238/299     4.51G    0.0298    0.0362  0.005066   0.07106       176       640: 100%|██████████| 8/8 [00:02<00:00,  3.63it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.83it/s]\n",
            "                 all         128         929        0.64       0.972       0.962        0.77\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   239/299     4.51G   0.03009   0.03225  0.004413   0.06675       148       640: 100%|██████████| 8/8 [00:02<00:00,  3.76it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.61it/s]\n",
            "                 all         128         929       0.651       0.973       0.964       0.777\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   240/299     4.51G   0.02912   0.03346  0.004483   0.06706       222       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.03it/s]\n",
            "                 all         128         929       0.648       0.974       0.966       0.772\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   241/299     4.51G   0.02993   0.03688  0.005111   0.07193       267       640: 100%|██████████| 8/8 [00:02<00:00,  3.59it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.08it/s]\n",
            "                 all         128         929        0.65       0.975       0.966       0.769\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   242/299     4.51G   0.02969   0.03402  0.004237   0.06795       179       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.12it/s]\n",
            "                 all         128         929       0.647       0.974       0.966       0.769\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   243/299     4.51G   0.02941   0.03399  0.004397    0.0678       189       640: 100%|██████████| 8/8 [00:02<00:00,  3.61it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.53it/s]\n",
            "                 all         128         929       0.648       0.974       0.966       0.768\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   244/299     4.51G   0.03018   0.03801  0.004622   0.07281       272       640: 100%|██████████| 8/8 [00:02<00:00,  3.70it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.68it/s]\n",
            "                 all         128         929       0.653       0.974       0.967       0.768\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   245/299     4.51G   0.03145    0.0355  0.004366   0.07131       208       640: 100%|██████████| 8/8 [00:02<00:00,  3.75it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.08it/s]\n",
            "                 all         128         929       0.652       0.974       0.967       0.779\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   246/299     4.51G   0.02997   0.03641  0.004538   0.07093       189       640: 100%|██████████| 8/8 [00:02<00:00,  3.65it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.31it/s]\n",
            "                 all         128         929       0.652       0.974       0.967       0.771\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   247/299     4.51G   0.03062   0.03608  0.004437   0.07114       133       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.11it/s]\n",
            "                 all         128         929       0.658       0.975       0.967        0.77\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   248/299     4.51G    0.0298     0.036  0.004372   0.07017       159       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.95it/s]\n",
            "                 all         128         929       0.661       0.976       0.968       0.763\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   249/299     4.51G   0.03029   0.03828  0.004254   0.07282       272       640: 100%|██████████| 8/8 [00:02<00:00,  3.57it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.16it/s]\n",
            "                 all         128         929       0.663       0.975       0.966       0.769\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   250/299     4.51G   0.03015   0.03279   0.00537    0.0683       184       640: 100%|██████████| 8/8 [00:02<00:00,  3.76it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.14it/s]\n",
            "                 all         128         929       0.664       0.977       0.965       0.764\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   251/299     4.51G   0.02754   0.03094  0.004842   0.06333       168       640: 100%|██████████| 8/8 [00:02<00:00,  3.68it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.10it/s]\n",
            "                 all         128         929       0.658       0.978       0.966       0.767\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   252/299     4.51G   0.02868   0.03224  0.004613   0.06553       235       640: 100%|██████████| 8/8 [00:02<00:00,  3.73it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.69it/s]\n",
            "                 all         128         929       0.655       0.977       0.965       0.767\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   253/299     4.51G   0.02721   0.03405  0.004569   0.06583       220       640: 100%|██████████| 8/8 [00:02<00:00,  3.69it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.64it/s]\n",
            "                 all         128         929       0.654       0.977       0.968       0.771\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   254/299     4.51G   0.02892   0.03289  0.004388    0.0662       242       640: 100%|██████████| 8/8 [00:02<00:00,  3.70it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.71it/s]\n",
            "                 all         128         929        0.66       0.978       0.967       0.771\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   255/299     4.51G   0.02946   0.03266  0.004921   0.06704       204       640: 100%|██████████| 8/8 [00:02<00:00,  3.58it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.83it/s]\n",
            "                 all         128         929       0.659       0.976       0.967       0.774\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   256/299     4.51G   0.02938   0.03458  0.004762   0.06872       224       640: 100%|██████████| 8/8 [00:02<00:00,  3.61it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.06it/s]\n",
            "                 all         128         929       0.664       0.975       0.965       0.772\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   257/299     4.51G   0.02905    0.0302  0.004532   0.06379       208       640: 100%|██████████| 8/8 [00:02<00:00,  3.68it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.03it/s]\n",
            "                 all         128         929       0.667       0.975       0.965       0.771\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   258/299     4.51G   0.02921   0.03497  0.004568   0.06876       208       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.05it/s]\n",
            "                 all         128         929       0.669       0.975       0.967       0.773\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   259/299     4.51G   0.02984   0.03554  0.005072   0.07045       225       640: 100%|██████████| 8/8 [00:02<00:00,  3.61it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.05it/s]\n",
            "                 all         128         929       0.662       0.975       0.965       0.778\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   260/299     4.51G   0.02947   0.03487  0.004759    0.0691       185       640: 100%|██████████| 8/8 [00:02<00:00,  3.58it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.89it/s]\n",
            "                 all         128         929       0.663       0.975       0.967       0.775\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   261/299     4.51G    0.0287   0.03148  0.004671   0.06485       263       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.09it/s]\n",
            "                 all         128         929       0.665       0.976       0.967       0.782\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   262/299     4.51G   0.02899   0.03485   0.00499   0.06883       174       640: 100%|██████████| 8/8 [00:02<00:00,  3.74it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.32it/s]\n",
            "                 all         128         929       0.667       0.975       0.968        0.78\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   263/299     4.51G   0.02817   0.03122  0.004366   0.06376       184       640: 100%|██████████| 8/8 [00:02<00:00,  3.67it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.18it/s]\n",
            "                 all         128         929       0.661       0.974       0.968       0.781\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   264/299     4.51G   0.02866   0.03371   0.00492   0.06729       129       640: 100%|██████████| 8/8 [00:02<00:00,  3.58it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.13it/s]\n",
            "                 all         128         929       0.664       0.975       0.968       0.789\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   265/299     4.51G   0.02686   0.03282  0.004042   0.06373       196       640: 100%|██████████| 8/8 [00:02<00:00,  3.68it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.87it/s]\n",
            "                 all         128         929       0.653       0.971       0.967        0.79\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   266/299     4.51G   0.02846   0.03085    0.0047   0.06401       159       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.10it/s]\n",
            "                 all         128         929       0.659       0.971       0.967       0.793\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   267/299     4.51G   0.02748   0.03144  0.004289   0.06321       226       640: 100%|██████████| 8/8 [00:02<00:00,  3.69it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.06it/s]\n",
            "                 all         128         929       0.657       0.975       0.967        0.79\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   268/299     4.51G   0.02815   0.03175  0.004436   0.06433       179       640: 100%|██████████| 8/8 [00:02<00:00,  3.67it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.03it/s]\n",
            "                 all         128         929        0.66       0.976       0.968       0.794\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   269/299     4.51G   0.02766   0.03208  0.004027   0.06376       223       640: 100%|██████████| 8/8 [00:02<00:00,  3.61it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.35it/s]\n",
            "                 all         128         929       0.661       0.975       0.968        0.79\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   270/299     4.51G   0.02794   0.03609  0.004436   0.06846       155       640: 100%|██████████| 8/8 [00:02<00:00,  3.58it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.88it/s]\n",
            "                 all         128         929       0.664       0.976       0.968       0.792\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   271/299     4.51G   0.02863    0.0319  0.004807   0.06534       104       640: 100%|██████████| 8/8 [00:02<00:00,  3.62it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.86it/s]\n",
            "                 all         128         929       0.662       0.976       0.967       0.785\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   272/299     4.51G   0.02992   0.03326  0.004418    0.0676       228       640: 100%|██████████| 8/8 [00:02<00:00,  3.56it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.48it/s]\n",
            "                 all         128         929       0.658       0.977       0.967       0.779\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   273/299     4.51G   0.03064   0.03568  0.004334   0.07065       309       640: 100%|██████████| 8/8 [00:02<00:00,  3.64it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.91it/s]\n",
            "                 all         128         929       0.663       0.976       0.968        0.79\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   274/299     4.51G   0.03003    0.0314  0.004449   0.06588       189       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.75it/s]\n",
            "                 all         128         929       0.663       0.975       0.967       0.777\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   275/299     4.51G   0.02958   0.03205  0.004162   0.06578       218       640: 100%|██████████| 8/8 [00:02<00:00,  3.61it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.21it/s]\n",
            "                 all         128         929       0.671       0.977       0.968       0.786\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   276/299     4.51G   0.02927   0.03221  0.004344   0.06582       249       640: 100%|██████████| 8/8 [00:02<00:00,  3.73it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.86it/s]\n",
            "                 all         128         929       0.676       0.976       0.967       0.783\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   277/299     4.51G   0.02847   0.03263  0.004072   0.06517       219       640: 100%|██████████| 8/8 [00:02<00:00,  3.69it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.98it/s]\n",
            "                 all         128         929       0.675       0.973       0.967       0.786\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   278/299     4.51G   0.02775   0.03293  0.004226   0.06491       237       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.98it/s]\n",
            "                 all         128         929        0.68       0.975       0.967       0.782\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   279/299     4.51G   0.02937   0.03453  0.004857   0.06876       198       640: 100%|██████████| 8/8 [00:02<00:00,  3.58it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.33it/s]\n",
            "                 all         128         929       0.673       0.975       0.966       0.786\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   280/299     4.51G   0.02776   0.03158  0.004254   0.06359       110       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.01it/s]\n",
            "                 all         128         929       0.665       0.977       0.966       0.785\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   281/299     4.51G   0.03084   0.03428  0.004973   0.07009       150       640: 100%|██████████| 8/8 [00:02<00:00,  3.70it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.93it/s]\n",
            "                 all         128         929       0.667       0.976       0.967       0.794\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   282/299     4.51G   0.02796   0.03259  0.004111   0.06466       175       640: 100%|██████████| 8/8 [00:02<00:00,  3.62it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.07it/s]\n",
            "                 all         128         929       0.667       0.977       0.967       0.793\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   283/299     4.51G   0.02705   0.02839  0.004076   0.05952       163       640: 100%|██████████| 8/8 [00:02<00:00,  3.77it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.82it/s]\n",
            "                 all         128         929       0.665       0.978       0.967         0.8\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   284/299     4.51G   0.02766   0.03697  0.004497   0.06912       291       640: 100%|██████████| 8/8 [00:02<00:00,  3.69it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.86it/s]\n",
            "                 all         128         929       0.667       0.978       0.967       0.788\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   285/299     4.51G   0.02882   0.03234  0.004528   0.06569       158       640: 100%|██████████| 8/8 [00:02<00:00,  3.67it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.13it/s]\n",
            "                 all         128         929       0.666       0.979       0.968       0.796\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   286/299     4.51G   0.02655     0.031  0.004058   0.06161       184       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.94it/s]\n",
            "                 all         128         929       0.662       0.979       0.968       0.792\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   287/299     4.51G   0.02787   0.03168  0.004392   0.06395       191       640: 100%|██████████| 8/8 [00:02<00:00,  3.60it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.84it/s]\n",
            "                 all         128         929       0.664       0.975       0.967       0.801\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   288/299     4.51G   0.02869   0.03579  0.004231   0.06872       223       640: 100%|██████████| 8/8 [00:02<00:00,  3.68it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.63it/s]\n",
            "                 all         128         929       0.666        0.98        0.97       0.796\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   289/299     4.51G   0.02856   0.03177  0.004318   0.06465       158       640: 100%|██████████| 8/8 [00:02<00:00,  3.64it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.56it/s]\n",
            "                 all         128         929       0.666       0.976       0.968       0.797\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   290/299     4.51G   0.02919    0.0334  0.004595   0.06718       256       640: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.10it/s]\n",
            "                 all         128         929       0.673       0.977       0.969       0.796\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   291/299     4.51G   0.02919   0.03293  0.004949   0.06706       238       640: 100%|██████████| 8/8 [00:02<00:00,  3.66it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.83it/s]\n",
            "                 all         128         929       0.672       0.974       0.968       0.794\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   292/299     4.51G   0.02757   0.03319  0.004447   0.06521       206       640: 100%|██████████| 8/8 [00:02<00:00,  3.75it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.81it/s]\n",
            "                 all         128         929       0.677       0.976        0.97       0.797\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   293/299     4.51G   0.02821    0.0358  0.004197   0.06821       199       640: 100%|██████████| 8/8 [00:02<00:00,  3.53it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.68it/s]\n",
            "                 all         128         929       0.684       0.976        0.97         0.8\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   294/299     4.51G   0.02796   0.03393  0.004671   0.06656       188       640: 100%|██████████| 8/8 [00:02<00:00,  3.73it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.43it/s]\n",
            "                 all         128         929       0.686       0.977       0.969       0.795\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   295/299     4.51G   0.02932   0.03204  0.004289   0.06565       253       640: 100%|██████████| 8/8 [00:02<00:00,  3.63it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.04it/s]\n",
            "                 all         128         929       0.682       0.977       0.969       0.794\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   296/299     4.51G    0.0269   0.03136  0.004083   0.06234       180       640: 100%|██████████| 8/8 [00:02<00:00,  3.65it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  5.57it/s]\n",
            "                 all         128         929       0.676       0.974       0.968       0.793\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   297/299     4.51G   0.02915   0.03452  0.004233    0.0679       235       640: 100%|██████████| 8/8 [00:02<00:00,  3.64it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.10it/s]\n",
            "                 all         128         929       0.672       0.974       0.967       0.797\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   298/299     4.51G   0.02706   0.03422  0.004324    0.0656       194       640: 100%|██████████| 8/8 [00:02<00:00,  3.63it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:01<00:00,  6.06it/s]\n",
            "                 all         128         929       0.671       0.977       0.967         0.8\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
            "   299/299     4.51G   0.02792   0.03442  0.003849   0.06619       274       640: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 8/8 [00:03<00:00,  2.03it/s]\n",
            "                 all         128         929       0.673       0.974       0.967       0.798\n",
            "Images sizes do not match. This will causes images to be display incorrectly in the UI.\n",
            "Optimizer stripped from runs/train/exp2/weights/last.pt, 15.2MB\n",
            "Images sizes do not match. This will causes images to be display incorrectly in the UI.\n",
            "300 epochs completed in 0.314 hours.\n",
            "\n",
            "\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Waiting for W&B process to finish, PID 430\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Program ended successfully.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                                                                                \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Find user logs for this run at: /content/yolov5/wandb/run-20201224_014226-29or7gxf/logs/debug.log\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Find internal logs for this run at: /content/yolov5/wandb/run-20201224_014226-29or7gxf/logs/debug-internal.log\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  _step 3904\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:               _runtime 1136\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:             _timestamp 1608775282\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         train/box_loss 0.02792\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         train/obj_loss 0.03442\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         train/cls_loss 0.00385\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:      metrics/precision 0.67345\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         metrics/recall 0.97435\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:        metrics/mAP_0.5 0.9667\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:   metrics/mAP_0.5:0.95 0.79767\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           val/box_loss 0.02129\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           val/obj_loss 0.01778\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           val/cls_loss 0.00247\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  x/lr0 0.002\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  x/lr1 0.002\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  x/lr2 0.002\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  _step ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:               _runtime ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:             _timestamp ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         train/box_loss ▇█▆▆▅▆▆▅▅▆▆▆▅▅▆▆▆█▆▆▅▅▅▃▄▃▄▃▃▃▁▃▂▁▂▁▂▁▂▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         train/obj_loss ██▆▇▆▆▅▄▄▄▄▄▄▄▅▃▄▅▄▄▄▃▄▃▃▂▂▂▃▂▂▃▂▂▂▂▂▁▂▂\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         train/cls_loss ██▆▅▅▄▄▃▃▃▃▃▃▃▃▃▃▃▂▃▃▃▂▂▂▂▂▂▂▂▂▂▁▂▂▁▁▁▂▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:      metrics/precision ▁▁▂▃▄▄▄▅▃▅▅▅▆▆▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇▇██▇███████\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         metrics/recall ▁▂▃▄▅▅▆▇▇▆▇▇▇▇▇▇▇▇▇▇████████████████████\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:        metrics/mAP_0.5 ▁▂▃▄▅▆▆▆▆▆▇▇▇▇▇▇▇▇▇▇▇█▇█████████████████\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:   metrics/mAP_0.5:0.95 ▁▁▂▃▄▄▅▅▃▄▄▅▄▄▄▅▅▄▄▅▅▅▆▆▆▆▆▇▇▇▇▇▇▇██████\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           val/box_loss ██▇▇▇▇▆▅█▇▆▆▆▆▇▆▆▆▇▆▅▅▅▃▄▃▄▃▃▂▂▂▂▂▂▁▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           val/obj_loss ██▇▆▅▅▄▄▄▄▃▃▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           val/cls_loss █▇▅▄▃▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  x/lr0 ▁▁▂▃▃▄▄▅▆▆▆▇▇▇████▇▇▇▇▆▆▆▅▅▅▄▄▄▄▃▃▃▃▃▃▃▃\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  x/lr1 ▁▁▂▃▃▄▄▅▆▆▆▇▇▇████▇▇▇▇▆▆▆▅▅▅▄▄▄▄▃▃▃▃▃▃▃▃\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  x/lr2 ██▇▇▇▆▆▅▅▄▄▄▃▃▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 46 media file(s), 0 artifact file(s) and 0 other file(s)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Synced \u001b[33mexp2\u001b[0m: \u001b[34mhttps://wandb.ai/wmh02240/YOLOv5/runs/29or7gxf\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "15glLzbQx5u0"
      },
      "source": [
        "# 4. Visualize"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DLI1JmHU7B0l"
      },
      "source": [
        "## Weights & Biases Logging 🌟 NEW\n",
        "\n",
        "[Weights & Biases](https://www.wandb.com/) (W&B) is now integrated with YOLOv5 for real-time visualization and cloud logging of training runs. This allows for better run comparison and introspection, as well improved visibility and collaboration for teams. To enable W&B `pip install wandb`, and then train normally (you will be guided through setup on first use). \n",
        "\n",
        "During training you will see live updates at [https://wandb.ai/home](https://wandb.ai/home), and you can create and share detailed [Reports](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY) of your results. For more information see the [YOLOv5 Weights & Biases Tutorial](https://github.com/ultralytics/yolov5/issues/1289). \n",
        "\n",
        "<img src=\"https://user-images.githubusercontent.com/26833433/98184457-bd3da580-1f0a-11eb-8461-95d908a71893.jpg\" width=\"800\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-WPvRbS5Swl6"
      },
      "source": [
        "## Local Logging\n",
        "\n",
        "All results are logged by default to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc. View train and test jpgs to see mosaics, labels, predictions and augmentation effects. Note a **Mosaic Dataloader** is used for training (shown below), a new concept developed by Ultralytics and first featured in [YOLOv4](https://arxiv.org/abs/2004.10934)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "riPdhraOTCO0"
      },
      "source": [
        "Image(filename='runs/train/exp/train_batch0.jpg', width=800)  # train batch 0 mosaics and labels\n",
        "Image(filename='runs/train/exp/test_batch0_labels.jpg', width=800)  # test batch 0 labels\n",
        "Image(filename='runs/train/exp/test_batch0_pred.jpg', width=800)  # test batch 0 predictions"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OYG4WFEnTVrI"
      },
      "source": [
        "> <img src=\"https://user-images.githubusercontent.com/26833433/83667642-90fcb200-a583-11ea-8fa3-338bbf7da194.jpeg\" width=\"750\">  \n",
        "`train_batch0.jpg` shows train batch 0 mosaics and labels\n",
        "\n",
        "> <img src=\"https://user-images.githubusercontent.com/26833433/83667626-8c37fe00-a583-11ea-997b-0923fe59b29b.jpeg\" width=\"750\">  \n",
        "`test_batch0_labels.jpg` shows test batch 0 labels\n",
        "\n",
        "> <img src=\"https://user-images.githubusercontent.com/26833433/83667635-90641b80-a583-11ea-8075-606316cebb9c.jpeg\" width=\"750\">  \n",
        "`test_batch0_pred.jpg` shows test batch 0 _predictions_\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7KN5ghjE6ZWh"
      },
      "source": [
        "Training losses and performance metrics are also logged to [Tensorboard](https://www.tensorflow.org/tensorboard) and a custom `results.txt` logfile which is plotted as `results.png` (below) after training completes. Here we show YOLOv5s trained on COCO128 to 300 epochs, starting from scratch (blue), and from pretrained `--weights yolov5s.pt` (orange)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4WMtw3KdmLKY",
        "outputId": "5ade1fea-7960-4a15-ee37-4c3dc5845b06"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f_9sOi2tndJa"
      },
      "source": [
        "# 新段落"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MDznIqPF7nk3"
      },
      "source": [
        "from utils.plots import plot_results \n",
        "plot_results(save_dir='runs/train/exp')  # plot all results*.txt as results.png\n",
        "Image(filename='runs/train/exp/results.png', width=800)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lfrEegCSW3fK"
      },
      "source": [
        "<img src=\"https://user-images.githubusercontent.com/26833433/97808309-8182b180-1c66-11eb-8461-bffe1a79511d.png\" width=\"800\">\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",
        "- **Google Colab Notebook** with free GPU: <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",
        "- **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov5](https://www.kaggle.com/ultralytics/yolov5)\n",
        "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) \n",
        "- **Docker Image** https://hub.docker.com/r/ultralytics/yolov5. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IEijrePND_2I"
      },
      "source": [
        "# Appendix\n",
        "\n",
        "Optional extras below. Unit tests validate repo functionality and should be run on any PRs submitted.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gI6NoBev8Ib1"
      },
      "source": [
        "# Re-clone repo\n",
        "%cd ..\n",
        "%rm -rf yolov5 && git clone https://github.com/ultralytics/yolov5\n",
        "%cd yolov5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mcKoSIK2WSzj"
      },
      "source": [
        "# Test all\n",
        "%%shell\n",
        "for x in s m l x; do\n",
        "  python test.py --weights yolov5$x.pt --data coco.yaml --img 640\n",
        "done"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FGH0ZjkGjejy"
      },
      "source": [
        "# Unit tests\n",
        "%%shell\n",
        "export PYTHONPATH=\"$PWD\"  # to run *.py. files in subdirectories\n",
        "\n",
        "rm -rf runs  # remove runs/\n",
        "for m in yolov5s; do  # models\n",
        "  python train.py --weights $m.pt --epochs 3 --img 320 --device 0  # train pretrained\n",
        "  python train.py --weights '' --cfg $m.yaml --epochs 3 --img 320 --device 0  # train scratch\n",
        "  for d in 0 cpu; do  # devices\n",
        "    python detect.py --weights $m.pt --device $d  # detect official\n",
        "    python detect.py --weights runs/train/exp/weights/best.pt --device $d  # detect custom\n",
        "    python test.py --weights $m.pt --device $d # test official\n",
        "    python test.py --weights runs/train/exp/weights/best.pt --device $d # test custom\n",
        "  done\n",
        "  python hubconf.py  # hub\n",
        "  python models/yolo.py --cfg $m.yaml  # inspect\n",
        "  python models/export.py --weights $m.pt --img 640 --batch 1  # export\n",
        "done"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gogI-kwi3Tye"
      },
      "source": [
        "# Profile\n",
        "from utils.torch_utils import profile \n",
        "\n",
        "m1 = lambda x: x * torch.sigmoid(x)\n",
        "m2 = torch.nn.SiLU()\n",
        "profile(x=torch.randn(16, 3, 640, 640), ops=[m1, m2], n=100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BSgFCAcMbk1R"
      },
      "source": [
        "# VOC\n",
        "for b, m in zip([64, 48, 32, 16], ['yolov5s', 'yolov5m', 'yolov5l', 'yolov5x']):  # zip(batch_size, model)\n",
        "  !python train.py --batch {b} --weights {m}.pt --data voc.yaml --epochs 50 --cache --img 512 --nosave --hyp hyp.finetune.yaml --project VOC --name {m}"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}