YOLOv4_Roboflow_PyTorch.ipynb

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  "metadata": {
    "colab": {
      "name": "YOLOv4-Roboflow-PyTorch.ipynb",
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      "name": "python3",
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sayan0506/Build-and-Implement-ML-pipeline-an-Object-Detector-for-FMCG-using-YOLOv4/blob/main/YOLOv4_Roboflow_PyTorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5q3s60oQjmHa"
      },
      "source": [
        "# Introduction\n",
        "\n",
        "In this notebook, we implement [YOLOv4](https://arxiv.org/pdf/2004.10934.pdf) for training on your own dataset in PyTorch.\n",
        "\n",
        "We also recommend reading our blog post on [Training YOLOv4 on custom data](https://blog.roboflow.ai/training-yolov4-on-a-custom-dataset/) side by side.\n",
        "\n",
        "We will take the following steps to implement YOLOv4 on our custom data:\n",
        "* Set up YOLOv4 environment\n",
        "* Download Custom Dataset via Roboflow\n",
        "* Train Custom YOLOv4 detector\n",
        "* Reload Custom YOLOv4 detector weights for inference\n",
        "\n",
        "When you are done you will have a custom detector that you can use. It will make inference like this:\n",
        "\n",
        "#### ![Chesse Image](https://i.imgur.com/cGOxffX.png)\n",
        "\n",
        "### **Reach out for support**\n",
        "\n",
        "If you run into any hurdles on your own data set or just want to share some cool results in your own domain, [reach out!](https://roboflow.ai/contact) \n",
        "\n",
        "\n",
        "#### ![Roboflow Workmark](https://i.imgur.com/WHFqYSJ.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E8Pr04DEjBtk"
      },
      "source": [
        "# Set up YOLOv4 Environment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rX_tj9SLtake",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "886d283d-90f4-48e7-a98b-260421844371"
      },
      "source": [
        "!git clone https://github.com/roboflow-ai/pytorch-YOLOv4.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'pytorch-YOLOv4'...\n",
            "remote: Enumerating objects: 395, done.\u001b[K\n",
            "remote: Total 395 (delta 0), reused 0 (delta 0), pack-reused 395\u001b[K\n",
            "Receiving objects: 100% (395/395), 922.22 KiB | 20.96 MiB/s, done.\n",
            "Resolving deltas: 100% (224/224), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2dcOE23gvbHj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b276621-8b9b-43b4-e202-c7e8d9ad4121"
      },
      "source": [
        "%cd /content/pytorch-YOLOv4\n",
        "!pip install -r requirements.txt\n",
        "# This restarts the runtime--ignore any error messages\n",
        "import os\n",
        "os.kill(os.getpid(), 9)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/pytorch-YOLOv4\n",
            "Collecting numpy==1.18.2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b7/ce/d0b92f0283faa4da76ea82587ff9da70104e81f59ba14f76c87e4196254e/numpy-1.18.2-cp37-cp37m-manylinux1_x86_64.whl (20.2MB)\n",
            "\u001b[K     |████████████████████████████████| 20.2MB 1.3MB/s \n",
            "\u001b[?25hCollecting torch==1.4.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1a/3b/fa92ece1e58a6a48ec598bab327f39d69808133e5b2fb33002ca754e381e/torch-1.4.0-cp37-cp37m-manylinux1_x86_64.whl (753.4MB)\n",
            "\u001b[K     |████████████████████████████████| 753.4MB 21kB/s \n",
            "\u001b[?25hRequirement already satisfied: scikit_image==0.16.2 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 3)) (0.16.2)\n",
            "Collecting matplotlib==2.2.3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/52/46/ff47fea8e5c528c497fc385c95887131c4319a3411814ba9a766b66a9367/matplotlib-2.2.3-cp37-cp37m-manylinux1_x86_64.whl (12.6MB)\n",
            "\u001b[K     |████████████████████████████████| 12.6MB 29.3MB/s \n",
            "\u001b[?25hCollecting tqdm==4.43.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/47/55/fd9170ba08a1a64a18a7f8a18f088037316f2a41be04d2fe6ece5a653e8f/tqdm-4.43.0-py2.py3-none-any.whl (59kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 7.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: easydict==1.9 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 6)) (1.9)\n",
            "Requirement already satisfied: Pillow==7.1.2 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 7)) (7.1.2)\n",
            "Collecting tensorboardX\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/07/84/46421bd3e0e89a92682b1a38b40efc22dafb6d8e3d947e4ceefd4a5fabc7/tensorboardX-2.2-py2.py3-none-any.whl (120kB)\n",
            "\u001b[K     |████████████████████████████████| 122kB 59.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit_image==0.16.2->-r requirements.txt (line 3)) (2.4.1)\n",
            "Requirement already satisfied: scipy>=0.19.0 in /usr/local/lib/python3.7/dist-packages (from scikit_image==0.16.2->-r requirements.txt (line 3)) (1.4.1)\n",
            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit_image==0.16.2->-r requirements.txt (line 3)) (2.5)\n",
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            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==2.2.3->-r requirements.txt (line 4)) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==2.2.3->-r requirements.txt (line 4)) (1.3.1)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib==2.2.3->-r requirements.txt (line 4)) (1.15.0)\n",
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            "Requirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.7/dist-packages (from tensorboardX->-r requirements.txt (line 8)) (3.12.4)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit_image==0.16.2->-r requirements.txt (line 3)) (4.4.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.8.0->tensorboardX->-r requirements.txt (line 8)) (54.2.0)\n",
            "\u001b[31mERROR: torchvision 0.9.1+cu101 has requirement torch==1.8.1, but you'll have torch 1.4.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: torchtext 0.9.1 has requirement torch==1.8.1, but you'll have torch 1.4.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 2.4.1 has requirement numpy~=1.19.2, but you'll have numpy 1.18.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: plotnine 0.6.0 has requirement matplotlib>=3.1.1, but you'll have matplotlib 2.2.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: mizani 0.6.0 has requirement matplotlib>=3.1.1, but you'll have matplotlib 2.2.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Installing collected packages: numpy, torch, matplotlib, tqdm, tensorboardX\n",
            "  Found existing installation: numpy 1.19.5\n",
            "    Uninstalling numpy-1.19.5:\n",
            "      Successfully uninstalled numpy-1.19.5\n",
            "  Found existing installation: torch 1.8.1+cu101\n",
            "    Uninstalling torch-1.8.1+cu101:\n",
            "      Successfully uninstalled torch-1.8.1+cu101\n",
            "  Found existing installation: matplotlib 3.2.2\n",
            "    Uninstalling matplotlib-3.2.2:\n",
            "      Successfully uninstalled matplotlib-3.2.2\n",
            "  Found existing installation: tqdm 4.41.1\n",
            "    Uninstalling tqdm-4.41.1:\n",
            "      Successfully uninstalled tqdm-4.41.1\n",
            "Successfully installed matplotlib-2.2.3 numpy-1.18.2 tensorboardX-2.2 torch-1.4.0 tqdm-4.43.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UYIS9QfDyCK9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2876af2e-a74c-48aa-f6a0-8ea1a1b7bef7"
      },
      "source": [
        "# download yolov4 weights that have already been converted to PyTorch\n",
        "%cd /content/pytorch-YOLOv4/cfg/\n",
        "!gdown https://drive.google.com/uc?id=1fcbR0bWzYfIEdLJPzOsn4R5mlvR6IQyA"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/pytorch-YOLOv4/cfg\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1fcbR0bWzYfIEdLJPzOsn4R5mlvR6IQyA\n",
            "To: /content/pytorch-YOLOv4/cfg/yolov4.conv.137.pth\n",
            "170MB [00:00, 216MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xvRAbH2PjLfg"
      },
      "source": [
        "# Download Custom Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1YW7jPF1BOAw"
      },
      "source": [
        "## Export Your Dataset from Roboflow\n",
        "\n",
        "Roboflow enables you to export your dataset in any format you need - including for this notebook.\n",
        "\n",
        "Create a [free account](https://app.roboflow.ai). Upload your private dataset. Generate a version (applying any preprocessing and augmentations you desire). Create an export. Select **YOLOv4 PyTorch** as the export format. Click **\"Show Download code\"**, copy your link, and paste it in the next cell. Magic."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lui6tBJKvqVS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "befaeaa5-684a-41df-a10e-573d196652cb"
      },
      "source": [
        "%cd /content/\n",
        "# REPLACE this link with your Roboflow dataset (export as YOLOv4 PyTorch format)\n",
        "!curl -L \"YOUR LINK HERE\" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n",
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
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            "100   897  100   897    0     0   1515      0 --:--:-- --:--:-- --:--:--  1512\n",
            "100 77.7M  100 77.7M    0     0  40.0M      0  0:00:01  0:00:01 --:--:-- 78.4M\n",
            "Archive:  roboflow.zip\n",
            " extracting: README.dataset.txt      \n",
            " extracting: README.roboflow.txt     \n",
            "   creating: test/\n",
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            " extracting: test/_annotations.txt   \n",
            " extracting: test/_classes.txt       \n",
            " extracting: test/a3863d0be6002c21b20ac88817b2c56f_jpg.rf.e421134b139d57e02e7df9468a35c1fb.jpg  \n",
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            "   creating: train/\n",
            " extracting: train/00bc0cacffdebe6b11bdeec56f63ee49_jpg.rf.fbcc3af401eca6bf01b4d031af33c1fc.jpg  \n",
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            " extracting: train/1a8a4abcba7c4ead35c01f05b9fae8e5_jpg.rf.8e60639ed4cbd253966be89b5a242834.jpg  \n",
            " extracting: train/1b4ccdf7d5ff45dc6c3885243bde5af2_jpg.rf.3ad9dc5fdd86856e5dc10e776f695818.jpg  \n",
            " extracting: train/22e74efb18b2d88fba63d25a61bf5f97_jpg.rf.72cda26bc69e8611a023dea25871bb26.jpg  \n",
            " extracting: train/23988893ef7381fece6d1ef32ef5428f_jpg.rf.c5e09b6b2996b5453a95e7facd4b4683.jpg  \n",
            " extracting: train/239c409d5c09b493fed01a70a3cda4bc_jpg.rf.a745921b91958a16c18c92b259aca5ea.jpg  \n",
            " extracting: train/247f9cf35a263dc7dd7886b187fd5480_jpg.rf.d77e03ae61805408a2c3f81f2217d3ca.jpg  \n",
            " extracting: train/254f92b18b2a81f88b85e7aed3cabc61_jpg.rf.a5d3749856054f63addc3c8282336d11.jpg  \n",
            " extracting: train/26d663ab5ffbec49f9dc8e592982cfd4_jpg.rf.b18d279ba4125fd80a1d12ac0e771a97.jpg  \n",
            " extracting: train/285d7c487a4e20ad832a74acb527b77f_jpg.rf.8925934caf22a38a85af6fd60453f502.jpg  \n",
            " extracting: train/292b0ddcacad7de06a628980954b6993_jpg.rf.c44c0299e2b2a05ba93ceadcd8e8568a.jpg  \n",
            " extracting: train/2c32afd520cc8bf076dfa5b6e2e1c4c1_jpg.rf.e9ad14fa3330976595bbd5162e607641.jpg  \n",
            " extracting: train/2ee0fd0963465ba29d8f27c6e605c55d_jpg.rf.416ed5a06f9bae303c04fb48287f167f.jpg  \n",
            " extracting: train/2fe75c34fd54e960146fb8b0ad8b3fd6_jpg.rf.9b91df20f1a711da331d1878d5687f13.jpg  \n",
            " extracting: train/300f80826bbb7dc4bf83e148614f2f77_jpg.rf.4a030c3f3f66357883fd1b9ae35cb892.jpg  \n",
            " extracting: train/3057eba7e9b0221ddbdc96a01f39ab79_jpg.rf.99cea19d83ccb4944b34919888dd0b71.jpg  \n",
            " extracting: train/3091c9b25d76e9cbd0af83ced9f354e5_jpg.rf.a96c979a388355dec7d4f832c0027f12.jpg  \n",
            " extracting: train/31419854b103ca6becc4cc394c449e95_jpg.rf.11b00b871f708bbe4d1421a0d9bc9daa.jpg  \n",
            " extracting: train/3161933dffedf8a859d6623a99492c53_jpg.rf.c58eabf189045a84e3c4389cebbcb9ec.jpg  \n",
            " extracting: train/3474d785b1b21d68163f56aa00a92bc9_jpg.rf.6bd40b33320a982005920d6d2d680c64.jpg  \n",
            " extracting: train/34ad1966ae7a17d4502ca141413ed8d2_jpg.rf.537d7ed41b558d70e1324f7ca1852ae0.jpg  \n",
            " extracting: train/36066ba85572ce99198f1a21c2c8bbff_jpg.rf.0b8c6b6b4c05ac617ba42138acb75757.jpg  \n",
            " extracting: train/3730ef213ac6aad431475a9ab28f349a_jpg.rf.f5f1fbb4d67fe92ce0d9acaa098cf5ac.jpg  \n",
            " extracting: train/3796db002cba7265bd32b0161ddd9127_jpg.rf.d39211b0dcef15258c6b963af339174e.jpg  \n",
            " extracting: train/37fe05bcf7d8568a9e55b569afdbccbd_jpg.rf.cf1a8be3a1ab89adc512c7969c0f1858.jpg  \n",
            " extracting: train/383c2ed7bbe2d327ab55a871db497c33_jpg.rf.52c7e74ebf109fe309e4b5c4def80ab7.jpg  \n",
            " extracting: train/389b4c47568c78c44df11dbb1377ffea_jpg.rf.cf94bc4ad387c2217d8cdf5402adcc1b.jpg  \n",
            " extracting: train/38f26ee82e38d332b2a831aa47bd363b_jpg.rf.69b9f7a3e7a10d7cdea51d7d7f01ba63.jpg  \n",
            " extracting: train/3914be0cea4aa8a6bbd1081ec3b034a7_jpg.rf.683bc62edd79c5db6b8b1dffcefc8d3f.jpg  \n",
            " extracting: train/3bab0eaaeb63a2ac9ae4942df4006a25_jpg.rf.1e2bc93d96300515f87808e09a5dd9a0.jpg  \n",
            " extracting: train/3e8fb24addda1a0945bd6b7777bc4018_jpg.rf.ec3432947d47720924cbb0dbf51956ba.jpg  \n",
            " extracting: train/446e75de1ffefc2115e79696bcf0e357_jpg.rf.3515dc451efed9a872f9268f3e9eb744.jpg  \n",
            " extracting: train/4667110b61b16e786673ed6126ccc35d_jpg.rf.8dd3ddf202a5cb346771cb4461f82884.jpg  \n",
            " extracting: train/479459fe5c8213a84fd55ba82f2670b1_jpg.rf.f481d875b91a96ca7982bfb5ba10e167.jpg  \n",
            " extracting: train/47e842dd95735a11cf92c0ddf1161193_jpg.rf.fa045b4cdedb6dbeea6226d5e5d58a5c.jpg  \n",
            " extracting: train/4807629b8df9c7eb4366b7feccd72e6a_jpg.rf.7d7f1b6fcb45b82fca8e8d6ce7ae33a9.jpg  \n",
            " extracting: train/4894f034a55eaa9252cd261a62b11d27_jpg.rf.5821daba2605c369ab52a6c1d3485b2a.jpg  \n",
            " extracting: train/48d3c59a99b2b5a5b9f1eb7d5ba63b60_jpg.rf.4360a206e865320f4f5dddea12152bf6.jpg  \n",
            " extracting: train/48e115dcbf1b3a67ca47a75a92da3f33_jpg.rf.b7f219d6adc0b92d43dc62bd98ee752b.jpg  \n",
            " extracting: train/4939035108d04ee672570a7cc937e270_jpg.rf.6fade1d3dc27011193c2fa8b9c10bd12.jpg  \n",
            " extracting: train/49c2afbbe5726160b289f7c0c62cdace_jpg.rf.018249b80783dd42d0d23496d4830902.jpg  \n",
            " extracting: train/49d365236ee4fb6bd982b0f00bff007e_jpg.rf.a2679c37a0afa53ab1a9394ff0585f46.jpg  \n",
            " extracting: train/49f78dc9aaeadd0c76ed2def75c358f3_jpg.rf.06d058407b83ba70ad2e41656d9d2195.jpg  \n",
            " extracting: train/4ae38537a74c5ed10d5223f8066659fc_jpg.rf.b8e4020f3f188e6ebe715200d8db0c27.jpg  \n",
            " extracting: train/4bf38c062fa7b5796d15ba90d6c3a456_jpg.rf.ca8cb7f5c79fb302acc4a6bd81e0b9a6.jpg  \n",
            " extracting: train/4d6b667ecbd41ebd603b38848366d9d0_jpg.rf.e51e6ab1c441b8766bf8503333a69db9.jpg  \n",
            " extracting: train/4d7820ad9fb4fe69d5168e1d7317dd02_jpg.rf.877766bf8a749843720f86abf3056207.jpg  \n",
            " extracting: train/4de23afff63bc169b4ebe547a9c9b692_jpg.rf.3c9bf54ca2de480c19e30d7808e4a2f6.jpg  \n",
            " extracting: train/4eb630d4dd38528dacf72355caf5c06d_jpg.rf.192a83d78be33d7b7f4a5bdc1b47702f.jpg  \n",
            " extracting: train/53de0674524ae6d77bdfff48136dec2a_jpg.rf.e3110b74dc7e779b42cf28f66c70a5d5.jpg  \n",
            " extracting: train/54a90aab8c73562975cc560d51a9d2d1_jpg.rf.27c69714af4c78fdd6f390a4def1e4a3.jpg  \n",
            " extracting: train/5758322233deed7ae7adc23536db2a4f_jpg.rf.8999db6b1f2c3f65d582bc066a6c55c5.jpg  \n",
            " extracting: train/5825608dccde6544eef91822136079d0_jpg.rf.7c4ac226ecdf109b9b74f91cefc46dde.jpg  \n",
            " extracting: train/59727dce26aaa6100078810b61404069_jpg.rf.d0c247bc8da0897e492a48e773adf35c.jpg  \n",
            " extracting: train/5a8433ec79c881f84ef19a07dc73665d_jpg.rf.2f0c3c38d635f03ea39475fd078c3414.jpg  \n",
            " extracting: train/5cecd3b3946aac5c713a51e0bd4617c9_jpg.rf.b0e3d523923d0224d5f5faa66949b7aa.jpg  \n",
            " extracting: train/5e71cb8d41c333a18e799ef0004b040c_jpg.rf.57a1424a00d13826fe3e771dd404ceff.jpg  \n",
            " extracting: train/614811e933a680fd6535ac8bf06bf530_jpg.rf.ace44d36688dfecc17409089f7d0720f.jpg  \n",
            " extracting: train/614aadadb4a7f5b475b027b8e11398ee_jpg.rf.e3506223ffe7f4878bc357cdc1f33cd2.jpg  \n",
            " extracting: train/61567b97353acc18ba9e8aac0f111326_jpg.rf.69222b87f379416d71b28d07b4914777.jpg  \n",
            " extracting: train/6179b463c8f503445e213b706d2a4de5_jpg.rf.8882e7b1020797df38ea13e02e43a69c.jpg  \n",
            " extracting: train/6403b91d63799cb9b5531c47b195d088_jpg.rf.77da8d8e3bc7557ac1396d597f5759c5.jpg  \n",
            " extracting: train/6589f4cfb37439d7d276f0d70f7ee1f0_jpg.rf.8d4dabde24fa1a51f62ebf8ae0ce02e6.jpg  \n",
            " extracting: train/65ba27557c78850168b1df70a3ce4ff7_jpg.rf.223d0d2c45a4796a00809534807bfea2.jpg  \n",
            " extracting: train/66f3c2c7c10a9263de9c6e056ba5c1b9_jpg.rf.9e391887b9da6213f12999e1628558bb.jpg  \n",
            " extracting: train/673bcd0d44f495fbe9dd88d5cacfceb3_jpg.rf.4c5902b014a2bdaf3057a3c74b6a44a2.jpg  \n",
            " extracting: train/675619f2c8078824cfd182cec2eeba95_jpg.rf.223c4b81b8fa7e3bd7a24577a10a5382.jpg  \n",
            " extracting: train/699edbacbfee5e6d4d6d2189bc88990a_jpg.rf.26706a6cf9de8ec15a6bdb7e53e6173c.jpg  \n",
            " extracting: train/6ba74e310dd824af891d057d674cedb9_jpg.rf.d5ce8eb38bcd1353757d902b6c07c670.jpg  \n",
            " extracting: train/6bb6f7cb96bf37230681d12ff7882f61_jpg.rf.f367c0f54253444f9c2f8f8968a1a47f.jpg  \n",
            " extracting: train/6f0a888f9e5aed9516e336fd04723ce1_jpg.rf.284ac83a4d78d901e17b6ca1f8d6222a.jpg  \n",
            " extracting: train/6f0de9b594de9f9b92c6a20daa51a28a_jpg.rf.bc6c392807dacf7a8a84cae9c09acd03.jpg  \n",
            " extracting: train/759a86e63667ca033255c4ab438dd392_jpg.rf.7ce77a0061d42d49bf91a4329cafdc59.jpg  \n",
            " extracting: train/76d01bada90581f55f1ae64c062cafcf_jpg.rf.d8d268060aa9f6fd260d483b10ac6a04.jpg  \n",
            " extracting: train/76dbe2ccf986a2a0d399d3d8a47279ad_jpg.rf.581770ac7f3816d67c2431339bc65592.jpg  \n",
            " extracting: train/76e118acf05a8ebe06957f8882cc06aa_jpg.rf.355e39cef189edb061cf20d6e59d42db.jpg  \n",
            " extracting: train/79e744a68d6e6f83be0a9e8761ea66a4_jpg.rf.433e4079f552b8e2c261a6a3052f30cd.jpg  \n",
            " extracting: train/7df16cd59fb40e0691948cc805e4801b_jpg.rf.c439322b6c3c711ba67ace0c0f3ebffe.jpg  \n",
            " extracting: train/7ee8d13861bdc45e40a7cfe190a8d8a6_jpg.rf.67d658f56e627e74416bc18ec92107c2.jpg  \n",
            " extracting: train/81f5c542ffe0f9eae4df59d29acbcced_jpg.rf.99b5d8d7a1f3fa6cbb6e9ecfb5e3d8b3.jpg  \n",
            " extracting: train/859e7157c6d544236a67463c08169b6e_jpg.rf.bdb4426cf64b8f8c1e1d693a1df74b76.jpg  \n",
            " extracting: train/8678864272a0a04c4c65ca96324105b4_jpg.rf.90118317e342f59a5e18a9c460e72a99.jpg  \n",
            " extracting: train/871597c145446cf58c1c2dd7db988864_jpg.rf.6cecb51bef82f71cd395832fa16411ba.jpg  \n",
            " extracting: train/889c420fb266b8d0e817306110042bda_jpg.rf.02d22a79820be51228296021ad0b6bbb.jpg  \n",
            " extracting: train/8967433350d3b3043902603430fccaab_jpg.rf.0e119efea84f75200b18702781a169ba.jpg  \n",
            " extracting: train/8bb72e70f0560095885586deba37a524_jpg.rf.436c6acb4c9d0a040b6a86ad0a41e770.jpg  \n",
            " extracting: train/8d6f722eadc015a393bd490f9b7a85e6_jpg.rf.85a7f0c7d2d1b7ce90664ff35b84fe8d.jpg  \n",
            " extracting: train/8d796de64b9eed1ffd5ebe550d4ca807_jpg.rf.bcadbba8ab4a943a30062047dd9951be.jpg  \n",
            " extracting: train/8dd12470c30e3b265e8933a6fee7ad28_jpg.rf.a583ec04d3044992cfd9acbebea7aa95.jpg  \n",
            " extracting: train/8de03901c64a80070048ead3fb0d32bd_jpg.rf.d4cd4d7336cee96c7c01731c6f3b81cd.jpg  \n",
            " extracting: train/8f84f1945fd993facc3368d13345f333_jpg.rf.3286223477d0f45d03a6a9854b7ff4a6.jpg  \n",
            " extracting: train/8ff64b3f770bfe96bdffc629efd16460_jpg.rf.f14ad67cdd66b17692929139aef233d6.jpg  \n",
            " extracting: train/9146a6989dac08f1769e677064ebfb49_jpg.rf.21c3f74a60d73237ceb25280df5cebe8.jpg  \n",
            " extracting: train/92992ff9c823e0420bf17e71db9ef4ef_jpg.rf.73292c1560eefbd1a84212b43be8d424.jpg  \n",
            " extracting: train/93557fc861304f7753089c244bc1e33e_jpg.rf.15d07a278e4c1061b2d99a2b95ff9921.jpg  \n",
            " extracting: train/969daa72bd7804ea1212e191820249b0_jpg.rf.9ac5120ab4af0484ace560bea2c39a7c.jpg  \n",
            " extracting: train/97aeb1f9b745a929e9ac0848acb53a1a_jpg.rf.a37313f9380bde8e4827749f1dba6d7c.jpg  \n",
            " extracting: train/9962a4d44388b9008aa0f466e4f4052c_jpg.rf.faa4d33a45e46b53fcc8f751377a8160.jpg  \n",
            " extracting: train/998222d9c93f1640829d4f0032dbf3e8_jpg.rf.28adf4b8b45dc5d0011897ab15e9419f.jpg  \n",
            " extracting: train/99ee6574b2a7afc0bb06269bbcf49a4c_jpg.rf.64986b73afba1223e13eadbd9b0fdb27.jpg  \n",
            " extracting: train/9a6b61a6d3b3e3ecddc201b097aa02d1_jpg.rf.d5534a244c0a9972da49c08b89f83f75.jpg  \n",
            " extracting: train/9c153a9c9798dab948d4260eb109b315_jpg.rf.8db7f32aeb3c84a69b2a7d79b921fc5b.jpg  \n",
            " extracting: train/9c5fb0c3cfd7b334a247cd87c139e8e6_jpg.rf.588e9ea4bfffb48edc0f99b7af98c96f.jpg  \n",
            " extracting: train/9d776e74e90c4f8092b060dd7567e2f8_jpg.rf.b70c4c285edf5789719b8f7bddac3ddc.jpg  \n",
            " extracting: train/9e943906fba1ec89edfacb2dd7976504_jpg.rf.aa1f7ea2f8c95e89aa701d9f87056330.jpg  \n",
            " extracting: train/9fc54a45feb5b01db8f6828d181fb075_jpg.rf.04e7c11036b566a522bc317e86e5ab6a.jpg  \n",
            " extracting: train/IMG_0166_JPG.rf.f228ddaecf7848a0520e929807b35af4.jpg  \n",
            " extracting: train/IMG_0167_JPG.rf.661ad368ce42cadb9dc497ceaa07e777.jpg  \n",
            " extracting: train/IMG_0291_JPG.rf.a72d476f25b25f6ef3f768e5876c2a55.jpg  \n",
            " extracting: train/IMG_0292_JPG.rf.dae866a073c030671cc199d0d06353e4.jpg  \n",
            " extracting: train/IMG_0294_JPG.rf.b7ae411904ff022e80b90f6267535f05.jpg  \n",
            " extracting: train/IMG_0295_JPG.rf.8abd802272b965c843eb1ecbc8df4abc.jpg  \n",
            " extracting: train/IMG_0296_JPG.rf.818119ea873100bb120049c72da67cbd.jpg  \n",
            " extracting: train/IMG_0297_JPG.rf.2609f7e3ad4d54b19aafc0623f5a8510.jpg  \n",
            " extracting: train/IMG_0298_JPG.rf.388fb04c02f369a0c79a524343fe590c.jpg  \n",
            " extracting: train/IMG_0299_JPG.rf.7d92f1ba0929b7fc5e77848b80148fc6.jpg  \n",
            " extracting: train/IMG_0317_JPG.rf.b9135e07e62170150fc67bc1f25b0d3c.jpg  \n",
            " extracting: train/IMG_0318_JPG.rf.37a8faa6e2f4efc18f2113466c1ec320.jpg  \n",
            " extracting: train/_annotations.txt  \n",
            " extracting: train/_classes.txt      \n",
            " extracting: train/a20eb4bb3cea2e394cfcf9ed969b628e_jpg.rf.9d2528623e7939a30ec82e76cac3df47.jpg  \n",
            " extracting: train/a4028b2361ce7ead654a86b07ac39d52_jpg.rf.03a20a2a674a95ceb6c61a5e531e8ca6.jpg  \n",
            " extracting: train/a4ebf4c268d80c4fe329331ea981b3a1_jpg.rf.d7e764e24afbdced1e4f8848f249bdfc.jpg  \n",
            " extracting: train/a5c65b40e0be3480c0ecfacaab399a87_jpg.rf.667ef27bf19775d48f8acf8dad04fa26.jpg  \n",
            " extracting: train/a8847f8fe8eaaa1c97bf83027a901760_jpg.rf.116b33357951f46fbbe0d5437561f61b.jpg  \n",
            " extracting: train/a932287da44b9dfacd0d16a5c1d27923_jpg.rf.b0950400898d51ce08945ad5c770ab03.jpg  \n",
            " extracting: train/a9768de3fceeeae2618f362870fb9a88_jpg.rf.055c7088ca1c11c42bcafa4950f2572f.jpg  \n",
            " extracting: train/a9987cf6cc5c6545818ec294d4a5bb9b_jpg.rf.21f51fbefe01648bbbb01e0d207c3843.jpg  \n",
            " extracting: train/b0f3d66c8be13f5f6aa25b67a06bdcfa_jpg.rf.30cc54e5f0d02160a2da3f6da04a9653.jpg  \n",
            " extracting: train/b3b002461f1c6b432e22964549767e5f_jpg.rf.6164bf4eadfd3dcb7f7ee2a2bd0764c2.jpg  \n",
            " extracting: train/b5102d7f9740eee7754ed268becb2163_jpg.rf.28a72d037ac692cc90b96708b1b05316.jpg  \n",
            " extracting: train/b5bcde459ca36f0d1f3c20e751336672_jpg.rf.0c6010e1e0afa0ff49d14eef51b5e6c8.jpg  \n",
            " extracting: train/b79ae5b70de58089ead6e32b235e30d3_jpg.rf.68c670de3208dc56613dc9b9f65ec934.jpg  \n",
            " extracting: train/b7a8c7de4fe1382d69f58ac97e819b5c_jpg.rf.d3b1cfbb0c618d6afff8ea3b96c4d331.jpg  \n",
            " extracting: train/bb54af2f0b83b174aecc29328c8fa001_jpg.rf.38630287afd7ffe84cd9468138ab7485.jpg  \n",
            " extracting: train/bc5decab88861286dcf78a367b4377cb_jpg.rf.c3e51d3ab678f7b49912e57cfd3d161c.jpg  \n",
            " extracting: train/beb11566e59775b61f0ca369952067cc_jpg.rf.726ac46a167828c2e34ad77f7abf895e.jpg  \n",
            " extracting: train/c0d68e012bb93c14bc333fc1d5e52621_jpg.rf.47fcf95dac60ca78902fc86cca7eab28.jpg  \n",
            " extracting: train/c3e9e81ba1540aae7961a4d8d96600ba_jpg.rf.deda5846f9437d81262d2e25f5808e36.jpg  \n",
            " extracting: train/c46bf04050a2a9323dfe563e8813602f_jpg.rf.634c5d0d551ab2b7c2554e4ce3c6aacb.jpg  \n",
            " extracting: train/c733616ab773817dd1a356dbbdf2ee33_jpg.rf.02fcb1d459e55b398bd4cc4edbd3bd10.jpg  \n",
            " extracting: train/c76c79e40bd9839a05237934cfa89ca3_jpg.rf.747f4292a1bb980bc392a787a773a0f1.jpg  \n",
            " extracting: train/c7890b749d14d3488066cbdfac4620fd_jpg.rf.eed3f658d1c9c65acfbcad5219f70716.jpg  \n",
            " extracting: train/ca869123d8a0cbcc6e54f4a445e5a78a_jpg.rf.de66695493176269696ee36a61dee1dc.jpg  \n",
            " extracting: train/cae099fe41d6aa30033d71e433c33c8d_jpg.rf.e346e0084281a8f6b63bbfeb576d1fc0.jpg  \n",
            " extracting: train/ce54969567273b9b8a275812ff56e16c_jpg.rf.af61be71f696438c30dc9bd87d299bd6.jpg  \n",
            " extracting: train/cf2784fa97151d5316b2961b1e62dc45_jpg.rf.d728eed31f107b29e913a6b2ad1f751a.jpg  \n",
            " extracting: train/d079f4e77b2445abceca7534356db743_jpg.rf.f7ee65ab8c16d3c9028443faaaf40ee2.jpg  \n",
            " extracting: train/d0cc2420bce5b14dfd39e55dc3737e57_jpg.rf.3914cdc1a02f2972d5316062da6f285f.jpg  \n",
            " extracting: train/d29148a2233950a7777285281cbfccff_jpg.rf.d23b4f180eb3b539d7ed88e2f4beb2cd.jpg  \n",
            " extracting: train/d33c33de41dbe1a95a43212c58fd12b7_jpg.rf.cdd968f5b7c71aa6ef1c732c4079c262.jpg  \n",
            " extracting: train/d3a4e1b8f13ef89f419251f5c5839d0d_jpg.rf.0c0623ed3bb8531690b2fbc7822d57bd.jpg  \n",
            " extracting: train/d415969922564f317be0d1433330626f_jpg.rf.1271853558336c8d9c5414b3a0466b8f.jpg  \n",
            " extracting: train/d494cb268ad7f9f55587de138edc1dc4_jpg.rf.b1bd99204b2ceacbda5bafa452a4d732.jpg  \n",
            " extracting: train/d67b5b9e900409b050dd9bd594f90709_jpg.rf.e07557fe78a4af8f4213b7c50edcc48e.jpg  \n",
            " extracting: train/d795f84f39716798482fb2937868ed8a_jpg.rf.57b7e9ee15d277b2694fdb94549a1487.jpg  \n",
            " extracting: train/d9acc69c5d57623cda22786e309201c9_jpg.rf.d37b5a883d97f31d33db5281c8ae0395.jpg  \n",
            " extracting: train/dd6b5c3cb2d7e77f38f1dfeb2bff0431_jpg.rf.f1dd1da676bf315e684a8e3a2ebbb82c.jpg  \n",
            " extracting: train/ddad9dc4d945006d66f5349d64498559_jpg.rf.cbac3a32273efcef96c08a2b0df10485.jpg  \n",
            " extracting: train/de60ba81aa78387928e4bdc11f3be301_jpg.rf.0cd8adb84fce0b83ee30a5fbdb4c1c4b.jpg  \n",
            " extracting: train/e40003d4bbcdac7196b9502bfe2fb6ed_jpg.rf.a92a3ed23c63f90ce84648a564f2a287.jpg  \n",
            " extracting: train/e6e3a2ff2c75970490079f00136885ad_jpg.rf.276c0ec51830ae8741a3e31c7f703638.jpg  \n",
            " extracting: train/e79deba8fe520409790b601ad61da4ee_jpg.rf.8cd55f2548d15a4c111f0a980d3ee4fe.jpg  \n",
            " extracting: train/e8480d7fb9881d8a0e88b7be4d103f6d_jpg.rf.eba2e44135388fd198939d843dca7e13.jpg  \n",
            " extracting: train/ea799d77875c399618c45cd9409f34ee_jpg.rf.077cbf5dc80bdc0bf0f59ddd6ce6f69e.jpg  \n",
            " extracting: train/eb9e7928e756c3cf9164e7afc08c4653_jpg.rf.77ace05280bcb7d839ab4cf9d586dfa2.jpg  \n",
            " extracting: train/ec4c30d88ecc70b6a3e76dbd9b17324a_jpg.rf.34d31473728f2b9a68bb71a3f40e1d6f.jpg  \n",
            " extracting: train/ec5ab1930d6aa16fff2582b48f82cada_jpg.rf.3164acc59bd3a1cade16f72b1f6e368a.jpg  \n",
            " extracting: train/edd285915356686fb53fb52c1ded0e53_jpg.rf.5104c70947989766361c9335d2f39d37.jpg  \n",
            " extracting: train/ef1d425fd5370fbf8b7adea43b755304_jpg.rf.4d4fe734367a2a5905da11a49184c767.jpg  \n",
            " extracting: train/f02d615907c77dc15f02bd1372e4398f_jpg.rf.714211d55651f47944d10b81622f1d39.jpg  \n",
            " extracting: train/f041d3171dfe3137390c85fc5437e447_jpg.rf.21a114abb3e64a6a241b6f7bdd0d8b9a.jpg  \n",
            " extracting: train/f1ea0167087976926d4fe0aa36b961ce_jpg.rf.245c4d4e37e289bbd69b90b60e6ee9bc.jpg  \n",
            " extracting: train/f2672cdc28767484b556da3ab6f1003e_jpg.rf.4397bb19b736490287e271bcf760ca16.jpg  \n",
            " extracting: train/f3302c754c6fd42130014199ee327d10_jpg.rf.0bef2494f0a7ebf9afda6b7688cf68b0.jpg  \n",
            " extracting: train/f3a5df526393445c6e2d38f66c1f5c27_jpg.rf.5c9ad833e2df809f6a0107ca7dd020ab.jpg  \n",
            " extracting: train/f52e1873b8583f8bf4f7ddf6e9649f07_jpg.rf.48248922519cd3c8f36539bc09dd8528.jpg  \n",
            " extracting: train/f587402be410b424bcbbac06e1dc6162_jpg.rf.a88d82401d5b97453bc047b92048ca39.jpg  \n",
            " extracting: train/f9a9a175f26d4b26bca3a5338cc1405e_jpg.rf.ec1a436f709ef7dc2e4f1fd3c9f6355e.jpg  \n",
            " extracting: train/fa3cf2724c1648a8822b59ac0759475f_jpg.rf.8b23c032ccc0fd6484f9d390cb6a2b9c.jpg  \n",
            " extracting: train/fa4e2b9a8cf58f405f69a56c662834f2_jpg.rf.9c9fb84a36bdfc518396a26a21758f9c.jpg  \n",
            " extracting: train/fb586797e8ad818c7e3e3a6411f73d84_jpg.rf.ad548be54dbd7087c07afbaa78746786.jpg  \n",
            " extracting: train/fc9d7bc0453cb3324406401c00224d30_jpg.rf.da31aaeddba327ec61dea2b7f515e62b.jpg  \n",
            "   creating: valid/\n",
            " extracting: valid/05ad7223827a29a8283f6c4b2490f52f_jpg.rf.5fb70e6e53b997c3015533194ea8e033.jpg  \n",
            " extracting: valid/0b2252c93c53e1b2e61d485b22328e2e_jpg.rf.78aea9d88ee5694eb75e7f05569e112a.jpg  \n",
            " extracting: valid/0c09b79cff39932c59ecc745dd827906_jpg.rf.a9966fa6e4e9a3da8246cd0e44c0e0d1.jpg  \n",
            " extracting: valid/0d9afc3d23392c3958f53d7fe71fd2f1_jpg.rf.38c69d3df99272f2d3c77c742ab1f50e.jpg  \n",
            " extracting: valid/15cc23c777b00d0e123f9df468f2852b_jpg.rf.a3f074f0d6beb76eb98cc6fa73ddce77.jpg  \n",
            " extracting: valid/18742c87a03866e042c5659ba04d1180_jpg.rf.ec481f84c7faf0dda7e20e721db5f89f.jpg  \n",
            " extracting: valid/1b7c1c9570e900f75eb974f99cbb3c60_jpg.rf.8f006641b9236815f6e4d16935747866.jpg  \n",
            " extracting: valid/1be2a621f309c7482e9a79ad5b23ecbe_jpg.rf.5cb426a7e2f8a4ef4c943f11bd62487b.jpg  \n",
            " extracting: valid/26fa37995fa5b18ec40e0a94e6d91104_jpg.rf.10d82beac7ac6234df91112c0276d4df.jpg  \n",
            " extracting: valid/302e7c10664be32b4fc000452149027c_jpg.rf.ec2e113c3ea11a78125c55887b60571f.jpg  \n",
            " extracting: valid/31b83afa654d5dd874c3f0111126ab7f_jpg.rf.755be19c63d3757e2127cc92d7b69869.jpg  \n",
            " extracting: valid/3312e3bb60e338e9c1a614f0f8960dd8_jpg.rf.effbdd6c0e43bd53a7e92240c0ba326b.jpg  \n",
            " extracting: valid/33b66ede234715fb46db40b33c4e26c1_jpg.rf.d59867e5d4f15a6cc37d2cc499b365d2.jpg  \n",
            " extracting: valid/3aafc2d38807dddd1b43a54cb70f500d_jpg.rf.7a1acfea51aff18b554e96c49beafb78.jpg  \n",
            " extracting: valid/3baf85c957b9d28a16c0b65cb2ef0d29_jpg.rf.ee68e051c8a61b8784b87686d828cf59.jpg  \n",
            " extracting: valid/3e0c67f38992fe16dfc163f7f5336263_jpg.rf.b541cee0030fd0edc7be9d4fed6a6abc.jpg  \n",
            " extracting: valid/424d6506342fa2471e71586675ed092c_jpg.rf.43baba77beb2f5588d5cc05ee7c081ec.jpg  \n",
            " extracting: valid/495019998442ddf85b59e387d4916cd3_jpg.rf.9d9d8ec43f8494e2bfe6603811f7b3de.jpg  \n",
            " extracting: valid/4b8f93069270a9f7bb523518a5088b9e_jpg.rf.ab3fbcd4d3542cb5bbb0b748053a358d.jpg  \n",
            " extracting: valid/55be99616328f83dcbfe8c18e1387c0e_jpg.rf.704f9d21a024a31afa058f00ea6d6ca9.jpg  \n",
            " extracting: valid/578029c06939a788cd5606ad17b49fb9_jpg.rf.6283a8b113fdfc84bf9f255a0df447c7.jpg  \n",
            " extracting: valid/57d1d1fb35ed875f9e770660bb03b6d7_jpg.rf.d3b0acfb689144d051a42d31205c791c.jpg  \n",
            " extracting: valid/5c19d3260762f5daa632d952bc0074d6_jpg.rf.9b21b95cd0d470a8c46ab298906e3745.jpg  \n",
            " extracting: valid/64061fd2e0e35bdf9ac4681eddf5fa2a_jpg.rf.7a9c55e842357fc0f8ed468afbdd4baa.jpg  \n",
            " extracting: valid/6859628a422c1b72be8b074841cd943e_jpg.rf.f580f9a20fdbf50ca1ab256112d11fb0.jpg  \n",
            " extracting: valid/6a41b6c8201604216ad196f842c6a2c6_jpg.rf.465cabf5502d8514f08087f183c3cb2a.jpg  \n",
            " extracting: valid/6e2dd4604b3a51d9be11b8809ed03803_jpg.rf.f6282009812141649ada9f73af2eee00.jpg  \n",
            " extracting: valid/73e6a751c50604d017541c11b28d8417_jpg.rf.81ee4214a166fd5de37da0bbfc5ef06c.jpg  \n",
            " extracting: valid/793c79d55c8a252b7a954d074b1d6498_jpg.rf.a9478a33f4491269886d3d92028cd059.jpg  \n",
            " extracting: valid/7e862b85e33cd247ed66447d129e5fb4_jpg.rf.d53dc427754d44718c33bb6291012ad2.jpg  \n",
            " extracting: valid/7e97f49e613a59a70b833e4c0b2c1c04_jpg.rf.e3d8cc08e136002cec4bf8350be37c47.jpg  \n",
            " extracting: valid/86afe95de5471af5cef08e6ae4d9acbb_jpg.rf.89e9e862697f76853ecd9142667e3aa7.jpg  \n",
            " extracting: valid/8ec14357f5f18fb98db86e0283623150_jpg.rf.dc1f65d43628062fda98946db19045ef.jpg  \n",
            " extracting: valid/9453c2097cb4ccc676e273939894b3da_jpg.rf.1b3941e5bbd87526c743197a542248ec.jpg  \n",
            " extracting: valid/97ed198b00b5491747d3b425df8e7096_jpg.rf.d4830d71ceb565084b77c6d7b7a09699.jpg  \n",
            " extracting: valid/IMG_0293_JPG.rf.f29eab19f33f4c8ef04f9188d7ff1de7.jpg  \n",
            " extracting: valid/IMG_0310_JPG.rf.6cf8e3d4550948ac9e5efafc66f1cdfd.jpg  \n",
            " extracting: valid/IMG_0311_JPG.rf.52d3ca104e59d1fcc846258e946e7dbd.jpg  \n",
            " extracting: valid/IMG_0319_JPG.rf.4ebd9c3d828dfa381411157d7b4ad4da.jpg  \n",
            " extracting: valid/IMG_0320_JPG.rf.63f3739202c30a04a1630bba0266fc4f.jpg  \n",
            " extracting: valid/_annotations.txt  \n",
            " extracting: valid/_classes.txt      \n",
            " extracting: valid/abd65798d9952a27e087710eb8bddf32_jpg.rf.99eb394f3994df8afcc2a5778016d1ec.jpg  \n",
            " extracting: valid/aec1aa6773dbbe004554f405cdef2bea_jpg.rf.748dcb4e54783f3bceb3c341d9eee5f1.jpg  \n",
            " extracting: valid/bb0de9761d16eee258ae09d8de32002c_jpg.rf.c11059695bd371034e76c84073775603.jpg  \n",
            " extracting: valid/c1f800417bc42263d141b5ed785e7707_jpg.rf.337b6ba78718cfc7228c7484c4a361f3.jpg  \n",
            " extracting: valid/c20ca9283ea51ac7707905894a7da703_jpg.rf.d23d9f998440d64312d949fdcdfa1b7d.jpg  \n",
            " extracting: valid/c5172afbdad90854b3d0f21a923c0c69_jpg.rf.f7da3947b94ff2b838d32b8fcd660104.jpg  \n",
            " extracting: valid/ca6484c259f286c5bdf1afefc868b753_jpg.rf.7aa1f1409074d5b0cbd19b20593cdcb5.jpg  \n",
            " extracting: valid/d114edc5cb4cae0ceb2f152afd15f57d_jpg.rf.047eea1174ccf45e4a09a9449a5611c0.jpg  \n",
            " extracting: valid/d3b9309d00a2b671407b918ea867a935_jpg.rf.cbc8557646c90e2b2764e2746a672acd.jpg  \n",
            " extracting: valid/d4f7caf01359b9a757c930140f746fad_jpg.rf.2b5cb1a63d242e69953a94e870bf8624.jpg  \n",
            " extracting: valid/d6e283a49b0395a6d5867c9e98e32045_jpg.rf.2571cdc4c5d218d1f8ecd4becc57c5ce.jpg  \n",
            " extracting: valid/d9ef98145d7d35393c75a51331a20e2c_jpg.rf.ff2352acde10b0f0eff169f22f55a1ce.jpg  \n",
            " extracting: valid/e1616dc9962fed075576ac4ea3553f51_jpg.rf.ad4a845557489e0b87666d3a34abd929.jpg  \n",
            " extracting: valid/e53bf8a0e692a4ccd5f1dc2bc19e7751_jpg.rf.181fa15089d35840f7c13edd8e22b1f5.jpg  \n",
            " extracting: valid/e7edc4f1b8d3cc1069b96d0358e066c9_jpg.rf.9dbe733e3efac3860d9ec4bf94e8674a.jpg  \n",
            " extracting: valid/ec418cafd39d7c5a69cc0642a08b2a08_jpg.rf.4d5b99fb671fd8cd28d64ed4f0293535.jpg  \n",
            " extracting: valid/eca42980852e6c5db10ee84aac23f9c6_jpg.rf.33b03eede8db8237276d8c962e735f2b.jpg  \n",
            " extracting: valid/fb7d97265a22bb1c1f908dadc6f9e7dc_jpg.rf.ebf5c65b740623772c9bd7490f81fc37.jpg  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lAoLxBEEz4FW"
      },
      "source": [
        "%cp train/_annotations.txt train/train.txt\n",
        "%cp train/_annotations.txt train.txt\n",
        "%mkdir data\n",
        "%cp valid/_annotations.txt data/val.txt\n",
        "%cp valid/*.jpg train/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TvwlqXkSlwUm"
      },
      "source": [
        "def file_len(fname):\n",
        "  with open(fname) as f:\n",
        "    for i, l in enumerate(f):\n",
        "      pass\n",
        "  return i + 1\n",
        "\n",
        "num_classes = file_len('train/_classes.txt')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m6xrTj1gREEc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c30f2369-6034-404c-ecda-49ce6adfa8d0"
      },
      "source": [
        "print(num_classes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "13\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uly0ZrqGjR9w"
      },
      "source": [
        "# Train Custom Detector"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mIlhqP2S57Ub",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e0cee78b-d034-4b86-da56-21d4c14a1254"
      },
      "source": [
        "#start training\n",
        "#-b batch size (you should keep this low (2-4) for training to work properly)\n",
        "#-s number of subdivisions in the batch, this was more relevant for the darknet framework\n",
        "#-l learning rate\n",
        "#-g direct training to the GPU device\n",
        "#pretrained invoke the pretrained weights that we downloaded above\n",
        "#classes - number of classes\n",
        "#dir - where the training data is\n",
        "#epoch - how long to train for\n",
        "!python ./pytorch-YOLOv4/train.py -b 2 -s 1 -l 0.001 -g 0 -pretrained /content/pytorch-YOLOv4/cfg/yolov4.conv.137.pth -classes {num_classes} -dir ./train -epochs 50"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "log file path:log/log_2021-04-10_00-59-46.txt\n",
            "2021-04-10 00:59:46,178 train.py[line:435] INFO: Using device cuda\n",
            "2021-04-10 00:59:48,613 train.py[line:282] INFO: Starting training:\n",
            "        Epochs:          50\n",
            "        Batch size:      2\n",
            "        Subdivisions:    1\n",
            "        Learning rate:   0.001\n",
            "        Training size:   202\n",
            "        Validation size: 58\n",
            "        Checkpoints:     True\n",
            "        Device:          cuda\n",
            "        Images size:     608\n",
            "        Optimizer:       adam\n",
            "        Dataset classes: 13\n",
            "        Train label path:train.txt\n",
            "        Pretrained:\n",
            "    \n",
            "Epoch 1/50:   0%|        | 0/202 [00:00<?, ?img/s]/usr/local/lib/python3.7/dist-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n",
            "  warnings.warn(warning.format(ret))\n",
            "Epoch 1/50:  19%|▏| 38/202 [00:24<01:05,  2.49img//usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:224: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`.\n",
            "  warnings.warn(\"To get the last learning rate computed by the scheduler, \"\n",
            "2021-04-10 01:00:14,184 train.py[line:353] INFO: Train step_20: loss : 32772.765625,loss xy : 115.34335327148438,loss wh : 14.456607818603516,loss obj : 32252.9375，loss cls : 390.0272216796875,loss l2 : 11902.8115234375,lr : 1.6000000000000002e-10\n",
            "Epoch 1/50:  39%|▍| 78/202 [00:36<00:42,  2.93img/2021-04-10 01:00:25,896 train.py[line:353] INFO: Train step_40: loss : 32487.685546875,loss xy : 30.783348083496094,loss wh : 3.9268531799316406,loss obj : 32348.556640625，loss cls : 104.4176254272461,loss l2 : 11794.185546875,lr : 2.5600000000000003e-09\n",
            "Epoch 1/50:  58%|▌| 118/202 [00:51<00:32,  2.59img2021-04-10 01:00:40,759 train.py[line:353] INFO: Train step_60: loss : 32750.32421875,loss xy : 101.72761535644531,loss wh : 20.545154571533203,loss obj : 32290.572265625，loss cls : 337.47918701171875,loss l2 : 11900.2294921875,lr : 1.2959999999999998e-08\n",
            "Epoch 1/50:  78%|▊| 158/202 [01:04<00:16,  2.74img2021-04-10 01:00:54,233 train.py[line:353] INFO: Train step_80: loss : 32463.53125,loss xy : 16.80966567993164,loss wh : 1.7372509241104126,loss obj : 32388.060546875，loss cls : 56.92368698120117,loss l2 : 11798.755859375,lr : 4.0960000000000004e-08\n",
            "Epoch 1/50:  98%|▉| 198/202 [01:13<00:00,  7.60img2021-04-10 01:01:02,394 train.py[line:353] INFO: Train step_100: loss : 32503.87109375,loss xy : 32.40509033203125,loss wh : 6.470165252685547,loss obj : 32363.513671875，loss cls : 101.48219299316406,loss l2 : 11812.9169921875,lr : 1.0000000000000002e-07\n",
            "Epoch 1/50: 100%|█| 202/202 [01:14<00:00,  7.71img2021-04-10 01:01:02,778 train.py[line:360] INFO: Created checkpoint directory\n",
            "2021-04-10 01:01:03,167 train.py[line:364] INFO: Checkpoint 1 saved !\n",
            "Epoch 1/50: 100%|█| 202/202 [01:14<00:00,  2.71img\n",
            "Epoch 2/50:  18%|▏| 36/202 [00:22<01:00,  2.74img/2021-04-10 01:01:25,948 train.py[line:353] INFO: Train step_120: loss : 32584.6171875,loss xy : 64.2480697631836,loss wh : 5.775518894195557,loss obj : 32302.111328125，loss cls : 212.48155212402344,loss l2 : 11822.3564453125,lr : 2.0735999999999997e-07\n",
            "Epoch 2/50:  38%|▍| 76/202 [00:36<00:45,  2.75img/2021-04-10 01:01:40,620 train.py[line:353] INFO: Train step_140: loss : 32671.216796875,loss xy : 71.43228912353516,loss wh : 8.843204498291016,loss obj : 32357.576171875，loss cls : 233.36524963378906,loss l2 : 11872.8330078125,lr : 3.841600000000001e-07\n",
            "Epoch 2/50:  57%|▌| 116/202 [00:49<00:29,  2.96img2021-04-10 01:01:53,577 train.py[line:353] INFO: Train step_160: loss : 32550.96484375,loss xy : 61.74879837036133,loss wh : 10.59247875213623,loss obj : 32275.41015625，loss cls : 203.21209716796875,loss l2 : 11815.986328125,lr : 6.553600000000001e-07\n",
            "Epoch 2/50:  77%|▊| 156/202 [01:04<00:17,  2.63img2021-04-10 01:02:08,437 train.py[line:353] INFO: Train step_180: loss : 32792.578125,loss xy : 144.05902099609375,loss wh : 37.333702087402344,loss obj : 32138.509765625，loss cls : 472.6768493652344,loss l2 : 11913.021484375,lr : 1.0497599999999998e-06\n",
            "Epoch 2/50:  97%|▉| 196/202 [01:15<00:00,  7.11img2021-04-10 01:02:18,976 train.py[line:353] INFO: Train step_200: loss : 32091.2109375,loss xy : 13.622049331665039,loss wh : 4.006208419799805,loss obj : 32026.78515625，loss cls : 46.79624938964844,loss l2 : 11631.1484375,lr : 1.6000000000000004e-06\n",
            "Epoch 2/50: 100%|█| 202/202 [01:16<00:00,  7.47img2021-04-10 01:02:20,025 train.py[line:364] INFO: Checkpoint 2 saved !\n",
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            "Epoch 3/50:  17%|▏| 34/202 [00:19<01:04,  2.60img/2021-04-10 01:02:40,463 train.py[line:353] INFO: Train step_220: loss : 32593.138671875,loss xy : 156.39700317382812,loss wh : 71.68461608886719,loss obj : 31850.287109375，loss cls : 514.7693481445312,loss l2 : 11856.8291015625,lr : 2.34256e-06\n",
            "Epoch 3/50:  37%|▎| 74/202 [00:33<00:41,  3.07img/2021-04-10 01:02:53,945 train.py[line:353] INFO: Train step_240: loss : 31731.171875,loss xy : 35.14031982421875,loss wh : 5.578735828399658,loss obj : 31571.10546875，loss cls : 119.3472900390625,loss l2 : 11434.669921875,lr : 3.3177599999999996e-06\n",
            "Epoch 3/50:  56%|▌| 114/202 [00:48<00:31,  2.80img2021-04-10 01:03:09,063 train.py[line:353] INFO: Train step_260: loss : 31439.048828125,loss xy : 92.37629699707031,loss wh : 11.389025688171387,loss obj : 31029.080078125，loss cls : 306.20220947265625,loss l2 : 11244.4384765625,lr : 4.569760000000001e-06\n",
            "Epoch 3/50:  76%|▊| 154/202 [01:03<00:17,  2.71img2021-04-10 01:03:23,934 train.py[line:353] INFO: Train step_280: loss : 31090.70703125,loss xy : 60.20925521850586,loss wh : 8.274884223937988,loss obj : 30825.423828125，loss cls : 196.79977416992188,loss l2 : 11108.0771484375,lr : 6.146560000000002e-06\n",
            "Epoch 3/50:  96%|▉| 194/202 [01:14<00:01,  6.35img2021-04-10 01:03:34,878 train.py[line:353] INFO: Train step_300: loss : 29898.13671875,loss xy : 46.74615478515625,loss wh : 4.651613235473633,loss obj : 29696.39453125，loss cls : 150.3447265625,loss l2 : 10512.947265625,lr : 8.1e-06\n",
            "Epoch 3/50: 100%|█| 202/202 [01:15<00:00,  7.33img2021-04-10 01:03:36,187 train.py[line:364] INFO: Checkpoint 3 saved !\n",
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            "Epoch 4/50:  16%|▏| 32/202 [00:19<01:04,  2.64img/2021-04-10 01:03:56,135 train.py[line:353] INFO: Train step_320: loss : 28632.0078125,loss xy : 51.44523620605469,loss wh : 6.293670654296875,loss obj : 28416.609375，loss cls : 157.659912109375,loss l2 : 9904.83203125,lr : 1.0485760000000001e-05\n",
            "Epoch 4/50:  36%|▎| 72/202 [00:33<00:43,  2.98img/2021-04-10 01:04:09,826 train.py[line:353] INFO: Train step_340: loss : 27486.708984375,loss xy : 56.58556365966797,loss wh : 4.706632614135742,loss obj : 27254.1953125，loss cls : 171.22100830078125,loss l2 : 9351.873046875,lr : 1.3363360000000005e-05\n",
            "Epoch 4/50:  55%|▌| 112/202 [00:47<00:31,  2.87img2021-04-10 01:04:24,212 train.py[line:353] INFO: Train step_360: loss : 26519.7109375,loss xy : 65.6025161743164,loss wh : 20.64641571044922,loss obj : 26236.53125，loss cls : 196.93197631835938,loss l2 : 8906.662109375,lr : 1.6796159999999997e-05\n",
            "Epoch 4/50:  75%|▊| 152/202 [01:02<00:19,  2.57img2021-04-10 01:04:39,303 train.py[line:353] INFO: Train step_380: loss : 24736.86328125,loss xy : 77.29884338378906,loss wh : 8.421697616577148,loss obj : 24427.947265625，loss cls : 223.19598388671875,loss l2 : 8027.24951171875,lr : 2.085136e-05\n",
            "Epoch 4/50:  95%|▉| 192/202 [01:13<00:01,  6.26img2021-04-10 01:04:49,586 train.py[line:353] INFO: Train step_400: loss : 23344.259765625,loss xy : 87.49908447265625,loss wh : 3.9455721378326416,loss obj : 23038.876953125，loss cls : 213.93768310546875,loss l2 : 7360.5595703125,lr : 2.5600000000000006e-05\n",
            "Epoch 4/50: 100%|█| 202/202 [01:14<00:00,  7.14img2021-04-10 01:04:51,177 train.py[line:364] INFO: Checkpoint 4 saved !\n",
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            "Epoch 5/50:  15%|▏| 30/202 [00:19<01:17,  2.21img/2021-04-10 01:05:11,476 train.py[line:353] INFO: Train step_420: loss : 21482.333984375,loss xy : 8.378119468688965,loss wh : 1.1377720832824707,loss obj : 21454.421875，loss cls : 18.39731216430664,loss l2 : 6541.40283203125,lr : 3.111696e-05\n",
            "Epoch 5/50:  35%|▎| 70/202 [00:36<00:57,  2.30img/2021-04-10 01:05:28,518 train.py[line:353] INFO: Train step_440: loss : 20644.744140625,loss xy : 108.82337951660156,loss wh : 29.169715881347656,loss obj : 20210.091796875，loss cls : 296.6602478027344,loss l2 : 6144.98095703125,lr : 3.748096e-05\n",
            "Epoch 5/50:  54%|▌| 110/202 [00:50<00:34,  2.64img2021-04-10 01:05:42,353 train.py[line:353] INFO: Train step_460: loss : 18934.375,loss xy : 98.11605834960938,loss wh : 15.860379219055176,loss obj : 18579.09375，loss cls : 241.3045654296875,loss l2 : 5367.662109375,lr : 4.477456000000001e-05\n",
            "Epoch 5/50:  74%|▋| 150/202 [01:05<00:17,  2.92img2021-04-10 01:05:56,921 train.py[line:353] INFO: Train step_480: loss : 16414.49609375,loss xy : 5.109506607055664,loss wh : 0.1273021697998047,loss obj : 16400.345703125，loss cls : 8.914955139160156,loss l2 : 4307.681640625,lr : 5.308415999999999e-05\n",
            "Epoch 5/50:  94%|▉| 190/202 [01:15<00:01,  6.54img2021-04-10 01:06:07,352 train.py[line:353] INFO: Train step_500: loss : 15415.4296875,loss xy : 20.873106002807617,loss wh : 0.952772319316864,loss obj : 15342.2119140625，loss cls : 51.39150619506836,loss l2 : 3893.45849609375,lr : 6.25e-05\n",
            "Epoch 5/50: 100%|█| 202/202 [01:17<00:00,  7.33img2021-04-10 01:06:09,241 train.py[line:364] INFO: Checkpoint 5 saved !\n",
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            "Epoch 6/50:  14%|▏| 28/202 [00:18<01:32,  1.89img/2021-04-10 01:06:28,272 train.py[line:353] INFO: Train step_520: loss : 15324.986328125,loss xy : 149.05715942382812,loss wh : 11.81901741027832,loss obj : 14839.015625，loss cls : 325.09503173828125,loss l2 : 3813.706298828125,lr : 7.311616000000002e-05\n",
            "Epoch 6/50:  34%|▎| 68/202 [00:33<00:47,  2.79img/2021-04-10 01:06:43,265 train.py[line:353] INFO: Train step_540: loss : 13057.2568359375,loss xy : 65.46493530273438,loss wh : 2.1129045486450195,loss obj : 12883.802734375，loss cls : 105.87566375732422,loss l2 : 2921.11083984375,lr : 8.503056000000002e-05\n",
            "Epoch 6/50:  53%|▌| 108/202 [00:45<00:28,  3.33img2021-04-10 01:06:55,704 train.py[line:353] INFO: Train step_560: loss : 12943.4091796875,loss xy : 74.250244140625,loss wh : 7.565037250518799,loss obj : 12701.8203125，loss cls : 159.77374267578125,loss l2 : 2899.2509765625,lr : 9.834496000000003e-05\n",
            "Epoch 6/50:  73%|▋| 148/202 [01:00<00:20,  2.65img2021-04-10 01:07:10,446 train.py[line:353] INFO: Train step_580: loss : 11976.98828125,loss xy : 144.93072509765625,loss wh : 28.32128143310547,loss obj : 11522.130859375，loss cls : 281.6058349609375,loss l2 : 2550.371337890625,lr : 0.00011316495999999998\n",
            "Epoch 6/50:  93%|▉| 188/202 [01:11<00:02,  6.44img2021-04-10 01:07:21,019 train.py[line:353] INFO: Train step_600: loss : 10536.8974609375,loss xy : 21.327190399169922,loss wh : 1.5748341083526611,loss obj : 10475.5859375，loss cls : 38.40911865234375,loss l2 : 2039.9881591796875,lr : 0.0001296\n",
            "Epoch 6/50: 100%|█| 202/202 [01:13<00:00,  7.43img2021-04-10 01:07:23,128 train.py[line:364] INFO: Checkpoint 6 saved !\n",
            "Epoch 6/50: 100%|█| 202/202 [01:13<00:00,  2.73img\n",
            "Epoch 7/50:  13%|▏| 26/202 [00:14<01:07,  2.62img/2021-04-10 01:07:38,130 train.py[line:353] INFO: Train step_620: loss : 9652.1962890625,loss xy : 13.012083053588867,loss wh : 0.35207200050354004,loss obj : 9618.15625，loss cls : 20.67618179321289,loss l2 : 1743.3516845703125,lr : 0.00014776335999999997\n",
            "Epoch 7/50:  33%|▎| 66/202 [00:28<00:44,  3.02img/2021-04-10 01:07:52,008 train.py[line:353] INFO: Train step_640: loss : 9219.7080078125,loss xy : 107.5146713256836,loss wh : 13.414280891418457,loss obj : 8923.955078125，loss cls : 174.824462890625,loss l2 : 1614.7227783203125,lr : 0.00016777216000000002\n",
            "Epoch 7/50:  52%|▌| 106/202 [00:42<00:39,  2.44img2021-04-10 01:08:06,565 train.py[line:353] INFO: Train step_660: loss : 8652.5537109375,loss xy : 52.3101921081543,loss wh : 4.732246398925781,loss obj : 8516.1650390625，loss cls : 79.34701538085938,loss l2 : 1448.692138671875,lr : 0.00018974736000000004\n",
            "Epoch 7/50:  72%|▋| 146/202 [00:58<00:18,  3.00img2021-04-10 01:08:22,683 train.py[line:353] INFO: Train step_680: loss : 7770.01611328125,loss xy : 90.9749984741211,loss wh : 10.54195499420166,loss obj : 7522.541015625，loss cls : 145.95822143554688,loss l2 : 1201.431640625,lr : 0.0002138137600000001\n",
            "Epoch 7/50:  92%|▉| 186/202 [01:09<00:02,  6.76img2021-04-10 01:08:32,552 train.py[line:353] INFO: Train step_700: loss : 6639.74560546875,loss xy : 4.981486797332764,loss wh : 0.22893348336219788,loss obj : 6625.43994140625，loss cls : 9.095010757446289,loss l2 : 890.771484375,lr : 0.00024009999999999995\n",
            "Epoch 7/50: 100%|█| 202/202 [01:11<00:00,  7.49img2021-04-10 01:08:34,920 train.py[line:364] INFO: Checkpoint 7 saved !\n",
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            "Epoch 8/50:  12%| | 24/202 [00:16<01:24,  2.10img/2021-04-10 01:08:52,204 train.py[line:353] INFO: Train step_720: loss : 5989.39697265625,loss xy : 25.173301696777344,loss wh : 0.46642622351646423,loss obj : 5932.45361328125，loss cls : 31.30350112915039,loss l2 : 737.4786376953125,lr : 0.00026873855999999996\n",
            "Epoch 8/50:  32%|▎| 64/202 [00:30<00:49,  2.79img/2021-04-10 01:09:05,630 train.py[line:353] INFO: Train step_740: loss : 5699.68310546875,loss xy : 57.844879150390625,loss wh : 5.5250444412231445,loss obj : 5528.306640625，loss cls : 108.00624084472656,loss l2 : 696.591552734375,lr : 0.00029986575999999996\n",
            "Epoch 8/50:  51%|▌| 104/202 [00:44<00:35,  2.79img2021-04-10 01:09:20,024 train.py[line:353] INFO: Train step_760: loss : 4811.78076171875,loss xy : 23.95888328552246,loss wh : 2.883162021636963,loss obj : 4749.046875，loss cls : 35.89164733886719,loss l2 : 498.836669921875,lr : 0.00033362176\n",
            "Epoch 8/50:  71%|▋| 144/202 [00:59<00:18,  3.06img2021-04-10 01:09:34,846 train.py[line:353] INFO: Train step_780: loss : 4630.7431640625,loss xy : 94.42303466796875,loss wh : 12.50103759765625,loss obj : 4385.09423828125，loss cls : 138.72457885742188,loss l2 : 494.6780090332031,lr : 0.00037015056000000006\n",
            "Epoch 8/50:  91%|▉| 184/202 [01:10<00:04,  4.15img2021-04-10 01:09:46,012 train.py[line:353] INFO: Train step_800: loss : 3991.020263671875,loss xy : 45.80610275268555,loss wh : 1.6267104148864746,loss obj : 3871.064453125，loss cls : 72.5229721069336,loss l2 : 364.19866943359375,lr : 0.0004096000000000001\n",
            "Epoch 8/50: 100%|█| 202/202 [01:13<00:00,  7.10img2021-04-10 01:09:48,699 train.py[line:364] INFO: Checkpoint 8 saved !\n",
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            "Epoch 9/50:  11%| | 22/202 [00:14<01:31,  1.98img/2021-04-10 01:10:03,460 train.py[line:353] INFO: Train step_820: loss : 3372.70458984375,loss xy : 21.170719146728516,loss wh : 1.1154953241348267,loss obj : 3326.514892578125，loss cls : 23.903593063354492,loss l2 : 262.0030212402344,lr : 0.0004521217599999999\n",
            "Epoch 9/50:  31%|▎| 62/202 [00:29<00:57,  2.45img/2021-04-10 01:10:19,185 train.py[line:353] INFO: Train step_840: loss : 3098.09375,loss xy : 78.63397216796875,loss wh : 3.369161605834961,loss obj : 2911.635498046875，loss cls : 104.4549560546875,loss l2 : 237.77125549316406,lr : 0.00049787136\n",
            "Epoch 9/50:  50%|▌| 102/202 [00:44<00:36,  2.73img2021-04-10 01:10:34,319 train.py[line:353] INFO: Train step_860: loss : 2657.9912109375,loss xy : 54.90793991088867,loss wh : 1.5738301277160645,loss obj : 2540.165771484375，loss cls : 61.343631744384766,loss l2 : 170.79736328125,lr : 0.00054700816\n",
            "Epoch 9/50:  70%|▋| 142/202 [00:59<00:21,  2.86img2021-04-10 01:10:48,789 train.py[line:353] INFO: Train step_880: loss : 2245.546630859375,loss xy : 12.607028007507324,loss wh : 0.42489883303642273,loss obj : 2210.7001953125，loss cls : 21.814380645751953,loss l2 : 119.50336456298828,lr : 0.00059969536\n",
            "Epoch 9/50:  90%|▉| 182/202 [01:12<00:05,  3.89img2021-04-10 01:11:01,953 train.py[line:353] INFO: Train step_900: loss : 2085.611572265625,loss xy : 64.54428100585938,loss wh : 3.0455784797668457,loss obj : 1931.0914306640625，loss cls : 86.93030548095703,loss l2 : 123.67848205566406,lr : 0.0006561000000000001\n",
            "Epoch 9/50: 100%|█| 202/202 [01:15<00:00,  7.38img2021-04-10 01:11:04,876 train.py[line:364] INFO: Checkpoint 9 saved !\n",
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            "Epoch 10/50:  10%| | 20/202 [00:16<01:47,  1.69img2021-04-10 01:11:21,747 train.py[line:353] INFO: Train step_920: loss : 1706.7701416015625,loss xy : 3.8836183547973633,loss wh : 0.19986188411712646,loss obj : 1692.774658203125，loss cls : 9.911972045898438,loss l2 : 69.37335205078125,lr : 0.0007163929600000001\n",
            "Epoch 10/50:  30%|▎| 60/202 [00:30<00:52,  2.68img2021-04-10 01:11:36,599 train.py[line:353] INFO: Train step_940: loss : 1720.30908203125,loss xy : 81.70631408691406,loss wh : 25.140727996826172,loss obj : 1515.0911865234375，loss cls : 98.37078857421875,loss l2 : 146.1929168701172,lr : 0.0007807489599999998\n",
            "Epoch 10/50:  50%|▍| 100/202 [00:45<00:27,  3.69im2021-04-10 01:11:50,770 train.py[line:353] INFO: Train step_960: loss : 1543.828857421875,loss xy : 84.18562316894531,loss wh : 10.571475982666016,loss obj : 1354.6884765625，loss cls : 94.38330841064453,loss l2 : 109.75900268554688,lr : 0.0008493465599999999\n",
            "Epoch 10/50:  69%|▋| 140/202 [01:00<00:21,  2.88im2021-04-10 01:12:05,964 train.py[line:353] INFO: Train step_980: loss : 1173.905029296875,loss xy : 22.875164031982422,loss wh : 1.216076374053955,loss obj : 1124.354248046875，loss cls : 25.459604263305664,loss l2 : 41.04911804199219,lr : 0.0009223681599999999\n",
            "Epoch 10/50:  89%|▉| 180/202 [01:13<00:06,  3.46im2021-04-10 01:12:19,177 train.py[line:353] INFO: Train step_1000: loss : 1097.6993408203125,loss xy : 40.726844787597656,loss wh : 2.394710063934326,loss obj : 1004.2213745117188，loss cls : 50.356483459472656,loss l2 : 49.039100646972656,lr : 0.001\n",
            "Epoch 10/50: 100%|█| 202/202 [01:17<00:00,  7.19im2021-04-10 01:12:22,489 train.py[line:364] INFO: Checkpoint 10 saved !\n",
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            "Epoch 11/50:   9%| | 18/202 [00:14<01:47,  1.71img2021-04-10 01:12:37,661 train.py[line:353] INFO: Train step_1020: loss : 1073.373779296875,loss xy : 74.859619140625,loss wh : 4.655176162719727,loss obj : 911.6824951171875，loss cls : 82.17646026611328,loss l2 : 61.61247634887695,lr : 0.001\n",
            "Epoch 11/50:  29%|▎| 58/202 [00:27<00:44,  3.21img2021-04-10 01:12:50,486 train.py[line:353] INFO: Train step_1040: loss : 789.32275390625,loss xy : 3.2152109146118164,loss wh : 0.10164730250835419,loss obj : 778.9810791015625，loss cls : 7.02485466003418,loss l2 : 16.731273651123047,lr : 0.001\n",
            "Epoch 11/50:  49%|▍| 98/202 [00:41<00:35,  2.91img2021-04-10 01:13:04,549 train.py[line:353] INFO: Train step_1060: loss : 780.4811401367188,loss xy : 26.070505142211914,loss wh : 6.315740585327148,loss obj : 720.0431518554688，loss cls : 28.051742553710938,loss l2 : 36.77384948730469,lr : 0.001\n",
            "Epoch 11/50:  68%|▋| 138/202 [00:54<00:23,  2.77im2021-04-10 01:13:17,506 train.py[line:353] INFO: Train step_1080: loss : 721.2149047851562,loss xy : 32.122920989990234,loss wh : 6.052211761474609,loss obj : 647.52099609375，loss cls : 35.51878356933594,loss l2 : 36.730525970458984,lr : 0.001\n",
            "Epoch 11/50:  88%|▉| 178/202 [01:06<00:06,  3.97im2021-04-10 01:13:29,278 train.py[line:353] INFO: Train step_1100: loss : 780.2396240234375,loss xy : 75.5717544555664,loss wh : 7.198024749755859,loss obj : 619.0293579101562，loss cls : 78.44051361083984,loss l2 : 57.481990814208984,lr : 0.001\n",
            "Epoch 11/50: 100%|█| 202/202 [01:09<00:00,  7.48im2021-04-10 01:13:32,760 train.py[line:364] INFO: Checkpoint 11 saved !\n",
            "Epoch 11/50: 100%|█| 202/202 [01:10<00:00,  2.87im\n",
            "Epoch 12/50:   8%| | 16/202 [00:12<01:55,  1.61img2021-04-10 01:13:46,400 train.py[line:353] INFO: Train step_1120: loss : 681.4876098632812,loss xy : 72.6382064819336,loss wh : 3.6731925010681152,loss obj : 548.400390625，loss cls : 56.77581787109375,loss l2 : 37.54755783081055,lr : 0.001\n",
            "Epoch 12/50:  28%|▎| 56/202 [00:28<00:42,  3.41img2021-04-10 01:14:02,141 train.py[line:353] INFO: Train step_1140: loss : 684.7960815429688,loss xy : 75.3163833618164,loss wh : 6.075346946716309,loss obj : 520.5287475585938，loss cls : 82.87562561035156,loss l2 : 52.79453659057617,lr : 0.001\n",
            "Epoch 12/50:  48%|▍| 96/202 [00:42<00:35,  2.95img2021-04-10 01:14:15,740 train.py[line:353] INFO: Train step_1160: loss : 576.2550048828125,loss xy : 56.54875946044922,loss wh : 3.2434401512145996,loss obj : 468.7911071777344，loss cls : 47.67169952392578,loss l2 : 33.986793518066406,lr : 0.001\n",
            "Epoch 12/50:  67%|▋| 136/202 [00:57<00:24,  2.73im2021-04-10 01:14:30,628 train.py[line:353] INFO: Train step_1180: loss : 813.4907836914062,loss xy : 134.38595581054688,loss wh : 21.957595825195312,loss obj : 531.3130493164062，loss cls : 125.83416748046875,loss l2 : 115.41153717041016,lr : 0.001\n",
            "Epoch 12/50:  87%|▊| 176/202 [01:09<00:07,  3.61im2021-04-10 01:14:42,561 train.py[line:353] INFO: Train step_1200: loss : 541.712890625,loss xy : 70.37268829345703,loss wh : 5.19504976272583,loss obj : 414.1119079589844，loss cls : 52.033206939697266,loss l2 : 38.4736213684082,lr : 0.001\n",
            "Epoch 12/50: 100%|█| 202/202 [01:12<00:00,  7.52im2021-04-10 01:14:46,283 train.py[line:364] INFO: Checkpoint 12 saved !\n",
            "Epoch 12/50: 100%|█| 202/202 [01:13<00:00,  2.75im\n",
            "Epoch 13/50:   7%| | 14/202 [00:11<02:15,  1.39img2021-04-10 01:14:58,481 train.py[line:353] INFO: Train step_1220: loss : 452.1607971191406,loss xy : 33.271087646484375,loss wh : 3.9712581634521484,loss obj : 382.8644714355469，loss cls : 32.053985595703125,loss l2 : 26.469364166259766,lr : 0.001\n",
            "Epoch 13/50:  27%|▎| 54/202 [00:26<00:53,  2.79img2021-04-10 01:15:13,802 train.py[line:353] INFO: Train step_1240: loss : 385.4522399902344,loss xy : 21.461212158203125,loss wh : 2.506112575531006,loss obj : 347.5452880859375，loss cls : 13.939635276794434,loss l2 : 14.841917037963867,lr : 0.001\n",
            "Epoch 13/50:  47%|▍| 94/202 [00:42<00:39,  2.74img2021-04-10 01:15:29,051 train.py[line:353] INFO: Train step_1260: loss : 434.29437255859375,loss xy : 41.111839294433594,loss wh : 8.060286521911621,loss obj : 348.9375915527344，loss cls : 36.18465042114258,loss l2 : 37.878326416015625,lr : 0.001\n",
            "Epoch 13/50:  66%|▋| 134/202 [00:54<00:23,  2.89im2021-04-10 01:15:41,764 train.py[line:353] INFO: Train step_1280: loss : 527.1268310546875,loss xy : 78.88926696777344,loss wh : 19.320476531982422,loss obj : 362.5418395996094，loss cls : 66.3752212524414,loss l2 : 76.93412017822266,lr : 0.001\n",
            "Epoch 13/50:  86%|▊| 174/202 [01:08<00:08,  3.36im2021-04-10 01:15:55,113 train.py[line:353] INFO: Train step_1300: loss : 324.0426330566406,loss xy : 6.973883152008057,loss wh : 0.9031527638435364,loss obj : 306.50213623046875，loss cls : 9.663450241088867,loss l2 : 11.559281349182129,lr : 0.001\n",
            "Epoch 13/50: 100%|█| 202/202 [01:13<00:00,  7.01im2021-04-10 01:15:59,837 train.py[line:364] INFO: Checkpoint 13 saved !\n",
            "Epoch 13/50: 100%|█| 202/202 [01:13<00:00,  2.75im\n",
            "Epoch 14/50:   6%| | 12/202 [00:11<03:02,  1.04img2021-04-10 01:16:12,103 train.py[line:353] INFO: Train step_1320: loss : 440.22869873046875,loss xy : 71.16517639160156,loss wh : 18.58950424194336,loss obj : 306.5763244628906，loss cls : 43.89768981933594,loss l2 : 63.262062072753906,lr : 0.001\n",
            "Epoch 14/50:  26%|▎| 52/202 [00:26<00:48,  3.06img2021-04-10 01:16:26,739 train.py[line:353] INFO: Train step_1340: loss : 462.9429931640625,loss xy : 90.12470245361328,loss wh : 6.831058979034424,loss obj : 311.20037841796875，loss cls : 54.78684997558594,loss l2 : 47.58488082885742,lr : 0.001\n",
            "Epoch 14/50:  46%|▍| 92/202 [00:40<00:39,  2.81img2021-04-10 01:16:40,666 train.py[line:353] INFO: Train step_1360: loss : 301.36260986328125,loss xy : 43.63067626953125,loss wh : 1.0938743352890015,loss obj : 240.81256103515625，loss cls : 15.825491905212402,loss l2 : 9.64744758605957,lr : 0.001\n",
            "Epoch 14/50:  65%|▋| 132/202 [00:53<00:20,  3.40im2021-04-10 01:16:53,895 train.py[line:353] INFO: Train step_1380: loss : 325.6573181152344,loss xy : 27.213024139404297,loss wh : 20.141414642333984,loss obj : 255.84349060058594，loss cls : 22.459367752075195,loss l2 : 54.806365966796875,lr : 0.001\n",
            "Epoch 14/50:  85%|▊| 172/202 [01:07<00:10,  2.86im2021-04-10 01:17:07,701 train.py[line:353] INFO: Train step_1400: loss : 262.2267150878906,loss xy : 26.657724380493164,loss wh : 1.1696854829788208,loss obj : 228.8769073486328，loss cls : 5.522387504577637,loss l2 : 8.33193588256836,lr : 0.001\n",
            "Epoch 14/50: 100%|█| 202/202 [01:12<00:00,  7.28im2021-04-10 01:17:12,714 train.py[line:364] INFO: Checkpoint 14 saved !\n",
            "Epoch 14/50: 100%|█| 202/202 [01:12<00:00,  2.77im\n",
            "Epoch 15/50:   5%| | 10/202 [00:10<03:37,  1.13s/i2021-04-10 01:17:23,671 train.py[line:353] INFO: Train step_1420: loss : 222.1258087158203,loss xy : 5.710448265075684,loss wh : 0.3299141228199005,loss obj : 207.20635986328125，loss cls : 8.879082679748535,loss l2 : 4.048442363739014,lr : 0.001\n",
            "Epoch 15/50:  25%|▏| 50/202 [00:26<00:58,  2.62img2021-04-10 01:17:39,636 train.py[line:353] INFO: Train step_1440: loss : 437.9598388671875,loss xy : 93.89312744140625,loss wh : 17.24468994140625,loss obj : 267.4040832519531，loss cls : 59.417930603027344,loss l2 : 70.3855972290039,lr : 0.001\n",
            "Epoch 15/50:  45%|▍| 90/202 [00:39<00:39,  2.81img2021-04-10 01:17:52,796 train.py[line:353] INFO: Train step_1460: loss : 246.65052795410156,loss xy : 32.632484436035156,loss wh : 1.5455999374389648,loss obj : 192.35267639160156，loss cls : 20.11977195739746,loss l2 : 12.441817283630371,lr : 0.001\n",
            "Epoch 15/50:  64%|▋| 130/202 [00:53<00:22,  3.21im2021-04-10 01:18:06,804 train.py[line:353] INFO: Train step_1480: loss : 307.2374267578125,loss xy : 66.05764770507812,loss wh : 1.7574889659881592,loss obj : 206.237060546875，loss cls : 33.18524169921875,loss l2 : 20.823627471923828,lr : 0.001\n",
            "Epoch 15/50:  84%|▊| 170/202 [01:07<00:10,  2.97im2021-04-10 01:18:20,653 train.py[line:353] INFO: Train step_1500: loss : 363.19549560546875,loss xy : 82.72697448730469,loss wh : 11.178084373474121,loss obj : 216.7362823486328，loss cls : 52.554134368896484,loss l2 : 50.88805389404297,lr : 0.001\n",
            "Epoch 15/50: 100%|█| 202/202 [01:13<00:00,  7.13im2021-04-10 01:18:26,636 train.py[line:364] INFO: Checkpoint 15 saved !\n",
            "Epoch 15/50: 100%|█| 202/202 [01:13<00:00,  2.73im\n",
            "Epoch 16/50:   4%| | 8/202 [00:08<04:15,  1.32s/im2021-04-10 01:18:36,017 train.py[line:353] INFO: Train step_1520: loss : 347.1835021972656,loss xy : 90.32067108154297,loss wh : 7.800396919250488,loss obj : 213.1803741455078，loss cls : 35.88205337524414,loss l2 : 39.29815673828125,lr : 0.001\n",
            "Epoch 16/50:  24%|▏| 48/202 [00:23<00:55,  2.80img2021-04-10 01:18:50,822 train.py[line:353] INFO: Train step_1540: loss : 270.7102966308594,loss xy : 74.23224639892578,loss wh : 3.930176258087158,loss obj : 170.5782928466797，loss cls : 21.96956443786621,loss l2 : 18.137292861938477,lr : 0.001\n",
            "Epoch 16/50:  44%|▍| 88/202 [00:37<00:43,  2.61img2021-04-10 01:19:04,804 train.py[line:353] INFO: Train step_1560: loss : 216.4896240234375,loss xy : 47.40498733520508,loss wh : 0.9778068661689758,loss obj : 155.61734008789062，loss cls : 12.48948860168457,loss l2 : 7.5124664306640625,lr : 0.001\n",
            "Epoch 16/50:  63%|▋| 128/202 [00:52<00:26,  2.77im2021-04-10 01:19:19,710 train.py[line:353] INFO: Train step_1580: loss : 175.29800415039062,loss xy : 17.192495346069336,loss wh : 1.1452572345733643,loss obj : 148.80625915527344，loss cls : 8.153995513916016,loss l2 : 6.656440258026123,lr : 0.001\n",
            "Epoch 16/50:  83%|▊| 168/202 [01:06<00:12,  2.73im2021-04-10 01:19:33,941 train.py[line:353] INFO: Train step_1600: loss : 311.9451599121094,loss xy : 62.53361892700195,loss wh : 4.028752326965332,loss obj : 197.60733032226562，loss cls : 47.77544021606445,loss l2 : 35.35568618774414,lr : 0.001\n",
            "Epoch 16/50: 100%|█| 202/202 [01:13<00:00,  6.90im2021-04-10 01:19:40,738 train.py[line:364] INFO: Checkpoint 16 saved !\n",
            "Epoch 16/50: 100%|█| 202/202 [01:14<00:00,  2.73im\n",
            "Epoch 17/50:   3%| | 6/202 [00:04<03:12,  1.02img/2021-04-10 01:19:46,356 train.py[line:353] INFO: Train step_1620: loss : 274.5317077636719,loss xy : 52.841033935546875,loss wh : 5.554619789123535,loss obj : 176.1073760986328，loss cls : 40.0286865234375,loss l2 : 35.705387115478516,lr : 0.001\n",
            "Epoch 17/50:  23%|▏| 46/202 [00:23<01:01,  2.54img2021-04-10 01:20:05,161 train.py[line:353] INFO: Train step_1640: loss : 192.9412078857422,loss xy : 43.465980529785156,loss wh : 1.0301940441131592,loss obj : 139.40911865234375，loss cls : 9.035916328430176,loss l2 : 7.3492560386657715,lr : 0.001\n",
            "Epoch 17/50:  43%|▍| 86/202 [00:37<00:39,  2.95img2021-04-10 01:20:19,250 train.py[line:353] INFO: Train step_1660: loss : 296.27655029296875,loss xy : 111.57107543945312,loss wh : 4.384405136108398,loss obj : 146.26878356933594，loss cls : 34.05228042602539,loss l2 : 24.679668426513672,lr : 0.001\n",
            "Epoch 17/50:  62%|▌| 126/202 [00:50<00:27,  2.81im2021-04-10 01:20:32,389 train.py[line:353] INFO: Train step_1680: loss : 244.5886688232422,loss xy : 71.96894073486328,loss wh : 2.0718789100646973,loss obj : 143.73422241210938，loss cls : 26.81363296508789,loss l2 : 17.016067504882812,lr : 0.001\n",
            "Epoch 17/50:  82%|▊| 166/202 [01:05<00:13,  2.68im2021-04-10 01:20:47,074 train.py[line:353] INFO: Train step_1700: loss : 119.29383850097656,loss xy : 2.6420559883117676,loss wh : 0.24361084401607513,loss obj : 115.54387664794922，loss cls : 0.8642950654029846,loss l2 : 1.095598816871643,lr : 0.001\n",
            "Epoch 17/50: 100%|█| 202/202 [01:12<00:00,  7.39im2021-04-10 01:20:53,871 train.py[line:364] INFO: Checkpoint 17 saved !\n",
            "Epoch 17/50: 100%|█| 202/202 [01:13<00:00,  2.76im\n",
            "Epoch 18/50:   2%| | 4/202 [00:07<07:30,  2.28s/im2021-04-10 01:21:01,608 train.py[line:353] INFO: Train step_1720: loss : 142.58670043945312,loss xy : 25.09360122680664,loss wh : 0.9028539657592773,loss obj : 115.9371337890625，loss cls : 0.6531046032905579,loss l2 : 3.403684377670288,lr : 0.001\n",
            "Epoch 18/50:  22%|▏| 44/202 [00:22<01:01,  2.58img2021-04-10 01:21:17,284 train.py[line:353] INFO: Train step_1740: loss : 273.88031005859375,loss xy : 73.22584533691406,loss wh : 14.412284851074219,loss obj : 150.12425231933594，loss cls : 36.1179313659668,loss l2 : 50.58515930175781,lr : 0.001\n",
            "Epoch 18/50:  42%|▍| 84/202 [00:37<00:41,  2.85img2021-04-10 01:21:31,762 train.py[line:353] INFO: Train step_1760: loss : 147.20281982421875,loss xy : 15.421636581420898,loss wh : 1.3109066486358643,loss obj : 119.55347442626953，loss cls : 10.916812896728516,loss l2 : 9.497794151306152,lr : 0.001\n",
            "Epoch 18/50:  61%|▌| 124/202 [00:53<00:33,  2.36im2021-04-10 01:21:48,329 train.py[line:353] INFO: Train step_1780: loss : 252.48423767089844,loss xy : 69.97637939453125,loss wh : 4.68612003326416,loss obj : 137.03689575195312，loss cls : 40.78483581542969,loss l2 : 30.52682876586914,lr : 0.001\n",
            "Epoch 18/50:  81%|▊| 164/202 [01:07<00:12,  2.99im2021-04-10 01:22:01,631 train.py[line:353] INFO: Train step_1800: loss : 106.47557830810547,loss xy : 5.448044300079346,loss wh : 0.3113382160663605,loss obj : 98.76520538330078，loss cls : 1.9509867429733276,loss l2 : 1.6629408597946167,lr : 0.001\n",
            "Epoch 18/50: 100%|█| 202/202 [01:15<00:00,  7.13im2021-04-10 01:22:09,791 train.py[line:364] INFO: Checkpoint 18 saved !\n",
            "Epoch 18/50: 100%|█| 202/202 [01:15<00:00,  2.66im\n",
            "Epoch 19/50:   1%| | 2/202 [00:03<05:10,  1.55s/im2021-04-10 01:22:13,729 train.py[line:353] INFO: Train step_1820: loss : 166.65444946289062,loss xy : 57.540260314941406,loss wh : 3.466761350631714,loss obj : 101.79084014892578，loss cls : 3.856588363647461,loss l2 : 9.057985305786133,lr : 0.001\n",
            "Epoch 19/50:  21%|▏| 42/202 [00:20<00:57,  2.76img2021-04-10 01:22:31,208 train.py[line:353] INFO: Train step_1840: loss : 158.22372436523438,loss xy : 50.38629150390625,loss wh : 0.835400402545929,loss obj : 103.48098754882812，loss cls : 3.521038770675659,loss l2 : 5.142797946929932,lr : 0.001\n",
            "Epoch 19/50:  41%|▍| 82/202 [00:35<00:41,  2.86img2021-04-10 01:22:46,302 train.py[line:353] INFO: Train step_1860: loss : 216.84112548828125,loss xy : 72.73320007324219,loss wh : 2.8602967262268066,loss obj : 116.97544860839844，loss cls : 24.272193908691406,loss l2 : 20.997556686401367,lr : 0.001\n",
            "Epoch 19/50:  60%|▌| 122/202 [00:51<00:29,  2.69im2021-04-10 01:23:01,831 train.py[line:353] INFO: Train step_1880: loss : 165.13607788085938,loss xy : 31.18318748474121,loss wh : 2.144110918045044,loss obj : 115.1828842163086，loss cls : 16.62590789794922,loss l2 : 15.441773414611816,lr : 0.001\n",
            "Epoch 19/50:  80%|▊| 162/202 [01:05<00:14,  2.80im2021-04-10 01:23:15,980 train.py[line:353] INFO: Train step_1900: loss : 164.0260009765625,loss xy : 34.59495544433594,loss wh : 1.1519355773925781,loss obj : 108.44751739501953，loss cls : 19.831592559814453,loss l2 : 13.65140438079834,lr : 0.001\n",
            "Epoch 19/50: 100%|█| 202/202 [01:14<00:00,  7.40im2021-04-10 01:23:24,855 train.py[line:364] INFO: Checkpoint 19 saved !\n",
            "Epoch 19/50: 100%|█| 202/202 [01:15<00:00,  2.69im\n",
            "Epoch 20/50:   0%|       | 0/202 [00:00<?, ?img/s]2021-04-10 01:23:30,834 train.py[line:353] INFO: Train step_1920: loss : 250.45199584960938,loss xy : 75.54389190673828,loss wh : 3.240633249282837,loss obj : 131.7310791015625，loss cls : 39.936397552490234,loss l2 : 31.350757598876953,lr : 0.001\n",
            "Epoch 20/50:  20%|▏| 40/202 [00:23<01:03,  2.57img2021-04-10 01:23:48,604 train.py[line:353] INFO: Train step_1940: loss : 85.58589935302734,loss xy : 3.221149206161499,loss wh : 0.2897467613220215,loss obj : 80.66756439208984，loss cls : 1.407443881034851,loss l2 : 1.3427767753601074,lr : 0.001\n",
            "Epoch 20/50:  40%|▍| 80/202 [00:36<00:44,  2.73img2021-04-10 01:24:01,876 train.py[line:353] INFO: Train step_1960: loss : 82.27224731445312,loss xy : 4.758033752441406,loss wh : 0.03991065174341202,loss obj : 77.41322326660156，loss cls : 0.06108124181628227,loss l2 : 0.32146188616752625,lr : 0.001\n",
            "Epoch 20/50:  59%|▌| 120/202 [00:50<00:31,  2.56im2021-04-10 01:24:16,144 train.py[line:353] INFO: Train step_1980: loss : 141.06399536132812,loss xy : 26.571392059326172,loss wh : 1.1562750339508057,loss obj : 91.9564437866211，loss cls : 21.379880905151367,loss l2 : 13.995737075805664,lr : 0.001\n",
            "Epoch 20/50:  79%|▊| 160/202 [01:04<00:13,  3.19im2021-04-10 01:24:30,409 train.py[line:353] INFO: Train step_2000: loss : 115.74748992919922,loss xy : 27.599414825439453,loss wh : 1.0889486074447632,loss obj : 82.36656188964844，loss cls : 4.692568302154541,loss l2 : 6.728229999542236,lr : 0.001\n",
            "Epoch 20/50:  99%|▉| 200/202 [01:14<00:00,  7.27im2021-04-10 01:24:39,328 train.py[line:353] INFO: Train step_2020: loss : 256.97784423828125,loss xy : 97.89114379882812,loss wh : 5.61295223236084,loss obj : 122.27913665771484，loss cls : 31.194623947143555,loss l2 : 32.47305679321289,lr : 0.001\n",
            "Epoch 20/50: 100%|█| 202/202 [01:14<00:00,  7.24im2021-04-10 01:24:39,844 train.py[line:364] INFO: Checkpoint 20 saved !\n",
            "Epoch 20/50: 100%|█| 202/202 [01:14<00:00,  2.69im\n",
            "Epoch 21/50:  19%|▏| 38/202 [00:21<01:05,  2.49img2021-04-10 01:25:01,828 train.py[line:353] INFO: Train step_2040: loss : 106.1452865600586,loss xy : 21.96831512451172,loss wh : 0.6725865006446838,loss obj : 79.4685287475586，loss cls : 4.035860538482666,loss l2 : 5.317651748657227,lr : 0.001\n",
            "Epoch 21/50:  39%|▍| 78/202 [00:35<00:38,  3.23img2021-04-10 01:25:15,807 train.py[line:353] INFO: Train step_2060: loss : 108.06607055664062,loss xy : 7.9900126457214355,loss wh : 20.581193923950195,loss obj : 74.12625122070312，loss cls : 5.368610858917236,loss l2 : 43.846622467041016,lr : 0.001\n",
            "Epoch 21/50:  58%|▌| 118/202 [00:49<00:32,  2.59im2021-04-10 01:25:30,300 train.py[line:353] INFO: Train step_2080: loss : 151.34194946289062,loss xy : 30.576183319091797,loss wh : 3.1665868759155273,loss obj : 99.1480712890625，loss cls : 18.45110511779785,loss l2 : 19.787382125854492,lr : 0.001\n",
            "Epoch 21/50:  78%|▊| 158/202 [01:04<00:17,  2.48im2021-04-10 01:25:44,910 train.py[line:353] INFO: Train step_2100: loss : 188.5850830078125,loss xy : 78.18869018554688,loss wh : 1.166166067123413,loss obj : 87.31880187988281，loss cls : 21.911418914794922,loss l2 : 17.430540084838867,lr : 0.001\n",
            "Epoch 21/50:  98%|▉| 198/202 [01:13<00:00,  7.25im2021-04-10 01:25:53,431 train.py[line:353] INFO: Train step_2120: loss : 254.32749938964844,loss xy : 127.62736511230469,loss wh : 2.7672152519226074,loss obj : 93.40637969970703，loss cls : 30.526538848876953,loss l2 : 23.166166305541992,lr : 0.001\n",
            "Epoch 21/50: 100%|█| 202/202 [01:13<00:00,  7.36im2021-04-10 01:25:54,194 train.py[line:364] INFO: Checkpoint 21 saved !\n",
            "Epoch 21/50: 100%|█| 202/202 [01:14<00:00,  2.72im\n",
            "Epoch 22/50:  18%|▏| 36/202 [00:21<01:02,  2.66img2021-04-10 01:26:16,325 train.py[line:353] INFO: Train step_2140: loss : 78.9598617553711,loss xy : 11.553237915039062,loss wh : 0.425189733505249,loss obj : 64.88835144042969，loss cls : 2.0930893421173096,loss l2 : 2.201160192489624,lr : 0.001\n",
            "Epoch 22/50:  38%|▍| 76/202 [00:35<00:44,  2.83img2021-04-10 01:26:30,123 train.py[line:353] INFO: Train step_2160: loss : 95.44962310791016,loss xy : 30.164289474487305,loss wh : 1.1726747751235962,loss obj : 63.05946350097656，loss cls : 1.0531996488571167,loss l2 : 3.331774950027466,lr : 0.001\n",
            "Epoch 22/50:  57%|▌| 116/202 [00:49<00:30,  2.84im2021-04-10 01:26:44,603 train.py[line:353] INFO: Train step_2180: loss : 130.51475524902344,loss xy : 26.780141830444336,loss wh : 1.9504488706588745,loss obj : 83.25973510742188，loss cls : 18.524433135986328,loss l2 : 16.24711036682129,lr : 0.001\n",
            "Epoch 22/50:  77%|▊| 156/202 [01:04<00:15,  2.97im2021-04-10 01:26:59,694 train.py[line:353] INFO: Train step_2200: loss : 176.8883056640625,loss xy : 68.75701141357422,loss wh : 3.72580623626709,loss obj : 83.04774475097656，loss cls : 21.35773468017578,loss l2 : 19.401779174804688,lr : 0.001\n",
            "Epoch 22/50:  97%|▉| 196/202 [01:14<00:00,  7.05im2021-04-10 01:27:09,133 train.py[line:353] INFO: Train step_2220: loss : 165.9166717529297,loss xy : 70.10470581054688,loss wh : 1.6012144088745117,loss obj : 70.74948120117188，loss cls : 23.46127700805664,loss l2 : 14.75564193725586,lr : 0.001\n",
            "Epoch 22/50: 100%|█| 202/202 [01:15<00:00,  7.24im2021-04-10 01:27:10,183 train.py[line:364] INFO: Checkpoint 22 saved !\n",
            "Epoch 22/50: 100%|█| 202/202 [01:15<00:00,  2.66im\n",
            "Epoch 23/50:  17%|▏| 34/202 [00:20<01:04,  2.62img2021-04-10 01:27:31,299 train.py[line:353] INFO: Train step_2240: loss : 194.25213623046875,loss xy : 95.08656311035156,loss wh : 1.9005577564239502,loss obj : 78.0340347290039，loss cls : 19.23099136352539,loss l2 : 15.233442306518555,lr : 0.001\n",
            "Epoch 23/50:  37%|▎| 74/202 [00:36<00:57,  2.21img2021-04-10 01:27:47,090 train.py[line:353] INFO: Train step_2260: loss : 183.12142944335938,loss xy : 81.78504180908203,loss wh : 9.722698211669922,loss obj : 78.23467254638672，loss cls : 13.379020690917969,loss l2 : 28.898466110229492,lr : 0.001\n",
            "Epoch 23/50:  56%|▌| 114/202 [00:49<00:31,  2.81im2021-04-10 01:28:00,793 train.py[line:353] INFO: Train step_2280: loss : 56.329795837402344,loss xy : 4.2033915519714355,loss wh : 0.35506632924079895,loss obj : 50.933712005615234，loss cls : 0.8376269340515137,loss l2 : 1.1248489618301392,lr : 0.001\n",
            "Epoch 23/50:  76%|▊| 154/202 [01:02<00:14,  3.37im2021-04-10 01:28:13,439 train.py[line:353] INFO: Train step_2300: loss : 134.27723693847656,loss xy : 63.032569885253906,loss wh : 1.1785352230072021,loss obj : 60.3862419128418，loss cls : 9.679889678955078,loss l2 : 7.429693222045898,lr : 0.001\n",
            "Epoch 23/50:  96%|▉| 194/202 [01:12<00:01,  6.87im2021-04-10 01:28:23,003 train.py[line:353] INFO: Train step_2320: loss : 117.98820495605469,loss xy : 38.961280822753906,loss wh : 1.67020845413208,loss obj : 61.706790924072266，loss cls : 15.649925231933594,loss l2 : 11.406393051147461,lr : 0.001\n",
            "Epoch 23/50: 100%|█| 202/202 [01:13<00:00,  7.40im2021-04-10 01:28:24,307 train.py[line:364] INFO: Checkpoint 23 saved !\n",
            "Epoch 23/50: 100%|█| 202/202 [01:14<00:00,  2.73im\n",
            "Epoch 24/50:  16%|▏| 32/202 [00:19<01:04,  2.63img2021-04-10 01:28:44,276 train.py[line:353] INFO: Train step_2340: loss : 183.89584350585938,loss xy : 98.71061706542969,loss wh : 2.452925443649292,loss obj : 67.69327545166016，loss cls : 15.039039611816406,loss l2 : 14.69758415222168,lr : 0.001\n",
            "Epoch 24/50:  36%|▎| 72/202 [00:35<00:51,  2.53img2021-04-10 01:29:00,128 train.py[line:353] INFO: Train step_2360: loss : 215.09632873535156,loss xy : 79.55375671386719,loss wh : 17.516599655151367,loss obj : 80.93571472167969，loss cls : 37.09025573730469,loss l2 : 53.03041458129883,lr : 0.001\n",
            "Epoch 24/50:  55%|▌| 112/202 [00:48<00:30,  2.95im2021-04-10 01:29:13,865 train.py[line:353] INFO: Train step_2380: loss : 222.71493530273438,loss xy : 87.1300048828125,loss wh : 2.8665859699249268,loss obj : 90.96439361572266，loss cls : 41.75395965576172,loss l2 : 29.94683837890625,lr : 0.001\n",
            "Epoch 24/50:  75%|▊| 152/202 [01:03<00:15,  3.22im2021-04-10 01:29:28,506 train.py[line:353] INFO: Train step_2400: loss : 161.61727905273438,loss xy : 80.82855987548828,loss wh : 2.4652795791625977,loss obj : 63.165504455566406，loss cls : 15.157930374145508,loss l2 : 14.293170928955078,lr : 0.001\n",
            "Epoch 24/50:  95%|▉| 192/202 [01:13<00:01,  6.79im2021-04-10 01:29:38,071 train.py[line:353] INFO: Train step_2420: loss : 48.627559661865234,loss xy : 4.936980247497559,loss wh : 0.07101692259311676,loss obj : 43.390499114990234，loss cls : 0.22906525433063507,loss l2 : 0.36374926567077637,lr : 0.001\n",
            "Epoch 24/50: 100%|█| 202/202 [01:14<00:00,  7.29im2021-04-10 01:29:39,705 train.py[line:364] INFO: Checkpoint 24 saved !\n",
            "Epoch 24/50: 100%|█| 202/202 [01:15<00:00,  2.68im\n",
            "Epoch 25/50:  15%|▏| 30/202 [00:18<01:08,  2.52img2021-04-10 01:29:59,437 train.py[line:353] INFO: Train step_2440: loss : 195.78286743164062,loss xy : 80.74955749511719,loss wh : 2.2480878829956055,loss obj : 74.33039855957031，loss cls : 38.454833984375,loss l2 : 24.726062774658203,lr : 0.001\n",
            "Epoch 25/50:  35%|▎| 70/202 [00:36<01:07,  1.95img2021-04-10 01:30:16,614 train.py[line:353] INFO: Train step_2460: loss : 72.06336212158203,loss xy : 25.631235122680664,loss wh : 0.379619300365448,loss obj : 44.89743423461914，loss cls : 1.1550729274749756,loss l2 : 2.2338271141052246,lr : 0.001\n",
            "Epoch 25/50:  54%|▌| 110/202 [00:49<00:29,  3.07im2021-04-10 01:30:30,457 train.py[line:353] INFO: Train step_2480: loss : 70.24506378173828,loss xy : 20.896318435668945,loss wh : 0.505260169506073,loss obj : 43.43391799926758，loss cls : 5.409569263458252,loss l2 : 3.3512110710144043,lr : 0.001\n",
            "Epoch 25/50:  74%|▋| 150/202 [01:05<00:17,  3.04im2021-04-10 01:30:45,825 train.py[line:353] INFO: Train step_2500: loss : 254.2509307861328,loss xy : 115.9969253540039,loss wh : 6.53272008895874,loss obj : 83.70872497558594，loss cls : 48.01255798339844,loss l2 : 37.139915466308594,lr : 0.001\n",
            "Epoch 25/50:  94%|▉| 190/202 [01:17<00:01,  6.51im2021-04-10 01:30:57,051 train.py[line:353] INFO: Train step_2520: loss : 193.37228393554688,loss xy : 86.6129150390625,loss wh : 3.0708370208740234,loss obj : 71.53744506835938，loss cls : 32.151100158691406,loss l2 : 25.13603401184082,lr : 0.001\n",
            "Epoch 25/50: 100%|█| 202/202 [01:18<00:00,  7.25im2021-04-10 01:30:58,917 train.py[line:364] INFO: Checkpoint 25 saved !\n",
            "Epoch 25/50: 100%|█| 202/202 [01:19<00:00,  2.55im\n",
            "Epoch 26/50:  14%|▏| 28/202 [00:17<01:08,  2.53img2021-04-10 01:31:17,502 train.py[line:353] INFO: Train step_2540: loss : 216.48971557617188,loss xy : 100.76866149902344,loss wh : 3.0877511501312256,loss obj : 72.63232421875，loss cls : 40.0009765625,loss l2 : 26.82193946838379,lr : 0.001\n",
            "Epoch 26/50:  34%|▎| 68/202 [00:32<00:45,  2.97img2021-04-10 01:31:31,942 train.py[line:353] INFO: Train step_2560: loss : 52.98696517944336,loss xy : 7.565877914428711,loss wh : 0.14191210269927979,loss obj : 41.2838134765625，loss cls : 3.995360851287842,loss l2 : 2.094816207885742,lr : 0.001\n",
            "Epoch 26/50:  53%|▌| 108/202 [00:46<00:36,  2.57im2021-04-10 01:31:46,639 train.py[line:353] INFO: Train step_2580: loss : 133.09193420410156,loss xy : 58.997711181640625,loss wh : 3.720747470855713,loss obj : 49.81911087036133，loss cls : 20.554372787475586,loss l2 : 17.17680549621582,lr : 0.001\n",
            "Epoch 26/50:  73%|▋| 148/202 [01:00<00:14,  3.70im2021-04-10 01:31:59,596 train.py[line:353] INFO: Train step_2600: loss : 128.65780639648438,loss xy : 69.7994613647461,loss wh : 1.108393907546997,loss obj : 43.86569595336914，loss cls : 13.884256362915039,loss l2 : 7.887696266174316,lr : 0.001\n",
            "Epoch 26/50:  93%|▉| 188/202 [01:11<00:02,  6.28im2021-04-10 01:32:10,373 train.py[line:353] INFO: Train step_2620: loss : 108.5072021484375,loss xy : 54.66748046875,loss wh : 0.9088927507400513,loss obj : 39.37493896484375，loss cls : 13.55589485168457,loss l2 : 5.195003986358643,lr : 0.001\n",
            "Epoch 26/50: 100%|█| 202/202 [01:13<00:00,  7.47im2021-04-10 01:32:12,461 train.py[line:364] INFO: Checkpoint 26 saved !\n",
            "Epoch 26/50: 100%|█| 202/202 [01:13<00:00,  2.75im\n",
            "Epoch 27/50:  13%|▏| 26/202 [00:15<01:16,  2.30img2021-04-10 01:32:28,841 train.py[line:353] INFO: Train step_2640: loss : 49.54951858520508,loss xy : 11.027167320251465,loss wh : 0.3540714383125305,loss obj : 37.24009704589844，loss cls : 0.9281797409057617,loss l2 : 1.9089659452438354,lr : 0.001\n",
            "Epoch 27/50:  33%|▎| 66/202 [00:31<00:50,  2.69img2021-04-10 01:32:44,397 train.py[line:353] INFO: Train step_2660: loss : 117.32654571533203,loss xy : 56.674964904785156,loss wh : 0.5221665501594543,loss obj : 41.154823303222656，loss cls : 18.974586486816406,loss l2 : 6.883397102355957,lr : 0.001\n",
            "Epoch 27/50:  52%|▌| 106/202 [00:45<00:33,  2.89im2021-04-10 01:32:58,946 train.py[line:353] INFO: Train step_2680: loss : 206.58078002929688,loss xy : 82.7354507446289,loss wh : 4.951923370361328,loss obj : 74.62193298339844，loss cls : 44.27147674560547,loss l2 : 30.356002807617188,lr : 0.001\n",
            "Epoch 27/50:  72%|▋| 146/202 [00:58<00:19,  2.88im2021-04-10 01:33:12,098 train.py[line:353] INFO: Train step_2700: loss : 45.424110412597656,loss xy : 9.572946548461914,loss wh : 0.37894973158836365,loss obj : 33.37376403808594，loss cls : 2.0984506607055664,loss l2 : 1.6514461040496826,lr : 0.001\n",
            "Epoch 27/50:  92%|▉| 186/202 [01:11<00:03,  5.16im2021-04-10 01:33:23,966 train.py[line:353] INFO: Train step_2720: loss : 131.42367553710938,loss xy : 64.9258041381836,loss wh : 1.505683422088623,loss obj : 48.4207649230957，loss cls : 16.571430206298828,loss l2 : 12.16895866394043,lr : 0.001\n",
            "Epoch 27/50: 100%|█| 202/202 [01:13<00:00,  7.48im2021-04-10 01:33:26,381 train.py[line:364] INFO: Checkpoint 27 saved !\n",
            "Epoch 27/50: 100%|█| 202/202 [01:13<00:00,  2.73im\n",
            "Epoch 28/50:  12%| | 24/202 [00:16<01:16,  2.31img2021-04-10 01:33:43,532 train.py[line:353] INFO: Train step_2740: loss : 69.40375518798828,loss xy : 18.962284088134766,loss wh : 0.6559587717056274,loss obj : 36.34981155395508，loss cls : 13.435702323913574,loss l2 : 6.87461519241333,lr : 0.001\n",
            "Epoch 28/50:  32%|▎| 64/202 [00:31<00:43,  3.15img2021-04-10 01:33:58,093 train.py[line:353] INFO: Train step_2760: loss : 138.0472412109375,loss xy : 70.09904479980469,loss wh : 1.8538485765457153,loss obj : 51.52067947387695，loss cls : 14.573670387268066,loss l2 : 13.891426086425781,lr : 0.001\n",
            "Epoch 28/50:  51%|▌| 104/202 [00:44<00:34,  2.83im2021-04-10 01:34:11,902 train.py[line:353] INFO: Train step_2780: loss : 143.7015380859375,loss xy : 64.28282165527344,loss wh : 1.2077524662017822,loss obj : 45.751888275146484，loss cls : 32.459068298339844,loss l2 : 15.064163208007812,lr : 0.001\n",
            "Epoch 28/50:  71%|▋| 144/202 [00:57<00:17,  3.32im2021-04-10 01:34:24,695 train.py[line:353] INFO: Train step_2800: loss : 74.03539276123047,loss xy : 28.984779357910156,loss wh : 1.1664822101593018,loss obj : 35.504913330078125，loss cls : 8.379217147827148,loss l2 : 6.825533866882324,lr : 0.001\n",
            "Epoch 28/50:  91%|▉| 184/202 [01:08<00:04,  4.43im2021-04-10 01:34:35,598 train.py[line:353] INFO: Train step_2820: loss : 156.45814514160156,loss xy : 72.66160583496094,loss wh : 3.2810726165771484,loss obj : 51.91535568237305，loss cls : 28.600116729736328,loss l2 : 20.533784866333008,lr : 0.001\n",
            "Epoch 28/50: 100%|█| 202/202 [01:11<00:00,  7.25im2021-04-10 01:34:38,244 train.py[line:364] INFO: Checkpoint 28 saved !\n",
            "Epoch 28/50: 100%|█| 202/202 [01:11<00:00,  2.81im\n",
            "Epoch 29/50:  11%| | 22/202 [00:15<01:52,  1.60img2021-04-10 01:34:54,263 train.py[line:353] INFO: Train step_2840: loss : 60.63152313232422,loss xy : 18.683828353881836,loss wh : 0.956782341003418,loss obj : 39.143287658691406，loss cls : 1.8476260900497437,loss l2 : 4.67448091506958,lr : 0.001\n",
            "Epoch 29/50:  31%|▎| 62/202 [00:29<00:46,  3.04img2021-04-10 01:35:08,514 train.py[line:353] INFO: Train step_2860: loss : 153.01742553710938,loss xy : 62.586524963378906,loss wh : 37.21589660644531,loss obj : 41.99471664428711，loss cls : 11.220287322998047,loss l2 : 81.41421508789062,lr : 0.001\n",
            "Epoch 29/50:  50%|▌| 102/202 [00:45<00:36,  2.72im2021-04-10 01:35:24,240 train.py[line:353] INFO: Train step_2880: loss : 97.49870300292969,loss xy : 43.30513000488281,loss wh : 2.0080864429473877,loss obj : 43.954734802246094，loss cls : 8.230749130249023,loss l2 : 11.612350463867188,lr : 0.001\n",
            "Epoch 29/50:  70%|▋| 142/202 [00:59<00:22,  2.71im2021-04-10 01:35:38,965 train.py[line:353] INFO: Train step_2900: loss : 224.70106506347656,loss xy : 88.91792297363281,loss wh : 30.02227210998535,loss obj : 74.43016815185547，loss cls : 31.33070945739746,loss l2 : 80.67632293701172,lr : 0.001\n",
            "Epoch 29/50:  90%|▉| 182/202 [01:12<00:04,  4.66im2021-04-10 01:35:51,210 train.py[line:353] INFO: Train step_2920: loss : 61.15920639038086,loss xy : 26.58614730834961,loss wh : 1.0336148738861084,loss obj : 30.45783233642578，loss cls : 3.081610679626465,loss l2 : 4.14040994644165,lr : 0.001\n",
            "Epoch 29/50: 100%|█| 202/202 [01:15<00:00,  7.42im2021-04-10 01:35:54,161 train.py[line:364] INFO: Checkpoint 29 saved !\n",
            "Epoch 29/50: 100%|█| 202/202 [01:15<00:00,  2.66im\n",
            "Epoch 30/50:  10%| | 20/202 [00:14<01:30,  2.00img2021-04-10 01:36:09,609 train.py[line:353] INFO: Train step_2940: loss : 353.46563720703125,loss xy : 170.62908935546875,loss wh : 4.242892265319824,loss obj : 101.47994995117188，loss cls : 77.11369323730469,loss l2 : 49.6078987121582,lr : 0.001\n",
            "Epoch 30/50:  30%|▎| 60/202 [00:29<00:59,  2.39img2021-04-10 01:36:24,703 train.py[line:353] INFO: Train step_2960: loss : 79.21976470947266,loss xy : 46.25642395019531,loss wh : 0.7142276167869568,loss obj : 28.37237548828125，loss cls : 3.8767426013946533,loss l2 : 3.754115581512451,lr : 0.001\n",
            "Epoch 30/50:  50%|▍| 100/202 [00:44<00:36,  2.81im2021-04-10 01:36:39,033 train.py[line:353] INFO: Train step_2980: loss : 40.08972930908203,loss xy : 6.961911678314209,loss wh : 0.01471896655857563,loss obj : 31.903779983520508，loss cls : 1.2093209028244019,loss l2 : 2.240676164627075,lr : 0.001\n",
            "Epoch 30/50:  69%|▋| 140/202 [00:59<00:21,  2.84im2021-04-10 01:36:54,193 train.py[line:353] INFO: Train step_3000: loss : 149.2912139892578,loss xy : 88.45133972167969,loss wh : 1.467912197113037,loss obj : 31.230684280395508，loss cls : 28.141277313232422,loss l2 : 11.387655258178711,lr : 0.001\n",
            "Epoch 30/50:  89%|▉| 180/202 [01:12<00:06,  3.42im2021-04-10 01:37:06,745 train.py[line:353] INFO: Train step_3020: loss : 51.67377471923828,loss xy : 15.577839851379395,loss wh : 0.2519364058971405,loss obj : 25.725828170776367，loss cls : 10.118171691894531,loss l2 : 3.0316648483276367,lr : 0.001\n",
            "Epoch 30/50: 100%|█| 202/202 [01:15<00:00,  7.16im2021-04-10 01:37:10,111 train.py[line:364] INFO: Checkpoint 30 saved !\n",
            "Epoch 30/50: 100%|█| 202/202 [01:15<00:00,  2.66im\n",
            "Epoch 31/50:   9%| | 18/202 [00:12<01:41,  1.81img2021-04-10 01:37:23,825 train.py[line:353] INFO: Train step_3040: loss : 199.57052612304688,loss xy : 120.98912811279297,loss wh : 4.086321830749512,loss obj : 56.40310287475586，loss cls : 18.09198760986328,loss l2 : 22.68833351135254,lr : 0.001\n",
            "Epoch 31/50:  29%|▎| 58/202 [00:29<00:58,  2.45img2021-04-10 01:37:40,049 train.py[line:353] INFO: Train step_3060: loss : 165.5474090576172,loss xy : 77.44949340820312,loss wh : 3.286858320236206,loss obj : 58.07544708251953，loss cls : 26.73560905456543,loss l2 : 23.63813018798828,lr : 0.001\n",
            "Epoch 31/50:  49%|▍| 98/202 [00:43<00:35,  2.97img2021-04-10 01:37:54,574 train.py[line:353] INFO: Train step_3080: loss : 130.39382934570312,loss xy : 82.8184814453125,loss wh : 1.4020055532455444,loss obj : 32.434242248535156，loss cls : 13.739097595214844,loss l2 : 9.766392707824707,lr : 0.001\n",
            "Epoch 31/50:  68%|▋| 138/202 [00:57<00:23,  2.72im2021-04-10 01:38:07,938 train.py[line:353] INFO: Train step_3100: loss : 203.9542236328125,loss xy : 95.50867462158203,loss wh : 4.061895847320557,loss obj : 61.70152282714844，loss cls : 42.68212127685547,loss l2 : 32.45840072631836,lr : 0.001\n",
            "Epoch 31/50:  88%|▉| 178/202 [01:11<00:08,  2.86im2021-04-10 01:38:22,018 train.py[line:353] INFO: Train step_3120: loss : 100.91659545898438,loss xy : 45.1386604309082,loss wh : 0.9427379369735718,loss obj : 38.90370559692383，loss cls : 15.93149185180664,loss l2 : 10.78454875946045,lr : 0.001\n",
            "Epoch 31/50: 100%|█| 202/202 [01:15<00:00,  7.11im2021-04-10 01:38:25,784 train.py[line:364] INFO: Checkpoint 31 saved !\n",
            "Epoch 31/50: 100%|█| 202/202 [01:15<00:00,  2.67im\n",
            "Epoch 32/50:   8%| | 16/202 [00:12<01:43,  1.81img2021-04-10 01:38:38,588 train.py[line:353] INFO: Train step_3140: loss : 32.215049743652344,loss xy : 4.898458480834961,loss wh : 0.04021868854761124,loss obj : 23.15422821044922，loss cls : 4.122145175933838,loss l2 : 1.9235212802886963,lr : 0.001\n",
            "Epoch 32/50:  28%|▎| 56/202 [00:27<00:57,  2.56img2021-04-10 01:38:54,178 train.py[line:353] INFO: Train step_3160: loss : 125.88191223144531,loss xy : 72.26771545410156,loss wh : 1.4731003046035767,loss obj : 35.652400970458984，loss cls : 16.488691329956055,loss l2 : 11.410181045532227,lr : 0.001\n",
            "Epoch 32/50:  48%|▍| 96/202 [00:38<00:24,  4.36img2021-04-10 01:39:05,113 train.py[line:353] INFO: Train step_3180: loss : 68.00161743164062,loss xy : 25.663679122924805,loss wh : 1.3593162298202515,loss obj : 35.80538558959961，loss cls : 5.173233509063721,loss l2 : 8.481352806091309,lr : 0.001\n",
            "Epoch 32/50:  67%|▋| 136/202 [00:52<00:22,  2.96im2021-04-10 01:39:19,232 train.py[line:353] INFO: Train step_3200: loss : 169.2762451171875,loss xy : 93.53239440917969,loss wh : 2.378110647201538,loss obj : 51.63856506347656，loss cls : 21.727170944213867,loss l2 : 17.55952262878418,lr : 0.001\n",
            "Epoch 32/50:  87%|▊| 176/202 [01:06<00:09,  2.84im2021-04-10 01:39:33,066 train.py[line:353] INFO: Train step_3220: loss : 174.27748107910156,loss xy : 118.3357925415039,loss wh : 2.045708656311035,loss obj : 44.961181640625，loss cls : 8.934799194335938,loss l2 : 14.032184600830078,lr : 0.001\n",
            "Epoch 32/50: 100%|█| 202/202 [01:10<00:00,  7.20im2021-04-10 01:39:37,141 train.py[line:364] INFO: Checkpoint 32 saved !\n",
            "Epoch 32/50: 100%|█| 202/202 [01:11<00:00,  2.83im\n",
            "Epoch 33/50:   7%| | 14/202 [00:13<02:39,  1.18img2021-04-10 01:39:51,048 train.py[line:353] INFO: Train step_3240: loss : 86.563720703125,loss xy : 36.5187873840332,loss wh : 1.5696085691452026,loss obj : 35.154537200927734，loss cls : 13.32078742980957,loss l2 : 10.916910171508789,lr : 0.001\n",
            "Epoch 33/50:  27%|▎| 54/202 [00:27<00:46,  3.16img2021-04-10 01:40:04,989 train.py[line:353] INFO: Train step_3260: loss : 52.945777893066406,loss xy : 20.565139770507812,loss wh : 0.7467235326766968,loss obj : 22.48993682861328，loss cls : 9.1439790725708,loss l2 : 5.07237434387207,lr : 0.001\n",
            "Epoch 33/50:  47%|▍| 94/202 [00:41<00:37,  2.89img2021-04-10 01:40:19,671 train.py[line:353] INFO: Train step_3280: loss : 186.959228515625,loss xy : 100.994873046875,loss wh : 4.40449333190918,loss obj : 45.1892204284668，loss cls : 36.370643615722656,loss l2 : 24.53280258178711,lr : 0.001\n",
            "Epoch 33/50:  66%|▋| 134/202 [00:56<00:24,  2.81im2021-04-10 01:40:34,099 train.py[line:353] INFO: Train step_3300: loss : 36.46684265136719,loss xy : 17.088882446289062,loss wh : 0.26906177401542664,loss obj : 19.001855850219727，loss cls : 0.10704183578491211,loss l2 : 0.6791365742683411,lr : 0.001\n",
            "Epoch 33/50:  86%|▊| 174/202 [01:11<00:10,  2.60im2021-04-10 01:40:49,432 train.py[line:353] INFO: Train step_3320: loss : 76.12960052490234,loss xy : 21.093276977539062,loss wh : 1.7639663219451904,loss obj : 47.837425231933594，loss cls : 5.43493127822876,loss l2 : 10.6649169921875,lr : 0.001\n",
            "Epoch 33/50: 100%|█| 202/202 [01:17<00:00,  6.73im2021-04-10 01:40:55,182 train.py[line:364] INFO: Checkpoint 33 saved !\n",
            "Epoch 33/50: 100%|█| 202/202 [01:18<00:00,  2.59im\n",
            "Epoch 34/50:   6%| | 12/202 [00:10<02:42,  1.17img2021-04-10 01:41:06,120 train.py[line:353] INFO: Train step_3340: loss : 106.69876098632812,loss xy : 53.55080795288086,loss wh : 1.2435567378997803,loss obj : 31.47883415222168，loss cls : 20.42556381225586,loss l2 : 12.464786529541016,lr : 0.001\n",
            "Epoch 34/50:  26%|▎| 52/202 [00:25<01:02,  2.41img2021-04-10 01:41:21,584 train.py[line:353] INFO: Train step_3360: loss : 146.57337951660156,loss xy : 98.6887435913086,loss wh : 1.6396448612213135,loss obj : 31.667051315307617，loss cls : 14.577940940856934,loss l2 : 11.748680114746094,lr : 0.001\n",
            "Epoch 34/50:  46%|▍| 92/202 [00:41<00:38,  2.85img2021-04-10 01:41:37,294 train.py[line:353] INFO: Train step_3380: loss : 122.43535614013672,loss xy : 57.377288818359375,loss wh : 2.044858932495117,loss obj : 39.83181381225586，loss cls : 23.1813907623291,loss l2 : 16.268468856811523,lr : 0.001\n",
            "Epoch 34/50:  65%|▋| 132/202 [00:55<00:27,  2.56im2021-04-10 01:41:51,701 train.py[line:353] INFO: Train step_3400: loss : 59.139404296875,loss xy : 25.727481842041016,loss wh : 1.00927734375,loss obj : 28.851558685302734，loss cls : 3.55108642578125,loss l2 : 6.792724609375,lr : 0.001\n",
            "Epoch 34/50:  85%|▊| 172/202 [01:11<00:11,  2.60im2021-04-10 01:42:07,712 train.py[line:353] INFO: Train step_3420: loss : 66.00241088867188,loss xy : 24.98310661315918,loss wh : 0.6027589440345764,loss obj : 29.164533615112305，loss cls : 11.252010345458984,loss l2 : 7.157970428466797,lr : 0.001\n",
            "Epoch 34/50: 100%|█| 202/202 [01:17<00:00,  7.15im2021-04-10 01:42:13,296 train.py[line:364] INFO: Checkpoint 34 saved !\n",
            "Epoch 34/50: 100%|█| 202/202 [01:18<00:00,  2.59im\n",
            "Epoch 35/50:   5%| | 10/202 [00:10<03:40,  1.15s/i2021-04-10 01:42:24,795 train.py[line:353] INFO: Train step_3440: loss : 105.9380874633789,loss xy : 44.3488655090332,loss wh : 12.714422225952148,loss obj : 33.483089447021484，loss cls : 15.391717910766602,loss l2 : 32.83182144165039,lr : 0.001\n",
            "Epoch 35/50:  25%|▏| 50/202 [00:26<00:58,  2.60img2021-04-10 01:42:40,914 train.py[line:353] INFO: Train step_3460: loss : 34.139217376708984,loss xy : 13.893292427062988,loss wh : 0.3539775013923645,loss obj : 18.477270126342773，loss cls : 1.4146769046783447,loss l2 : 1.6019160747528076,lr : 0.001\n",
            "Epoch 35/50:  45%|▍| 90/202 [00:42<00:41,  2.72img2021-04-10 01:42:56,259 train.py[line:353] INFO: Train step_3480: loss : 85.90414428710938,loss xy : 46.642940521240234,loss wh : 1.0781023502349854,loss obj : 25.173864364624023，loss cls : 13.009239196777344,loss l2 : 9.57879638671875,lr : 0.001\n",
            "Epoch 35/50:  64%|▋| 130/202 [00:56<00:27,  2.59im2021-04-10 01:43:10,764 train.py[line:353] INFO: Train step_3500: loss : 150.8695831298828,loss xy : 78.42143249511719,loss wh : 3.1234130859375,loss obj : 43.44267654418945，loss cls : 25.882062911987305,loss l2 : 21.3369140625,lr : 0.001\n",
            "Epoch 35/50:  84%|▊| 170/202 [01:12<00:11,  2.77im2021-04-10 01:43:26,065 train.py[line:353] INFO: Train step_3520: loss : 165.51722717285156,loss xy : 75.08485412597656,loss wh : 2.199430465698242,loss obj : 52.91416931152344，loss cls : 35.31876754760742,loss l2 : 23.950855255126953,lr : 0.001\n",
            "Epoch 35/50: 100%|█| 202/202 [01:18<00:00,  7.05im2021-04-10 01:43:32,254 train.py[line:364] INFO: Checkpoint 35 saved !\n",
            "Epoch 35/50: 100%|█| 202/202 [01:18<00:00,  2.56im\n",
            "Epoch 36/50:   4%| | 8/202 [00:07<03:04,  1.05img/2021-04-10 01:43:40,701 train.py[line:353] INFO: Train step_3540: loss : 71.11544799804688,loss xy : 44.52970886230469,loss wh : 0.7105406522750854,loss obj : 18.24599838256836，loss cls : 7.629199981689453,loss l2 : 4.663638114929199,lr : 0.001\n",
            "Epoch 36/50:  24%|▏| 48/202 [00:24<00:57,  2.70img2021-04-10 01:43:57,947 train.py[line:353] INFO: Train step_3560: loss : 136.4510955810547,loss xy : 68.67300415039062,loss wh : 1.2251094579696655,loss obj : 43.255149841308594，loss cls : 23.297832489013672,loss l2 : 17.382904052734375,lr : 0.001\n",
            "Epoch 36/50:  44%|▍| 88/202 [00:39<00:43,  2.61img2021-04-10 01:44:12,657 train.py[line:353] INFO: Train step_3580: loss : 163.16754150390625,loss xy : 79.27442932128906,loss wh : 2.863271951675415,loss obj : 44.91518020629883，loss cls : 36.114654541015625,loss l2 : 25.183204650878906,lr : 0.001\n",
            "Epoch 36/50:  63%|▋| 128/202 [00:54<00:27,  2.69im2021-04-10 01:44:27,868 train.py[line:353] INFO: Train step_3600: loss : 163.87197875976562,loss xy : 95.42047119140625,loss wh : 5.911027908325195,loss obj : 44.41814041137695，loss cls : 18.122344970703125,loss l2 : 25.205549240112305,lr : 0.001\n",
            "Epoch 36/50:  83%|▊| 168/202 [01:09<00:12,  2.82im2021-04-10 01:44:42,582 train.py[line:353] INFO: Train step_3620: loss : 102.1566390991211,loss xy : 59.84489059448242,loss wh : 1.8378000259399414,loss obj : 24.349058151245117，loss cls : 16.124893188476562,loss l2 : 10.433525085449219,lr : 0.001\n",
            "Epoch 36/50: 100%|█| 202/202 [01:16<00:00,  7.32im2021-04-10 01:44:48,940 train.py[line:364] INFO: Checkpoint 36 saved !\n",
            "Epoch 36/50: 100%|█| 202/202 [01:16<00:00,  2.63im\n",
            "Epoch 37/50:   3%| | 6/202 [00:10<06:52,  2.10s/im2021-04-10 01:45:00,021 train.py[line:353] INFO: Train step_3640: loss : 32.667869567871094,loss xy : 10.734224319458008,loss wh : 0.2990868389606476,loss obj : 17.447479248046875，loss cls : 4.187080383300781,loss l2 : 2.786860466003418,lr : 0.001\n",
            "Epoch 37/50:  23%|▏| 46/202 [00:25<01:00,  2.59img2021-04-10 01:45:15,179 train.py[line:353] INFO: Train step_3660: loss : 51.1323356628418,loss xy : 31.536460876464844,loss wh : 0.6428993940353394,loss obj : 17.309345245361328，loss cls : 1.6436293125152588,loss l2 : 2.9146578311920166,lr : 0.001\n",
            "Epoch 37/50:  43%|▍| 86/202 [00:40<00:35,  3.29img2021-04-10 01:45:30,177 train.py[line:353] INFO: Train step_3680: loss : 126.76195526123047,loss xy : 70.4312744140625,loss wh : 2.5226619243621826,loss obj : 42.48526382446289，loss cls : 11.322758674621582,loss l2 : 15.951590538024902,lr : 0.001\n",
            "Epoch 37/50:  62%|▌| 126/202 [00:56<00:31,  2.45im2021-04-10 01:45:45,790 train.py[line:353] INFO: Train step_3700: loss : 82.5103988647461,loss xy : 51.90684509277344,loss wh : 1.1287606954574585,loss obj : 19.393251419067383，loss cls : 10.08154010772705,loss l2 : 7.372613906860352,lr : 0.001\n",
            "Epoch 37/50:  82%|▊| 166/202 [01:10<00:13,  2.76im2021-04-10 01:46:00,343 train.py[line:353] INFO: Train step_3720: loss : 30.446796417236328,loss xy : 12.473281860351562,loss wh : 0.39263978600502014,loss obj : 17.27913475036621，loss cls : 0.3017393946647644,loss l2 : 1.506569266319275,lr : 0.001\n",
            "Epoch 37/50: 100%|█| 202/202 [01:17<00:00,  7.48im2021-04-10 01:46:06,906 train.py[line:364] INFO: Checkpoint 37 saved !\n",
            "Epoch 37/50: 100%|█| 202/202 [01:17<00:00,  2.59im\n",
            "Epoch 38/50:   2%| | 4/202 [00:08<10:02,  3.04s/im2021-04-10 01:46:16,452 train.py[line:353] INFO: Train step_3740: loss : 80.7332534790039,loss xy : 48.15760040283203,loss wh : 0.795521080493927,loss obj : 25.05402374267578，loss cls : 6.726109504699707,loss l2 : 7.6406450271606445,lr : 0.001\n",
            "Epoch 38/50:  22%|▏| 44/202 [00:23<01:02,  2.51img2021-04-10 01:46:31,286 train.py[line:353] INFO: Train step_3760: loss : 72.0944595336914,loss xy : 32.774085998535156,loss wh : 1.69230055809021,loss obj : 32.478843688964844，loss cls : 5.149233818054199,loss l2 : 10.804075241088867,lr : 0.001\n",
            "Epoch 38/50:  42%|▍| 84/202 [00:37<00:39,  2.95img2021-04-10 01:46:45,832 train.py[line:353] INFO: Train step_3780: loss : 110.19515991210938,loss xy : 62.0908317565918,loss wh : 1.3473594188690186,loss obj : 26.195659637451172，loss cls : 20.56131362915039,loss l2 : 12.63768196105957,lr : 0.001\n",
            "Epoch 38/50:  61%|▌| 124/202 [00:54<00:31,  2.45im2021-04-10 01:47:01,771 train.py[line:353] INFO: Train step_3800: loss : 96.41455841064453,loss xy : 56.36219024658203,loss wh : 1.0708599090576172,loss obj : 32.18807601928711，loss cls : 6.793436050415039,loss l2 : 10.34492015838623,lr : 0.001\n",
            "Epoch 38/50:  81%|▊| 164/202 [01:08<00:12,  3.08im2021-04-10 01:47:15,585 train.py[line:353] INFO: Train step_3820: loss : 72.99484252929688,loss xy : 27.041006088256836,loss wh : 0.8224427103996277,loss obj : 41.566593170166016，loss cls : 3.564803123474121,loss l2 : 10.952316284179688,lr : 0.001\n",
            "Epoch 38/50: 100%|█| 202/202 [01:15<00:00,  7.17im2021-04-10 01:47:23,225 train.py[line:364] INFO: Checkpoint 38 saved !\n",
            "Epoch 38/50: 100%|█| 202/202 [01:16<00:00,  2.65im\n",
            "Epoch 39/50:   1%| | 2/202 [00:03<05:38,  1.69s/im2021-04-10 01:47:29,827 train.py[line:353] INFO: Train step_3840: loss : 96.80764770507812,loss xy : 67.59537506103516,loss wh : 1.8019826412200928,loss obj : 18.936996459960938，loss cls : 8.473299026489258,loss l2 : 8.718544960021973,lr : 0.001\n",
            "Epoch 39/50:  21%|▏| 42/202 [00:24<01:14,  2.16img2021-04-10 01:47:47,781 train.py[line:353] INFO: Train step_3860: loss : 140.27735900878906,loss xy : 66.79119873046875,loss wh : 7.111976623535156,loss obj : 43.629058837890625，loss cls : 22.74513053894043,loss l2 : 27.6653995513916,lr : 0.001\n",
            "Epoch 39/50:  41%|▍| 82/202 [00:37<00:43,  2.75img2021-04-10 01:48:01,698 train.py[line:353] INFO: Train step_3880: loss : 81.57186889648438,loss xy : 62.25200653076172,loss wh : 1.0601986646652222,loss obj : 17.700740814208984，loss cls : 0.5589189529418945,loss l2 : 3.7697064876556396,lr : 0.001\n",
            "Epoch 39/50:  60%|▌| 122/202 [00:53<00:28,  2.86im2021-04-10 01:48:17,105 train.py[line:353] INFO: Train step_3900: loss : 60.56303787231445,loss xy : 34.65085220336914,loss wh : 0.36632031202316284,loss obj : 24.567840576171875，loss cls : 0.9780246019363403,loss l2 : 4.916754722595215,lr : 0.001\n",
            "Epoch 39/50:  80%|▊| 162/202 [01:06<00:12,  3.24im2021-04-10 01:48:30,117 train.py[line:353] INFO: Train step_3920: loss : 55.62213897705078,loss xy : 21.106159210205078,loss wh : 1.2099785804748535,loss obj : 21.87906265258789，loss cls : 11.426942825317383,loss l2 : 8.814064025878906,lr : 0.001\n",
            "Epoch 39/50: 100%|█| 202/202 [01:13<00:00,  7.24im2021-04-10 01:48:37,724 train.py[line:364] INFO: Checkpoint 39 saved !\n",
            "Epoch 39/50: 100%|█| 202/202 [01:14<00:00,  2.71im\n",
            "Epoch 40/50:   0%|       | 0/202 [00:00<?, ?img/s]2021-04-10 01:48:41,347 train.py[line:353] INFO: Train step_3940: loss : 105.27919006347656,loss xy : 86.84148406982422,loss wh : 1.0913074016571045,loss obj : 14.205808639526367，loss cls : 3.140584707260132,loss l2 : 3.66939377784729,lr : 0.001\n",
            "Epoch 40/50:  20%|▏| 40/202 [00:21<01:04,  2.52img2021-04-10 01:48:59,548 train.py[line:353] INFO: Train step_3960: loss : 48.217533111572266,loss xy : 26.4991455078125,loss wh : 1.098109483718872,loss obj : 17.996984481811523，loss cls : 2.6232943534851074,loss l2 : 5.877288818359375,lr : 0.001\n",
            "Epoch 40/50:  40%|▍| 80/202 [00:35<00:42,  2.87img2021-04-10 01:49:13,834 train.py[line:353] INFO: Train step_3980: loss : 191.15228271484375,loss xy : 89.91839599609375,loss wh : 9.814468383789062,loss obj : 43.72764587402344，loss cls : 47.69176483154297,loss l2 : 39.20841979980469,lr : 0.001\n",
            "Epoch 40/50:  59%|▌| 120/202 [00:50<00:36,  2.26im2021-04-10 01:49:28,444 train.py[line:353] INFO: Train step_4000: loss : 19.405235290527344,loss xy : 4.651256084442139,loss wh : 0.05204316973686218,loss obj : 11.048940658569336，loss cls : 3.6529970169067383,loss l2 : 1.5609498023986816,lr : 0.001\n",
            "Epoch 40/50:  79%|▊| 160/202 [01:03<00:12,  3.39im2021-04-10 01:49:42,355 train.py[line:353] INFO: Train step_4020: loss : 185.0587158203125,loss xy : 101.70748901367188,loss wh : 2.9319562911987305,loss obj : 53.275917053222656，loss cls : 27.143348693847656,loss l2 : 26.00059700012207,lr : 0.001\n",
            "Epoch 40/50:  99%|▉| 200/202 [01:11<00:00,  7.56im2021-04-10 01:49:49,557 train.py[line:353] INFO: Train step_4040: loss : 34.73850631713867,loss xy : 13.276859283447266,loss wh : 0.2066086083650589,loss obj : 14.948697090148926，loss cls : 6.306342601776123,loss l2 : 4.279098987579346,lr : 0.001\n",
            "Epoch 40/50: 100%|█| 202/202 [01:11<00:00,  7.64im2021-04-10 01:49:50,067 train.py[line:364] INFO: Checkpoint 40 saved !\n",
            "Epoch 40/50: 100%|█| 202/202 [01:12<00:00,  2.79im\n",
            "Epoch 41/50:  19%|▏| 38/202 [00:20<01:02,  2.64img2021-04-10 01:50:11,895 train.py[line:353] INFO: Train step_4060: loss : 109.24125671386719,loss xy : 59.44545364379883,loss wh : 2.794358730316162,loss obj : 30.27922248840332，loss cls : 16.722217559814453,loss l2 : 16.42584800720215,lr : 0.001\n",
            "Epoch 41/50:  39%|▍| 78/202 [00:37<00:55,  2.25img2021-04-10 01:50:28,438 train.py[line:353] INFO: Train step_4080: loss : 77.6385726928711,loss xy : 41.963035583496094,loss wh : 0.8668279051780701,loss obj : 30.32660675048828，loss cls : 4.482100486755371,loss l2 : 7.9183831214904785,lr : 0.001\n",
            "Epoch 41/50:  58%|▌| 118/202 [00:52<00:31,  2.68im2021-04-10 01:50:43,642 train.py[line:353] INFO: Train step_4100: loss : 84.34735107421875,loss xy : 69.2588882446289,loss wh : 0.5240182280540466,loss obj : 10.63731575012207，loss cls : 3.9271292686462402,loss l2 : 2.389564275741577,lr : 0.001\n",
            "Epoch 41/50:  78%|▊| 158/202 [01:07<00:16,  2.73im2021-04-10 01:50:58,743 train.py[line:353] INFO: Train step_4120: loss : 174.82119750976562,loss xy : 102.92715454101562,loss wh : 2.6708157062530518,loss obj : 42.03089904785156，loss cls : 27.192319869995117,loss l2 : 21.572349548339844,lr : 0.001\n",
            "Epoch 41/50:  98%|▉| 198/202 [01:18<00:00,  6.86im2021-04-10 01:51:08,859 train.py[line:353] INFO: Train step_4140: loss : 56.47295379638672,loss xy : 35.863521575927734,loss wh : 0.9962843656539917,loss obj : 12.523568153381348，loss cls : 7.089580535888672,loss l2 : 4.563507556915283,lr : 0.001\n",
            "Epoch 41/50: 100%|█| 202/202 [01:19<00:00,  7.26im2021-04-10 01:51:09,640 train.py[line:364] INFO: Checkpoint 41 saved !\n",
            "Epoch 41/50: 100%|█| 202/202 [01:19<00:00,  2.54im\n",
            "Epoch 42/50:  18%|▏| 36/202 [00:20<01:09,  2.37img2021-04-10 01:51:31,059 train.py[line:353] INFO: Train step_4160: loss : 58.90973663330078,loss xy : 45.21086120605469,loss wh : 0.48412999510765076,loss obj : 11.023834228515625，loss cls : 2.190910577774048,loss l2 : 2.049077272415161,lr : 0.001\n",
            "Epoch 42/50:  38%|▍| 76/202 [00:36<00:44,  2.83img2021-04-10 01:51:46,483 train.py[line:353] INFO: Train step_4180: loss : 29.770627975463867,loss xy : 9.626681327819824,loss wh : 0.3349738121032715,loss obj : 15.750000953674316，loss cls : 4.05897331237793,loss l2 : 3.0414137840270996,lr : 0.001\n",
            "Epoch 42/50:  57%|▌| 116/202 [00:51<00:35,  2.43im2021-04-10 01:52:02,098 train.py[line:353] INFO: Train step_4200: loss : 109.34095001220703,loss xy : 70.85594940185547,loss wh : 1.7209761142730713,loss obj : 23.861419677734375，loss cls : 12.902606010437012,loss l2 : 11.298346519470215,lr : 0.001\n",
            "Epoch 42/50:  77%|▊| 156/202 [01:07<00:20,  2.29im2021-04-10 01:52:17,795 train.py[line:353] INFO: Train step_4220: loss : 92.4408187866211,loss xy : 54.25838088989258,loss wh : 2.442873954772949,loss obj : 19.43777084350586，loss cls : 16.301794052124023,loss l2 : 13.272829055786133,lr : 0.001\n",
            "Epoch 42/50:  97%|▉| 196/202 [01:18<00:00,  6.72im2021-04-10 01:52:28,146 train.py[line:353] INFO: Train step_4240: loss : 135.08517456054688,loss xy : 79.69969940185547,loss wh : 2.827829360961914,loss obj : 35.051021575927734，loss cls : 17.506628036499023,loss l2 : 17.834426879882812,lr : 0.001\n",
            "Epoch 42/50: 100%|█| 202/202 [01:19<00:00,  7.07im2021-04-10 01:52:29,223 train.py[line:364] INFO: Checkpoint 42 saved !\n",
            "Epoch 42/50: 100%|█| 202/202 [01:19<00:00,  2.54im\n",
            "Epoch 43/50:  17%|▏| 34/202 [00:19<01:03,  2.64img2021-04-10 01:52:49,288 train.py[line:353] INFO: Train step_4260: loss : 55.42074966430664,loss xy : 33.9538688659668,loss wh : 0.17364653944969177,loss obj : 13.039650917053223，loss cls : 8.253586769104004,loss l2 : 4.40983247756958,lr : 0.001\n",
            "Epoch 43/50:  37%|▎| 74/202 [00:35<00:48,  2.66img2021-04-10 01:53:05,078 train.py[line:353] INFO: Train step_4280: loss : 164.69192504882812,loss xy : 86.91133117675781,loss wh : 2.1695454120635986,loss obj : 46.97634506225586，loss cls : 28.63469886779785,loss l2 : 22.594148635864258,lr : 0.001\n",
            "Epoch 43/50:  56%|▌| 114/202 [00:49<00:29,  3.00im2021-04-10 01:53:19,953 train.py[line:353] INFO: Train step_4300: loss : 205.83572387695312,loss xy : 113.72273254394531,loss wh : 2.8213486671447754,loss obj : 52.51863479614258，loss cls : 36.77301788330078,loss l2 : 27.94570541381836,lr : 0.001\n",
            "Epoch 43/50:  76%|▊| 154/202 [01:04<00:16,  2.98im2021-04-10 01:53:33,984 train.py[line:353] INFO: Train step_4320: loss : 10.687223434448242,loss xy : 2.1013126373291016,loss wh : 0.07103577256202698,loss obj : 8.401480674743652，loss cls : 0.11339402198791504,loss l2 : 0.20455512404441833,lr : 0.001\n",
            "Epoch 43/50:  96%|▉| 194/202 [01:14<00:01,  6.79im2021-04-10 01:53:44,200 train.py[line:353] INFO: Train step_4340: loss : 17.12689781188965,loss xy : 7.575411796569824,loss wh : 0.13695555925369263,loss obj : 9.389041900634766，loss cls : 0.025488339364528656,loss l2 : 0.33262577652931213,lr : 0.001\n",
            "Epoch 43/50: 100%|█| 202/202 [01:15<00:00,  7.34im2021-04-10 01:53:45,523 train.py[line:364] INFO: Checkpoint 43 saved !\n",
            "Epoch 43/50: 100%|█| 202/202 [01:16<00:00,  2.65im\n",
            "Epoch 44/50:  16%|▏| 32/202 [00:18<01:06,  2.56img2021-04-10 01:54:04,537 train.py[line:353] INFO: Train step_4360: loss : 103.16606140136719,loss xy : 82.6559066772461,loss wh : 1.1254451274871826,loss obj : 10.813058853149414，loss cls : 8.571651458740234,loss l2 : 5.444635391235352,lr : 0.001\n",
            "Epoch 44/50:  36%|▎| 72/202 [00:31<00:46,  2.80img2021-04-10 01:54:18,059 train.py[line:353] INFO: Train step_4380: loss : 107.06356048583984,loss xy : 66.07278442382812,loss wh : 1.9618656635284424,loss obj : 26.055374145507812，loss cls : 12.973535537719727,loss l2 : 11.284493446350098,lr : 0.001\n",
            "Epoch 44/50:  55%|▌| 112/202 [00:46<00:33,  2.67im2021-04-10 01:54:33,189 train.py[line:353] INFO: Train step_4400: loss : 232.04763793945312,loss xy : 146.2596435546875,loss wh : 3.881687879562378,loss obj : 44.764068603515625，loss cls : 37.142234802246094,loss l2 : 26.76336669921875,lr : 0.001\n",
            "Epoch 44/50:  75%|▊| 152/202 [00:59<00:14,  3.33im2021-04-10 01:54:46,108 train.py[line:353] INFO: Train step_4420: loss : 73.95208740234375,loss xy : 54.219818115234375,loss wh : 1.4918798208236694,loss obj : 17.635334014892578，loss cls : 0.6050581932067871,loss l2 : 6.488824844360352,lr : 0.001\n",
            "Epoch 44/50:  95%|▉| 192/202 [01:10<00:01,  6.66im2021-04-10 01:54:55,882 train.py[line:353] INFO: Train step_4440: loss : 186.5491943359375,loss xy : 97.81295776367188,loss wh : 9.496563911437988,loss obj : 52.91252899169922，loss cls : 26.32714080810547,loss l2 : 37.87314987182617,lr : 0.001\n",
            "Epoch 44/50: 100%|█| 202/202 [01:11<00:00,  7.33im2021-04-10 01:54:57,488 train.py[line:364] INFO: Checkpoint 44 saved !\n",
            "Epoch 44/50: 100%|█| 202/202 [01:11<00:00,  2.81im\n",
            "Epoch 45/50:  15%|▏| 30/202 [00:18<01:15,  2.28img2021-04-10 01:55:16,694 train.py[line:353] INFO: Train step_4460: loss : 13.196932792663574,loss xy : 5.115166664123535,loss wh : 0.20942531526088715,loss obj : 7.811856746673584，loss cls : 0.06048363447189331,loss l2 : 0.47308748960494995,lr : 0.001\n",
            "Epoch 45/50:  35%|▎| 70/202 [00:32<00:51,  2.58img2021-04-10 01:55:30,997 train.py[line:353] INFO: Train step_4480: loss : 187.30068969726562,loss xy : 78.46356201171875,loss wh : 48.418155670166016,loss obj : 37.78488540649414，loss cls : 22.63408660888672,loss l2 : 111.0335464477539,lr : 0.001\n",
            "Epoch 45/50:  54%|▌| 110/202 [00:45<00:33,  2.77im2021-04-10 01:55:43,417 train.py[line:353] INFO: Train step_4500: loss : 82.34003448486328,loss xy : 56.60877990722656,loss wh : 0.6226610541343689,loss obj : 11.914111137390137，loss cls : 13.194482803344727,loss l2 : 6.52796745300293,lr : 0.001\n",
            "Epoch 45/50:  74%|▋| 150/202 [01:00<00:20,  2.55im2021-04-10 01:55:59,070 train.py[line:353] INFO: Train step_4520: loss : 126.52964782714844,loss xy : 63.97054672241211,loss wh : 8.719338417053223,loss obj : 33.669979095458984，loss cls : 20.169780731201172,loss l2 : 29.139751434326172,lr : 0.001\n",
            "Epoch 45/50:  94%|▉| 190/202 [01:13<00:02,  5.63im2021-04-10 01:56:11,033 train.py[line:353] INFO: Train step_4540: loss : 61.92212677001953,loss xy : 38.177364349365234,loss wh : 1.0709648132324219,loss obj : 20.958087921142578，loss cls : 1.7157100439071655,loss l2 : 6.011211395263672,lr : 0.001\n",
            "Epoch 45/50: 100%|█| 202/202 [01:14<00:00,  7.30im2021-04-10 01:56:12,863 train.py[line:364] INFO: Checkpoint 45 saved !\n",
            "Epoch 45/50: 100%|█| 202/202 [01:15<00:00,  2.68im\n",
            "Epoch 46/50:  14%|▏| 28/202 [00:17<01:11,  2.42img2021-04-10 01:56:31,079 train.py[line:353] INFO: Train step_4560: loss : 27.57488250732422,loss xy : 19.854473114013672,loss wh : 0.2954190969467163,loss obj : 7.240232467651367，loss cls : 0.18475745618343353,loss l2 : 0.8540119528770447,lr : 0.001\n",
            "Epoch 46/50:  34%|▎| 68/202 [00:32<00:52,  2.54img2021-04-10 01:56:46,387 train.py[line:353] INFO: Train step_4580: loss : 185.05421447753906,loss xy : 126.4425048828125,loss wh : 4.463772773742676,loss obj : 32.892459869384766，loss cls : 21.255477905273438,loss l2 : 20.35314178466797,lr : 0.001\n",
            "Epoch 46/50:  53%|▌| 108/202 [00:49<00:41,  2.25im2021-04-10 01:57:02,781 train.py[line:353] INFO: Train step_4600: loss : 14.969882011413574,loss xy : 6.67616605758667,loss wh : 0.22645336389541626,loss obj : 8.049373626708984，loss cls : 0.01788925938308239,loss l2 : 0.5672749280929565,lr : 0.001\n",
            "Epoch 46/50:  73%|▋| 148/202 [01:01<00:19,  2.82im2021-04-10 01:57:15,569 train.py[line:353] INFO: Train step_4620: loss : 84.02337646484375,loss xy : 53.14573287963867,loss wh : 0.7748813629150391,loss obj : 21.111223220825195，loss cls : 8.991538047790527,loss l2 : 9.073150634765625,lr : 0.001\n",
            "Epoch 46/50:  93%|▉| 188/202 [01:13<00:02,  5.94im2021-04-10 01:57:26,462 train.py[line:353] INFO: Train step_4640: loss : 75.98233032226562,loss xy : 46.66680145263672,loss wh : 0.9679925441741943,loss obj : 24.822294235229492，loss cls : 3.5252490043640137,loss l2 : 7.300305366516113,lr : 0.001\n",
            "Epoch 46/50: 100%|█| 202/202 [01:15<00:00,  7.32im2021-04-10 01:57:28,620 train.py[line:364] INFO: Checkpoint 46 saved !\n",
            "Epoch 46/50: 100%|█| 202/202 [01:15<00:00,  2.67im\n",
            "Epoch 47/50:  13%|▏| 26/202 [00:16<01:07,  2.60img2021-04-10 01:57:45,620 train.py[line:353] INFO: Train step_4660: loss : 31.789344787597656,loss xy : 13.733321189880371,loss wh : 0.21504148840904236,loss obj : 9.214225769042969，loss cls : 8.626757621765137,loss l2 : 2.2452409267425537,lr : 0.001\n",
            "Epoch 47/50:  33%|▎| 66/202 [00:32<00:53,  2.57img2021-04-10 01:58:01,525 train.py[line:353] INFO: Train step_4680: loss : 149.67079162597656,loss xy : 90.11243438720703,loss wh : 3.6662378311157227,loss obj : 34.926456451416016，loss cls : 20.965669631958008,loss l2 : 23.19738006591797,lr : 0.001\n",
            "Epoch 47/50:  52%|▌| 106/202 [00:47<00:36,  2.65im2021-04-10 01:58:17,018 train.py[line:353] INFO: Train step_4700: loss : 10.803067207336426,loss xy : 2.7800920009613037,loss wh : 0.11025682091712952,loss obj : 7.602912425994873，loss cls : 0.3098055124282837,loss l2 : 0.3664452135562897,lr : 0.001\n",
            "Epoch 47/50:  72%|▋| 146/202 [01:01<00:18,  3.01im2021-04-10 01:58:31,351 train.py[line:353] INFO: Train step_4720: loss : 45.64558410644531,loss xy : 27.59922981262207,loss wh : 0.24190787971019745,loss obj : 6.806130409240723，loss cls : 10.998316764831543,loss l2 : 2.73081111907959,lr : 0.001\n",
            "Epoch 47/50:  92%|▉| 186/202 [01:15<00:02,  5.80im2021-04-10 01:58:44,179 train.py[line:353] INFO: Train step_4740: loss : 61.19879913330078,loss xy : 41.92533874511719,loss wh : 0.5063541531562805,loss obj : 11.162384986877441，loss cls : 7.604720115661621,loss l2 : 4.629415035247803,lr : 0.001\n",
            "Epoch 47/50: 100%|█| 202/202 [01:17<00:00,  7.49im2021-04-10 01:58:46,585 train.py[line:364] INFO: Checkpoint 47 saved !\n",
            "Epoch 47/50: 100%|█| 202/202 [01:17<00:00,  2.59im\n",
            "Epoch 48/50:  12%| | 24/202 [00:13<01:04,  2.77img2021-04-10 01:59:01,137 train.py[line:353] INFO: Train step_4760: loss : 14.885295867919922,loss xy : 7.2548980712890625,loss wh : 0.27276426553726196,loss obj : 6.996101379394531，loss cls : 0.3615322411060333,loss l2 : 0.6859076619148254,lr : 0.001\n",
            "Epoch 48/50:  32%|▎| 64/202 [00:29<00:54,  2.54img2021-04-10 01:59:16,795 train.py[line:353] INFO: Train step_4780: loss : 128.7412109375,loss xy : 72.6383056640625,loss wh : 2.4229326248168945,loss obj : 25.444704055786133，loss cls : 28.235260009765625,loss l2 : 17.559162139892578,lr : 0.001\n",
            "Epoch 48/50:  51%|▌| 104/202 [00:44<00:38,  2.56im2021-04-10 01:59:31,927 train.py[line:353] INFO: Train step_4800: loss : 54.19400405883789,loss xy : 46.458988189697266,loss wh : 0.520455539226532,loss obj : 6.83593225479126，loss cls : 0.37862783670425415,loss l2 : 1.4339029788970947,lr : 0.001\n",
            "Epoch 48/50:  71%|▋| 144/202 [00:58<00:23,  2.48im2021-04-10 01:59:46,030 train.py[line:353] INFO: Train step_4820: loss : 10.792025566101074,loss xy : 4.404359340667725,loss wh : 0.032599739730358124,loss obj : 6.240937232971191，loss cls : 0.11412867158651352,loss l2 : 0.11614162474870682,lr : 0.001\n",
            "Epoch 48/50:  91%|▉| 184/202 [01:13<00:04,  4.48im2021-04-10 02:00:00,164 train.py[line:353] INFO: Train step_4840: loss : 61.08609390258789,loss xy : 37.52228927612305,loss wh : 0.6140186786651611,loss obj : 15.175597190856934，loss cls : 7.7741899490356445,loss l2 : 6.889069080352783,lr : 0.001\n",
            "Epoch 48/50: 100%|█| 202/202 [01:15<00:00,  7.03im2021-04-10 02:00:03,045 train.py[line:364] INFO: Checkpoint 48 saved !\n",
            "Epoch 48/50: 100%|█| 202/202 [01:16<00:00,  2.64im\n",
            "Epoch 49/50:  11%| | 22/202 [00:14<01:33,  1.94img2021-04-10 02:00:18,524 train.py[line:353] INFO: Train step_4860: loss : 7.634584903717041,loss xy : 1.6083428859710693,loss wh : 0.043099116533994675,loss obj : 5.957683086395264，loss cls : 0.025459783151745796,loss l2 : 0.10165367275476456,lr : 0.001\n",
            "Epoch 49/50:  31%|▎| 62/202 [00:31<00:53,  2.60img2021-04-10 02:00:34,929 train.py[line:353] INFO: Train step_4880: loss : 210.6729278564453,loss xy : 110.49584197998047,loss wh : 15.422073364257812,loss obj : 46.60116958618164，loss cls : 38.153839111328125,loss l2 : 50.12958908081055,lr : 0.001\n",
            "Epoch 49/50:  50%|▌| 102/202 [00:43<00:32,  3.05im2021-04-10 02:00:47,439 train.py[line:353] INFO: Train step_4900: loss : 15.672450065612793,loss xy : 4.649868965148926,loss wh : 0.1691644787788391,loss obj : 10.84400463104248，loss cls : 0.009411338716745377,loss l2 : 1.374551773071289,lr : 0.001\n",
            "Epoch 49/50:  70%|▋| 142/202 [00:58<00:23,  2.61im2021-04-10 02:01:02,516 train.py[line:353] INFO: Train step_4920: loss : 59.58451843261719,loss xy : 52.09547424316406,loss wh : 1.0972295999526978,loss obj : 6.136442184448242，loss cls : 0.25537002086639404,loss l2 : 2.5546414852142334,lr : 0.001\n",
            "Epoch 49/50:  90%|▉| 182/202 [01:12<00:04,  4.06im2021-04-10 02:01:15,475 train.py[line:353] INFO: Train step_4940: loss : 89.3915786743164,loss xy : 70.64282989501953,loss wh : 2.033982515335083,loss obj : 11.46895980834961，loss cls : 5.24581241607666,loss l2 : 7.412510395050049,lr : 0.001\n",
            "Epoch 49/50: 100%|█| 202/202 [01:14<00:00,  7.39im2021-04-10 02:01:18,378 train.py[line:364] INFO: Checkpoint 49 saved !\n",
            "Epoch 49/50: 100%|█| 202/202 [01:15<00:00,  2.68im\n",
            "Epoch 50/50:  10%| | 20/202 [00:16<02:03,  1.47img2021-04-10 02:01:36,014 train.py[line:353] INFO: Train step_4960: loss : 61.170249938964844,loss xy : 49.924617767333984,loss wh : 0.9082503318786621,loss obj : 8.833102226257324，loss cls : 1.5042808055877686,loss l2 : 3.3124568462371826,lr : 0.001\n",
            "Epoch 50/50:  30%|▎| 60/202 [00:31<00:45,  3.13img2021-04-10 02:01:50,697 train.py[line:353] INFO: Train step_4980: loss : 84.06372833251953,loss xy : 51.80705642700195,loss wh : 0.8616183996200562,loss obj : 12.08135986328125，loss cls : 19.31369972229004,loss l2 : 9.028738975524902,lr : 0.001\n",
            "Epoch 50/50:  50%|▍| 100/202 [00:45<00:32,  3.11im2021-04-10 02:02:04,675 train.py[line:353] INFO: Train step_5000: loss : 165.71893310546875,loss xy : 130.04986572265625,loss wh : 3.130160093307495,loss obj : 20.623279571533203，loss cls : 11.915633201599121,loss l2 : 12.63451862335205,lr : 0.001\n",
            "Epoch 50/50:  69%|▋| 140/202 [01:01<00:25,  2.46im2021-04-10 02:02:20,999 train.py[line:353] INFO: Train step_5020: loss : 55.640907287597656,loss xy : 41.888145446777344,loss wh : 0.604092001914978,loss obj : 7.2660932540893555，loss cls : 5.882574558258057,loss l2 : 3.5911266803741455,lr : 0.001\n",
            "Epoch 50/50:  89%|▉| 180/202 [01:13<00:04,  4.65im2021-04-10 02:02:32,227 train.py[line:353] INFO: Train step_5040: loss : 135.17059326171875,loss xy : 87.88284301757812,loss wh : 2.0706658363342285,loss obj : 17.37583351135254，loss cls : 27.84125518798828,loss l2 : 14.21749496459961,lr : 0.001\n",
            "Epoch 50/50: 100%|█| 202/202 [01:16<00:00,  7.53im2021-04-10 02:02:35,419 train.py[line:364] INFO: Checkpoint 50 saved !\n",
            "Epoch 50/50: 100%|█| 202/202 [01:17<00:00,  2.62im\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CR87FGKijXbI"
      },
      "source": [
        "# Load Trained Weights for Custom Detection\n",
        "\n",
        "You can also use this to load previously saved weights!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5iR0H_DUoOl-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "820efe67-bc02-47d0-a999-19840baefed5"
      },
      "source": [
        "#looking at the weights that our model has saved during training\n",
        "!ls checkpoints"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Yolov4_epoch10.pth  Yolov4_epoch22.pth\tYolov4_epoch34.pth  Yolov4_epoch46.pth\n",
            "Yolov4_epoch11.pth  Yolov4_epoch23.pth\tYolov4_epoch35.pth  Yolov4_epoch47.pth\n",
            "Yolov4_epoch12.pth  Yolov4_epoch24.pth\tYolov4_epoch36.pth  Yolov4_epoch48.pth\n",
            "Yolov4_epoch13.pth  Yolov4_epoch25.pth\tYolov4_epoch37.pth  Yolov4_epoch49.pth\n",
            "Yolov4_epoch14.pth  Yolov4_epoch26.pth\tYolov4_epoch38.pth  Yolov4_epoch4.pth\n",
            "Yolov4_epoch15.pth  Yolov4_epoch27.pth\tYolov4_epoch39.pth  Yolov4_epoch50.pth\n",
            "Yolov4_epoch16.pth  Yolov4_epoch28.pth\tYolov4_epoch3.pth   Yolov4_epoch5.pth\n",
            "Yolov4_epoch17.pth  Yolov4_epoch29.pth\tYolov4_epoch40.pth  Yolov4_epoch6.pth\n",
            "Yolov4_epoch18.pth  Yolov4_epoch2.pth\tYolov4_epoch41.pth  Yolov4_epoch7.pth\n",
            "Yolov4_epoch19.pth  Yolov4_epoch30.pth\tYolov4_epoch42.pth  Yolov4_epoch8.pth\n",
            "Yolov4_epoch1.pth   Yolov4_epoch31.pth\tYolov4_epoch43.pth  Yolov4_epoch9.pth\n",
            "Yolov4_epoch20.pth  Yolov4_epoch32.pth\tYolov4_epoch44.pth\n",
            "Yolov4_epoch21.pth  Yolov4_epoch33.pth\tYolov4_epoch45.pth\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vutw9YXmhvWo"
      },
      "source": [
        "#choose random test image\n",
        "import os\n",
        "test_images = [f for f in os.listdir('test') if f.endswith('.jpg')]\n",
        "import random\n",
        "img_path = \"test/\" + random.choice(test_images);"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "091QOGGihsuV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3fd6e571-1161-485a-dafa-635e5be07e13"
      },
      "source": [
        "%%time\n",
        "##change the epoch here to the one you would like to use for inference\n",
        "!python /content/pytorch-YOLOv4/models.py {num_classes} checkpoints/Yolov4_epoch46.pth {img_path} test/_classes.txt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "white-bishop: 0.999999\n",
            "black-bishop: 0.999405\n",
            "black-bishop: 0.999306\n",
            "black-pawn: 1.000000\n",
            "white-pawn: 1.000000\n",
            "black-pawn: 1.000000\n",
            "black-king: 0.999978\n",
            "white-king: 0.999680\n",
            "save plot results to predictions.jpg\n",
            "CPU times: user 22 ms, sys: 6.02 ms, total: 28 ms\n",
            "Wall time: 5.53 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Af8tiXQLrGgk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "outputId": "8d0127a1-f2ca-4aef-fba4-401cb8d7bc6d"
      },
      "source": [
        "#visualize inference\n",
        "from IPython.display import Image\n",
        "Image('predictions.jpg')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    }
  ]
}