multi_label_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "multi_label_classification.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNahJsg8SVRPetgaZ7GSNY2",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/novoforce/Exploring-Tensorflow/blob/main/multi_label_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UDZy7_xCHEu3"
      },
      "source": [
        "# Get the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rKm7U9K1Ci5v"
      },
      "source": [
        "mkdir dataset"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bI779K63HjMr",
        "outputId": "0f8f43f3-93d3-43f6-8932-58e7755dc73d"
      },
      "source": [
        "cd dataset"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/dataset\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2B2utP1ZHrrR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21bcfbfb-1573-4873-b6a9-fb949148bc78"
      },
      "source": [
        "! curl -L \"https://app.roboflow.com/ds/EYWP025nf4?key=bOv6xVqzBY\" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100   889  100   889    0     0    814      0  0:00:01  0:00:01 --:--:--   814\n",
            "100  213M  100  213M    0     0  50.1M      0  0:00:04  0:00:04 --:--:-- 72.1M\n",
            "Archive:  roboflow.zip\n",
            " extracting: README.dataset.txt      \n",
            " extracting: README.roboflow.txt     \n",
            "   creating: test/\n",
            "   creating: test/black_jeans/\n",
            " extracting: test/black_jeans/00000018_jpg.rf.4b4c71f74f61993513be7311a618396f.jpg  \n",
            " extracting: test/black_jeans/00000021_jpg.rf.3908dd338a0e69598462582c44853d24.jpg  \n",
            " extracting: test/black_jeans/00000028_jpg.rf.00cbda5ac28a1f91a59b02dea79259d9.jpg  \n",
            " extracting: test/black_jeans/00000046_jpeg.rf.1bdb2329204fae0d96d115dcb43bfee4.jpg  \n",
            " extracting: test/black_jeans/00000060_jpg.rf.bb9816dc6de2d8978161bd6b6f4dcd53.jpg  \n",
            " extracting: test/black_jeans/00000068_jpg.rf.871d13e54ac6b657e27d286e1242fbd2.jpg  \n",
            " extracting: test/black_jeans/00000070_jpeg.rf.af26eac3ece26fd89304e2d637ccab99.jpg  \n",
            " extracting: test/black_jeans/00000071_jpg.rf.7a5df2cb7d707876e5cd95d5af08e89d.jpg  \n",
            " extracting: test/black_jeans/00000082_jpg.rf.0fb1228d0d1d13d1a0f9e7c1ddbdf12a.jpg  \n",
            " extracting: test/black_jeans/00000116_jpeg.rf.8b379b28551949a73fec03b65b359e90.jpg  \n",
            " extracting: test/black_jeans/00000127_jpeg.rf.c576586d95d2b9af4c3b5086fc8ef71e.jpg  \n",
            " extracting: test/black_jeans/00000138_jpg.rf.c55fb0ca4f65e61286c9b21322d86e3c.jpg  \n",
            " extracting: test/black_jeans/00000141_jpg.rf.cc5788068508e5ed23a0a3314eb4af0f.jpg  \n",
            " extracting: test/black_jeans/00000147_jpeg.rf.73150b0926f3e02f7537309380638fbc.jpg  \n",
            " extracting: test/black_jeans/00000177_jpg.rf.9b44d3f9673446e895712ed93cf0c497.jpg  \n",
            " extracting: test/black_jeans/00000213_jpg.rf.2d12113b9a8d4f8544dc598ea2aa5208.jpg  \n",
            " extracting: test/black_jeans/00000218_jpg.rf.a3821a2fc2da51d011c9b5da1adff921.jpg  \n",
            " extracting: test/black_jeans/00000222_JPG.rf.9756985a7d4fe0b85582926c19c79607.jpg  \n",
            " extracting: test/black_jeans/00000240_jpeg.rf.9f21209a1daf1ce82387d69bc121baa5.jpg  \n",
            " extracting: test/black_jeans/00000247_jpg.rf.46d824f6d085a0421e93cc341f630977.jpg  \n",
            " extracting: test/black_jeans/00000249_jpg.rf.ebe44c9363956ea9c3f8150f7752740f.jpg  \n",
            " extracting: test/black_jeans/00000255_jpg.rf.a038ca37759748c444cdefc50a553c9c.jpg  \n",
            " extracting: test/black_jeans/00000258_jpg.rf.62fd554aa5ce03f260210bd8611734fa.jpg  \n",
            " extracting: test/black_jeans/00000288_jpg.rf.9fa1ee3dbc58417045290c157a863211.jpg  \n",
            " extracting: test/black_jeans/00000293_jpg.rf.ffc7d7d140e3c2fe873f673edffb35a2.jpg  \n",
            " extracting: test/black_jeans/00000297_jpeg.rf.8cb68ad03569774a078cfae9cb5980c7.jpg  \n",
            " extracting: test/black_jeans/00000306_jpeg.rf.ac586b214c6813749399d4cafaa78ece.jpg  \n",
            " extracting: test/black_jeans/00000312_jpg.rf.f92b3e45ffd0d790fc9fac711584e836.jpg  \n",
            " extracting: test/black_jeans/00000357_jpg.rf.32e1c3b8e66411ba96cb49e9aecfe2ce.jpg  \n",
            " extracting: test/black_jeans/00000358_jpg.rf.d68368e6a42cffbdce234c92420dda14.jpg  \n",
            "   creating: test/blue_dress/\n",
            " extracting: test/blue_dress/00000008_jpg.rf.f1a9729c8e57489adce84c3251a35372.jpg  \n",
            " extracting: test/blue_dress/00000015_jpg.rf.39f21cce7211c6bc49621c4c0071925b.jpg  \n",
            " extracting: test/blue_dress/00000026_jpeg.rf.b10540f2643fb152b5a09aef2806aea6.jpg  \n",
            " extracting: test/blue_dress/00000030_jpg.rf.deec9e1da2ec244600f4ba243453b1c4.jpg  \n",
            " extracting: test/blue_dress/00000035_jpeg.rf.b8c5497620d50a5a33d256674fbe10fb.jpg  \n",
            " extracting: test/blue_dress/00000038_jpg.rf.7699268e6b0429346a674e6e767f2aaa.jpg  \n",
            " extracting: test/blue_dress/00000045_jpg.rf.cb7f762315a8162298e4ffe6cf4bb7e4.jpg  \n",
            " extracting: test/blue_dress/00000067_jpg.rf.5166c0f4c80411717e7e1c69ec4a6a2f.jpg  \n",
            " extracting: test/blue_dress/00000068_jpg.rf.64066b5309f7093ed5d18b7dbf899f31.jpg  \n",
            " extracting: test/blue_dress/00000074_jpg.rf.bdbeac93c6f0647be262181891c68ac2.jpg  \n",
            " extracting: test/blue_dress/00000088_jpg.rf.6ae4c6bf8f4b72cc83d3761a4bcf4b07.jpg  \n",
            " extracting: test/blue_dress/00000091_jpg.rf.0789c7fdc563b2ec6dfc58b8703042df.jpg  \n",
            " extracting: test/blue_dress/00000096_jpeg.rf.2aaca071c0e6a0312fded5423e697cd7.jpg  \n",
            " extracting: test/blue_dress/00000116_jpg.rf.af8ddb14e6ee9fcc7250896af151dc0e.jpg  \n",
            " extracting: test/blue_dress/00000120_jpeg.rf.5d729a385bcb89dba1baab1c4e4b6c93.jpg  \n",
            " extracting: test/blue_dress/00000129_jpeg.rf.a7c9540b569143e28b9e53c1d2c6fd00.jpg  \n",
            " extracting: test/blue_dress/00000130_jpg.rf.9853071156a360c22a27ac80378a3b79.jpg  \n",
            " extracting: test/blue_dress/00000137_jpeg.rf.1ed694b0b6003bf3362a4963b754c3a8.jpg  \n",
            " extracting: test/blue_dress/00000152_jpg.rf.55b4b2f615d1e9939162a5bdfcb9f861.jpg  \n",
            " extracting: test/blue_dress/00000154_jpeg.rf.624545143ba62b310124eea099e74726.jpg  \n",
            " extracting: test/blue_dress/00000159_jpg.rf.ca52c431243dee25604d1b32e62c4b81.jpg  \n",
            " extracting: test/blue_dress/00000163_jpg.rf.9313456797e829a79052c8768a4adb04.jpg  \n",
            " extracting: test/blue_dress/00000167_jpg.rf.a36da8edf8e99ea854cdc272ab4c4f73.jpg  \n",
            " extracting: test/blue_dress/00000187_jpg.rf.d3161166b5507fba438eb9bc74517216.jpg  \n",
            " extracting: test/blue_dress/00000191_jpg.rf.30a6a5a4125657f319539a9ec7799a64.jpg  \n",
            " extracting: test/blue_dress/00000210_jpg.rf.7de2b283aa38204d5c38f7da804fe2a1.jpg  \n",
            " extracting: test/blue_dress/00000239_jpg.rf.df5f7232087041b1509f9948f5fec521.jpg  \n",
            " extracting: test/blue_dress/00000246_jpg.rf.f7cf22018af3b2ba7a04860b139c39e1.jpg  \n",
            " extracting: test/blue_dress/00000262_jpg.rf.c1bdf94401faff8091cbf0eb0493d1a9.jpg  \n",
            " extracting: test/blue_dress/00000264_jpg.rf.4489acb42373df8275d6a9d6fed049e7.jpg  \n",
            " extracting: test/blue_dress/00000281_jpg.rf.79aa430350717d59a3b58423e48ffe60.jpg  \n",
            " extracting: test/blue_dress/00000307_jpeg.rf.09238328de068357695096eae0d998f9.jpg  \n",
            " extracting: test/blue_dress/00000321_jpg.rf.9646a5da4e0642054fb25369c0015cd0.jpg  \n",
            " extracting: test/blue_dress/00000324_jpg.rf.a7866f06ad09c65b99b5c7b88ed9bfb9.jpg  \n",
            " extracting: test/blue_dress/00000354_jpg.rf.022a2e878b2d2d6c85a1c50c608a640c.jpg  \n",
            " extracting: test/blue_dress/00000357_JPG.rf.cc7a5e0e279833460b753e9baabc98c6.jpg  \n",
            " extracting: test/blue_dress/00000375_jpg.rf.30a26e492abd89e53b7c81de6a1582e7.jpg  \n",
            " extracting: test/blue_dress/00000376_jpg.rf.f1bf2a225b49877da9a11aa515306a0b.jpg  \n",
            "   creating: test/blue_jeans/\n",
            " extracting: test/blue_jeans/00000012_jpg.rf.eeddb96d4ac86000097b936405b9f504.jpg  \n",
            " extracting: test/blue_jeans/00000015_jpg.rf.c8af66fe2e13fd2367dce7f39894b41c.jpg  \n",
            " extracting: test/blue_jeans/00000030_jpg.rf.71e9aa3ea7d10e071f171f0e7f9b4d7c.jpg  \n",
            " extracting: test/blue_jeans/00000033_jpg.rf.5bd4de7ced7953f30229f87bd4809e81.jpg  \n",
            " extracting: test/blue_jeans/00000038_jpeg.rf.becd23531c1e341892dea4d6142019fb.jpg  \n",
            " extracting: test/blue_jeans/00000051_jpg.rf.0560a53e80b822bc0d1d982eabca2921.jpg  \n",
            " extracting: test/blue_jeans/00000054_jpg.rf.15d4df2e8a744e2af5d3a49d199030c3.jpg  \n",
            " extracting: test/blue_jeans/00000064_jpg.rf.2d29617b36dd683b4fceddbfa4f79b39.jpg  \n",
            " extracting: test/blue_jeans/00000084_jpg.rf.67341194afa0ea149f1e14ef1a3fa495.jpg  \n",
            " extracting: test/blue_jeans/00000085_jpeg.rf.f702a698f59623404d52d3d10e370477.jpg  \n",
            " extracting: test/blue_jeans/00000109_jpg.rf.0e5b79ff14ac5321f423c372545a7380.jpg  \n",
            " extracting: test/blue_jeans/00000118_jpg.rf.0201433d8bbc86a07b700018356383e4.jpg  \n",
            " extracting: test/blue_jeans/00000138_jpeg.rf.05ae2ed4884e5f8ce0057335d10d7b35.jpg  \n",
            " extracting: test/blue_jeans/00000157_jpg.rf.7399bc49e3bc993e27d2b4c9289dd48a.jpg  \n",
            " extracting: test/blue_jeans/00000162_jpg.rf.f3ddd1738fd4e5e3e5f50fc5e2495513.jpg  \n",
            " extracting: test/blue_jeans/00000170_jpg.rf.b998fd98e8091fbea76cf62c0ce649f1.jpg  \n",
            " extracting: test/blue_jeans/00000172_jpg.rf.812f1d3d3fb6a8e34eef3210a88cb002.jpg  \n",
            " extracting: test/blue_jeans/00000175_jpg.rf.7c9fb3bbc1135c9c91c57bd924efa1bb.jpg  \n",
            " extracting: test/blue_jeans/00000177_jpg.rf.d7504b9853140690cb7448985c914bda.jpg  \n",
            " extracting: test/blue_jeans/00000183_jpg.rf.d1b2ae683035f49e2629d6cd11c6440e.jpg  \n",
            " extracting: test/blue_jeans/00000193_jpeg.rf.01386400cfb76e49f78cb3efd8986720.jpg  \n",
            " extracting: test/blue_jeans/00000202_jpeg.rf.a1deb914e4ceeb85a914fc77a8984405.jpg  \n",
            " extracting: test/blue_jeans/00000226_jpg.rf.43c538bb4a076a0d348a64f4365589f5.jpg  \n",
            " extracting: test/blue_jeans/00000237_jpg.rf.c99808f8231c6f30c5dd6dde822a3c2b.jpg  \n",
            " extracting: test/blue_jeans/00000245_jpg.rf.59472d995ba9ce31fa3809e46d7b2b2b.jpg  \n",
            " extracting: test/blue_jeans/00000271_jpg.rf.968a62fb67c9d86921bd931da333964d.jpg  \n",
            " extracting: test/blue_jeans/00000275_jpeg.rf.1710b780f58d5592ee0fc7039c84e05c.jpg  \n",
            " extracting: test/blue_jeans/00000282_jpg.rf.c379c63b9a29acf8e95758e8bf23d367.jpg  \n",
            " extracting: test/blue_jeans/00000292_jpeg.rf.3aabbed9810950313de24f4ca5e26438.jpg  \n",
            " extracting: test/blue_jeans/00000299_jpg.rf.c44b27d9ff6e84c18482caf404ccb6ba.jpg  \n",
            " extracting: test/blue_jeans/00000309_jpg.rf.bde5e96379e8fab17a20b4b136044af4.jpg  \n",
            " extracting: test/blue_jeans/00000323_jpg.rf.2cff7d68e775bfbf5aec3bc00d5b8b22.jpg  \n",
            " extracting: test/blue_jeans/00000334_jpeg.rf.351a550b38d8fa6b19ffeaf48d790e70.jpg  \n",
            " extracting: test/blue_jeans/00000346_jpg.rf.aedc1d7b71e18b6362af427c7ffccebf.jpg  \n",
            " extracting: test/blue_jeans/00000348_jpg.rf.00b6fe19a8d2a3bca52189b0e28ba961.jpg  \n",
            " extracting: test/blue_jeans/00000349_jpg.rf.df5eac0c568827adbb411bf821a01ea7.jpg  \n",
            " extracting: test/blue_jeans/00000353_jpg.rf.099139be5d70fa4731e21ab8a635da8a.jpg  \n",
            "   creating: test/blue_shirt/\n",
            " extracting: test/blue_shirt/00000000_jpg.rf.3de62844694e9c6567301e0cdddb5ea8.jpg  \n",
            " extracting: test/blue_shirt/00000020_png.rf.ae8a9d13acf53e7b667fc923463ee53b.jpg  \n",
            " extracting: test/blue_shirt/00000035_jpg.rf.1427065255118dc19f46b2de1238b65b.jpg  \n",
            " extracting: test/blue_shirt/00000045_jpg.rf.e32aef6173380d6f6132705749803e83.jpg  \n",
            " extracting: test/blue_shirt/00000054_jpg.rf.dccc24809d24b30c73daa8968d87d52c.jpg  \n",
            " extracting: test/blue_shirt/00000055_jpg.rf.0406ebab63a1d10b2cae0cef46e7218e.jpg  \n",
            " extracting: test/blue_shirt/00000096_jpg.rf.fdaab349cf836e68bfb73bfec550a62e.jpg  \n",
            " extracting: test/blue_shirt/00000097_jpg.rf.717789ab3e0c01821726abb5957da5a3.jpg  \n",
            " extracting: test/blue_shirt/00000153_jpg.rf.46521cb7cf3620ecfc3330e84cb03034.jpg  \n",
            " extracting: test/blue_shirt/00000180_jpg.rf.616a8e034662e9a2402c024cd4225e84.jpg  \n",
            " extracting: test/blue_shirt/00000186_jpg.rf.0b913ea2ae314922505707630f8dc59e.jpg  \n",
            " extracting: test/blue_shirt/00000189_jpeg.rf.01520eec394dd17e8b9584b473f9c79d.jpg  \n",
            " extracting: test/blue_shirt/00000191_jpg.rf.f021230047c1603794267bec976f7e33.jpg  \n",
            " extracting: test/blue_shirt/00000207_jpeg.rf.5ad3ba6f519f5c278b6b555a105e3233.jpg  \n",
            " extracting: test/blue_shirt/00000221_jpg.rf.141c860fae114fb80a3c808e058c9fd8.jpg  \n",
            " extracting: test/blue_shirt/00000226_jpg.rf.db70b9d4a6f3894b51a6b663be020ef3.jpg  \n",
            " extracting: test/blue_shirt/00000229_jpg.rf.649015c7e6276ca28c7b702ece468e0b.jpg  \n",
            " extracting: test/blue_shirt/00000234_jpg.rf.c69fdccde788c536f1d701d215fae000.jpg  \n",
            " extracting: test/blue_shirt/00000243_jpg.rf.3843d3f4f37f5aac674f7a8d466c7f8f.jpg  \n",
            " extracting: test/blue_shirt/00000245_jpg.rf.55935194bccb5d2bbe508248c9b86450.jpg  \n",
            " extracting: test/blue_shirt/00000246_png.rf.2c0f53e535968e59cba0ffe5e5958f90.jpg  \n",
            " extracting: test/blue_shirt/00000248_jpg.rf.2e234c77637201ec89b294fcd0ce23df.jpg  \n",
            " extracting: test/blue_shirt/00000251_jpg.rf.1ba176a30e3d57940e1a16791a5ab27d.jpg  \n",
            " extracting: test/blue_shirt/00000278_jpg.rf.757e4f4808a0c9f3b79dc70d25ff487d.jpg  \n",
            " extracting: test/blue_shirt/00000290_png.rf.445b3895ffb124cf0a4ae39e07db6680.jpg  \n",
            " extracting: test/blue_shirt/00000299_jpg.rf.a01287934ec1450daf9066dce058acca.jpg  \n",
            " extracting: test/blue_shirt/00000313_jpg.rf.1215e1febbab57fb20fbbd730d046238.jpg  \n",
            " extracting: test/blue_shirt/00000330_jpg.rf.25b3112e69ea1f8f83fd106cfb87c766.jpg  \n",
            " extracting: test/blue_shirt/00000347_jpg.rf.8e1cf2c5198a748d098af8d09eec8c65.jpg  \n",
            " extracting: test/blue_shirt/00000348_jpg.rf.a616e6a128a4330bcb2b38e063c0ec41.jpg  \n",
            " extracting: test/blue_shirt/00000360_jpg.rf.d1dec792396beace64eaefc4b0960442.jpg  \n",
            " extracting: test/blue_shirt/00000364_jpeg.rf.8109b95d0bcb82e0058e7833b673c311.jpg  \n",
            " extracting: test/blue_shirt/00000374_jpg.rf.92a0da6efb58329acd83897a1e1808cd.jpg  \n",
            " extracting: test/blue_shirt/00000375_jpeg.rf.acc5973660227bef1f18b54ad0991f71.jpg  \n",
            "   creating: test/red_dress/\n",
            " extracting: test/red_dress/00000001_jpg.rf.1a51030879e09b6b5e020e567cdcbc5c.jpg  \n",
            " extracting: test/red_dress/00000006_jpg.rf.102106427358cd6d6f133795aa79496d.jpg  \n",
            " extracting: test/red_dress/00000064_jpg.rf.c2d496ec425a4bab7d6e154827ca4bbb.jpg  \n",
            " extracting: test/red_dress/00000083_jpg.rf.8a232a5e1e286c32a7881e3414dc328b.jpg  \n",
            " extracting: test/red_dress/00000091_jpeg.rf.a2603132f259c96fe6e57c654e06f889.jpg  \n",
            " extracting: test/red_dress/00000102_jpg.rf.7c683c2610f93ea6f6071b472f9daa8b.jpg  \n",
            " extracting: test/red_dress/00000105_jpg.rf.ca971bdc707281774a12507cf0175e70.jpg  \n",
            " extracting: test/red_dress/00000110_jpg.rf.397b6bf25f3583bae344abfd2eab4fc4.jpg  \n",
            " extracting: test/red_dress/00000153_jpg.rf.6c8eeac18ddb9ebc1e251bda02a49ae7.jpg  \n",
            " extracting: test/red_dress/00000161_jpg.rf.3c7b862e9bce39191dfc3b52b733677d.jpg  \n",
            " extracting: test/red_dress/00000168_jpg.rf.c50ce424041362f2b37a08aa813ebfab.jpg  \n",
            " extracting: test/red_dress/00000180_jpeg.rf.32101bfd93d5fc94c71150dd94ba1928.jpg  \n",
            " extracting: test/red_dress/00000192_jpg.rf.d05b1177a27eca7d3015c489232aeadf.jpg  \n",
            " extracting: test/red_dress/00000199_jpg.rf.83e98dd28d287f8bdd2bc2dbdeab0fa3.jpg  \n",
            " extracting: test/red_dress/00000206_jpg.rf.d0329117d69c7d2dea0ddf6b151bf883.jpg  \n",
            " extracting: test/red_dress/00000218_jpg.rf.b64c07ae88e56a115dbe192000a98140.jpg  \n",
            " extracting: test/red_dress/00000226_jpeg.rf.488e26f24f9bc8b9092299f7bb724db9.jpg  \n",
            " extracting: test/red_dress/00000231_jpg.rf.3fa8bcedf632191372ed6f493acf0e02.jpg  \n",
            " extracting: test/red_dress/00000237_jpg.rf.4ce3c66cfd487877dd0242c635a48d49.jpg  \n",
            " extracting: test/red_dress/00000241_jpg.rf.d0c06bdf5b58aa9fe397d8cb11f5a634.jpg  \n",
            " extracting: test/red_dress/00000248_jpg.rf.74947f31d4fa4d3a218bcec22bf2c0eb.jpg  \n",
            " extracting: test/red_dress/00000258_jpg.rf.deb048cae14480e5afd42beae053fdf4.jpg  \n",
            " extracting: test/red_dress/00000264_jpg.rf.aa0a9987ee03cc167da6c53126ed9b3e.jpg  \n",
            " extracting: test/red_dress/00000265_jpg.rf.360a48c2d16c75527adbfad70c66a0b5.jpg  \n",
            " extracting: test/red_dress/00000272_jpg.rf.f53b1756fb8e68bbcf8487dd995c1487.jpg  \n",
            " extracting: test/red_dress/00000284_jpg.rf.ea51ae49c185fa6b23a1bcf9568c8937.jpg  \n",
            " extracting: test/red_dress/00000286_png.rf.eacdcac751541284c614d038e59758e6.jpg  \n",
            " extracting: test/red_dress/00000323_jpg.rf.a52d1966877753d4c2b4ebb0921c8c95.jpg  \n",
            " extracting: test/red_dress/00000326_jpg.rf.d7191ad8ec67e9bddba6256ebf676671.jpg  \n",
            " extracting: test/red_dress/00000329_jpg.rf.e501653d363edd8ce8949bcf69922256.jpg  \n",
            " extracting: test/red_dress/00000339_jpg.rf.9d61ea4a924df16563fb198d45205fec.jpg  \n",
            " extracting: test/red_dress/00000348_jpeg.rf.4d485cf9268fc0990e1252ab6b8e1396.jpg  \n",
            " extracting: test/red_dress/00000350_jpg.rf.dabb38490d5f5f5d9fe6074c06ced234.jpg  \n",
            " extracting: test/red_dress/00000359_jpg.rf.2a09730bb2c494b129ff5f6aa8a35794.jpg  \n",
            " extracting: test/red_dress/00000372_jpg.rf.153097ee2d0eba5248c00a968d9dabf3.jpg  \n",
            " extracting: test/red_dress/00000377_jpg.rf.9f0e2a967bf140ca9fa38b025dd828e9.jpg  \n",
            " extracting: test/red_dress/00000380_jpg.rf.ace983ad177286dcb47692b9ddd57538.jpg  \n",
            " extracting: test/red_dress/00000386_jpg.rf.636b8e3f76c86b48c1d9b34625762e06.jpg  \n",
            " extracting: test/red_dress/00000389_jpg.rf.1ef1c3ab8792faded338bb2fe0592e4b.jpg  \n",
            "   creating: test/red_shirt/\n",
            " extracting: test/red_shirt/00000027_jpg.rf.23210875ccc6b2112b93764f6a03a0ea.jpg  \n",
            " extracting: test/red_shirt/00000037_jpg.rf.a23cdca5ba26c2f22270803050339ff7.jpg  \n",
            " extracting: test/red_shirt/00000071_jpg.rf.b428979faa3a4a329d2961fe6b9904c7.jpg  \n",
            " extracting: test/red_shirt/00000082_jpg.rf.63c06bffdc0281941ce00eee00db3c4f.jpg  \n",
            " extracting: test/red_shirt/00000083_jpg.rf.ec74822a24fa15efb3679ee2952699ad.jpg  \n",
            " extracting: test/red_shirt/00000101_jpg.rf.665f8dc9d82b5948f0f4b167b1ae2b54.jpg  \n",
            " extracting: test/red_shirt/00000128_jpg.rf.bb5e05bce4da18da0ac2ab4533b34c92.jpg  \n",
            " extracting: test/red_shirt/00000168_jpg.rf.a5295cd6afa94ccce1d415228c9a6a48.jpg  \n",
            " extracting: test/red_shirt/00000169_jpg.rf.c8db68b823b7cb45a72df90ba010ac0c.jpg  \n",
            " extracting: test/red_shirt/00000188_jpg.rf.b7c64bd75969dabc58a1c5cedc15d2d9.jpg  \n",
            " extracting: test/red_shirt/00000191_jpg.rf.4757d70db72515a123be04c79570a29f.jpg  \n",
            " extracting: test/red_shirt/00000197_jpg.rf.4e7824c91c386a1439d92287037daef6.jpg  \n",
            " extracting: test/red_shirt/00000198_jpg.rf.06fe02e46a7723a8934aa2ceb953f380.jpg  \n",
            " extracting: test/red_shirt/00000204_png.rf.f5e1c781589620d616ce88fe29d0f4cf.jpg  \n",
            " extracting: test/red_shirt/00000215_jpg.rf.4f360e75272038f4b6ede979674dbcc4.jpg  \n",
            " extracting: test/red_shirt/00000216_jpg.rf.d293b89897d021b5327a1ac610b80b61.jpg  \n",
            " extracting: test/red_shirt/00000219_jpg.rf.66ba113c596f0970d99b063356f0bded.jpg  \n",
            " extracting: test/red_shirt/00000220_jpg.rf.be6132a8019143856fe9e3822d292c2a.jpg  \n",
            " extracting: test/red_shirt/00000237_jpg.rf.b905176b084da738c39e06045d404293.jpg  \n",
            " extracting: test/red_shirt/00000238_jpg.rf.4e1ecbbd84d1869af45c11568151f191.jpg  \n",
            " extracting: test/red_shirt/00000252_jpg.rf.2a49bd2bf2cb765a51a68ab78d4cb06b.jpg  \n",
            " extracting: test/red_shirt/00000269_jpeg.rf.d5cbf2bdb7a0fc70ea708962e661f1a0.jpg  \n",
            " extracting: test/red_shirt/00000322_jpg.rf.8d356c96880bcf6188065099defbb66d.jpg  \n",
            " extracting: test/red_shirt/00000339_jpg.rf.54a54180f427358f307def61ef94dfb1.jpg  \n",
            " extracting: test/red_shirt/00000343_jpeg.rf.f17f917cdd3f1c20702f34a766409788.jpg  \n",
            " extracting: test/red_shirt/00000355_jpg.rf.683dadd0522ef4420d27703ea6fe93ed.jpg  \n",
            " extracting: test/red_shirt/00000364_jpg.rf.75e0eaef06e5d685fe5007b9baa6e2c6.jpg  \n",
            " extracting: test/red_shirt/00000370_jpg.rf.602af0bdbbb428583c50ce48ba41c71a.jpg  \n",
            " extracting: test/red_shirt/00000374_jpg.rf.1d48dc078b84afb2de34d4724d175fe2.jpg  \n",
            " extracting: test/red_shirt/00000379_jpg.rf.8cc96ae2b1b70a1349105b942191792b.jpg  \n",
            "   creating: train/\n",
            "   creating: train/black_jeans/\n",
            " extracting: train/black_jeans/00000000_jpg.rf.c2c6673dccaa69f7feaa5c7e77036d4a.jpg  \n",
            " extracting: train/black_jeans/00000001_jpeg.rf.080a408df60c46c91e5d697416362e1d.jpg  \n",
            " extracting: train/black_jeans/00000002_jpeg.rf.2b7e0a4b7f8dc1de0c548cb91cfd0623.jpg  \n",
            " extracting: train/black_jeans/00000003_jpg.rf.d982db8cd955e958c2928c58b6b3046a.jpg  \n",
            " extracting: train/black_jeans/00000005_jpeg.rf.a5c3dabc119a827d39f664da36d9d70e.jpg  \n",
            " extracting: train/black_jeans/00000006_jpg.rf.bddfb61e81c354a1023851dca74d8762.jpg  \n",
            " extracting: train/black_jeans/00000009_jpg.rf.7cb7545fb3255b2dbb9af8335bf26ed2.jpg  \n",
            " extracting: train/black_jeans/00000010_jpg.rf.b80f17ff4e224ddb6f7b4bf4b5e549bb.jpg  \n",
            " extracting: train/black_jeans/00000011_jpg.rf.8ef101b6a6d622feeefde5acf29155ab.jpg  \n",
            " extracting: train/black_jeans/00000013_jpg.rf.d950c34a59f08bd9820f42af7d83f0b9.jpg  \n",
            " extracting: train/black_jeans/00000014_jpg.rf.ff246df51d15f02c4c413afa9a7d3658.jpg  \n",
            " extracting: train/black_jeans/00000015_jpg.rf.41399628f7c64566da1b8af061591395.jpg  \n",
            " extracting: train/black_jeans/00000016_jpg.rf.4fdc3989bd4af60fe9f78a54f19b09ee.jpg  \n",
            " extracting: train/black_jeans/00000017_jpg.rf.702073e9e0b5635117e777a3fcc2bfba.jpg  \n",
            " extracting: train/black_jeans/00000019_jpg.rf.5099ffe4082702de03fd0b30951e2fbf.jpg  \n",
            " extracting: train/black_jeans/00000020_jpg.rf.69339417c07268a9b5a591bf318c0ead.jpg  \n",
            " extracting: train/black_jeans/00000022_jpg.rf.0c0100d26df6d67929c86fde5da68051.jpg  \n",
            " extracting: train/black_jeans/00000023_jpg.rf.d4c5d6d5675700072147b7602471dc00.jpg  \n",
            " extracting: train/black_jeans/00000024_jpg.rf.df4effb4f77e97db936aab96c0d7cfd1.jpg  \n",
            " extracting: train/black_jeans/00000025_jpg.rf.ebae185d24e8f94709acf25fed843778.jpg  \n",
            " extracting: train/black_jeans/00000027_jpg.rf.8cc97a5119f4cc28f9e09580ef9dd4c7.jpg  \n",
            " extracting: train/black_jeans/00000029_jpg.rf.ed44e81be9e2e498aeff3f39d475d408.jpg  \n",
            " extracting: train/black_jeans/00000030_JPG.rf.cf4e6765f55559ad328ae45bd4cb40fb.jpg  \n",
            " extracting: train/black_jeans/00000032_jpeg.rf.ac7a08a350b7b8507113f3aed67abb0c.jpg  \n",
            " extracting: train/black_jeans/00000034_jpeg.rf.baf1ff329998810fb27f3a0f796a8e19.jpg  \n",
            " extracting: train/black_jeans/00000035_jpg.rf.a89af6200012c272819c933adfc9d5b0.jpg  \n",
            " extracting: train/black_jeans/00000037_jpg.rf.63127463bdf97ef8e3641470089a27e0.jpg  \n",
            " extracting: train/black_jeans/00000038_jpg.rf.a6fb0e25c8895809a0c7173ef0658049.jpg  \n",
            " extracting: train/black_jeans/00000040_jpg.rf.29329b37ea07cc297a22fba576129c64.jpg  \n",
            " extracting: train/black_jeans/00000041_jpg.rf.cee4bd0892befc542893cab82f7a96fa.jpg  \n",
            " extracting: train/black_jeans/00000043_jpeg.rf.0a1f32d956bc5fb741cb3d99c291ea2e.jpg  \n",
            " extracting: train/black_jeans/00000047_jpg.rf.2129dc488d903f266577554aa973c3b4.jpg  \n",
            " extracting: train/black_jeans/00000048_jpg.rf.18cdb8bb203e029a84cdec8388d86cc3.jpg  \n",
            " extracting: train/black_jeans/00000049_jpg.rf.8e218e6d44a7c14bb02158b60960a4a5.jpg  \n",
            " extracting: train/black_jeans/00000050_jpeg.rf.b96ca5819f0c701bb4cb435844d032b1.jpg  \n",
            " extracting: train/black_jeans/00000051_jpg.rf.32c38dab55caabef6ebfadab2010b9f1.jpg  \n",
            " extracting: train/black_jeans/00000052_jpg.rf.c27c8f4711a79d812c4d499cd48b6ce9.jpg  \n",
            " extracting: train/black_jeans/00000053_jpg.rf.708ef63396f84fad2c1665d1cecb4cc1.jpg  \n",
            " extracting: train/black_jeans/00000055_jpg.rf.22c9811a5d6861340261a5ef282ee94a.jpg  \n",
            " extracting: train/black_jeans/00000057_jpg.rf.94a8c5c05828f7c9b40f75b21415c16a.jpg  \n",
            " extracting: train/black_jeans/00000058_jpeg.rf.718354eea049a182668d2788eafd60f5.jpg  \n",
            " extracting: train/black_jeans/00000059_jpeg.rf.1a1be5ff71336d47a1e5bdbefa137d7e.jpg  \n",
            " extracting: train/black_jeans/00000061_jpg.rf.8e2edc1e444af6bf10dc79f04e1f52f2.jpg  \n",
            " extracting: train/black_jeans/00000062_jpg.rf.f3a384798f1f145832bf7aabaa81179e.jpg  \n",
            " extracting: train/black_jeans/00000063_jpg.rf.3fb824873ada43cca57ef45d2fca7d93.jpg  \n",
            " extracting: train/black_jeans/00000064_jpg.rf.5e3756faf4f60b8ad8707d786de85fb0.jpg  \n",
            " extracting: train/black_jeans/00000065_jpg.rf.a516b01abf5564277125dc720a4e7279.jpg  \n",
            " extracting: train/black_jeans/00000066_jpg.rf.f43203d83d0cb5beae94243ad68240f5.jpg  \n",
            " extracting: train/black_jeans/00000067_jpg.rf.87123e32cb60b8cd56ea819c4503e93c.jpg  \n",
            " extracting: train/black_jeans/00000069_jpeg.rf.24ddbd285cf7444cfc6bd55c66a5043b.jpg  \n",
            " extracting: train/black_jeans/00000073_jpg.rf.9dd80bc885a11ace566eb656ab8ee576.jpg  \n",
            " extracting: train/black_jeans/00000076_jpg.rf.6a5c0d52fcbd09460fd311e6c2b651cc.jpg  \n",
            " extracting: train/black_jeans/00000077_jpeg.rf.872d5d2863ec5d5611148a079baea4d3.jpg  \n",
            " extracting: train/black_jeans/00000078_jpg.rf.75cf02736fa66c88420bdca88c80598a.jpg  \n",
            " extracting: train/black_jeans/00000080_jpg.rf.24e0b1a80d7114f0fbe35bf1fe17cac2.jpg  \n",
            " extracting: train/black_jeans/00000081_jpg.rf.857fb78558a7b4bff72bdd4951d72e9c.jpg  \n",
            " extracting: train/black_jeans/00000085_jpg.rf.a22074008912099e887f69431012bd1f.jpg  \n",
            " extracting: train/black_jeans/00000086_jpg.rf.c2dd7276ba783a75e0bb9c163c33ee1f.jpg  \n",
            " extracting: train/black_jeans/00000091_jpeg.rf.6aef4e07efa2e133795e8d6cefdf5b4a.jpg  \n",
            " extracting: train/black_jeans/00000092_jpg.rf.c0d8b970270eed96c4a92d7f4d285d09.jpg  \n",
            " extracting: train/black_jeans/00000093_jpg.rf.7ac86617278bd647b4e7c5c4b1c21560.jpg  \n",
            " extracting: train/black_jeans/00000094_jpg.rf.aed0a6c76c3c795061242aa561d8caf2.jpg  \n",
            " extracting: train/black_jeans/00000096_jpg.rf.a3f2213bff544f80945347b8728f7463.jpg  \n",
            " extracting: train/black_jeans/00000097_jpg.rf.2212be8ca6f37a8f7631706c1c29b6cb.jpg  \n",
            " extracting: train/black_jeans/00000098_jpg.rf.41de84309a12cbd2097788ce3c057aea.jpg  \n",
            " extracting: train/black_jeans/00000099_jpeg.rf.31f9646217d4c9a1d801a1ec0d71cb50.jpg  \n",
            " extracting: train/black_jeans/00000100_jpg.rf.641d6b654b01e2949aa22cc2e3783762.jpg  \n",
            " extracting: train/black_jeans/00000101_jpg.rf.cafb10d4b419b253c00c477bb5ed7cb7.jpg  \n",
            " extracting: train/black_jeans/00000102_jpg.rf.35a86fca13c7b606df0157e23c85e579.jpg  \n",
            " extracting: train/black_jeans/00000104_jpg.rf.784b152278b1ba64f1c49da62865a586.jpg  \n",
            " extracting: train/black_jeans/00000107_jpg.rf.1210bdfc5e3e638a89d7ade97516ef4c.jpg  \n",
            " extracting: train/black_jeans/00000108_jpg.rf.c2de97c2bb752014e1d074f5f27c1993.jpg  \n",
            " extracting: train/black_jeans/00000109_jpeg.rf.48fa907a599c2fe4a09f56f949cd748a.jpg  \n",
            " extracting: train/black_jeans/00000110_jpg.rf.d4889d4ffc05f639771fd42c5826a14a.jpg  \n",
            " extracting: train/black_jeans/00000111_jpg.rf.2fa898398d556cb01e37365a97600fff.jpg  \n",
            " extracting: train/black_jeans/00000112_jpg.rf.26418e3d6c5c371cc7ec141bdb950bf2.jpg  \n",
            " extracting: train/black_jeans/00000113_jpg.rf.b9b90883a79a1c0830471a95306245d4.jpg  \n",
            " extracting: train/black_jeans/00000114_jpeg.rf.f8a2c265005180fcdadb207eb0d459f6.jpg  \n",
            " extracting: train/black_jeans/00000118_jpg.rf.2481bc1b5ff76897fe2b4f0c730b661c.jpg  \n",
            " extracting: train/black_jeans/00000120_jpg.rf.2eee1122bbd16c3f270f83ba56a94f2b.jpg  \n",
            " extracting: train/black_jeans/00000121_jpg.rf.0fcb9637ee8ff67101f9497116df8c70.jpg  \n",
            " extracting: train/black_jeans/00000124_jpg.rf.6d12aed3775db180c0513c590db49ee4.jpg  \n",
            " extracting: train/black_jeans/00000125_jpg.rf.6bf6f2f7180b9a8f8bb018c63a8b84f3.jpg  \n",
            " extracting: train/black_jeans/00000126_jpg.rf.b653266d420aae242ddf5c5f491b9d21.jpg  \n",
            " extracting: train/black_jeans/00000129_jpg.rf.781d64feac3dba6a1d47a3d67546092c.jpg  \n",
            " extracting: train/black_jeans/00000130_jpg.rf.1b9d43056f8422b152c140deda1130fb.jpg  \n",
            " extracting: train/black_jeans/00000136_jpg.rf.5ae14d84919d204278a4095db417bbc9.jpg  \n",
            " extracting: train/black_jeans/00000139_jpeg.rf.444ceb8799748459a3a92747d44eb381.jpg  \n",
            " extracting: train/black_jeans/00000140_jpg.rf.d5c5236a5b3cd71d50e3849803532b1c.jpg  \n",
            " extracting: train/black_jeans/00000142_jpeg.rf.726182582bc0cbb087fc3042a3895932.jpg  \n",
            " extracting: train/black_jeans/00000143_jpg.rf.511cc7b2706aeb8136e486a118370b4f.jpg  \n",
            " extracting: train/black_jeans/00000148_jpg.rf.1687702ef11a328417d4af3000e9800f.jpg  \n",
            " extracting: train/black_jeans/00000149_jpg.rf.0a102d99475ed26d9a75b4a3fbcd4f18.jpg  \n",
            " extracting: train/black_jeans/00000150_jpg.rf.f7daf713babcf83c859a98114bcc545c.jpg  \n",
            " extracting: train/black_jeans/00000151_jpg.rf.3a75662bd9cd1d0fd8807d91ab13c75b.jpg  \n",
            " extracting: train/black_jeans/00000152_jpeg.rf.3f691c7c0a9bc4666c7cc5fef349fce5.jpg  \n",
            " extracting: train/black_jeans/00000153_jpg.rf.847377244043b56bc20e68dd3661c9eb.jpg  \n",
            " extracting: train/black_jeans/00000154_jpeg.rf.4a1e3508b717a6f5bbb33b7f6afb641c.jpg  \n",
            " extracting: train/black_jeans/00000155_jpg.rf.251b164db8c49c54303963acbc91b0bd.jpg  \n",
            " extracting: train/black_jeans/00000158_jpeg.rf.94d5fe0c0912476a18a9efe0d2b4a243.jpg  \n",
            " extracting: train/black_jeans/00000159_jpg.rf.0e25e45f8892f5117e3ba99fcfb565c5.jpg  \n",
            " extracting: train/black_jeans/00000160_jpg.rf.1d5016dde96ead733d2856edb8803f90.jpg  \n",
            " extracting: train/black_jeans/00000162_jpg.rf.91ca2e49a2352530390456e23d565b00.jpg  \n",
            " extracting: train/black_jeans/00000164_JPG.rf.03b00544f283554bc767b47e067a02a0.jpg  \n",
            " extracting: train/black_jeans/00000166_jpg.rf.308ba88033a379e496302fc7646ee9e0.jpg  \n",
            " extracting: train/black_jeans/00000167_jpg.rf.78b165a8987ef5caf1fdb78fa931f41b.jpg  \n",
            " extracting: train/black_jeans/00000170_jpeg.rf.7d3182ebd72c08941fcd9ee39c84f4b9.jpg  \n",
            " extracting: train/black_jeans/00000171_jpg.rf.c665fd3b8c1c120220c755be384c564a.jpg  \n",
            " extracting: train/black_jeans/00000174_jpeg.rf.d8fdc2342e965c755d9408e6e076f032.jpg  \n",
            " extracting: train/black_jeans/00000175_jpeg.rf.94efd55e4215cffc3547a3bb0ac62576.jpg  \n",
            " extracting: train/black_jeans/00000178_jpeg.rf.3401dfdfab4e39427726a6c36e4fb769.jpg  \n",
            " extracting: train/black_jeans/00000179_jpeg.rf.c6365be8a4aeab28c067ec7b68a52e31.jpg  \n",
            " extracting: train/black_jeans/00000184_jpg.rf.1453194ae35d79641bfb62bc857a31be.jpg  \n",
            " extracting: train/black_jeans/00000185_jpg.rf.05a8b516cb182e9c45c5d0cf351a16bd.jpg  \n",
            " extracting: train/black_jeans/00000186_jpg.rf.8c5c1ca4135643dc60f5fcd2ccbcaf18.jpg  \n",
            " extracting: train/black_jeans/00000187_jpeg.rf.4f3af36f6923cb8c0524f4b3be5b0322.jpg  \n",
            " extracting: train/black_jeans/00000188_jpeg.rf.4668ba6287a902ee7188d11a34935b76.jpg  \n",
            " extracting: train/black_jeans/00000189_jpg.rf.d156f5512974591d54eb8b1d3b378d27.jpg  \n",
            " extracting: train/black_jeans/00000190_jpg.rf.58831e516f2cd95b5b38b5f904aba244.jpg  \n",
            " extracting: train/black_jeans/00000191_jpg.rf.74422436526e97bcc36c86346d364d9f.jpg  \n",
            " extracting: train/black_jeans/00000194_jpg.rf.eb6470159e4750d9b6f54837229f7642.jpg  \n",
            " extracting: train/black_jeans/00000195_jpg.rf.dcb405d70c25f126a4b4b9812ffa4bda.jpg  \n",
            " extracting: train/black_jeans/00000197_jpg.rf.da94cd9ddcff510d8c4d88065172dee7.jpg  \n",
            " extracting: train/black_jeans/00000198_jpg.rf.b3103e7f5ab501b02b618998e1109ad0.jpg  \n",
            " extracting: train/black_jeans/00000199_jpg.rf.796c8be63bf2c3ec308b2d7c10922784.jpg  \n",
            " extracting: train/black_jeans/00000200_jpg.rf.280a7746279bc88ff334c7c568d1052a.jpg  \n",
            " extracting: train/black_jeans/00000201_jpeg.rf.7b5fc079bff95cb824f17b7604fa1ae2.jpg  \n",
            " extracting: train/black_jeans/00000202_jpeg.rf.dd5647864b6204b66f5d777d22288287.jpg  \n",
            " extracting: train/black_jeans/00000203_jpg.rf.80c35e1269248b3de5289729273180f0.jpg  \n",
            " extracting: train/black_jeans/00000204_jpeg.rf.e010d382adc8e3ee041b8beba62d2aaa.jpg  \n",
            " extracting: train/black_jeans/00000207_jpeg.rf.9f9e0a2e67b716f9867026862579cc38.jpg  \n",
            " extracting: train/black_jeans/00000208_jpg.rf.68fc46079c5e524c7278b4b28abe3ddf.jpg  \n",
            " extracting: train/black_jeans/00000209_jpg.rf.a9d115c27b481b6414582f942bd2928c.jpg  \n",
            " extracting: train/black_jeans/00000210_jpg.rf.1fd0060f1a1bafe0310981a15ae8c86b.jpg  \n",
            " extracting: train/black_jeans/00000212_jpg.rf.28d7c3498a680237fb3ada2ea8d94ab0.jpg  \n",
            " extracting: train/black_jeans/00000214_jpg.rf.1254f48ba17d031f184067243dc2c4af.jpg  \n",
            " extracting: train/black_jeans/00000215_jpeg.rf.a31d39d1b6824745cb18af7d059e179b.jpg  \n",
            " extracting: train/black_jeans/00000216_jpeg.rf.805bf31b278203cc503567a9374af466.jpg  \n",
            " extracting: train/black_jeans/00000217_jpg.rf.05892e57597c56e30231604e6816ef75.jpg  \n",
            " extracting: train/black_jeans/00000219_jpg.rf.ab6454eaf25f0d9506d61cc7cdd23fa9.jpg  \n",
            " extracting: train/black_jeans/00000220_jpg.rf.fe7ec3e9730dca30e184edc0141bbdd5.jpg  \n",
            " extracting: train/black_jeans/00000221_jpg.rf.f7851ae40216d4a3bfb2a37ad1582b50.jpg  \n",
            " extracting: train/black_jeans/00000223_jpg.rf.78b99d7c2f696dd125627a609f166b0c.jpg  \n",
            " extracting: train/black_jeans/00000228_jpg.rf.1826aa7fed7ad1cc993c27b21b6825bd.jpg  \n",
            " extracting: train/black_jeans/00000229_jpg.rf.10b47d7cc0b51d82c6e1fce32e05a2a0.jpg  \n",
            " extracting: train/black_jeans/00000230_jpeg.rf.7858531afb56a49c8f5ac3a4204356f6.jpg  \n",
            " extracting: train/black_jeans/00000231_jpeg.rf.56f0233736ddc27af4c431f1d024c978.jpg  \n",
            " extracting: train/black_jeans/00000232_jpg.rf.8a207f7a2550723feb71db826ce4bba2.jpg  \n",
            " extracting: train/black_jeans/00000233_jpg.rf.a2a0d29a1a847b0477838fc613e3e4e1.jpg  \n",
            " extracting: train/black_jeans/00000235_jpeg.rf.9ce964b6d42da4bc38ea5213448abd1d.jpg  \n",
            " extracting: train/black_jeans/00000236_jpeg.rf.baa1d78b4a916af1f08f971a7c56074a.jpg  \n",
            " extracting: train/black_jeans/00000237_jpg.rf.da667efd085b5beabc4582091b6ca923.jpg  \n",
            " extracting: train/black_jeans/00000238_jpeg.rf.2bc4982eacf9040cca5e0fdd4f52dfea.jpg  \n",
            " extracting: train/black_jeans/00000239_jpg.rf.7baf1c6bfea7d9d672e2eac64c6cf233.jpg  \n",
            " extracting: train/black_jeans/00000241_jpg.rf.d00ea3689b5f2afc2c2b0cd86feb5f15.jpg  \n",
            " extracting: train/black_jeans/00000243_jpg.rf.d7bcde5e06e2858968c02ff6d676d35d.jpg  \n",
            " extracting: train/black_jeans/00000244_jpeg.rf.8d7d1e5804861768d6f7c088b39ce980.jpg  \n",
            " extracting: train/black_jeans/00000245_jpg.rf.011afca03e1ec951752ffa901d2d25bf.jpg  \n",
            " extracting: train/black_jeans/00000248_jpeg.rf.267a92113b2c3f31dc39657641928fee.jpg  \n",
            " extracting: train/black_jeans/00000251_jpg.rf.a71b441fea9368c611dc2f6d5f8fb30d.jpg  \n",
            " extracting: train/black_jeans/00000253_jpg.rf.b14391112286b6c6fcf019b737fdfd8a.jpg  \n",
            " extracting: train/black_jeans/00000254_jpg.rf.4adf0ac1ecdf1d839835ec78dc3e5b45.jpg  \n",
            " extracting: train/black_jeans/00000257_jpg.rf.b0f284cb24091be77ff01372ab4c8a0e.jpg  \n",
            " extracting: train/black_jeans/00000259_jpg.rf.fbba7fc4b9d37749fbb3e178f1830826.jpg  \n",
            " extracting: train/black_jeans/00000262_jpg.rf.b7d18fd1443086ec37dd41b4ab067ebe.jpg  \n",
            " extracting: train/black_jeans/00000263_jpg.rf.dd9ec0633855d6e976531d11539f45ba.jpg  \n",
            " extracting: train/black_jeans/00000264_jpg.rf.e22c79e2d84836b7c0c079cd086b9bcd.jpg  \n",
            " extracting: train/black_jeans/00000265_jpg.rf.4132ae7e1f448c81b2c2db18f9936db0.jpg  \n",
            " extracting: train/black_jeans/00000266_jpg.rf.e1b4a0ee4c8d5d5d1450d462a30dd19f.jpg  \n",
            " extracting: train/black_jeans/00000268_jpg.rf.88b608a35f72491258fab7f43df229b6.jpg  \n",
            " extracting: train/black_jeans/00000269_jpg.rf.a8698b21d3c2f931cc8d34633dedcf72.jpg  \n",
            " extracting: train/black_jeans/00000270_jpg.rf.817cc0c080d5ae84d55c950d292a2628.jpg  \n",
            " extracting: train/black_jeans/00000271_jpg.rf.769a9718326bc88da8c1f3950c9e4380.jpg  \n",
            " extracting: train/black_jeans/00000272_jpg.rf.62a87ee9ed30b2ab39b066a386eb398d.jpg  \n",
            " extracting: train/black_jeans/00000273_jpg.rf.a26e4a019288d441816eaea7fc2ed2e9.jpg  \n",
            " extracting: train/black_jeans/00000274_jpg.rf.ecd0d3b0d559d8720ebbf0023a974277.jpg  \n",
            " extracting: train/black_jeans/00000275_jpg.rf.0e33f81c82b8b6f438f7be3c9e7ee962.jpg  \n",
            " extracting: train/black_jeans/00000277_jpeg.rf.e75bc7c05c830a6658be1cbb2ca52214.jpg  \n",
            " extracting: train/black_jeans/00000278_jpeg.rf.460b5ef865c836965b5650030d8a87ef.jpg  \n",
            " extracting: train/black_jeans/00000280_jpeg.rf.2c52401641da4c3c5ab0c9e3856b4cf4.jpg  \n",
            " extracting: train/black_jeans/00000281_jpeg.rf.ee3733c31b22323fdef7353f8e7d47bf.jpg  \n",
            " extracting: train/black_jeans/00000282_jpg.rf.0311630123e5dc35aacee315596f86c9.jpg  \n",
            " extracting: train/black_jeans/00000283_png.rf.6b8bf6c47c46909fc002a84b70a1bcf5.jpg  \n",
            " extracting: train/black_jeans/00000284_jpg.rf.37ba1cfdd39617b38d7c3a429bd467ce.jpg  \n",
            " extracting: train/black_jeans/00000285_jpg.rf.fc0a4c0c2c1b8d10b3bb49b1f14ef26a.jpg  \n",
            " extracting: train/black_jeans/00000286_jpg.rf.75496de5272a2a4eb575deb8d137bbf8.jpg  \n",
            " extracting: train/black_jeans/00000287_jpeg.rf.08931a1458c40cb50dffd992104cc287.jpg  \n",
            " extracting: train/black_jeans/00000289_jpg.rf.50c0b75a15fb5398fb8536f4cd6c9a32.jpg  \n",
            " extracting: train/black_jeans/00000290_jpg.rf.b8099d97a6011f6948e7e5dc970f8604.jpg  \n",
            " extracting: train/black_jeans/00000291_jpeg.rf.d61c586b7221c248381d1e430d1a4c87.jpg  \n",
            " extracting: train/black_jeans/00000294_jpeg.rf.65b1fced3b18b6059322ead5f22a2a61.jpg  \n",
            " extracting: train/black_jeans/00000295_jpg.rf.116b88866071cb1c7b1f3d03d626ff49.jpg  \n",
            " extracting: train/black_jeans/00000296_jpg.rf.f1921dec8253b734071d7c9d3504ae15.jpg  \n",
            " extracting: train/black_jeans/00000298_jpeg.rf.5475816974a3d8f4ae9eaef63b143b92.jpg  \n",
            " extracting: train/black_jeans/00000300_jpg.rf.77f313649667819c70584df544ada8c5.jpg  \n",
            " extracting: train/black_jeans/00000301_jpg.rf.7cd5df3ac9bf621baff38faa35383442.jpg  \n",
            " extracting: train/black_jeans/00000303_jpeg.rf.ccde6033a9c8673aab98597ae1f8b94c.jpg  \n",
            " extracting: train/black_jeans/00000304_jpg.rf.b4b523923ec8ce70fbd71a4156bfa906.jpg  \n",
            " extracting: train/black_jeans/00000307_jpg.rf.6e5323ea2af33911e28acf1e0c4d7c7d.jpg  \n",
            " extracting: train/black_jeans/00000308_jpg.rf.1a167ed9e93fcabc22046d2da2985279.jpg  \n",
            " extracting: train/black_jeans/00000311_jpeg.rf.485435309fb3cd17be0c39f46f32e58c.jpg  \n",
            " extracting: train/black_jeans/00000313_jpeg.rf.bda4d58f3851b6e6cb7f64050b97d8d7.jpg  \n",
            " extracting: train/black_jeans/00000314_jpg.rf.9b836a8ec4ef05473d8a31b1c5f6af38.jpg  \n",
            " extracting: train/black_jeans/00000316_jpg.rf.61d29a74508b75ebb2e4b96ed1e055a3.jpg  \n",
            " extracting: train/black_jeans/00000318_jpg.rf.5db7df20f981f22844ebce5eb3c69579.jpg  \n",
            " extracting: train/black_jeans/00000322_jpg.rf.33f08c66947155bdd252c9bfd9d6d219.jpg  \n",
            " extracting: train/black_jeans/00000323_jpeg.rf.072a161e053af4e69c005b76e5814283.jpg  \n",
            " extracting: train/black_jeans/00000324_jpg.rf.f577272a20711eaca4e5a326950ff2c0.jpg  \n",
            " extracting: train/black_jeans/00000325_jpg.rf.0e5995bebc561a75d9dacbf8bca610f2.jpg  \n",
            " extracting: train/black_jeans/00000327_jpeg.rf.4cd418a5fcca10e9174a15ad42966229.jpg  \n",
            " extracting: train/black_jeans/00000332_jpg.rf.405ad4749fe99f90b0806b6e1356b536.jpg  \n",
            " extracting: train/black_jeans/00000333_jpeg.rf.07f576df9061f9bd61997e65dfd9eddb.jpg  \n",
            " extracting: train/black_jeans/00000335_jpg.rf.a1a4d068c1c8e4f87be76b55836e3d49.jpg  \n",
            " extracting: train/black_jeans/00000336_jpg.rf.f6ab96837a8d077b28a4a66baa8a877e.jpg  \n",
            " extracting: train/black_jeans/00000337_jpeg.rf.add8b104929e94c2aef745703128c391.jpg  \n",
            " extracting: train/black_jeans/00000338_jpg.rf.822184c22e5bef59e655b60e86803d85.jpg  \n",
            " extracting: train/black_jeans/00000339_jpeg.rf.5b830415a78438252cde6f599df80aff.jpg  \n",
            " extracting: train/black_jeans/00000340_jpeg.rf.be29c1971d54bd5e0049ae1a07116d5c.jpg  \n",
            " extracting: train/black_jeans/00000342_jpg.rf.cdde055799ff9d24b5b52539f76f4896.jpg  \n",
            " extracting: train/black_jeans/00000344_jpg.rf.b77e4875359b9c3e5775b8549cac5fec.jpg  \n",
            " extracting: train/black_jeans/00000345_jpeg.rf.277caaf6e3d3505d834458b81c952cbb.jpg  \n",
            " extracting: train/black_jeans/00000346_jpg.rf.1809d938865833dc75be0a2d42a9e788.jpg  \n",
            " extracting: train/black_jeans/00000347_jpg.rf.3577438531dc13f5a522265174d629cc.jpg  \n",
            " extracting: train/black_jeans/00000348_jpeg.rf.5e3f1baf269a17915c8813e7b9f50565.jpg  \n",
            " extracting: train/black_jeans/00000349_jpg.rf.8760fc51285bc5d023e8949b9b5b195d.jpg  \n",
            " extracting: train/black_jeans/00000351_jpg.rf.6b0bc0b3e9deae7680caf9825c6c3947.jpg  \n",
            " extracting: train/black_jeans/00000352_jpeg.rf.51455e756bac7d30de42fc3b51316815.jpg  \n",
            " extracting: train/black_jeans/00000353_jpeg.rf.459b0e2add0d0ce5ecc80bd97c7a46f8.jpg  \n",
            " extracting: train/black_jeans/00000354_jpeg.rf.a8b0090db56e43f98b9b3734da9b4340.jpg  \n",
            " extracting: train/black_jeans/00000356_jpg.rf.2cbb568ac7920f8b6281c4947f0e58bc.jpg  \n",
            " extracting: train/black_jeans/00000359_jpg.rf.97eb571b0ac8c9563cd700f821126033.jpg  \n",
            " extracting: train/black_jeans/00000360_jpg.rf.9811db345a25fd5f870a6a409c38677d.jpg  \n",
            " extracting: train/black_jeans/00000361_jpg.rf.ea7c7e401841a06ca3d84dca7f3ccb2a.jpg  \n",
            " extracting: train/black_jeans/00000362_jpg.rf.8d3b8f1a09ee5699d7a1bed4206cd053.jpg  \n",
            " extracting: train/black_jeans/00000363_jpeg.rf.da609515bda51106b0e3bbb6820bc646.jpg  \n",
            " extracting: train/black_jeans/00000364_jpeg.rf.e394687f8225d90af473acb8cf42bd73.jpg  \n",
            " extracting: train/black_jeans/00000365_jpg.rf.996b69a05ee14f9fa0df8aaa0827e4f9.jpg  \n",
            "   creating: train/blue_dress/\n",
            " extracting: train/blue_dress/00000000_jpg.rf.a895f0d2bf0ab1383e54a907116fd2c9.jpg  \n",
            " extracting: train/blue_dress/00000002_jpg.rf.bb46c3e6dfe673ba099f2e1b5bf23b4c.jpg  \n",
            " extracting: train/blue_dress/00000003_jpeg.rf.66bacb7850d02a23abc16cbe2d094f30.jpg  \n",
            " extracting: train/blue_dress/00000004_jpg.rf.954b98ad18bbde7543e0b4999183aa00.jpg  \n",
            " extracting: train/blue_dress/00000005_jpg.rf.f44273165ba08d3a060ef6e04e34b414.jpg  \n",
            " extracting: train/blue_dress/00000007_jpg.rf.dc4ce118f358e77a4a54da835fea0a7d.jpg  \n",
            " extracting: train/blue_dress/00000010_jpg.rf.68499622578ef2225347a99534d729c6.jpg  \n",
            " extracting: train/blue_dress/00000011_jpg.rf.6c5d40655a73f8c96cfc630a4f7374e3.jpg  \n",
            " extracting: train/blue_dress/00000012_jpg.rf.c1bf6dfb4e3b1e1ad9fd3797aa5324bc.jpg  \n",
            " extracting: train/blue_dress/00000013_jpg.rf.7f464a2930592dacf985c8ca3a85bbc8.jpg  \n",
            " extracting: train/blue_dress/00000014_jpg.rf.3bef72e069784dfe55441c2b95323aa4.jpg  \n",
            " extracting: train/blue_dress/00000016_jpg.rf.e7741c4bb042f3ef4e03ebaf91845402.jpg  \n",
            " extracting: train/blue_dress/00000018_jpg.rf.5b026a41be71a51aae4113402d606143.jpg  \n",
            " extracting: train/blue_dress/00000019_jpg.rf.6cf66dbd597098e5ddd4b3df437439d8.jpg  \n",
            " extracting: train/blue_dress/00000021_jpg.rf.e191ed9107884b865de3f619add17d5d.jpg  \n",
            " extracting: train/blue_dress/00000022_jpg.rf.150d351a80b95be44ebbeb40c7c9a5e1.jpg  \n",
            " extracting: train/blue_dress/00000023_jpg.rf.8959c6a7a9e0c4e2b6f66a749daa2ee8.jpg  \n",
            " extracting: train/blue_dress/00000024_jpg.rf.04b9de211ac1fe818bbd462cd027ca1c.jpg  \n",
            " extracting: train/blue_dress/00000025_jpg.rf.4440db956f3c4dd3168e3ea7cb69e62f.jpg  \n",
            " extracting: train/blue_dress/00000028_jpeg.rf.92bdde825acad228ccd801317168b204.jpg  \n",
            " extracting: train/blue_dress/00000029_jpg.rf.5885ff92b884b4338edc8b5de755a0a2.jpg  \n",
            " extracting: train/blue_dress/00000031_jpg.rf.4d64ad7395d433e318c0c9ea695867c9.jpg  \n",
            " extracting: train/blue_dress/00000032_jpg.rf.f4cd78ec1ea9f8fccaa2d76a16c21428.jpg  \n",
            " extracting: train/blue_dress/00000033_jpg.rf.413dbf40d5443f58539523076aef9924.jpg  \n",
            " extracting: train/blue_dress/00000034_jpeg.rf.4cb709f0a877761c96d1bf36d4f9ac04.jpg  \n",
            " extracting: train/blue_dress/00000039_jpg.rf.2766eb718791fd3e7ae500caf153684b.jpg  \n",
            " extracting: train/blue_dress/00000040_jpg.rf.e9f4bf5ee76fbf76af83bf52963f548b.jpg  \n",
            " extracting: train/blue_dress/00000041_jpeg.rf.d4945feedfc9ea396b2747b4f6b59166.jpg  \n",
            " extracting: train/blue_dress/00000042_jpg.rf.5642affc50cc4ef4f0f30a175e23b2bb.jpg  \n",
            " extracting: train/blue_dress/00000043_jpg.rf.27d66b5fde91ac699218dbd71e3918d6.jpg  \n",
            " extracting: train/blue_dress/00000044_jpg.rf.d70f7061436045318eb2626e0cb31ce0.jpg  \n",
            " extracting: train/blue_dress/00000046_jpg.rf.86b60f99465dc72a3bb56ee2dcd4ba00.jpg  \n",
            " extracting: train/blue_dress/00000047_jpg.rf.fa02b442f2f1a39ea05d37c66296c0d3.jpg  \n",
            " extracting: train/blue_dress/00000048_jpg.rf.ae3e86cd5c4cc562dc6e33615a0d49dc.jpg  \n",
            " extracting: train/blue_dress/00000049_jpg.rf.812a1ef69754362269a73accb2883f6c.jpg  \n",
            " extracting: train/blue_dress/00000051_jpg.rf.98bc4d6eafdfa14de668ad7f34469d47.jpg  \n",
            " extracting: train/blue_dress/00000052_jpg.rf.be8a1fe08533ac1ca187fe4dd7dd044d.jpg  \n",
            " extracting: train/blue_dress/00000053_jpg.rf.8a0a5072292313a8be7001821a29478b.jpg  \n",
            " extracting: train/blue_dress/00000054_jpg.rf.227525804191c1b43b1e8df49690b341.jpg  \n",
            " extracting: train/blue_dress/00000056_jpg.rf.61f2760199d4d974e4332b695a363446.jpg  \n",
            " extracting: train/blue_dress/00000057_jpg.rf.1d32fd4db3abf6cccf171c89244187ec.jpg  \n",
            " extracting: train/blue_dress/00000058_jpg.rf.60717f0666d9001af2b607f8f525f222.jpg  \n",
            " extracting: train/blue_dress/00000059_jpg.rf.6569519dd8dd7293e3f0b86d540dfd9c.jpg  \n",
            " extracting: train/blue_dress/00000060_jpg.rf.d8584e51baba529f3d2d5b67626876b1.jpg  \n",
            " extracting: train/blue_dress/00000061_jpg.rf.e1cddbd80a6c76a724187a22ec452de7.jpg  \n",
            " extracting: train/blue_dress/00000062_jpg.rf.f640f5ac8827ed6321ca7b415af81cd9.jpg  \n",
            " extracting: train/blue_dress/00000063_jpg.rf.4c1c2a7a4a9976838703021b9b7b9087.jpg  \n",
            " extracting: train/blue_dress/00000064_jpg.rf.ed557cfc5198453b10998fa210709591.jpg  \n",
            " extracting: train/blue_dress/00000065_jpg.rf.2cbf61fb52c1a57223f4097c3aacaf6b.jpg  \n",
            " extracting: train/blue_dress/00000066_jpg.rf.f1af71cbe6203679ee6fff489a84763f.jpg  \n",
            " extracting: train/blue_dress/00000070_jpeg.rf.5b9f0a92aa7b2e99c5e518a7eb9edecf.jpg  \n",
            " extracting: train/blue_dress/00000071_jpg.rf.8adf02b26b9af1a27de1c4b3351ce49e.jpg  \n",
            " extracting: train/blue_dress/00000072_jpeg.rf.71ac25ea62b547f5723e2725dfbc45e9.jpg  \n",
            " extracting: train/blue_dress/00000073_jpg.rf.7f84c7ab764a88cf0398f5db38061796.jpg  \n",
            " extracting: train/blue_dress/00000075_jpg.rf.2247cbfbdb39b388c0e71c31cd161905.jpg  \n",
            " extracting: train/blue_dress/00000076_jpg.rf.318b8d55e7812ece98e1759f6067c2bb.jpg  \n",
            " extracting: train/blue_dress/00000077_jpg.rf.bed7f333e2d9ec3419d1a165b651cde7.jpg  \n",
            " extracting: train/blue_dress/00000079_jpg.rf.92a8844c12ba4726254808280544c383.jpg  \n",
            " extracting: train/blue_dress/00000080_jpg.rf.aea765cf1a1bc436c1dba543392b83c5.jpg  \n",
            " extracting: train/blue_dress/00000082_jpg.rf.ad6dec46191bd725f64e7a72707471e0.jpg  \n",
            " extracting: train/blue_dress/00000085_jpg.rf.8eb0f894421b66ffe9e955ee18352b4f.jpg  \n",
            " extracting: train/blue_dress/00000086_jpg.rf.e8c19f5de6a301fb904b2e45337757b5.jpg  \n",
            " extracting: train/blue_dress/00000087_jpg.rf.1d67c8194fa022748d87b5ed676e659b.jpg  \n",
            " extracting: train/blue_dress/00000090_jpg.rf.b9225122baf45e5058c7702c01f36ad1.jpg  \n",
            " extracting: train/blue_dress/00000092_jpg.rf.ea953b8fa58c3527fc3ce68c89abf894.jpg  \n",
            " extracting: train/blue_dress/00000094_jpg.rf.26d376933e4e564376e4b7c0b51be2ba.jpg  \n",
            " extracting: train/blue_dress/00000095_jpg.rf.18acbe32d7b0cc657f2ad375745384bd.jpg  \n",
            " extracting: train/blue_dress/00000097_jpg.rf.c182d43e56aaca4cb8093b80c199e35c.jpg  \n",
            " extracting: train/blue_dress/00000098_jpeg.rf.093903e37de4cb93372e3f9b06bea57d.jpg  \n",
            " extracting: train/blue_dress/00000100_jpg.rf.e1b1f3049ee70a955df0b13fd6f144ef.jpg  \n",
            " extracting: train/blue_dress/00000101_jpg.rf.9418fcc2b584ce4b871642c1153d3d1c.jpg  \n",
            " extracting: train/blue_dress/00000102_jpg.rf.e987c746ad3f84e90da374532350faab.jpg  \n",
            " extracting: train/blue_dress/00000104_jpg.rf.614884781d82905e04023d9095602f0f.jpg  \n",
            " extracting: train/blue_dress/00000105_jpg.rf.d42f529ac459c465cb303350306dbb07.jpg  \n",
            " extracting: train/blue_dress/00000109_jpg.rf.0fe2a92bfc3e3e4f973f8ab1a592ec8c.jpg  \n",
            " extracting: train/blue_dress/00000110_jpg.rf.76168b411d56cbaa3b546ae29d6190d5.jpg  \n",
            " extracting: train/blue_dress/00000111_jpg.rf.2559e2ef687421406923e8444706082e.jpg  \n",
            " extracting: train/blue_dress/00000114_jpg.rf.74bba6d92e5e8be734da3b22c1afef65.jpg  \n",
            " extracting: train/blue_dress/00000115_jpg.rf.9bb8456a96244e0284ab57ae502f7cac.jpg  \n",
            " extracting: train/blue_dress/00000117_jpg.rf.9dcc197f1162dcafae10c6291248c3f7.jpg  \n",
            " extracting: train/blue_dress/00000118_jpg.rf.e30422aee018a8d02ca3783b9ecf2bb9.jpg  \n",
            " extracting: train/blue_dress/00000119_jpg.rf.d79a3c80816cac213b30f51f015359ec.jpg  \n",
            " extracting: train/blue_dress/00000121_jpg.rf.01b784e34e64142fefd6395301a8f49a.jpg  \n",
            " extracting: train/blue_dress/00000123_jpg.rf.4f59280b5d193cb388d5d1e6e1b757e8.jpg  \n",
            " extracting: train/blue_dress/00000124_jpg.rf.9a2a2938b3bae8eb6d8bd265f8fed3a5.jpg  \n",
            " extracting: train/blue_dress/00000125_jpg.rf.460a063da42f2f40e41f5f1fb243462e.jpg  \n",
            " extracting: train/blue_dress/00000126_jpg.rf.b4f8268a0ad4376afefa47a0b2f17953.jpg  \n",
            " extracting: train/blue_dress/00000127_jpg.rf.8f1112bbf121ba953c5d3a7c1919c89a.jpg  \n",
            " extracting: train/blue_dress/00000132_jpg.rf.b10aa5673f90cc7f3f6253a137e3cf77.jpg  \n",
            " extracting: train/blue_dress/00000134_jpg.rf.a381e80b16892df93a3c6c9a97377471.jpg  \n",
            " extracting: train/blue_dress/00000135_jpg.rf.ec124c50c4f6208774c1804210c6da9a.jpg  \n",
            " extracting: train/blue_dress/00000138_jpg.rf.ba2c485172b523c193b80904dac34d11.jpg  \n",
            " extracting: train/blue_dress/00000140_jpeg.rf.286b7c1976450ddd4b3e989d422d53f2.jpg  \n",
            " extracting: train/blue_dress/00000141_jpg.rf.77569bc342deefd9c8113a1c2dee3cbb.jpg  \n",
            " extracting: train/blue_dress/00000143_jpg.rf.d8689f8e1ca406bda2806bcbc5b9c269.jpg  \n",
            " extracting: train/blue_dress/00000144_jpg.rf.ecd0cc937d1bcc6f488f72d1ef157764.jpg  \n",
            " extracting: train/blue_dress/00000145_jpeg.rf.aaf0bb228d9dc1424b3eb4c224c90dfc.jpg  \n",
            " extracting: train/blue_dress/00000146_jpg.rf.ff5515b91a9b12d06353492527233db8.jpg  \n",
            " extracting: train/blue_dress/00000147_jpg.rf.24c10804f481868dfbb14d664bdd661d.jpg  \n",
            " extracting: train/blue_dress/00000148_jpg.rf.e8cf1857daa8dbdb486bfc33698637d8.jpg  \n",
            " extracting: train/blue_dress/00000149_jpg.rf.c050c30232c8e9ab485ba9b673859188.jpg  \n",
            " extracting: train/blue_dress/00000150_jpg.rf.367f68934976ccb9303ea3ec33a8e055.jpg  \n",
            " extracting: train/blue_dress/00000151_jpeg.rf.ae28cc6af719ba42a766a687ab665ac7.jpg  \n",
            " extracting: train/blue_dress/00000153_jpg.rf.209aa5cb5e7095f251d37f539a59647a.jpg  \n",
            " extracting: train/blue_dress/00000155_jpg.rf.108a97370a4f82e975e6f2e0f8b75573.jpg  \n",
            " extracting: train/blue_dress/00000156_jpg.rf.9ad2af8de44fe241b8ae11f4d3dfb2fd.jpg  \n",
            " extracting: train/blue_dress/00000158_jpg.rf.3787390b2a75ea0f449c41227dd9516a.jpg  \n",
            " extracting: train/blue_dress/00000161_jpg.rf.4b9911bb3d8da87de3420028c4e35147.jpg  \n",
            " extracting: train/blue_dress/00000162_jpeg.rf.db6fb3da05d6023f5d27a98f8780ec37.jpg  \n",
            " extracting: train/blue_dress/00000164_jpg.rf.f2ace99d7ef1b199e66db9b40f8630cc.jpg  \n",
            " extracting: train/blue_dress/00000166_jpg.rf.122cdaf3b7bb624f8883c0b60bf5a20d.jpg  \n",
            " extracting: train/blue_dress/00000168_jpg.rf.c24e6eb889c516bd1d1a446aca9d278e.jpg  \n",
            " extracting: train/blue_dress/00000169_jpg.rf.aec86b13fe5835c13b144ab03a71343f.jpg  \n",
            " extracting: train/blue_dress/00000170_jpg.rf.26b807ac1ced1ba4c4e756ee986bb86a.jpg  \n",
            " extracting: train/blue_dress/00000171_jpg.rf.4c9ee2087038980049d27959c140beae.jpg  \n",
            " extracting: train/blue_dress/00000172_jpg.rf.0dcf6d20ccf6cfbc08a72730255d94ae.jpg  \n",
            " extracting: train/blue_dress/00000173_jpg.rf.c297dd214aa920d0c6ab610dc82cc247.jpg  \n",
            " extracting: train/blue_dress/00000174_jpg.rf.fe3a90be9c9cf21a332b2a9f5741c8b8.jpg  \n",
            " extracting: train/blue_dress/00000176_jpg.rf.2c5e65d07a4139cbbc04d0b44a84acf5.jpg  \n",
            " extracting: train/blue_dress/00000177_jpg.rf.c34f52f9929eb975b1e94fa02fb1961b.jpg  \n",
            " extracting: train/blue_dress/00000178_jpg.rf.87207f8fae417a6dc6135a98f943ed4c.jpg  \n",
            " extracting: train/blue_dress/00000179_jpg.rf.c04556197044dd28be18863515194410.jpg  \n",
            " extracting: train/blue_dress/00000180_jpg.rf.ccb31f06c9663a166579caa1c40eaf4e.jpg  \n",
            " extracting: train/blue_dress/00000182_jpg.rf.2a3bae96bdbe2e2c8332c2abb9fa36e4.jpg  \n",
            " extracting: train/blue_dress/00000184_jpg.rf.b20965b2dd8148f926db7ffe18bc949f.jpg  \n",
            " extracting: train/blue_dress/00000185_jpg.rf.d1083b737e89fa0641d458307be809b0.jpg  \n",
            " extracting: train/blue_dress/00000186_jpg.rf.d2cff53390c8dd3c80cf774a3e0dbcec.jpg  \n",
            " extracting: train/blue_dress/00000190_jpg.rf.348624f41c97ae031d5f59f16374237a.jpg  \n",
            " extracting: train/blue_dress/00000193_jpg.rf.43956ef00b1f1f171fb7725e6d5f83bf.jpg  \n",
            " extracting: train/blue_dress/00000194_jpg.rf.2d27331120bc2dbe93400aab05aa7a0c.jpg  \n",
            " extracting: train/blue_dress/00000195_jpg.rf.8ca610beaf775a5a81ad762331296efb.jpg  \n",
            " extracting: train/blue_dress/00000196_jpg.rf.e7db9557477c7bae1e1347c7c2984e00.jpg  \n",
            " extracting: train/blue_dress/00000197_jpg.rf.76ceabbfa3dce8115fe9ee88dd4d9883.jpg  \n",
            " extracting: train/blue_dress/00000200_jpg.rf.0a16dff51834e8d98492b223f65cb135.jpg  \n",
            " extracting: train/blue_dress/00000201_jpg.rf.07665243da7c217e92562ce38451bd2b.jpg  \n",
            " extracting: train/blue_dress/00000202_jpg.rf.7c93ff065e5ae8e27277305cd3d9b587.jpg  \n",
            " extracting: train/blue_dress/00000203_jpg.rf.e8443a51b28e5aadb9e193bea6916044.jpg  \n",
            " extracting: train/blue_dress/00000205_png.rf.dfd3ee34749c4e450f31f99ba2b611e0.jpg  \n",
            " extracting: train/blue_dress/00000212_jpg.rf.319129e8ffee4c4ca01f2273f284ac5a.jpg  \n",
            " extracting: train/blue_dress/00000214_jpg.rf.b20faae29ff5310c005d5830733dd1cf.jpg  \n",
            " extracting: train/blue_dress/00000217_jpg.rf.79adc8044686758fa3c354e4c6badda1.jpg  \n",
            " extracting: train/blue_dress/00000219_jpg.rf.f6d9c5a08b52dc73ab0fdeb3473e24f9.jpg  \n",
            " extracting: train/blue_dress/00000220_jpg.rf.39fdd47ed79cf18d7efb4c17a2d92b5a.jpg  \n",
            " extracting: train/blue_dress/00000221_jpg.rf.8ad61913ff5d2862849b888c17c1f875.jpg  \n",
            " extracting: train/blue_dress/00000222_jpeg.rf.5a4a9cb5bac6436ac78183a00d646602.jpg  \n",
            " extracting: train/blue_dress/00000227_jpg.rf.cf9f96806cf71481ae2ffd3681622591.jpg  \n",
            " extracting: train/blue_dress/00000228_jpg.rf.8b0e19d76cc0a96996a88a778d79efe1.jpg  \n",
            " extracting: train/blue_dress/00000229_jpg.rf.5f09ebbb0fb182e58b711e34a515ed0f.jpg  \n",
            " extracting: train/blue_dress/00000230_jpg.rf.766070450c85c1e7d0410a0752df3a4d.jpg  \n",
            " extracting: train/blue_dress/00000231_jpg.rf.35212c239ae9137df99c4c2065231467.jpg  \n",
            " extracting: train/blue_dress/00000236_jpg.rf.6e43a8b5b3781203dc58e98ccaeae257.jpg  \n",
            " extracting: train/blue_dress/00000237_jpg.rf.fac25d93683ec2db423bdacda80276fc.jpg  \n",
            " extracting: train/blue_dress/00000238_jpg.rf.be69718e172e5d2f178a34cfed29d063.jpg  \n",
            " extracting: train/blue_dress/00000240_jpg.rf.3eb388b72a86b3b6099e5c6e034031c3.jpg  \n",
            " extracting: train/blue_dress/00000242_jpg.rf.8b19dd9479d4dec0f406f17feffbba72.jpg  \n",
            " extracting: train/blue_dress/00000243_jpg.rf.ab15c799b4534f3b048b52a43078865f.jpg  \n",
            " extracting: train/blue_dress/00000247_jpg.rf.96d8b29eee0b34f1a72cf9428ce0a08c.jpg  \n",
            " extracting: train/blue_dress/00000248_jpg.rf.dba09c86fc7d3025f83222389345bc17.jpg  \n",
            " extracting: train/blue_dress/00000250_jpg.rf.89cb22f6fb3ecb0ce987969559301482.jpg  \n",
            " extracting: train/blue_dress/00000256_jpg.rf.e46f30d5665d5875a5f0b93308c272a2.jpg  \n",
            " extracting: train/blue_dress/00000259_jpeg.rf.978f535369cadc42948dfcf1f1d47c93.jpg  \n",
            " extracting: train/blue_dress/00000260_jpg.rf.d0d9737878b31a6b8a1ebd8c5ac956c7.jpg  \n",
            " extracting: train/blue_dress/00000263_jpg.rf.7a8a5514ed9e6efad60d0cf4dd123b11.jpg  \n",
            " extracting: train/blue_dress/00000265_jpg.rf.f360dd2d018bc5790349ab04de984edd.jpg  \n",
            " extracting: train/blue_dress/00000266_jpg.rf.97a74fbe48199da2d520186f3d261707.jpg  \n",
            " extracting: train/blue_dress/00000267_jpg.rf.36a618b888b1786f7f8b197762ec23c7.jpg  \n",
            " extracting: train/blue_dress/00000268_jpg.rf.8e80d4d4a5a0b3915c6ae26f50f4a6f2.jpg  \n",
            " extracting: train/blue_dress/00000269_jpeg.rf.d56569ba1a2849a77ed24f9bdf46417f.jpg  \n",
            " extracting: train/blue_dress/00000270_jpg.rf.13a31cd4b3d884cc1792078c757d58b4.jpg  \n",
            " extracting: train/blue_dress/00000272_jpg.rf.8b6eb61712f508040290b5ce9d4aa184.jpg  \n",
            " extracting: train/blue_dress/00000273_jpg.rf.3ac9598f8eb3a6645a648f3596de2f12.jpg  \n",
            " extracting: train/blue_dress/00000274_jpg.rf.f2e11f39279a461005538691c226dadb.jpg  \n",
            " extracting: train/blue_dress/00000275_jpg.rf.141d8092d8e6fbacdac5a933bb7b455e.jpg  \n",
            " extracting: train/blue_dress/00000278_jpg.rf.beb7f80b378391f5615588676da8bbb7.jpg  \n",
            " extracting: train/blue_dress/00000279_jpg.rf.c1b4f54d1efe3557513d6c4d1085519e.jpg  \n",
            " extracting: train/blue_dress/00000280_jpg.rf.7339869d3b3d1cba5448c749f157a661.jpg  \n",
            " extracting: train/blue_dress/00000282_jpg.rf.266504e4db72a0b5a4d4515d25664c11.jpg  \n",
            " extracting: train/blue_dress/00000286_jpg.rf.3db97c5cbc5c440f3bba5b82dc105afc.jpg  \n",
            " extracting: train/blue_dress/00000288_jpg.rf.061f48f0151a690436ffffff17ce5211.jpg  \n",
            " extracting: train/blue_dress/00000289_jpg.rf.db3d0d17c1319f99be269cdbe81023c3.jpg  \n",
            " extracting: train/blue_dress/00000290_jpg.rf.e1e078e28677e6127e36647b75e9a4a8.jpg  \n",
            " extracting: train/blue_dress/00000291_jpg.rf.ca10a9ed1541e3f15f0370521617b692.jpg  \n",
            " extracting: train/blue_dress/00000293_jpg.rf.d011514087d6f8b8a9d9e28fee07560d.jpg  \n",
            " extracting: train/blue_dress/00000294_jpg.rf.166962ba9403441fa5b98be35e5daa2b.jpg  \n",
            " extracting: train/blue_dress/00000295_jpg.rf.0f51707b78cdb3f354368740596385e4.jpg  \n",
            " extracting: train/blue_dress/00000297_jpg.rf.3083788140b0b488e750d47aec34d1e0.jpg  \n",
            " extracting: train/blue_dress/00000300_jpg.rf.1f69c9cd917e0fc266a38d8be96fcd54.jpg  \n",
            " extracting: train/blue_dress/00000302_jpg.rf.73797e2fb481909cd976c1e38b493917.jpg  \n",
            " extracting: train/blue_dress/00000303_jpg.rf.7770aa4ffb1aadbdb0dcdeaca53704b9.jpg  \n",
            " extracting: train/blue_dress/00000304_jpg.rf.b8f2a8921af4e4704c617db27d0ea9d3.jpg  \n",
            " extracting: train/blue_dress/00000309_jpg.rf.14f2e6a73e233aeee3d14f9956706062.jpg  \n",
            " extracting: train/blue_dress/00000310_jpg.rf.848884a4c1ff04b7396305bf8b71392f.jpg  \n",
            " extracting: train/blue_dress/00000311_jpg.rf.259e61f54fbad9750b0577e8aeda8d85.jpg  \n",
            " extracting: train/blue_dress/00000312_jpg.rf.7feac5005b79a1528c5a70ff47efef93.jpg  \n",
            " extracting: train/blue_dress/00000313_jpg.rf.941e8f1dce3cdb1a2caf10bb77fd98f2.jpg  \n",
            " extracting: train/blue_dress/00000314_jpg.rf.ea9fdd65a615f4e3e797bfb16362c463.jpg  \n",
            " extracting: train/blue_dress/00000315_png.rf.73c91af4aeb517d76c7bc9afee6a0845.jpg  \n",
            " extracting: train/blue_dress/00000316_jpeg.rf.469f6a3b1a04f4608a4df6594bb9804d.jpg  \n",
            " extracting: train/blue_dress/00000317_jpg.rf.b7974f45c424546b7567a2d6b9e1d100.jpg  \n",
            " extracting: train/blue_dress/00000318_jpg.rf.5929db752d9477b56ab6a8ecd6479fd8.jpg  \n",
            " extracting: train/blue_dress/00000319_jpeg.rf.34eda824fc996b6d1b4c9f292d6d1ccd.jpg  \n",
            " extracting: train/blue_dress/00000322_jpg.rf.0ce39ad7e4c4053cef68c567fecc9786.jpg  \n",
            " extracting: train/blue_dress/00000323_jpeg.rf.7b75f889e724922cea6334e57c8130a0.jpg  \n",
            " extracting: train/blue_dress/00000325_jpg.rf.37d84c6e9dcc54d22286d8f618fdfaae.jpg  \n",
            " extracting: train/blue_dress/00000328_jpg.rf.f3a01b7029a206148890a93185939384.jpg  \n",
            " extracting: train/blue_dress/00000329_jpg.rf.6617dd05556e411b0bb13b909e718c4b.jpg  \n",
            " extracting: train/blue_dress/00000331_jpg.rf.6f57f40b6aa6a2ef4de4a14619dfa514.jpg  \n",
            " extracting: train/blue_dress/00000332_jpg.rf.f7ee2ac7705153d0d0e9c1e5c76a28f9.jpg  \n",
            " extracting: train/blue_dress/00000333_jpg.rf.a87090c284b867b1e293fbce298fc21e.jpg  \n",
            " extracting: train/blue_dress/00000334_jpg.rf.def01383050114889be522bdc036f804.jpg  \n",
            " extracting: train/blue_dress/00000336_jpg.rf.9e953da180558546313f7fe4298b4fb1.jpg  \n",
            " extracting: train/blue_dress/00000337_jpg.rf.3715d5e62d29f30edba9b2a065ecf4f3.jpg  \n",
            " extracting: train/blue_dress/00000338_jpg.rf.aafd296fd9d675581e25701c91673cfa.jpg  \n",
            " extracting: train/blue_dress/00000339_jpg.rf.b9270a1b51397f431c6483bcdf9e1716.jpg  \n",
            " extracting: train/blue_dress/00000340_jpg.rf.4f4d925239895fae67cbc66e46537f32.jpg  \n",
            " extracting: train/blue_dress/00000341_jpg.rf.124da8fe636ab647cd0edd6e1b0f0ab8.jpg  \n",
            " extracting: train/blue_dress/00000342_jpeg.rf.c08612735b42ed0929564c9052a5a815.jpg  \n",
            " extracting: train/blue_dress/00000343_jpg.rf.76fddc1ce9234def9b7e2146eaacaf6c.jpg  \n",
            " extracting: train/blue_dress/00000345_jpg.rf.7189695afe9062826112da567240e69a.jpg  \n",
            " extracting: train/blue_dress/00000346_jpg.rf.4c55eaf1e814d782c54ab891790abd82.jpg  \n",
            " extracting: train/blue_dress/00000348_jpeg.rf.95d24d41868d50ef690aec61bf2c2b51.jpg  \n",
            " extracting: train/blue_dress/00000349_jpg.rf.4fb0601e69956108988a0a7567078f39.jpg  \n",
            " extracting: train/blue_dress/00000350_jpg.rf.fe66322595f57d8292cf88e19a75370a.jpg  \n",
            " extracting: train/blue_dress/00000351_jpg.rf.596a99b593aaace88a1e1bc382dcade8.jpg  \n",
            " extracting: train/blue_dress/00000352_jpg.rf.1ffeb90feb3ac74888d18842d8118cfa.jpg  \n",
            " extracting: train/blue_dress/00000353_jpg.rf.4db785ae6b49b7b2f0d8686406cce787.jpg  \n",
            " extracting: train/blue_dress/00000356_jpg.rf.02c0a5f6691ba13bd41e67b97cfd94c5.jpg  \n",
            " extracting: train/blue_dress/00000359_jpg.rf.06bdb5cb132a4db061803cdb66509e6a.jpg  \n",
            " extracting: train/blue_dress/00000360_jpg.rf.31c857244f2aa9a3a7e4c924e5390cf2.jpg  \n",
            " extracting: train/blue_dress/00000363_jpg.rf.d7395a6a9fce21bf72269ee12ec5fbdd.jpg  \n",
            " extracting: train/blue_dress/00000364_jpg.rf.9e471362aa0f7c884a22720bec3251fa.jpg  \n",
            " extracting: train/blue_dress/00000365_jpg.rf.eb072b13f8e634042f46f794a7872c1a.jpg  \n",
            " extracting: train/blue_dress/00000366_jpg.rf.e257e7533ff4b10481448d1dcec2bf79.jpg  \n",
            " extracting: train/blue_dress/00000367_jpg.rf.d6e06dca75df8dad360ca6fcd1887a15.jpg  \n",
            " extracting: train/blue_dress/00000369_jpg.rf.b22ffca1aa5aad05f8a12eef56aa57e3.jpg  \n",
            " extracting: train/blue_dress/00000370_jpg.rf.e942405b3f52f89168e8a574507f362a.jpg  \n",
            " extracting: train/blue_dress/00000371_jpeg.rf.e67c5e219843aa5985a790687b9a23dd.jpg  \n",
            " extracting: train/blue_dress/00000372_jpg.rf.5997c9f8ca43f0d9c0f9a22f1041b373.jpg  \n",
            " extracting: train/blue_dress/00000374_jpg.rf.4544494f1153bb26005b2ee9ddbeb4a8.jpg  \n",
            " extracting: train/blue_dress/00000379_jpg.rf.f3e565b5e5c5f4bd56a633bb41929317.jpg  \n",
            " extracting: train/blue_dress/00000380_jpg.rf.821bafd6f5d5ff6b8a97a2d712445bb5.jpg  \n",
            " extracting: train/blue_dress/00000382_jpg.rf.c5efecd9aff22da02bf9c132be636cec.jpg  \n",
            " extracting: train/blue_dress/00000383_jpg.rf.c4706ce2a2b6989979e3d0299e4d9446.jpg  \n",
            " extracting: train/blue_dress/00000384_jpg.rf.b00bcd1f56c7856540062ab67ccb5c15.jpg  \n",
            " extracting: train/blue_dress/00000385_jpg.rf.7fc78dc4532ba9660fd9cbebf7930aad.jpg  \n",
            " extracting: train/blue_dress/00000386_jpg.rf.ec60a1cf66401dd325714635344ed05f.jpg  \n",
            " extracting: train/blue_dress/00000388_jpeg.rf.1a4f531bfdd069c54524c3593d3c5f92.jpg  \n",
            " extracting: train/blue_dress/00000389_jpg.rf.151bd52840cf7c5f30d0744d54847ae5.jpg  \n",
            " extracting: train/blue_dress/00000390_jpg.rf.81ad45f6db0fa600bf6f651b249c1328.jpg  \n",
            " extracting: train/blue_dress/00000391_jpg.rf.8b1febd9142df1e6093ded858c0464fc.jpg  \n",
            " extracting: train/blue_dress/00000392_jpg.rf.b469251d64c8d5101848d843b93e259b.jpg  \n",
            " extracting: train/blue_dress/00000393_jpg.rf.3460a9fc6495c22d1635b350514020a5.jpg  \n",
            " extracting: train/blue_dress/00000395_jpg.rf.40b20b84b040875414aeb40adaac0508.jpg  \n",
            "   creating: train/blue_jeans/\n",
            " extracting: train/blue_jeans/00000000_jpg.rf.17fc85d472157e24eaa6baa850378227.jpg  \n",
            " extracting: train/blue_jeans/00000001_jpeg.rf.a0e0619c011aec4d87dc598e5b6c6049.jpg  \n",
            " extracting: train/blue_jeans/00000004_jpg.rf.c06049115a0e3c0ef081a3dbf1223036.jpg  \n",
            " extracting: train/blue_jeans/00000006_jpg.rf.742168c4a5ab9f83a3d0314f34162dd3.jpg  \n",
            " extracting: train/blue_jeans/00000007_jpg.rf.f1e06678ec34a9490863f8227557fae1.jpg  \n",
            " extracting: train/blue_jeans/00000009_jpg.rf.3d0f1a09347984b961628f7293c902fe.jpg  \n",
            " extracting: train/blue_jeans/00000010_jpg.rf.3f3207efcdad267fc03b623f4b0400ab.jpg  \n",
            " extracting: train/blue_jeans/00000011_jpg.rf.16dd2081386c3f50f04b1a6ea6a2523e.jpg  \n",
            " extracting: train/blue_jeans/00000013_jpg.rf.db942a1490868a6f7983747d6032265d.jpg  \n",
            " extracting: train/blue_jeans/00000014_jpg.rf.310af2b5624553b70bbfab323ae65d2b.jpg  \n",
            " extracting: train/blue_jeans/00000016_jpg.rf.700a1a59368d77381511fcccb2071594.jpg  \n",
            " extracting: train/blue_jeans/00000018_jpeg.rf.b8e49fd38d43b456ad3c8d2d646a420c.jpg  \n",
            " extracting: train/blue_jeans/00000020_jpg.rf.8f0f8d2785eca57fc19d9187d4c7b42f.jpg  \n",
            " extracting: train/blue_jeans/00000021_jpg.rf.6e45d494a5646ffc810cac4ec83d1808.jpg  \n",
            " extracting: train/blue_jeans/00000022_jpg.rf.fca2d6fe7a9ad2c1a9f3e83624bad91b.jpg  \n",
            " extracting: train/blue_jeans/00000023_jpg.rf.65c3d46d78e8985213b78f2d9031de21.jpg  \n",
            " extracting: train/blue_jeans/00000024_png.rf.5682d8f380da15e1420c46f1423cedef.jpg  \n",
            " extracting: train/blue_jeans/00000028_jpg.rf.f94651c7adafb9b26acdf9c1c05d5b32.jpg  \n",
            " extracting: train/blue_jeans/00000031_jpg.rf.e9c942eb775f5dc0598fd085efee9dd5.jpg  \n",
            " extracting: train/blue_jeans/00000034_jpeg.rf.5690dabc373ebc688922f7e291deef94.jpg  \n",
            " extracting: train/blue_jeans/00000036_jpg.rf.0fcd790c5a606507e37fa4b7cbbeb364.jpg  \n",
            " extracting: train/blue_jeans/00000037_jpg.rf.616b8b0027abca0ae509269641bb4d93.jpg  \n",
            " extracting: train/blue_jeans/00000039_jpg.rf.89e5c6775a73766af5e94b23e5d9521e.jpg  \n",
            " extracting: train/blue_jeans/00000041_jpg.rf.64fe2b52fa5d152274de89563fce13c9.jpg  \n",
            " extracting: train/blue_jeans/00000042_jpg.rf.159e43e6ea3f3156ba2baaf8294bfdab.jpg  \n",
            " extracting: train/blue_jeans/00000044_jpg.rf.3ea61fd66dd134c74f8dbe3d54d4a28e.jpg  \n",
            " extracting: train/blue_jeans/00000045_jpg.rf.1d09521f200e46f285ff3b10e9f67856.jpg  \n",
            " extracting: train/blue_jeans/00000046_jpg.rf.4301258a15f4cf8e2b7436106e787142.jpg  \n",
            " extracting: train/blue_jeans/00000047_jpg.rf.d8fa38f00acbbfc6b1b69066e6793a79.jpg  \n",
            " extracting: train/blue_jeans/00000050_jpg.rf.f96542c1da41e642ea7ca89d48e0dc26.jpg  \n",
            " extracting: train/blue_jeans/00000052_jpg.rf.efa612b4e63c74859dc77e1f59a08ca3.jpg  \n",
            " extracting: train/blue_jeans/00000053_jpg.rf.c44b7e5aaf81d02b6f27956aec38d6a6.jpg  \n",
            " extracting: train/blue_jeans/00000055_jpg.rf.dea13c1fccd8e118909b619455565831.jpg  \n",
            " extracting: train/blue_jeans/00000056_jpg.rf.95f6f9ada49f277db3e3f21686d36e1c.jpg  \n",
            " extracting: train/blue_jeans/00000058_jpg.rf.e54ed1e6634b7a978373d0e0d70fff94.jpg  \n",
            " extracting: train/blue_jeans/00000059_jpg.rf.b0e27a24da1aab18314a571960cf9784.jpg  \n",
            " extracting: train/blue_jeans/00000062_jpg.rf.b7e83b3598cb0cd0be42a8a74531a850.jpg  \n",
            " extracting: train/blue_jeans/00000063_jpg.rf.daf642daf55a40e6803531e251618277.jpg  \n",
            " extracting: train/blue_jeans/00000065_jpg.rf.5f514a12e00b1f39a154811743fdf33e.jpg  \n",
            " extracting: train/blue_jeans/00000066_jpeg.rf.feaa895fcf0ba9b0280bf73980552808.jpg  \n",
            " extracting: train/blue_jeans/00000067_jpg.rf.d583b81204db00606041ddceeaf24e4a.jpg  \n",
            " extracting: train/blue_jeans/00000069_jpg.rf.6507d1ba8d9c0616a7c1ba71bfdaa806.jpg  \n",
            " extracting: train/blue_jeans/00000070_jpeg.rf.95dec4489129eb3d873c9ef7b0c420d9.jpg  \n",
            " extracting: train/blue_jeans/00000071_jpg.rf.3c539d35af88ebe478a8b4e3aa169bef.jpg  \n",
            " extracting: train/blue_jeans/00000073_jpg.rf.096b01d54ce7d12658e63515e0477e7d.jpg  \n",
            " extracting: train/blue_jeans/00000074_jpg.rf.ae7c684a6c35338eb7c6448145a0f37a.jpg  \n",
            " extracting: train/blue_jeans/00000075_jpeg.rf.16a15c329688f1f5ecd2738f2327ae3d.jpg  \n",
            " extracting: train/blue_jeans/00000076_jpeg.rf.c6ba5b47b7abe1f0a8606fa94e926922.jpg  \n",
            " extracting: train/blue_jeans/00000079_jpeg.rf.7765e871b0eb2d51fc2b22487b6ea8b5.jpg  \n",
            " extracting: train/blue_jeans/00000080_jpg.rf.255d5cc016ef4a40fe014e5fcdf4fa12.jpg  \n",
            " extracting: train/blue_jeans/00000081_jpg.rf.8b68c7d78a89398c0e7db5946c3959cf.jpg  \n",
            " extracting: train/blue_jeans/00000082_jpg.rf.91cea0090f947b42b4625ba380b12846.jpg  \n",
            " extracting: train/blue_jeans/00000086_jpg.rf.83f439dbccb8556e7d5276c242048b97.jpg  \n",
            " extracting: train/blue_jeans/00000089_jpg.rf.876297fd18fc1f3b4708bd5dd579c46f.jpg  \n",
            " extracting: train/blue_jeans/00000090_jpg.rf.f844b8f3fd7829c985af0c913812e5fd.jpg  \n",
            " extracting: train/blue_jeans/00000091_jpg.rf.62a2f4b2e1a615fd1ca46ea7be7fb7e4.jpg  \n",
            " extracting: train/blue_jeans/00000092_jpg.rf.0584e8cd301d2c05dd991a037185e75f.jpg  \n",
            " extracting: train/blue_jeans/00000093_jpg.rf.76b9082bed578cb13207b3ea5c16a9f2.jpg  \n",
            " extracting: train/blue_jeans/00000094_jpg.rf.c85e2f4e9854a16fdb6461bce4e2708e.jpg  \n",
            " extracting: train/blue_jeans/00000095_jpg.rf.0978235589c9cb0f0ebaf5a8516e2337.jpg  \n",
            " extracting: train/blue_jeans/00000096_jpg.rf.86cea7a9c00f3f8635644ef5e2362ca7.jpg  \n",
            " extracting: train/blue_jeans/00000102_jpg.rf.fb1844e316120048be4d8bc9b419e9ee.jpg  \n",
            " extracting: train/blue_jeans/00000103_jpg.rf.0265001a70586131a65ddb567bf4cd26.jpg  \n",
            " extracting: train/blue_jeans/00000105_jpg.rf.8ac1c4f0da62d4e9863ad714217a2ace.jpg  \n",
            " extracting: train/blue_jeans/00000108_jpg.rf.5a099da2f088eec6a78c10aa98aa35db.jpg  \n",
            " extracting: train/blue_jeans/00000110_jpg.rf.7b456ac1bf93abe1fb72e62a03273fe2.jpg  \n",
            " extracting: train/blue_jeans/00000111_jpg.rf.2e508743ecc3bb2625f3edb81dd1783d.jpg  \n",
            " extracting: train/blue_jeans/00000112_jpg.rf.9de889554a0259198b8419d1baf5fb5a.jpg  \n",
            " extracting: train/blue_jeans/00000113_jpg.rf.37677d1dbce392f72705b7e8ce85a186.jpg  \n",
            " extracting: train/blue_jeans/00000114_jpeg.rf.b087904ddbabdf5d3c232fc9eeef18b0.jpg  \n",
            " extracting: train/blue_jeans/00000115_jpeg.rf.39b8dea6dfc8cf7d7df04959431a1e30.jpg  \n",
            " extracting: train/blue_jeans/00000116_jpeg.rf.ea6b7b544f91fd56cdb8212c0ccefeee.jpg  \n",
            " extracting: train/blue_jeans/00000117_jpg.rf.03f1c0ecc0dd40a7c9b61b5abe883397.jpg  \n",
            " extracting: train/blue_jeans/00000119_jpg.rf.20395ca4ac7f758a2dd22e06febc3ab7.jpg  \n",
            " extracting: train/blue_jeans/00000120_jpg.rf.e6e0e01efc4e60c2ebd6a94a7452eeb0.jpg  \n",
            " extracting: train/blue_jeans/00000121_jpeg.rf.bbf26f1bcdf5053b5b249a0c30ee0a83.jpg  \n",
            " extracting: train/blue_jeans/00000122_jpeg.rf.cc1821a427f30ba3372e92f27b2f04e7.jpg  \n",
            " extracting: train/blue_jeans/00000123_png.rf.8fa8ec297d6497eabb82ad10f7b06dd9.jpg  \n",
            " extracting: train/blue_jeans/00000124_jpg.rf.a482f0aeb791d604cc9b0dc68d19ae3f.jpg  \n",
            " extracting: train/blue_jeans/00000126_jpg.rf.541c8563cfabf14c34f028b627e09aa7.jpg  \n",
            " extracting: train/blue_jeans/00000127_jpg.rf.4dc62d1fe2ed741713e9379e671afdd2.jpg  \n",
            " extracting: train/blue_jeans/00000128_jpg.rf.473f0ad17a53c4ad5924369f31114e7b.jpg  \n",
            " extracting: train/blue_jeans/00000129_jpg.rf.1fc9dba80dc5738d476fa97c921719d8.jpg  \n",
            " extracting: train/blue_jeans/00000130_jpg.rf.54597078f1d5bd25a8bd9fe01682d8c1.jpg  \n",
            " extracting: train/blue_jeans/00000132_jpg.rf.6682c558e93d78b16912f268d1de4907.jpg  \n",
            " extracting: train/blue_jeans/00000133_jpg.rf.7afe1efe37417652fd20500ae2030971.jpg  \n",
            " extracting: train/blue_jeans/00000134_jpg.rf.dc5e3706948e25446bd2138aedd8cd98.jpg  \n",
            " extracting: train/blue_jeans/00000136_jpg.rf.0d5219c089eb627d8ad9425a8e4290fe.jpg  \n",
            " extracting: train/blue_jeans/00000139_jpg.rf.16dd2b89a120df8c2b4df6d56fc3528d.jpg  \n",
            " extracting: train/blue_jeans/00000141_jpg.rf.a7be785a06bceed1811f54e2441f32fe.jpg  \n",
            " extracting: train/blue_jeans/00000143_jpeg.rf.5a0c30e9ac30da67cd801f827be8d736.jpg  \n",
            " extracting: train/blue_jeans/00000144_jpg.rf.50169a4570d6b23c3686c68bcd9a8254.jpg  \n",
            " extracting: train/blue_jeans/00000147_jpeg.rf.06762ff2addbde742924c1a5610304a3.jpg  \n",
            " extracting: train/blue_jeans/00000148_jpg.rf.51b5c79a42acb3ba4249be4b133bd6b0.jpg  \n",
            " extracting: train/blue_jeans/00000149_jpg.rf.b4d80f597a9a276fbd1c4e74ab6f97c5.jpg  \n",
            " extracting: train/blue_jeans/00000151_jpg.rf.4f30b236b49b411b7041d564be275c51.jpg  \n",
            " extracting: train/blue_jeans/00000152_jpeg.rf.ba8b1b6dd3132df53a8a0846279178b4.jpg  \n",
            " extracting: train/blue_jeans/00000153_jpg.rf.ba64a6a363449f21edfc3ae4a9028612.jpg  \n",
            " extracting: train/blue_jeans/00000155_jpg.rf.dc5c91d23fc35b519c50432387846c82.jpg  \n",
            " extracting: train/blue_jeans/00000156_jpg.rf.f0c992bc3458e0167b81d27a306bc162.jpg  \n",
            " extracting: train/blue_jeans/00000158_jpg.rf.1fb8c9364b4c3af3fccca3b412ce720b.jpg  \n",
            " extracting: train/blue_jeans/00000161_jpg.rf.ac3adcdebbb76d670292c25540fed660.jpg  \n",
            " extracting: train/blue_jeans/00000163_jpg.rf.b3a90e630ed74796cf499a16c158ff8a.jpg  \n",
            " extracting: train/blue_jeans/00000164_jpg.rf.421d891ed580868daef858208c14fa92.jpg  \n",
            " extracting: train/blue_jeans/00000166_jpeg.rf.9bf3f90cd62e8d4b240a96c24b3f378b.jpg  \n",
            " extracting: train/blue_jeans/00000167_jpg.rf.a892ea42017b27a9ef0c064f04e7d2c0.jpg  \n",
            " extracting: train/blue_jeans/00000173_jpg.rf.e51d9bb9ae8902f4244422347bc06d22.jpg  \n",
            " extracting: train/blue_jeans/00000174_jpg.rf.e2a17f903f83ce0fbc9b60762682b7d8.jpg  \n",
            " extracting: train/blue_jeans/00000178_jpeg.rf.6d49a5a7050b189ef8b414168beee35b.jpg  \n",
            " extracting: train/blue_jeans/00000180_jpg.rf.cb5c0e85f5b6dc19a59b79a2165b6770.jpg  \n",
            " extracting: train/blue_jeans/00000181_jpeg.rf.4a930e1b3b53fc8fbcd45a90f7ba926b.jpg  \n",
            " extracting: train/blue_jeans/00000182_jpg.rf.225ecd89b2b3d1fb0670b56a27d039ec.jpg  \n",
            " extracting: train/blue_jeans/00000184_jpeg.rf.4bb03c53aa6e339944690bc6dce68920.jpg  \n",
            " extracting: train/blue_jeans/00000186_jpg.rf.330a837694452a51f5eab9693c829bbf.jpg  \n",
            " extracting: train/blue_jeans/00000187_jpg.rf.5885e3887960e839e55946a965b04e14.jpg  \n",
            " extracting: train/blue_jeans/00000188_jpg.rf.ef3d53e4e1704285832a6355268f45d1.jpg  \n",
            " extracting: train/blue_jeans/00000189_jpg.rf.e964e6a008e02142a278c24a131e5967.jpg  \n",
            " extracting: train/blue_jeans/00000190_jpg.rf.fc2aab50a6cf25239759dd936c12e53b.jpg  \n",
            " extracting: train/blue_jeans/00000191_jpeg.rf.c1e3f866c7578cf14737c1d0f8090f5d.jpg  \n",
            " extracting: train/blue_jeans/00000192_jpeg.rf.b70464ec03595aa624a0ede218d61bf4.jpg  \n",
            " extracting: train/blue_jeans/00000195_png.rf.909121d89fd04b99b12aff1cbabf8faa.jpg  \n",
            " extracting: train/blue_jeans/00000196_jpg.rf.777f1377325351e48120dd13319fbec2.jpg  \n",
            " extracting: train/blue_jeans/00000199_jpeg.rf.873dc9ae3fb8eb10c1bb104fd98fc0ca.jpg  \n",
            " extracting: train/blue_jeans/00000200_jpg.rf.80c6957b97413f0cd61e908c71ab18b8.jpg  \n",
            " extracting: train/blue_jeans/00000201_jpg.rf.d275bdd83820faadb123220dc05ae55b.jpg  \n",
            " extracting: train/blue_jeans/00000204_jpg.rf.b1317a47025a6b96d6f435abba06a7de.jpg  \n",
            " extracting: train/blue_jeans/00000207_jpeg.rf.62a86c32a54241d4f2d46f34fd574c5e.jpg  \n",
            " extracting: train/blue_jeans/00000208_jpg.rf.a7224a58bfdfba05e958325866ffead7.jpg  \n",
            " extracting: train/blue_jeans/00000209_jpg.rf.727689787020fbea97d6c5d31e935b56.jpg  \n",
            " extracting: train/blue_jeans/00000210_jpg.rf.0e233dbfc5b276bdb931f90c6cb61156.jpg  \n",
            " extracting: train/blue_jeans/00000211_jpeg.rf.1e4102b10a0d6e65497d11b2e9d1920c.jpg  \n",
            " extracting: train/blue_jeans/00000212_jpg.rf.71721c804cfff23ae0c43ee9f606858d.jpg  \n",
            " extracting: train/blue_jeans/00000215_jpg.rf.4f65195856090b26471279543df30bd1.jpg  \n",
            " extracting: train/blue_jeans/00000216_jpeg.rf.f41d96540c2a089be9bcb72a23f0bedf.jpg  \n",
            " extracting: train/blue_jeans/00000217_jpeg.rf.c3e60daece9d24add9ad190a6578584a.jpg  \n",
            " extracting: train/blue_jeans/00000218_jpg.rf.045250a10991eb9902cc7204cdcece32.jpg  \n",
            " extracting: train/blue_jeans/00000219_jpg.rf.c0befcf0e6656b2e281631c41e45ab8c.jpg  \n",
            " extracting: train/blue_jeans/00000220_jpg.rf.9acb33ea779ebc442ad4e970a00491eb.jpg  \n",
            " extracting: train/blue_jeans/00000222_jpeg.rf.6f47a587e840712a20c84179e3f726a6.jpg  \n",
            " extracting: train/blue_jeans/00000223_jpeg.rf.b8be17143e503a7f1b13fd56d5a1c90c.jpg  \n",
            " extracting: train/blue_jeans/00000224_jpeg.rf.9bca00305b296ec55894b9ba5cff45f9.jpg  \n",
            " extracting: train/blue_jeans/00000225_jpg.rf.e012172a02a5c8078d684b9681be83c8.jpg  \n",
            " extracting: train/blue_jeans/00000227_jpg.rf.f30d7b6757e36d5b851213174cec5c37.jpg  \n",
            " extracting: train/blue_jeans/00000228_jpg.rf.42ad4a2da619ea63200957c02722733a.jpg  \n",
            " extracting: train/blue_jeans/00000230_jpg.rf.6b4957763cf09ce729c6fe5150ed0861.jpg  \n",
            " extracting: train/blue_jeans/00000234_jpg.rf.f53a6fbfb555849ab63a257192d0619c.jpg  \n",
            " extracting: train/blue_jeans/00000238_jpeg.rf.718b229c609a310fe5d991f8c7434486.jpg  \n",
            " extracting: train/blue_jeans/00000239_jpeg.rf.09af54ef080c2ad664a71a5c5da5e86f.jpg  \n",
            " extracting: train/blue_jeans/00000241_jpg.rf.a108d821f82411af2ccd70695545f972.jpg  \n",
            " extracting: train/blue_jeans/00000243_jpg.rf.07e4a55e89e632e2e92f4041b48e63ad.jpg  \n",
            " extracting: train/blue_jeans/00000244_jpg.rf.b6d40e4391f54ed7d1118e0b1f7dca3d.jpg  \n",
            " extracting: train/blue_jeans/00000246_jpg.rf.eabe360c975a4fd1cff95c891041ce4c.jpg  \n",
            " extracting: train/blue_jeans/00000247_jpeg.rf.6b17b9847c0ae3c6d3ee613c23d1bc30.jpg  \n",
            " extracting: train/blue_jeans/00000249_jpg.rf.ff5119824af4377eaee5288fa09944ec.jpg  \n",
            " extracting: train/blue_jeans/00000250_jpg.rf.c2a71052fbc8c0b151823195ac3308f1.jpg  \n",
            " extracting: train/blue_jeans/00000251_jpg.rf.348ec891e5bdcac7bb3d0942f4107571.jpg  \n",
            " extracting: train/blue_jeans/00000252_jpg.rf.863a69dddb3fd0760533ce392b2c89e8.jpg  \n",
            " extracting: train/blue_jeans/00000254_jpg.rf.89d6be58defdc9bf26da6749d5681edf.jpg  \n",
            " extracting: train/blue_jeans/00000256_jpg.rf.0215c89caf7cf8b33c3a780c07e1b58d.jpg  \n",
            " extracting: train/blue_jeans/00000257_jpeg.rf.3244d1f57c21b6363e396cd51e83e02b.jpg  \n",
            " extracting: train/blue_jeans/00000258_jpg.rf.6cb46f034c6cdfab3a2a4fba36882898.jpg  \n",
            " extracting: train/blue_jeans/00000259_jpg.rf.637a14c1ee9159a3664f637aacab21b6.jpg  \n",
            " extracting: train/blue_jeans/00000260_jpeg.rf.eddd1beff277a2968b8fa625e8d8337b.jpg  \n",
            " extracting: train/blue_jeans/00000261_jpg.rf.b9758e19f88a1afb6f7558f15174c85c.jpg  \n",
            " extracting: train/blue_jeans/00000263_jpg.rf.fd322aa173cec1048b45d724b5f2564f.jpg  \n",
            " extracting: train/blue_jeans/00000264_jpg.rf.ef4aee4b6a684a97a7cba2b64f338927.jpg  \n",
            " extracting: train/blue_jeans/00000265_jpg.rf.01444d3918c060f48d2e6691346269a3.jpg  \n",
            " extracting: train/blue_jeans/00000266_jpg.rf.3d068c37d4faf870e2e858574bae5718.jpg  \n",
            " extracting: train/blue_jeans/00000270_jpg.rf.fe405ba20fae53c1c4e52ec939453a12.jpg  \n",
            " extracting: train/blue_jeans/00000272_jpg.rf.7ac7d0e7d2c0d3db145206f47bb0c97a.jpg  \n",
            " extracting: train/blue_jeans/00000273_jpg.rf.bee989cf44c36410bc02dc7485966b28.jpg  \n",
            " extracting: train/blue_jeans/00000276_jpg.rf.0327bfd7ad6be8649e2eefb3e6fbdaba.jpg  \n",
            " extracting: train/blue_jeans/00000278_jpg.rf.f98da1a4246fb54200dd0f61ff258cdc.jpg  \n",
            " extracting: train/blue_jeans/00000283_jpeg.rf.f9bf77c07a1a9d1f0d3d78be23315b02.jpg  \n",
            " extracting: train/blue_jeans/00000284_jpeg.rf.d3a4034f75e7cab254e12575438e29c6.jpg  \n",
            " extracting: train/blue_jeans/00000285_jpg.rf.1e5ae0f42e6aaaa08dbb2222590d36c5.jpg  \n",
            " extracting: train/blue_jeans/00000286_jpg.rf.324716b6ecc37e9ea6fe6c74631814d0.jpg  \n",
            " extracting: train/blue_jeans/00000287_jpg.rf.fe1a35862fcb2b4aaf7aa0ca9432a369.jpg  \n",
            " extracting: train/blue_jeans/00000288_jpeg.rf.92691e9ec62069020e5767370a89fd52.jpg  \n",
            " extracting: train/blue_jeans/00000289_jpg.rf.9f6be3d9ba9098e06f38982b140dbd5f.jpg  \n",
            " extracting: train/blue_jeans/00000291_png.rf.6c4c213f10ebe6d9c6ddbb33082adc8a.jpg  \n",
            " extracting: train/blue_jeans/00000297_JPG.rf.797ca08980ba8fafdd5a48de47bb9377.jpg  \n",
            " extracting: train/blue_jeans/00000298_jpeg.rf.32a2e8d8c1c33f9450a6e5be0ea1481b.jpg  \n",
            " extracting: train/blue_jeans/00000300_jpg.rf.c3cc142aed9bc8813d7fe66ceb83596e.jpg  \n",
            " extracting: train/blue_jeans/00000301_jpg.rf.ac892a890630619e92d601e4c08b61c7.jpg  \n",
            " extracting: train/blue_jeans/00000303_jpeg.rf.06407bdf6ba6749b4edb61ee5b983d18.jpg  \n",
            " extracting: train/blue_jeans/00000304_jpg.rf.c4fd8fb6ab1db70f34b68e76b41f65d8.jpg  \n",
            " extracting: train/blue_jeans/00000306_jpeg.rf.4600a079d8b9ed801d0518bbf5ef6aff.jpg  \n",
            " extracting: train/blue_jeans/00000307_jpg.rf.d6846b4c24faafaef6e0867b581d6ad0.jpg  \n",
            " extracting: train/blue_jeans/00000308_jpeg.rf.e5475fbaea24687564ecb5ddfe6fb11a.jpg  \n",
            " extracting: train/blue_jeans/00000310_jpg.rf.5c0ecb443216ac64f38f4e29b151bd2c.jpg  \n",
            " extracting: train/blue_jeans/00000311_jpeg.rf.90c6da287c875fa40c6a0fd26acacc99.jpg  \n",
            " extracting: train/blue_jeans/00000312_jpeg.rf.7b68036021c0b838d5e4776672c1cbf7.jpg  \n",
            " extracting: train/blue_jeans/00000313_jpg.rf.51c7506f934e4c6a4e6ed4304ac9021b.jpg  \n",
            " extracting: train/blue_jeans/00000315_jpeg.rf.2a7cf401470d6d87b9fe746a92eebbcb.jpg  \n",
            " extracting: train/blue_jeans/00000316_jpg.rf.f5638b8c4787e3cd3f7cf3f5eacd0f0a.jpg  \n",
            " extracting: train/blue_jeans/00000317_jpg.rf.6486ea442f313269fc871f54fd96c2da.jpg  \n",
            " extracting: train/blue_jeans/00000318_jpeg.rf.d9f324624d773ecb1ff818e77c4389a5.jpg  \n",
            " extracting: train/blue_jeans/00000319_jpeg.rf.335090d71e4df2c5511ba466bba48ee9.jpg  \n",
            " extracting: train/blue_jeans/00000320_jpg.rf.f4a686266e7df5dc839f2feda30ad07e.jpg  \n",
            " extracting: train/blue_jeans/00000321_jpg.rf.5b2657a9d604d123a477d574d170c693.jpg  \n",
            " extracting: train/blue_jeans/00000322_jpeg.rf.7d9444b49c98fc6fb6d04e393cb83426.jpg  \n",
            " extracting: train/blue_jeans/00000324_jpeg.rf.b9d4bd773bab61574bcac72bcce03511.jpg  \n",
            " extracting: train/blue_jeans/00000325_jpeg.rf.c978b9aaf4ff4388f4a067ecd0f1ad4f.jpg  \n",
            " extracting: train/blue_jeans/00000328_jpg.rf.e538a6bb393f9ebfeb2d8cec69b6a2db.jpg  \n",
            " extracting: train/blue_jeans/00000329_jpeg.rf.fd2bc34070f2e77d84f97c3340bf332e.jpg  \n",
            " extracting: train/blue_jeans/00000335_jpg.rf.47a4f97d4ebc0ee8aa07f462fcb10044.jpg  \n",
            " extracting: train/blue_jeans/00000336_jpg.rf.899451cb219efc609897349d291a60b8.jpg  \n",
            " extracting: train/blue_jeans/00000337_jpeg.rf.8c49dc8d82e38b2dffcba20c30d9a13c.jpg  \n",
            " extracting: train/blue_jeans/00000338_jpg.rf.e598f782624434abf12b0aba68f8d4eb.jpg  \n",
            " extracting: train/blue_jeans/00000339_jpg.rf.01010543b832577aa3b499961bd62a8d.jpg  \n",
            " extracting: train/blue_jeans/00000340_jpeg.rf.5366b0d0bd2715f60e237ded9a42c835.jpg  \n",
            " extracting: train/blue_jeans/00000341_jpg.rf.27e9d85386f9c388ef1ff83d4caeb937.jpg  \n",
            " extracting: train/blue_jeans/00000342_jpg.rf.05876ecbbba3b8e15441bba9239f4692.jpg  \n",
            " extracting: train/blue_jeans/00000344_jpeg.rf.2529df707a10c9f088a85d64a1c76a6b.jpg  \n",
            " extracting: train/blue_jeans/00000350_jpg.rf.55c7f77e175ef3f76955060b558ed6b5.jpg  \n",
            " extracting: train/blue_jeans/00000351_jpg.rf.20d3a756daedb9561d7d76c3057d2c08.jpg  \n",
            " extracting: train/blue_jeans/00000354_jpeg.rf.70cd1d8bd3ba0af52af5fe828462f864.jpg  \n",
            " extracting: train/blue_jeans/00000357_jpg.rf.de50af26d2ae83da99cc5fa604d0e629.jpg  \n",
            " extracting: train/blue_jeans/00000360_jpg.rf.ee9a5a91b5346f39b5d147e5956fd6f6.jpg  \n",
            " extracting: train/blue_jeans/00000361_jpeg.rf.e7fc5a16ffb5ac88bd5fe7c0870b81c4.jpg  \n",
            " extracting: train/blue_jeans/00000362_jpg.rf.bf325ad5c22858fbd6ac4da23477652c.jpg  \n",
            " extracting: train/blue_jeans/00000363_JPG.rf.827009de31cc411d590242944d0cb5e6.jpg  \n",
            " extracting: train/blue_jeans/00000366_jpg.rf.44a1f62d1647060ac4a53a1387350a15.jpg  \n",
            " extracting: train/blue_jeans/00000367_jpg.rf.9265d8f25312debaa0747ca6bf813eff.jpg  \n",
            " extracting: train/blue_jeans/00000369_jpeg.rf.629eeb116ff9e56f648108a4a2615b69.jpg  \n",
            " extracting: train/blue_jeans/00000370_jpeg.rf.af34e38498541e3f9d25e00e24ff0678.jpg  \n",
            " extracting: train/blue_jeans/00000371_jpeg.rf.e9429a23560e99ff7efcd78f212ea5d5.jpg  \n",
            " extracting: train/blue_jeans/00000372_jpeg.rf.bb9d645b4df1e889ae07556ad1348b30.jpg  \n",
            " extracting: train/blue_jeans/00000373_jpeg.rf.dce89e82174a62789a38fa3b85e462a7.jpg  \n",
            " extracting: train/blue_jeans/00000374_jpeg.rf.dad2dbd9c38fa2a3b551926cf35ccc17.jpg  \n",
            "   creating: train/blue_shirt/\n",
            " extracting: train/blue_shirt/00000001_jpg.rf.c0acca1fb3c2a564c715555eccfb6a03.jpg  \n",
            " extracting: train/blue_shirt/00000002_jpeg.rf.073322d23e4eff4be33609f7b33e4f76.jpg  \n",
            " extracting: train/blue_shirt/00000003_jpg.rf.6a8ef32bbb919264bd8411758401758f.jpg  \n",
            " extracting: train/blue_shirt/00000004_jpg.rf.ec49b3e46176b763a671963ea6c10f12.jpg  \n",
            " extracting: train/blue_shirt/00000007_jpg.rf.c67041e6cd53ea55ad4c46af246ba5ac.jpg  \n",
            " extracting: train/blue_shirt/00000008_jpg.rf.f76b6a15ea29438324cd5178ea37b385.jpg  \n",
            " extracting: train/blue_shirt/00000009_JPG.rf.9283f6dd941d92ec697444caa5bd0a10.jpg  \n",
            " extracting: train/blue_shirt/00000011_jpg.rf.2ef376a96899fd96f0ed8870a7d346b4.jpg  \n",
            " extracting: train/blue_shirt/00000012_jpg.rf.c731f8fe5a55b9713efe48e8579e8b8e.jpg  \n",
            " extracting: train/blue_shirt/00000014_jpg.rf.e3cd4bb540414ef6994ea8b2b738759c.jpg  \n",
            " extracting: train/blue_shirt/00000015_jpg.rf.59fe4270d8df074e2e3faf250c12df89.jpg  \n",
            " extracting: train/blue_shirt/00000016_jpg.rf.1405df0bb3ae0360663eca013a22fbd8.jpg  \n",
            " extracting: train/blue_shirt/00000017_jpg.rf.fc797f22136c37977eaeee35908cf1c5.jpg  \n",
            " extracting: train/blue_shirt/00000018_jpg.rf.218897e584ea093d53b33c15bae8edcb.jpg  \n",
            " extracting: train/blue_shirt/00000019_jpg.rf.bb3a8c4fc933bc2fcaa7977fe03bb828.jpg  \n",
            " extracting: train/blue_shirt/00000021_jpg.rf.fdcf90f01de645251b4b680c9274bf81.jpg  \n",
            " extracting: train/blue_shirt/00000022_png.rf.381f6fb1ab6f2e16865d0a6118435589.jpg  \n",
            " extracting: train/blue_shirt/00000023_jpg.rf.baba5a72c914feca440228fe1aba5226.jpg  \n",
            " extracting: train/blue_shirt/00000024_jpg.rf.ea7fc98ec8c184098996abc103837346.jpg  \n",
            " extracting: train/blue_shirt/00000026_jpg.rf.c678f6d5bbd674288f6bb8790a7b0b33.jpg  \n",
            " extracting: train/blue_shirt/00000027_jpg.rf.624e137ff20673942000cda29800f6dc.jpg  \n",
            " extracting: train/blue_shirt/00000028_jpeg.rf.2c6d6bd9cb390149d6ab19bb3930f2d8.jpg  \n",
            " extracting: train/blue_shirt/00000029_jpg.rf.83b16b1a3714cd093ce0eadcd3b2aa42.jpg  \n",
            " extracting: train/blue_shirt/00000030_jpg.rf.3e664a61707a1cfc0794b74e4cf80fa4.jpg  \n",
            " extracting: train/blue_shirt/00000031_jpg.rf.6273d33b34ac4be8b3a83151e7c3a2f9.jpg  \n",
            " extracting: train/blue_shirt/00000032_png.rf.20999a5fff9b9ec7737e809d00e17af0.jpg  \n",
            " extracting: train/blue_shirt/00000033_jpg.rf.24efad0497b494326926a386dd6e0507.jpg  \n",
            " extracting: train/blue_shirt/00000037_jpg.rf.9297a29a6ca6e55a8035e3eb82cfe6df.jpg  \n",
            " extracting: train/blue_shirt/00000038_jpg.rf.2e8aa1fb6a3460a950217880c96b200e.jpg  \n",
            " extracting: train/blue_shirt/00000039_jpg.rf.bf8414269f61395316f38c1ae3f98ffe.jpg  \n",
            " extracting: train/blue_shirt/00000040_png.rf.4b42ee49c39447621402754ca9bbab0b.jpg  \n",
            " extracting: train/blue_shirt/00000041_jpg.rf.26b8bf4cc7e1d54254e3ea07b4d1d6d0.jpg  \n",
            " extracting: train/blue_shirt/00000042_jpg.rf.a06a3817d03ac1ad2c91c923eab365c0.jpg  \n",
            " extracting: train/blue_shirt/00000044_jpg.rf.0dfbe9c60457d5520f1ab69cabb1c7b2.jpg  \n",
            " extracting: train/blue_shirt/00000048_jpeg.rf.42205146467dc60d03fdc4a43a6fbd01.jpg  \n",
            " extracting: train/blue_shirt/00000050_jpg.rf.70a94791850d1eddcdcab17dde3c8584.jpg  \n",
            " extracting: train/blue_shirt/00000051_jpg.rf.0b3706e894e83c41a755d68e069c2d93.jpg  \n",
            " extracting: train/blue_shirt/00000052_jpg.rf.19152b9fff9bc2d61e5c812798416a27.jpg  \n",
            " extracting: train/blue_shirt/00000056_png.rf.be9914c0244e35fd8f4d8ad97b1937c5.jpg  \n",
            " extracting: train/blue_shirt/00000057_jpg.rf.3dcc466c06e5212534b8d2821794197c.jpg  \n",
            " extracting: train/blue_shirt/00000058_jpg.rf.832c689cb739b4c1f3cdb1b5fa85f163.jpg  \n",
            " extracting: train/blue_shirt/00000059_jpeg.rf.ed0d8ab97317e95547abef0fb2d45542.jpg  \n",
            " extracting: train/blue_shirt/00000060_jpg.rf.88c244919fbc6f7bb64dbc752936ef27.jpg  \n",
            " extracting: train/blue_shirt/00000061_jpg.rf.3880efedf14c80c005a4aa5cba8cf714.jpg  \n",
            " extracting: train/blue_shirt/00000062_jpg.rf.fcc008c2b70546b5767c080b974a538e.jpg  \n",
            " extracting: train/blue_shirt/00000063_jpg.rf.eee8f62e3a8c4dbeea16e8443e7ae8e6.jpg  \n",
            " extracting: train/blue_shirt/00000067_jpg.rf.9c3d6d6320d70bc277516b69aacefb56.jpg  \n",
            " extracting: train/blue_shirt/00000068_jpg.rf.2dc8b20f820650bb12671a384fea2785.jpg  \n",
            " extracting: train/blue_shirt/00000069_jpg.rf.bffd42bc36262fdbbfb40a4251a0bcb3.jpg  \n",
            " extracting: train/blue_shirt/00000070_jpg.rf.6c58ef660e424897e5f6b5a9cd76d1b3.jpg  \n",
            " extracting: train/blue_shirt/00000071_jpg.rf.8455a66a2a1b17cad46264a137b39fc9.jpg  \n",
            " extracting: train/blue_shirt/00000072_jpg.rf.10cafae9e35d2f4f97efa77f1eaa0a54.jpg  \n",
            " extracting: train/blue_shirt/00000073_jpg.rf.fd4afdd81123f79262e5757a06a39eef.jpg  \n",
            " extracting: train/blue_shirt/00000074_jpg.rf.be9b744353cdb7385d2176deda851916.jpg  \n",
            " extracting: train/blue_shirt/00000076_jpeg.rf.a95680285154737563d4ad439ccc691a.jpg  \n",
            " extracting: train/blue_shirt/00000077_jpeg.rf.4601f726f54f2fb9aeb9ee06f19d7aa3.jpg  \n",
            " extracting: train/blue_shirt/00000078_jpg.rf.9f14fe8da11801311c20ed1d7bbcf390.jpg  \n",
            " extracting: train/blue_shirt/00000079_jpg.rf.1b0071fe1037ab3dc711c6181e8bd702.jpg  \n",
            " extracting: train/blue_shirt/00000081_jpg.rf.2f4df821b2dc6f3291f191dce10e49a1.jpg  \n",
            " extracting: train/blue_shirt/00000082_jpg.rf.60c45cb5fb07065a9c9782c71e2c2f44.jpg  \n",
            " extracting: train/blue_shirt/00000083_jpg.rf.244996ae85c10dc5d5b55505e2b249cc.jpg  \n",
            " extracting: train/blue_shirt/00000084_jpg.rf.dfb3a74718eb903cbbe20a7f5cc0f37c.jpg  \n",
            " extracting: train/blue_shirt/00000085_jpg.rf.791630a608344298746bde065c24928a.jpg  \n",
            " extracting: train/blue_shirt/00000086_jpg.rf.805fb33fa3cd256cf7666613853e65b5.jpg  \n",
            " extracting: train/blue_shirt/00000087_jpeg.rf.dcad65ecba2ea44eb5ae91f707cfab4b.jpg  \n",
            " extracting: train/blue_shirt/00000088_jpg.rf.15b1273f5fd883923b0a81d884b59ee8.jpg  \n",
            " extracting: train/blue_shirt/00000089_jpg.rf.65a9f0f68edc3902e92e5bf7a1b91067.jpg  \n",
            " extracting: train/blue_shirt/00000090_jpg.rf.947d4381b8c3e4f498f75dc4bdbfcb45.jpg  \n",
            " extracting: train/blue_shirt/00000091_png.rf.f7b6f306a8030c11221595daa464b6bf.jpg  \n",
            " extracting: train/blue_shirt/00000092_png.rf.5867c430764b04a2c65b770993f29c82.jpg  \n",
            " extracting: train/blue_shirt/00000093_jpg.rf.f90192b5612039eff7db5f0e47afeff1.jpg  \n",
            " extracting: train/blue_shirt/00000094_jpg.rf.994430bc5d491db46fecac6836ed9e5f.jpg  \n",
            " extracting: train/blue_shirt/00000095_jpg.rf.d50a91c2281488843d7a9024ed48466d.jpg  \n",
            " extracting: train/blue_shirt/00000099_png.rf.11109bfc721f435c34404fa2d0016dfc.jpg  \n",
            " extracting: train/blue_shirt/00000100_png.rf.ebeca5139ba922908b33259642cded48.jpg  \n",
            " extracting: train/blue_shirt/00000101_jpg.rf.76e6bc4d17a8bb4aa2a1a9885695724c.jpg  \n",
            " extracting: train/blue_shirt/00000102_jpg.rf.6577a4be69d8e24898402f969c2e0be3.jpg  \n",
            " extracting: train/blue_shirt/00000103_jpg.rf.a929448f7e3322bbab2cf6c360b33778.jpg  \n",
            " extracting: train/blue_shirt/00000104_jpg.rf.cdcbc71ad8d621f645e4be90222be354.jpg  \n",
            " extracting: train/blue_shirt/00000105_jpg.rf.b1e0a81917d0a35214c96d024b41473b.jpg  \n",
            " extracting: train/blue_shirt/00000106_png.rf.a9edc83c4d806d417317e5c3e9cc1bd7.jpg  \n",
            " extracting: train/blue_shirt/00000107_jpg.rf.936426a2ffc4751a0c7d706eb08d3b4b.jpg  \n",
            " extracting: train/blue_shirt/00000108_jpg.rf.eb5726cfc9ea3c885ae7702a2612e319.jpg  \n",
            " extracting: train/blue_shirt/00000109_jpg.rf.1527214d4a5692f1625f87689849cde7.jpg  \n",
            " extracting: train/blue_shirt/00000111_jpg.rf.ca69c41bc579b4fa8007d19bb88234a3.jpg  \n",
            " extracting: train/blue_shirt/00000112_jpg.rf.c58c77e4a57ed26327fc542eadecedfc.jpg  \n",
            " extracting: train/blue_shirt/00000113_jpeg.rf.f209543ae33a441384f8ea386919ab76.jpg  \n",
            " extracting: train/blue_shirt/00000114_jpg.rf.f935c389103040008d5c4f2107e42948.jpg  \n",
            " extracting: train/blue_shirt/00000115_jpg.rf.219054fb3fd2e3a42fcbe46edcab8ae4.jpg  \n",
            " extracting: train/blue_shirt/00000116_jpg.rf.eda8d9ffce4e1dce8080226eb32eba26.jpg  \n",
            " extracting: train/blue_shirt/00000117_jpg.rf.19d27600275965807eadf22ffdaccad8.jpg  \n",
            " extracting: train/blue_shirt/00000118_jpg.rf.f5ac62e781730e75fc9fc3a1dca2b05a.jpg  \n",
            " extracting: train/blue_shirt/00000120_jpg.rf.fa171d77df95813e9d8fce00cfe7dfa8.jpg  \n",
            " extracting: train/blue_shirt/00000121_jpg.rf.77ff175829ff0b667c5e6c1f370599a3.jpg  \n",
            " extracting: train/blue_shirt/00000123_jpg.rf.6520b9cfb3a015aaf0c604e0da79fb0c.jpg  \n",
            " extracting: train/blue_shirt/00000125_jpg.rf.46718eaa8d0d8b95a30fbe49c22587cf.jpg  \n",
            " extracting: train/blue_shirt/00000126_jpg.rf.0770c65e1f9bd9baa71af63bd22fe30f.jpg  \n",
            " extracting: train/blue_shirt/00000127_jpg.rf.a487ab59583888bf6378f3a075cc29e5.jpg  \n",
            " extracting: train/blue_shirt/00000130_jpg.rf.56cc887b42c5194f31f54a0f9ca9eba5.jpg  \n",
            " extracting: train/blue_shirt/00000131_jpeg.rf.05d03c5b8302068e0d3684ad8b6779ad.jpg  \n",
            " extracting: train/blue_shirt/00000133_jpg.rf.016ad2b5f5737a9acdb4486ec6174f29.jpg  \n",
            " extracting: train/blue_shirt/00000134_jpg.rf.4eafbef19e90c80d546a4cd85be894eb.jpg  \n",
            " extracting: train/blue_shirt/00000135_jpg.rf.480b646ef7428b5fe043c8b968dee1e8.jpg  \n",
            " extracting: train/blue_shirt/00000136_jpg.rf.14d5598b550e258fcd9850175d9b01a5.jpg  \n",
            " extracting: train/blue_shirt/00000137_jpg.rf.f0ae7e4fe985103782bb474805472ec7.jpg  \n",
            " extracting: train/blue_shirt/00000138_jpg.rf.e3b5734b43ecc0fb6f2a5fb2879af117.jpg  \n",
            " extracting: train/blue_shirt/00000139_jpg.rf.a7c9f6d44066515d5bde9deb208354b9.jpg  \n",
            " extracting: train/blue_shirt/00000140_jpg.rf.b744d7b2c4640e1df523d515d8ff57ae.jpg  \n",
            " extracting: train/blue_shirt/00000141_jpg.rf.fd17113585b02b6ded162a6934157615.jpg  \n",
            " extracting: train/blue_shirt/00000143_jpg.rf.f8f630389298ac1805360b6244b6f9ca.jpg  \n",
            " extracting: train/blue_shirt/00000144_JPG.rf.a6358984db82db9861fb2c0269d59dfe.jpg  \n",
            " extracting: train/blue_shirt/00000145_jpeg.rf.a3662d304d29f89764e6a28bd74a9f94.jpg  \n",
            " extracting: train/blue_shirt/00000146_jpg.rf.c85bdd0d44b9ee0e5079422913c58b6f.jpg  \n",
            " extracting: train/blue_shirt/00000147_jpg.rf.5907002be912bdf0efd57af1c78248db.jpg  \n",
            " extracting: train/blue_shirt/00000148_jpg.rf.03e2eb0cde223d35679e5dcec4aa40ee.jpg  \n",
            " extracting: train/blue_shirt/00000150_jpeg.rf.00e3abe0cf33fb878a0f24be90b6215a.jpg  \n",
            " extracting: train/blue_shirt/00000151_jpg.rf.70fab82d354092adb55ace17069b2c71.jpg  \n",
            " extracting: train/blue_shirt/00000152_jpg.rf.6fcebff75942fafeab2bb443fe754489.jpg  \n",
            " extracting: train/blue_shirt/00000154_jpg.rf.d3af5eb109ff8668fd6176bc0b37179f.jpg  \n",
            " extracting: train/blue_shirt/00000156_JPG.rf.d0a5e4e6546fe4b1cb00d08e85c531b5.jpg  \n",
            " extracting: train/blue_shirt/00000157_jpg.rf.83f9f4ebbb5ce4b523b70080affc7d70.jpg  \n",
            " extracting: train/blue_shirt/00000160_jpg.rf.680de1a11a53ddcac5ca0ae284f49964.jpg  \n",
            " extracting: train/blue_shirt/00000161_jpg.rf.d0911cd7a38092da1ce4f61cf5bd7117.jpg  \n",
            " extracting: train/blue_shirt/00000162_jpg.rf.cf29aec420be643590af12a985435ba2.jpg  \n",
            " extracting: train/blue_shirt/00000163_jpg.rf.76cee5550d02e047b98f9c96b568c483.jpg  \n",
            " extracting: train/blue_shirt/00000164_jpg.rf.a9508f1a1217d6b1985e118aff504833.jpg  \n",
            " extracting: train/blue_shirt/00000165_jpg.rf.4674efef8cd7cd241e79bd40bfea438f.jpg  \n",
            " extracting: train/blue_shirt/00000167_jpg.rf.4d4f21bd24d3b83de0a822a46b43990b.jpg  \n",
            " extracting: train/blue_shirt/00000168_jpg.rf.e7b797f86e700d93733bf6467a3db1e7.jpg  \n",
            " extracting: train/blue_shirt/00000169_png.rf.14ec402731444a175b14b6f77ce49011.jpg  \n",
            " extracting: train/blue_shirt/00000170_jpeg.rf.4941dd4955acba043057be67274226eb.jpg  \n",
            " extracting: train/blue_shirt/00000171_jpg.rf.f98de7b67b9643708774e145126a00b6.jpg  \n",
            " extracting: train/blue_shirt/00000175_jpg.rf.0a546acf965f95fac23021ba95701ca0.jpg  \n",
            " extracting: train/blue_shirt/00000181_png.rf.3ba445590a23fb956ed8ffaa08ea8435.jpg  \n",
            " extracting: train/blue_shirt/00000182_jpg.rf.2d5f617b9e7cdebf7eb136edf9046f7b.jpg  \n",
            " extracting: train/blue_shirt/00000183_jpg.rf.a457a134eeb4d02483b3387b7fe77024.jpg  \n",
            " extracting: train/blue_shirt/00000184_jpg.rf.c92c9b8971282812ee88123d42e6d783.jpg  \n",
            " extracting: train/blue_shirt/00000185_jpg.rf.1753065fec511662ec91889b68248cdf.jpg  \n",
            " extracting: train/blue_shirt/00000187_jpg.rf.0eaedd08b9b9d041e11958dda327bf44.jpg  \n",
            " extracting: train/blue_shirt/00000190_jpg.rf.a145a1d6cb370d266c1aecc0a24364d4.jpg  \n",
            " extracting: train/blue_shirt/00000192_jpg.rf.3247942cc180395a712184fe53d8c92c.jpg  \n",
            " extracting: train/blue_shirt/00000197_jpg.rf.d06ec95bc6ccb036e70965e2fd2dbdcd.jpg  \n",
            " extracting: train/blue_shirt/00000198_jpg.rf.2d978eefade0a8f5a37d2dc9ad96cab6.jpg  \n",
            " extracting: train/blue_shirt/00000199_jpeg.rf.bfa7ef012b0b6a5245117eb45b82c054.jpg  \n",
            " extracting: train/blue_shirt/00000201_jpg.rf.f42589b6f5d2fe51da1035cd32847559.jpg  \n",
            " extracting: train/blue_shirt/00000203_jpg.rf.997b9afcc6369b4232dfdf7c77dfcfa3.jpg  \n",
            " extracting: train/blue_shirt/00000204_jpg.rf.832d6513a35318709f82a9680469c072.jpg  \n",
            " extracting: train/blue_shirt/00000205_jpg.rf.4179f76229dbce4eb647594668b4f6b3.jpg  \n",
            " extracting: train/blue_shirt/00000208_jpeg.rf.3016180a28f4ef585d5d55f673d404c2.jpg  \n",
            " extracting: train/blue_shirt/00000209_jpg.rf.a89f7d60af52ca1a67cc417b991ae7a0.jpg  \n",
            " extracting: train/blue_shirt/00000210_jpg.rf.8cf8c4279b00b7f5f2385daa58e23531.jpg  \n",
            " extracting: train/blue_shirt/00000211_jpeg.rf.adab14b699c202e3a92f5cb1b1deb747.jpg  \n",
            " extracting: train/blue_shirt/00000213_jpg.rf.7ec3322a8dddcd3e02308074ab89ab1e.jpg  \n",
            " extracting: train/blue_shirt/00000214_jpg.rf.f42035a72d6df1e738f888f90fe0ef0b.jpg  \n",
            " extracting: train/blue_shirt/00000217_jpg.rf.3530551deb74f70a0983f75e880bf4a3.jpg  \n",
            " extracting: train/blue_shirt/00000218_jpeg.rf.9527db70b1c0ed716500ac778bc06e8a.jpg  \n",
            " extracting: train/blue_shirt/00000222_jpg.rf.08e6764a8e9fa658dd4f615e838b85d3.jpg  \n",
            " extracting: train/blue_shirt/00000223_jpg.rf.10205c374bfc93d40460465c8dd2fe61.jpg  \n",
            " extracting: train/blue_shirt/00000224_jpg.rf.4973885964807a75c7cec8834eb43a9e.jpg  \n",
            " extracting: train/blue_shirt/00000227_jpg.rf.a1575e29b8714aaa4bc6ba998b9969af.jpg  \n",
            " extracting: train/blue_shirt/00000230_jpg.rf.12f7fa2450b780633bc51ecd50bc80b6.jpg  \n",
            " extracting: train/blue_shirt/00000231_jpg.rf.78c2a08e7b4b73aba731912cfc67cc0e.jpg  \n",
            " extracting: train/blue_shirt/00000232_jpg.rf.dba155c36f35596631beb10b6aaa8071.jpg  \n",
            " extracting: train/blue_shirt/00000233_jpg.rf.6d4cc98426b50533ae8afac0f550b0bb.jpg  \n",
            " extracting: train/blue_shirt/00000236_jpg.rf.ed3c5323141a6827180b6b023f37df12.jpg  \n",
            " extracting: train/blue_shirt/00000238_jpg.rf.6de884aa03f870977c6b61d77d234431.jpg  \n",
            " extracting: train/blue_shirt/00000241_jpg.rf.3fa01b32bf82a177c96b140b92e30a6e.jpg  \n",
            " extracting: train/blue_shirt/00000242_jpg.rf.86eed8a423bb3eda72ad6d60fe0d9561.jpg  \n",
            " extracting: train/blue_shirt/00000244_jpg.rf.cd0e396990142b530aa2377f6d0be3e9.jpg  \n",
            " extracting: train/blue_shirt/00000249_jpg.rf.f84dfb1ae6cf25a1f20ba547c0c73f4d.jpg  \n",
            " extracting: train/blue_shirt/00000250_jpg.rf.e3d6e99b8e6830848b4e98d96e543fbe.jpg  \n",
            " extracting: train/blue_shirt/00000252_jpg.rf.75092f030b2e876e76898437bcee534c.jpg  \n",
            " extracting: train/blue_shirt/00000253_jpg.rf.66e9dde159add056f545b598c3e85a33.jpg  \n",
            " extracting: train/blue_shirt/00000254_jpg.rf.8c33b9b67b277c6ad58048102d9726d5.jpg  \n",
            " extracting: train/blue_shirt/00000255_jpg.rf.fc5af4d86fef71ba7aaf885f056e0f74.jpg  \n",
            " extracting: train/blue_shirt/00000258_jpg.rf.0b3eb3b04bf558c352fc4318e079c718.jpg  \n",
            " extracting: train/blue_shirt/00000259_jpg.rf.ae2ad90179f9c0fc5dd9446b90249627.jpg  \n",
            " extracting: train/blue_shirt/00000260_jpg.rf.0e46be7bd114aad495f085728b855fb0.jpg  \n",
            " extracting: train/blue_shirt/00000261_jpg.rf.1dee7d86ec7c2cfd0f84bea7ac73e3ba.jpg  \n",
            " extracting: train/blue_shirt/00000263_jpeg.rf.750d284f63bb107bc2fec106ca527ac6.jpg  \n",
            " extracting: train/blue_shirt/00000264_jpg.rf.69edb220b8379bf98bdb98f58c974b95.jpg  \n",
            " extracting: train/blue_shirt/00000265_jpg.rf.3eec42e95a8e85a5087a3bc4aa6890a2.jpg  \n",
            " extracting: train/blue_shirt/00000266_jpg.rf.24aa63591ae8f22b0dc10844c0af7209.jpg  \n",
            " extracting: train/blue_shirt/00000267_jpg.rf.001b2b2d9041087412ff6250b48e8e76.jpg  \n",
            " extracting: train/blue_shirt/00000268_jpg.rf.f9a0c563722eb0e56b1d152f67cb916d.jpg  \n",
            " extracting: train/blue_shirt/00000269_jpg.rf.5c12db519aa708133c042b949b917fc0.jpg  \n",
            " extracting: train/blue_shirt/00000270_jpg.rf.4eab652ee6c562821be6c29bb3649a86.jpg  \n",
            " extracting: train/blue_shirt/00000272_jpeg.rf.6c7c24d915b1eb8354e7ca9645606721.jpg  \n",
            " extracting: train/blue_shirt/00000273_jpg.rf.971200c139386636bf609c13961ed71d.jpg  \n",
            " extracting: train/blue_shirt/00000274_jpeg.rf.a473d0232be149b9bb7261424d199bff.jpg  \n",
            " extracting: train/blue_shirt/00000275_jpeg.rf.a1d1e35f2d776f692de86f1240a5e203.jpg  \n",
            " extracting: train/blue_shirt/00000277_jpg.rf.38991cd06f68a9130e13595191223db4.jpg  \n",
            " extracting: train/blue_shirt/00000279_jpg.rf.0073ef14359b541d545ad9fb941ab6f1.jpg  \n",
            " extracting: train/blue_shirt/00000282_jpg.rf.20ca2fc45909ff955cbb64b8a34b6f2f.jpg  \n",
            " extracting: train/blue_shirt/00000283_jpeg.rf.4fc174478e36585d1f9c6d801ad2148c.jpg  \n",
            " extracting: train/blue_shirt/00000284_png.rf.95f9d7196d2d85f3c32484f3498c0ca2.jpg  \n",
            " extracting: train/blue_shirt/00000289_jpeg.rf.dda847ba8934b34385ca55039ea96517.jpg  \n",
            " extracting: train/blue_shirt/00000291_jpg.rf.7cc3ef759705bc83250d2ed22830c998.jpg  \n",
            " extracting: train/blue_shirt/00000293_jpg.rf.091de6b78c5da01edd16068e17c8ff26.jpg  \n",
            " extracting: train/blue_shirt/00000296_jpg.rf.eeb5d0774b476d415a96368da61b12cf.jpg  \n",
            " extracting: train/blue_shirt/00000298_png.rf.651a1d5f3e8f46e281dfeb43033f978b.jpg  \n",
            " extracting: train/blue_shirt/00000300_jpg.rf.70f638376c6d3e55d26eef2bc63b5ffe.jpg  \n",
            " extracting: train/blue_shirt/00000301_jpg.rf.632527c6455bc5b19e8e998de8f76459.jpg  \n",
            " extracting: train/blue_shirt/00000302_jpeg.rf.e4ca384118435740b6eecb06e53a75b8.jpg  \n",
            " extracting: train/blue_shirt/00000304_jpg.rf.071ec0ad38556b8c78aa0ad1988cf62d.jpg  \n",
            " extracting: train/blue_shirt/00000305_jpg.rf.d6d130f8dbfeabd5e2f805c038c3462f.jpg  \n",
            " extracting: train/blue_shirt/00000308_jpg.rf.f27cc49932ba043249b5e6418f1817f3.jpg  \n",
            " extracting: train/blue_shirt/00000309_jpeg.rf.9bd1297e5d123e3378c04de5509c3ec5.jpg  \n",
            " extracting: train/blue_shirt/00000310_jpg.rf.8f7251a0c0c65cb8a314e9519af69a1f.jpg  \n",
            " extracting: train/blue_shirt/00000311_jpg.rf.fa57a81b0716ac4bd6036cf7e8bdb3c9.jpg  \n",
            " extracting: train/blue_shirt/00000312_jpg.rf.41a38f2ed4c77622a4e183f56ba1c40d.jpg  \n",
            " extracting: train/blue_shirt/00000314_jpg.rf.36e3e7a545454293064e5b5f3fbfa3c6.jpg  \n",
            " extracting: train/blue_shirt/00000315_jpeg.rf.f4d68530056613af83f390bdb3fcb5f5.jpg  \n",
            " extracting: train/blue_shirt/00000316_jpg.rf.22c6e4e947e911cff773d4543ea753f8.jpg  \n",
            " extracting: train/blue_shirt/00000317_jpg.rf.802b39e2c55c1e2c22a53dd2e30fd062.jpg  \n",
            " extracting: train/blue_shirt/00000318_jpeg.rf.e0d119e93bf84d092871fe68919de02a.jpg  \n",
            " extracting: train/blue_shirt/00000319_jpg.rf.c7955cdd180df5c031d3e396888740f3.jpg  \n",
            " extracting: train/blue_shirt/00000320_jpg.rf.0712ca71418298b764ba729e7ebdc6dc.jpg  \n",
            " extracting: train/blue_shirt/00000321_jpg.rf.ae8237f86de03af3ca02c0a0617460cd.jpg  \n",
            " extracting: train/blue_shirt/00000322_jpg.rf.9fedad9e7478b20c1f275753f3144228.jpg  \n",
            " extracting: train/blue_shirt/00000328_jpg.rf.980ef3f4afcc5ce82fa3a965c9a170d7.jpg  \n",
            " extracting: train/blue_shirt/00000329_jpg.rf.cab2ee595bc0988d4e73fb7dd553bfde.jpg  \n",
            " extracting: train/blue_shirt/00000331_jpg.rf.3ccc82993816b9ccc0fa999888ab87ec.jpg  \n",
            " extracting: train/blue_shirt/00000335_jpg.rf.6cd8c0067d2fcbc927b97bfdedf37229.jpg  \n",
            " extracting: train/blue_shirt/00000337_png.rf.ef8b69aed52021924d1c34e525c12224.jpg  \n",
            " extracting: train/blue_shirt/00000339_jpeg.rf.8c8e7d67e3c4ca4b47659ced2c1197c5.jpg  \n",
            " extracting: train/blue_shirt/00000341_jpg.rf.8601e64f76547905d4e7b4f4207cb0f5.jpg  \n",
            " extracting: train/blue_shirt/00000342_jpg.rf.f2fb036c12b134d0a2e828f3c8d41b0f.jpg  \n",
            " extracting: train/blue_shirt/00000343_jpeg.rf.7a472e58da7c3bad079a34729a619bab.jpg  \n",
            " extracting: train/blue_shirt/00000344_jpg.rf.1cf2dfb3b9414d3e53d0304f816b1d8f.jpg  \n",
            " extracting: train/blue_shirt/00000345_jpg.rf.b08858cde20269d4fdbaa69addaa3083.jpg  \n",
            " extracting: train/blue_shirt/00000346_jpg.rf.f539d60acbd368989d031019c2280467.jpg  \n",
            " extracting: train/blue_shirt/00000349_jpg.rf.95e4c23f8351af572ed93c14b1475979.jpg  \n",
            " extracting: train/blue_shirt/00000350_jpg.rf.67a3b55128a20e611c9385fc662c0ee7.jpg  \n",
            " extracting: train/blue_shirt/00000351_jpg.rf.064e8ea95bf49755d4ab3921328fed6d.jpg  \n",
            " extracting: train/blue_shirt/00000352_jpg.rf.02ed0d50866d2d35ded03f2ca8f78db1.jpg  \n",
            " extracting: train/blue_shirt/00000353_jpg.rf.d1ccf0351fb1afa3234661031b7761e8.jpg  \n",
            " extracting: train/blue_shirt/00000354_jpg.rf.916a0e47c71548a97c88a4f8c329bc0b.jpg  \n",
            " extracting: train/blue_shirt/00000355_jpeg.rf.51a2f8e6009ca77b6d217da4124888b8.jpg  \n",
            " extracting: train/blue_shirt/00000358_jpg.rf.0c116227e6c1a1b4d48274110b14b62a.jpg  \n",
            " extracting: train/blue_shirt/00000362_jpg.rf.49cd9d5cd803ab80c5176ee1248be7b9.jpg  \n",
            " extracting: train/blue_shirt/00000363_jpg.rf.fb89f5caedb76f0d118c4b758c17995a.jpg  \n",
            " extracting: train/blue_shirt/00000365_jpg.rf.de52e340395d01d1ee6f6a7f11faa79b.jpg  \n",
            " extracting: train/blue_shirt/00000366_jpg.rf.d02df4202450593acc9dac94010d87d3.jpg  \n",
            " extracting: train/blue_shirt/00000368_jpg.rf.50d7a867b549e3030d3381dfa7ab5d69.jpg  \n",
            " extracting: train/blue_shirt/00000369_jpg.rf.ff656db9cd198c4cfd4dfd86b08f70c9.jpg  \n",
            " extracting: train/blue_shirt/00000370_jpeg.rf.766ab4c2553857c734b34a43274875e6.jpg  \n",
            " extracting: train/blue_shirt/00000372_jpg.rf.f371a374d81eb816361eaf1cea55c4f4.jpg  \n",
            " extracting: train/blue_shirt/00000376_jpg.rf.85eaa48642d6a1b6358f13c792c24268.jpg  \n",
            " extracting: train/blue_shirt/00000377_jpeg.rf.cb1367558a7ea379ab1e6c36acc6b1fd.jpg  \n",
            " extracting: train/blue_shirt/00000382_jpg.rf.8e0555d60f07d34149bae0181cde1c4c.jpg  \n",
            " extracting: train/blue_shirt/00000383_jpg.rf.06556d8195684dc47703f03a6f9b358a.jpg  \n",
            " extracting: train/blue_shirt/00000384_jpg.rf.b9dc8b332bee5317ae9f8de165e62e3b.jpg  \n",
            "   creating: train/red_dress/\n",
            " extracting: train/red_dress/00000000_jpg.rf.ce68238a3cff7dca1fd1a94ca3c173b3.jpg  \n",
            " extracting: train/red_dress/00000002_jpg.rf.9555739da0efeefe0851127b44465986.jpg  \n",
            " extracting: train/red_dress/00000003_jpg.rf.e182a06c0200394bbf580bd1a98f78ae.jpg  \n",
            " extracting: train/red_dress/00000005_jpg.rf.7c4e45d9542f1f48a6e89b8f89ee6e9c.jpg  \n",
            " extracting: train/red_dress/00000007_jpg.rf.eda9a60b4109a760ed78450bdb60f454.jpg  \n",
            " extracting: train/red_dress/00000008_jpg.rf.7b280ff89adfb5a1608fa64de8468102.jpg  \n",
            " extracting: train/red_dress/00000009_JPG.rf.1dda183d3c57e762d50d8df9ac6b5dbf.jpg  \n",
            " extracting: train/red_dress/00000010_jpg.rf.430e8a887dc4bf7f95d4670a1561e1db.jpg  \n",
            " extracting: train/red_dress/00000011_jpg.rf.afca5f0e0e437ca18e01bccaf939f4d8.jpg  \n",
            " extracting: train/red_dress/00000012_jpg.rf.17cfad9487b6e1420d4b301af0050b55.jpg  \n",
            " extracting: train/red_dress/00000015_jpg.rf.179602e2f1f391454563f2e350cf9b2d.jpg  \n",
            " extracting: train/red_dress/00000017_jpg.rf.b5b45a72a9359fdfb78cb000ee915305.jpg  \n",
            " extracting: train/red_dress/00000018_jpg.rf.87cdd724aa5670ace4bc3347d9a80ac5.jpg  \n",
            " extracting: train/red_dress/00000019_jpg.rf.ea2f0185880ecbbef3c5b895a005d26f.jpg  \n",
            " extracting: train/red_dress/00000020_jpg.rf.b1ec1fcce250c0ec38936ba4b51cda88.jpg  \n",
            " extracting: train/red_dress/00000021_jpg.rf.01dbd1897ee3c4b35f168821f49e7a52.jpg  \n",
            " extracting: train/red_dress/00000022_jpg.rf.fa05b0450c5ea04db13a477d3e0cfa71.jpg  \n",
            " extracting: train/red_dress/00000023_jpg.rf.794463799786d7a1fbc86e7d30205a60.jpg  \n",
            " extracting: train/red_dress/00000024_jpg.rf.66f77a4caeee766606d482d51acedda6.jpg  \n",
            " extracting: train/red_dress/00000025_jpg.rf.5022fc0366ab09034e6d1e084d7084ae.jpg  \n",
            " extracting: train/red_dress/00000026_jpg.rf.ef5669a9eea5416bcb0d29faf58bebdf.jpg  \n",
            " extracting: train/red_dress/00000027_jpg.rf.13f3fe924186d5b5bafbc6ccc85b3a51.jpg  \n",
            " extracting: train/red_dress/00000029_png.rf.7a874d1b91b21fac597cd66f50dbfef8.jpg  \n",
            " extracting: train/red_dress/00000030_jpg.rf.bc39a0c95e063502b9b7c7e70c07faaf.jpg  \n",
            " extracting: train/red_dress/00000031_jpeg.rf.682966fa71dab71b00386920b7491ed9.jpg  \n",
            " extracting: train/red_dress/00000032_jpg.rf.e057d2a9c9bf92bd3e84b4047a04f7ce.jpg  \n",
            " extracting: train/red_dress/00000033_jpg.rf.331a189b51d34b86c575f2740861cccc.jpg  \n",
            " extracting: train/red_dress/00000035_jpg.rf.edd39240166193601e86b326e840b883.jpg  \n",
            " extracting: train/red_dress/00000038_jpg.rf.b141bce61e43bf8665b9c9aa8c4642ba.jpg  \n",
            " extracting: train/red_dress/00000039_jpg.rf.cc46d9f40321729b84c659361510fbe4.jpg  \n",
            " extracting: train/red_dress/00000042_jpg.rf.d39c9d44d3cd43c054549e460d36ca4e.jpg  \n",
            " extracting: train/red_dress/00000043_jpg.rf.b8a92dad63be3066a3fd47346e9f1368.jpg  \n",
            " extracting: train/red_dress/00000044_jpg.rf.00087dc27a91b729cb12e676cfe1912c.jpg  \n",
            " extracting: train/red_dress/00000045_jpg.rf.665785d935a28869eab6bd1daae968dd.jpg  \n",
            " extracting: train/red_dress/00000046_jpg.rf.a1ac4babd4776763853e62a537506307.jpg  \n",
            " extracting: train/red_dress/00000052_jpg.rf.26cac415d5fff24aae789906c9802bfb.jpg  \n",
            " extracting: train/red_dress/00000053_jpg.rf.ef26bc91842618b311eff13c7d8f1cdb.jpg  \n",
            " extracting: train/red_dress/00000054_jpg.rf.4d6ef1cc4bad53abff9f5d47a0deee60.jpg  \n",
            " extracting: train/red_dress/00000055_jpg.rf.8754276d9f626315efbd3c837e8f68b9.jpg  \n",
            " extracting: train/red_dress/00000056_png.rf.4b4742a990b8f8a83eeab996815fcc2c.jpg  \n",
            " extracting: train/red_dress/00000057_jpg.rf.14dd812c61f7faacc255c2f61f21b95e.jpg  \n",
            " extracting: train/red_dress/00000058_jpg.rf.21fec8713bd437b6b8baf3c5d249771e.jpg  \n",
            " extracting: train/red_dress/00000059_jpg.rf.e1180088bacbbb3fb0e89a66c3f9df04.jpg  \n",
            " extracting: train/red_dress/00000060_jpg.rf.d888f6f0bd6d3f2cff1783db0f4f3765.jpg  \n",
            " extracting: train/red_dress/00000061_jpg.rf.de3e2551dfbfa0a75b275effa099cc7f.jpg  \n",
            " extracting: train/red_dress/00000062_jpg.rf.a89baa4001a8e1d80c64da8e92598072.jpg  \n",
            " extracting: train/red_dress/00000065_jpg.rf.7e5bdd91b006f2d34e36ea51c2fc506d.jpg  \n",
            " extracting: train/red_dress/00000066_jpg.rf.552cc8c247b1b458e523727cda087a97.jpg  \n",
            " extracting: train/red_dress/00000067_jpg.rf.af44fe8cab13fa14de85c06a5199566c.jpg  \n",
            " extracting: train/red_dress/00000070_jpeg.rf.f8edc22aa9b4100702e5d630ba5a1ffd.jpg  \n",
            " extracting: train/red_dress/00000071_jpg.rf.a63ed1077acdcf87e5ddaed0066c3b89.jpg  \n",
            " extracting: train/red_dress/00000073_jpg.rf.c260e2a00da5ed9c93421c96b61a52ef.jpg  \n",
            " extracting: train/red_dress/00000074_jpg.rf.4775f3e1ae372766964cee2ec8f62e89.jpg  \n",
            " extracting: train/red_dress/00000075_jpg.rf.287a6b2ad6108285c22df3fe41f6d0ef.jpg  \n",
            " extracting: train/red_dress/00000076_jpg.rf.9903ffa0cd0b082d8035d9e71f5c059e.jpg  \n",
            " extracting: train/red_dress/00000077_jpg.rf.b51e97ebc50bd8da66f95351ac118667.jpg  \n",
            " extracting: train/red_dress/00000078_jpg.rf.00469da58ce729e7b18cfb071bb2e07e.jpg  \n",
            " extracting: train/red_dress/00000079_jpg.rf.1cd56725866234c151ab102cd9bef43d.jpg  \n",
            " extracting: train/red_dress/00000080_JPG.rf.9037c752c90abbe8ed2a7632ce71b053.jpg  \n",
            " extracting: train/red_dress/00000081_jpg.rf.26fb6eb1a0c6dfbf4af45d72bcaaff0a.jpg  \n",
            " extracting: train/red_dress/00000085_jpg.rf.22dbcdb686d929b2fa889dd41d821d1d.jpg  \n",
            " extracting: train/red_dress/00000086_jpg.rf.cd35fdcec6868e01554a59d6305d0430.jpg  \n",
            " extracting: train/red_dress/00000087_jpg.rf.6daec5f75419e9558a6e7435c2cdb24b.jpg  \n",
            " extracting: train/red_dress/00000088_jpg.rf.68befc4105ce706458e9642b7443c696.jpg  \n",
            " extracting: train/red_dress/00000089_jpg.rf.b6e84782516846930f220259812098d9.jpg  \n",
            " extracting: train/red_dress/00000090_jpg.rf.62382bddb1647fc7fe34234e9cd8ff48.jpg  \n",
            " extracting: train/red_dress/00000092_jpg.rf.5502192639cb585603521f83e31bdf2c.jpg  \n",
            " extracting: train/red_dress/00000093_jpg.rf.b000964e56e3f69632f5958d600ee76a.jpg  \n",
            " extracting: train/red_dress/00000094_jpg.rf.7f78411dd22df40512c2c91942687e33.jpg  \n",
            " extracting: train/red_dress/00000095_jpg.rf.4826454762a2783dd2c487b633d1cd64.jpg  \n",
            " extracting: train/red_dress/00000101_jpg.rf.019f1e91d09719331d9561f703df1b85.jpg  \n",
            " extracting: train/red_dress/00000103_jpeg.rf.c9f0b82ad90d09c44a633a70d92bcb1f.jpg  \n",
            " extracting: train/red_dress/00000104_jpg.rf.5c9597fecdcd5ec5a53b460db1f266d5.jpg  \n",
            " extracting: train/red_dress/00000106_jpg.rf.662c0dad297c1cb565cd14e6ed797c05.jpg  \n",
            " extracting: train/red_dress/00000107_jpg.rf.bb1114ba604f011f42239a42abbc5375.jpg  \n",
            " extracting: train/red_dress/00000108_jpg.rf.e98b52f3d57cf4621256267b983570d1.jpg  \n",
            " extracting: train/red_dress/00000111_jpg.rf.f771d9b8318d7527879a86e95242bbe1.jpg  \n",
            " extracting: train/red_dress/00000112_jpg.rf.4c06a4be93bf1de69151b405f32fdcf0.jpg  \n",
            " extracting: train/red_dress/00000113_jpg.rf.c22fb44747e54c608b871bf754f27194.jpg  \n",
            " extracting: train/red_dress/00000114_jpg.rf.eee1c7a9b3541ea3e0384c0cbba1a104.jpg  \n",
            " extracting: train/red_dress/00000115_jpg.rf.b95fa0abe5dfc33c6c301839967583a5.jpg  \n",
            " extracting: train/red_dress/00000117_jpg.rf.30afdcae28dcd2dd83a4088e667af77a.jpg  \n",
            " extracting: train/red_dress/00000118_jpg.rf.0ef3f9b6c0b3fcaa058c75e98b4214fe.jpg  \n",
            " extracting: train/red_dress/00000120_jpeg.rf.004f42a8f02112b5e083cb6ff97b3f83.jpg  \n",
            " extracting: train/red_dress/00000121_jpg.rf.908ae0b00985395bcc6ea0c54ce99c4b.jpg  \n",
            " extracting: train/red_dress/00000122_JPG.rf.d9dcdf684ad78b8de760de84cc8de8b3.jpg  \n",
            " extracting: train/red_dress/00000124_jpg.rf.91b88a6bc7f3f7510f5a4e6695a990e5.jpg  \n",
            " extracting: train/red_dress/00000125_jpeg.rf.ddff89136e56389eff3cbaedeba96824.jpg  \n",
            " extracting: train/red_dress/00000127_jpg.rf.9813427f579068ee1376b43032d88f81.jpg  \n",
            " extracting: train/red_dress/00000128_jpg.rf.5e07ab0e8872b3b6f90d8a2f8416f87a.jpg  \n",
            " extracting: train/red_dress/00000129_jpg.rf.a7e49d3e93ad3f7fa623a7a459f36978.jpg  \n",
            " extracting: train/red_dress/00000130_jpg.rf.08832fa31aaf1004937075de40dd8c14.jpg  \n",
            " extracting: train/red_dress/00000131_jpg.rf.3a4497763b8c7b42ed10c4a2a6b7b2dd.jpg  \n",
            " extracting: train/red_dress/00000132_jpg.rf.bb63133f19cae16c7ba8773bc53cd7f7.jpg  \n",
            " extracting: train/red_dress/00000133_jpg.rf.813ea79da256f3ffac0bd8c9ab8bf541.jpg  \n",
            " extracting: train/red_dress/00000134_jpg.rf.4ae3b02dc910c508d56e7f08844e404c.jpg  \n",
            " extracting: train/red_dress/00000135_jpg.rf.99520a2a18751d4a2a21e0b0c52785b8.jpg  \n",
            " extracting: train/red_dress/00000136_jpg.rf.23b46794742d8b039d2cc29935a99b16.jpg  \n",
            " extracting: train/red_dress/00000137_jpeg.rf.eb5bf9a606beb1fb91613d9488e6c50f.jpg  \n",
            " extracting: train/red_dress/00000140_jpg.rf.91dd52af6cf161d75b0f56415db048e5.jpg  \n",
            " extracting: train/red_dress/00000141_jpg.rf.b4e25414556963d891aa1dacb4b23049.jpg  \n",
            " extracting: train/red_dress/00000142_jpg.rf.67d64b5c647060281b5c1496f2287876.jpg  \n",
            " extracting: train/red_dress/00000144_jpeg.rf.89a9fa52dc945349a86394f13a3e10a1.jpg  \n",
            " extracting: train/red_dress/00000146_jpg.rf.56d1fb441be9ea9feb2c72789c2c9cb0.jpg  \n",
            " extracting: train/red_dress/00000148_jpg.rf.dd8d08e7f0cc37f741ec5c58d190fc08.jpg  \n",
            " extracting: train/red_dress/00000149_jpg.rf.33e0100951e69828bcfda4abca397ace.jpg  \n",
            " extracting: train/red_dress/00000150_jpeg.rf.3808fe068ddd80062ed47abc3d355eb9.jpg  \n",
            " extracting: train/red_dress/00000152_jpg.rf.17043e42a3d3cf73695c1a1e54a3ca46.jpg  \n",
            " extracting: train/red_dress/00000154_jpg.rf.e681264d50defffde1fbaa113a1d96b6.jpg  \n",
            " extracting: train/red_dress/00000155_jpg.rf.a5d889428436e6ce348217d6a7531ddb.jpg  \n",
            " extracting: train/red_dress/00000157_jpeg.rf.464c70a8e97e59b8f38e5b56f5c0d109.jpg  \n",
            " extracting: train/red_dress/00000159_jpg.rf.7f1af8cd22dfa22257fa7b245e9702ef.jpg  \n",
            " extracting: train/red_dress/00000160_jpg.rf.f2780d72d8b8ed165563c433df55f1c6.jpg  \n",
            " extracting: train/red_dress/00000162_jpg.rf.a8a101967ac1ecd296f2fde9ef8ff3f7.jpg  \n",
            " extracting: train/red_dress/00000163_jpg.rf.949402e07128f0f6f6337c232888b663.jpg  \n",
            " extracting: train/red_dress/00000164_jpg.rf.bd1c6501f5d40f0de2b13606b6547676.jpg  \n",
            " extracting: train/red_dress/00000165_jpg.rf.feba9b2fbfadfabe0ae4263dbff7abeb.jpg  \n",
            " extracting: train/red_dress/00000166_jpg.rf.e449bf82cd42610be3742754537537a1.jpg  \n",
            " extracting: train/red_dress/00000167_jpg.rf.9570561421e069b317eccedf91d1fbcc.jpg  \n",
            " extracting: train/red_dress/00000169_jpg.rf.13ad364bb87d71bf73fb03c54d9730a3.jpg  \n",
            " extracting: train/red_dress/00000170_jpg.rf.9d59a3d540763fe88faad23fc515ed61.jpg  \n",
            " extracting: train/red_dress/00000171_jpeg.rf.e99ab0ae936b2cc6dff8ff71599bed88.jpg  \n",
            " extracting: train/red_dress/00000173_jpg.rf.691fb467daf2b99ce71ba8af1609b9ce.jpg  \n",
            " extracting: train/red_dress/00000174_jpg.rf.49115adf76d6b25ab51467a240e9d5eb.jpg  \n",
            " extracting: train/red_dress/00000175_jpg.rf.e220f49086ce93cf1738693299fa8718.jpg  \n",
            " extracting: train/red_dress/00000176_jpg.rf.401bbc3de6325d590172c337b3feac45.jpg  \n",
            " extracting: train/red_dress/00000177_jpg.rf.5aa43d164d86b267048a34669443a839.jpg  \n",
            " extracting: train/red_dress/00000179_jpg.rf.e2bbe50fd98000f413e94b77b6eb1105.jpg  \n",
            " extracting: train/red_dress/00000182_jpg.rf.65aa7ae70f7e1925ce43063a8c5e2272.jpg  \n",
            " extracting: train/red_dress/00000183_jpg.rf.b1e8241579d43d3abff3b4c6a8e62ebe.jpg  \n",
            " extracting: train/red_dress/00000184_jpg.rf.660c48a178d4efd878d6c6601b05e720.jpg  \n",
            " extracting: train/red_dress/00000185_jpg.rf.c3b9973ab6815762b75de8a2b1530aa1.jpg  \n",
            " extracting: train/red_dress/00000186_jpg.rf.d846fcc3268a78b1b7a70cf2665fe7cd.jpg  \n",
            " extracting: train/red_dress/00000189_jpg.rf.cf8ab54b82b8c67d9359656f43ac6f65.jpg  \n",
            " extracting: train/red_dress/00000190_jpg.rf.702c152d97c4f7e4c7f7469a7f3b740f.jpg  \n",
            " extracting: train/red_dress/00000191_jpg.rf.056c2fe9b501f558117d4385cda66a71.jpg  \n",
            " extracting: train/red_dress/00000193_jpg.rf.f18c14a855c9e35fbb4725f944219638.jpg  \n",
            " extracting: train/red_dress/00000195_jpg.rf.fdb108162621ce7cfdbd87ff79b5f0ac.jpg  \n",
            " extracting: train/red_dress/00000196_jpg.rf.caa49658ca7db6102e63893828b603ed.jpg  \n",
            " extracting: train/red_dress/00000197_jpg.rf.d984bbc1fd19092c87c719b7fe77bd82.jpg  \n",
            " extracting: train/red_dress/00000198_jpg.rf.e0feb96df79649db2dc8d7d19a406f28.jpg  \n",
            " extracting: train/red_dress/00000201_jpg.rf.a9ac7ab9cf76ac90754b0bf64f44dfda.jpg  \n",
            " extracting: train/red_dress/00000202_jpg.rf.e745fa743a9e86d7e7b68a75fbdbcb9a.jpg  \n",
            " extracting: train/red_dress/00000203_jpeg.rf.864cb2ce0574c0dfd3e0488ccb78d197.jpg  \n",
            " extracting: train/red_dress/00000208_jpg.rf.e058a40eaa3d428309d830f003a06df0.jpg  \n",
            " extracting: train/red_dress/00000209_jpg.rf.42ee322901902a60a522a77772fc1b52.jpg  \n",
            " extracting: train/red_dress/00000210_jpg.rf.146629797e498ed721a515196a08188c.jpg  \n",
            " extracting: train/red_dress/00000212_jpg.rf.ed6f6d61a7504b5bc4a70b44a5fa7a46.jpg  \n",
            " extracting: train/red_dress/00000213_jpg.rf.43f130944899b66c19e2f66ebbd34c45.jpg  \n",
            " extracting: train/red_dress/00000215_jpg.rf.7ab5d38e730932705c4d9123ebddd783.jpg  \n",
            " extracting: train/red_dress/00000216_jpg.rf.306421aef15efaa96926f14f0370c340.jpg  \n",
            " extracting: train/red_dress/00000217_jpg.rf.08fb387ce3feac790f0426bd283e36e5.jpg  \n",
            " extracting: train/red_dress/00000219_jpeg.rf.9357b23614c23df184dc25dded094d08.jpg  \n",
            " extracting: train/red_dress/00000220_jpg.rf.7c0ba993e04c5c4b40bc8049cb1010f1.jpg  \n",
            " extracting: train/red_dress/00000221_jpg.rf.8acb2e01b3bfcece5ab708a7b47b1dba.jpg  \n",
            " extracting: train/red_dress/00000224_jpg.rf.f4122955d574354f7af62990cd577c70.jpg  \n",
            " extracting: train/red_dress/00000225_jpg.rf.2193e67c53d461c0027946e03f8e3b13.jpg  \n",
            " extracting: train/red_dress/00000227_jpg.rf.d2e1f5852c1e5ce21c9bf6d5a23df31e.jpg  \n",
            " extracting: train/red_dress/00000230_jpg.rf.c3792fb2e06ded8eee1d2300eea3dac3.jpg  \n",
            " extracting: train/red_dress/00000234_jpg.rf.754e415351a4057e61b79cf95911d884.jpg  \n",
            " extracting: train/red_dress/00000235_jpg.rf.49c7cd74ef780c638526df386dadca14.jpg  \n",
            " extracting: train/red_dress/00000238_jpg.rf.dd921910cc2b93539e6172f412ba0cf0.jpg  \n",
            " extracting: train/red_dress/00000239_jpg.rf.64751004ffa09991529f5b171a3366d8.jpg  \n",
            " extracting: train/red_dress/00000240_jpeg.rf.3a3c5dc7407875d383a442ee0b9a150c.jpg  \n",
            " extracting: train/red_dress/00000242_jpg.rf.099479379fb0c6a8904c2343994a3ae3.jpg  \n",
            " extracting: train/red_dress/00000243_jpg.rf.21959a74c7197327da648fdff4567014.jpg  \n",
            " extracting: train/red_dress/00000245_jpg.rf.b037fc99c861e73102a5faaf0711c9e3.jpg  \n",
            " extracting: train/red_dress/00000246_jpg.rf.df8828699c2318e050817bb9be24248f.jpg  \n",
            " extracting: train/red_dress/00000249_jpg.rf.5212b9095f324fadf846ceca29b34252.jpg  \n",
            " extracting: train/red_dress/00000250_jpg.rf.5546ef1e4d445b0426587a873fa2fd32.jpg  \n",
            " extracting: train/red_dress/00000254_jpg.rf.49ef950664127a03e285739f7f6b4f4e.jpg  \n",
            " extracting: train/red_dress/00000255_jpg.rf.46bffa71c4c6c291214778485c272ea8.jpg  \n",
            " extracting: train/red_dress/00000256_jpg.rf.09f080064f6b096931348786f67a038f.jpg  \n",
            " extracting: train/red_dress/00000260_jpg.rf.47254c090429497ef12c31ecbe984d06.jpg  \n",
            " extracting: train/red_dress/00000261_jpg.rf.4a20f9136655dca9442cbbeb26213f0b.jpg  \n",
            " extracting: train/red_dress/00000262_jpg.rf.64cfb86d796b94190d4b861b197f82c6.jpg  \n",
            " extracting: train/red_dress/00000263_jpg.rf.42d3f64a182000dd4814616a55f3b90a.jpg  \n",
            " extracting: train/red_dress/00000266_jpg.rf.f65fd20e84c08933fd61c6d1a4fc656b.jpg  \n",
            " extracting: train/red_dress/00000267_jpg.rf.acb9ad6b9201da6c0da4c650dcd6f9f4.jpg  \n",
            " extracting: train/red_dress/00000268_jpg.rf.cd533b58c5627d89b1edee4a5863c4e6.jpg  \n",
            " extracting: train/red_dress/00000269_jpg.rf.c3d55a9f9d7b49d94c6259406987a205.jpg  \n",
            " extracting: train/red_dress/00000270_jpg.rf.ff5caf89df15d5b6c22af291ae52567a.jpg  \n",
            " extracting: train/red_dress/00000271_jpeg.rf.1d2b7c3f721543b6566429e101d2cc73.jpg  \n",
            " extracting: train/red_dress/00000273_jpg.rf.487479d10d37788a128ee2973ea69e71.jpg  \n",
            " extracting: train/red_dress/00000275_jpg.rf.bfb0090b398df857e81e83fa98907dbf.jpg  \n",
            " extracting: train/red_dress/00000276_JPG.rf.2b3add5d2cd143d676fdfca6c38f64da.jpg  \n",
            " extracting: train/red_dress/00000277_jpg.rf.77f02f0b14318f3a7707ae829e2e0772.jpg  \n",
            " extracting: train/red_dress/00000278_jpg.rf.ebee634c656a94d05e97311c857f047d.jpg  \n",
            " extracting: train/red_dress/00000279_jpg.rf.eb8c5b08ea70ce72d5dd14bfdc6144c2.jpg  \n",
            " extracting: train/red_dress/00000280_jpeg.rf.31d8e341c44d6556a9717d842f2424ca.jpg  \n",
            " extracting: train/red_dress/00000282_jpeg.rf.ee29d37ba83d1f08f872ab95906515f2.jpg  \n",
            " extracting: train/red_dress/00000283_jpg.rf.ec7e37f2b2dfe76577a9c4e3010ced89.jpg  \n",
            " extracting: train/red_dress/00000285_jpeg.rf.2e7e3f3771b0ac4c89de480234a16a5c.jpg  \n",
            " extracting: train/red_dress/00000287_jpg.rf.d7c7561285d15fb2e694ff97b0672dd8.jpg  \n",
            " extracting: train/red_dress/00000289_jpg.rf.b94f20cd4df480f36d08a732765b11fb.jpg  \n",
            " extracting: train/red_dress/00000290_jpg.rf.eb29ad80cda82c09c8291c8aa02a877f.jpg  \n",
            " extracting: train/red_dress/00000291_jpg.rf.e5dc270c65761deea1fd6ea5e08d1cf2.jpg  \n",
            " extracting: train/red_dress/00000298_jpg.rf.c8e7045a808765c31054c1066f37cb4f.jpg  \n",
            " extracting: train/red_dress/00000299_jpg.rf.d51c18d057336783547a204eb219ec49.jpg  \n",
            " extracting: train/red_dress/00000300_jpg.rf.6bb3f1756c2f08d2700a2b9a6618ce0d.jpg  \n",
            " extracting: train/red_dress/00000301_jpeg.rf.a69e6198a437373ae4f71d9835d07a2f.jpg  \n",
            " extracting: train/red_dress/00000302_jpeg.rf.0debacfdc92b338c5f894e0aa709af15.jpg  \n",
            " extracting: train/red_dress/00000303_jpeg.rf.f8d7a2d5d30205223326fc5cde535144.jpg  \n",
            " extracting: train/red_dress/00000304_jpeg.rf.c543bb206fa9d61c1203e449465e0a2b.jpg  \n",
            " extracting: train/red_dress/00000305_jpg.rf.f1d59ebba2edd440b63536200cb69055.jpg  \n",
            " extracting: train/red_dress/00000307_jpeg.rf.369df50f82759b82e7a4d3a16dadb185.jpg  \n",
            " extracting: train/red_dress/00000309_jpg.rf.f96a366737f236e52a863e431a427b69.jpg  \n",
            " extracting: train/red_dress/00000312_jpg.rf.e0fe2ddc04adc57c9a8e31774aa28dd3.jpg  \n",
            " extracting: train/red_dress/00000313_jpg.rf.655e02371cc57a366fed3810126921c6.jpg  \n",
            " extracting: train/red_dress/00000314_jpg.rf.c2e4d3c44da1b2eec043e0e98d9dc27c.jpg  \n",
            " extracting: train/red_dress/00000316_jpg.rf.cc578478e433977c3fedb3436befa5bd.jpg  \n",
            " extracting: train/red_dress/00000318_jpg.rf.ecc34c1456c3b905f5a7e6ca86a9a6e3.jpg  \n",
            " extracting: train/red_dress/00000320_jpg.rf.f21617a2a91bc200a8068ce1094cdc2d.jpg  \n",
            " extracting: train/red_dress/00000321_jpg.rf.e869238e7e0387261ef3598d0d3e433e.jpg  \n",
            " extracting: train/red_dress/00000322_jpg.rf.9c925f3ce639dd01da7c84b083bb278d.jpg  \n",
            " extracting: train/red_dress/00000324_jpg.rf.cabd7691e6069ca72b14cbf7e2745ba4.jpg  \n",
            " extracting: train/red_dress/00000325_jpg.rf.94bd03c9827b0b57850bee683074ba6a.jpg  \n",
            " extracting: train/red_dress/00000327_jpeg.rf.df061fa426e5bb81aacd106c1effeb17.jpg  \n",
            " extracting: train/red_dress/00000331_jpg.rf.612336402a2c52448455139f2caa0c01.jpg  \n",
            " extracting: train/red_dress/00000332_jpg.rf.0bf3c504b57ab9b5f35d35c0850303af.jpg  \n",
            " extracting: train/red_dress/00000333_jpg.rf.0f9c4a76a616f8a665af1e9537461352.jpg  \n",
            " extracting: train/red_dress/00000335_jpg.rf.886d115e0c9014c3d588b09f9ff67edd.jpg  \n",
            " extracting: train/red_dress/00000336_jpg.rf.35a449663adac7cb0d4a470044c92827.jpg  \n",
            " extracting: train/red_dress/00000337_jpg.rf.e4443c24f337132f73a414572b2048f0.jpg  \n",
            " extracting: train/red_dress/00000340_jpg.rf.bc329d9c5dd31bd38662e548e2ce80f8.jpg  \n",
            " extracting: train/red_dress/00000341_jpg.rf.8732071607b41ef1ad53c9f24971724c.jpg  \n",
            " extracting: train/red_dress/00000342_jpg.rf.74b87b69a3d4a3dec4a478c0106f90a6.jpg  \n",
            " extracting: train/red_dress/00000345_jpg.rf.a165d37611489d99abff348c9b963a69.jpg  \n",
            " extracting: train/red_dress/00000346_jpg.rf.a3470661bd98dea2ee72bfa65609963d.jpg  \n",
            " extracting: train/red_dress/00000349_jpg.rf.7f98268ef6271e9411468427fbdec5fb.jpg  \n",
            " extracting: train/red_dress/00000351_jpg.rf.09c67d816998b27cfead50ae8904bae0.jpg  \n",
            " extracting: train/red_dress/00000354_jpg.rf.7617cbb4b6f5e8a2188fe4d49cb566b4.jpg  \n",
            " extracting: train/red_dress/00000356_jpg.rf.a19dab5cc0a80e26d10fed05eeb01b85.jpg  \n",
            " extracting: train/red_dress/00000357_jpg.rf.d6d7ca31adcf982cb1ede2924f1a0439.jpg  \n",
            " extracting: train/red_dress/00000358_jpg.rf.0c3c94823a1d2ffafbd470dc19fc3379.jpg  \n",
            " extracting: train/red_dress/00000360_jpg.rf.42c8d7605f58d448b722256158a85949.jpg  \n",
            " extracting: train/red_dress/00000362_jpg.rf.a81e3d114a9ea43f4482b9796328fbb3.jpg  \n",
            " extracting: train/red_dress/00000364_jpg.rf.606807e44714430738d6f6fb24553a36.jpg  \n",
            " extracting: train/red_dress/00000365_jpg.rf.df7a31ccf1f0461e0128b7159c38f42b.jpg  \n",
            " extracting: train/red_dress/00000367_jpg.rf.24d41b8e009ecd215bcd27cb9cd4916b.jpg  \n",
            " extracting: train/red_dress/00000368_jpg.rf.1aa5ba0c81f0c187aad76c3e848b6d22.jpg  \n",
            " extracting: train/red_dress/00000369_jpg.rf.f7949fb719febbca131132b856071ce6.jpg  \n",
            " extracting: train/red_dress/00000371_jpg.rf.f8a6265c43e0e28390a442cf59b02cb0.jpg  \n",
            " extracting: train/red_dress/00000373_jpg.rf.7aa4b90d16f22b68d780d6dea8adecc9.jpg  \n",
            " extracting: train/red_dress/00000374_jpg.rf.49cace46579c0a186e06e89003b75332.jpg  \n",
            " extracting: train/red_dress/00000375_JPG.rf.b6f2bbe2100bbbd82e5ec744f33f12a4.jpg  \n",
            " extracting: train/red_dress/00000378_jpg.rf.9de5d68f28ed1882d1395ff9af82f5e3.jpg  \n",
            " extracting: train/red_dress/00000379_jpg.rf.acca894bc80d1d82af8b225a2b44582e.jpg  \n",
            " extracting: train/red_dress/00000381_jpg.rf.807389afce2cc0cab511e276273b3747.jpg  \n",
            " extracting: train/red_dress/00000382_jpg.rf.cbe002a84e33ef9d5708c0072e743d57.jpg  \n",
            " extracting: train/red_dress/00000388_jpg.rf.67a610f07a37720ad6148ffebf8286a0.jpg  \n",
            "   creating: train/red_shirt/\n",
            " extracting: train/red_shirt/00000001_png.rf.658be9590c801fcd57f3e07d94d487d2.jpg  \n",
            " extracting: train/red_shirt/00000002_jpg.rf.6da2249f8a4b6c68d96d3534519ad96d.jpg  \n",
            " extracting: train/red_shirt/00000003_jpeg.rf.ffdd38f51eaf5fa970eb85e826372658.jpg  \n",
            " extracting: train/red_shirt/00000005_png.rf.5e3f21e2c754629902cb73edd81c62cd.jpg  \n",
            " extracting: train/red_shirt/00000006_png.rf.ab1d7588eab09f96a5761375d067aeab.jpg  \n",
            " extracting: train/red_shirt/00000007_png.rf.b93e44a8e559c2d4bd1dfdc2babd3ecb.jpg  \n",
            " extracting: train/red_shirt/00000008_png.rf.ed3eeaf8d601ea720afb129979b5f53e.jpg  \n",
            " extracting: train/red_shirt/00000009_jpeg.rf.d31cfc2c3dd8bacbd5e665ddd8cb8373.jpg  \n",
            " extracting: train/red_shirt/00000011_jpg.rf.0bfeabd5b2a8a5b539ef246cf312a158.jpg  \n",
            " extracting: train/red_shirt/00000012_png.rf.b246688eb8c9d8cd926870c9c0f3390f.jpg  \n",
            " extracting: train/red_shirt/00000013_png.rf.68bd5f3c25db1846004cac8c4fee5abf.jpg  \n",
            " extracting: train/red_shirt/00000014_jpg.rf.892e9958c2508ff1e0e5ce2d1561b8cf.jpg  \n",
            " extracting: train/red_shirt/00000015_jpg.rf.e15e3edbcb1fbc5e6885623611886db6.jpg  \n",
            " extracting: train/red_shirt/00000016_jpg.rf.f8f934be36fcd96842dd8fe4472b1ec0.jpg  \n",
            " extracting: train/red_shirt/00000017_jpeg.rf.9de046e78d6bade4055961d16f1ea0a3.jpg  \n",
            " extracting: train/red_shirt/00000019_png.rf.9fd397da339de1ca5fd1c02075002a88.jpg  \n",
            " extracting: train/red_shirt/00000020_jpg.rf.1571cc6a453af267802901f6c0e6d9ac.jpg  \n",
            " extracting: train/red_shirt/00000021_jpg.rf.5c5445219770685caf151388bbea9e35.jpg  \n",
            " extracting: train/red_shirt/00000023_png.rf.c7bc3a1c90016ca33d4e05bfe7513daa.jpg  \n",
            " extracting: train/red_shirt/00000024_jpg.rf.51f43355b3658c34ab54b5ee26a85a06.jpg  \n",
            " extracting: train/red_shirt/00000025_jpg.rf.56c1e7e4dce42d380db4f37b59bdc92c.jpg  \n",
            " extracting: train/red_shirt/00000028_png.rf.5774b1060f2567f848962d7e9c2c1d88.jpg  \n",
            " extracting: train/red_shirt/00000029_jpg.rf.40abbf783e29007df28d037e38a847bc.jpg  \n",
            " extracting: train/red_shirt/00000031_png.rf.22bd3d1bfbf4ef5d3db342abb7ca6c84.jpg  \n",
            " extracting: train/red_shirt/00000033_jpg.rf.e05004f9f7ad86ada61ef0ebdc631c69.jpg  \n",
            " extracting: train/red_shirt/00000034_png.rf.cb5b2b5cf09a6255db691f46dd0e1f9b.jpg  \n",
            " extracting: train/red_shirt/00000035_jpg.rf.14d8ddc4d96850ba794b33903be1ec46.jpg  \n",
            " extracting: train/red_shirt/00000036_jpg.rf.7701d6729edb0a3cb8ac4c6ef0b5b1b9.jpg  \n",
            " extracting: train/red_shirt/00000038_png.rf.71e5b73b9cb142ee8a7eaa18ea319192.jpg  \n",
            " extracting: train/red_shirt/00000041_jpg.rf.bfac0af72877a81d14140ac5e011a45f.jpg  \n",
            " extracting: train/red_shirt/00000042_jpg.rf.67c0fb60b62fd27cef1f86ccdd5717de.jpg  \n",
            " extracting: train/red_shirt/00000043_jpg.rf.47e81e72e4472879493467138633d5e8.jpg  \n",
            " extracting: train/red_shirt/00000044_png.rf.1686b2d90e2d43058389cde1439e7ff5.jpg  \n",
            " extracting: train/red_shirt/00000046_jpg.rf.1899f390ca2ddbc0fcccb65d511816f7.jpg  \n",
            " extracting: train/red_shirt/00000047_jpg.rf.0706af24f65df31ee82d79695d90f0c2.jpg  \n",
            " extracting: train/red_shirt/00000049_jpg.rf.b9ee916444c7c9006e16c2733b5c5e14.jpg  \n",
            " extracting: train/red_shirt/00000051_jpg.rf.6f0fd12ebf4c5c9a641984212d83700e.jpg  \n",
            " extracting: train/red_shirt/00000053_jpg.rf.9a958e72a6fe5b8dc11a39085da6f745.jpg  \n",
            " extracting: train/red_shirt/00000054_jpg.rf.f823087bbdd01a3a0b4929eb80a41995.jpg  \n",
            " extracting: train/red_shirt/00000055_jpg.rf.d304cd508fd4c9cd1e2568527820e706.jpg  \n",
            " extracting: train/red_shirt/00000056_jpg.rf.c19f91f7e50e5cb046b11b875da28486.jpg  \n",
            " extracting: train/red_shirt/00000057_jpg.rf.5b906e06ec19321f0265cdf8166d3ba9.jpg  \n",
            " extracting: train/red_shirt/00000058_png.rf.a12812a1f5c6fc0235f49257e87cccf1.jpg  \n",
            " extracting: train/red_shirt/00000059_jpg.rf.cc22819f02f4323091a403dda97f7343.jpg  \n",
            " extracting: train/red_shirt/00000060_jpg.rf.949b549fa7eecd2b11dec55ece8eb18b.jpg  \n",
            " extracting: train/red_shirt/00000061_jpeg.rf.b2c499de6851c716b7b680fa140022ff.jpg  \n",
            " extracting: train/red_shirt/00000062_jpg.rf.947e8d2fcf9be0381dbce56d9bece072.jpg  \n",
            " extracting: train/red_shirt/00000063_jpg.rf.9c971b4053ffdbeba4be91af459532a6.jpg  \n",
            " extracting: train/red_shirt/00000064_jpg.rf.57ead3562962375cd6e8e193a2a1bd6a.jpg  \n",
            " extracting: train/red_shirt/00000065_jpg.rf.60bb402edeee8fda1bb3b09f234503ca.jpg  \n",
            " extracting: train/red_shirt/00000066_jpg.rf.bfc3fc72cd45199ff509e8bdcbf8f046.jpg  \n",
            " extracting: train/red_shirt/00000068_jpg.rf.19964e1710a322a8be05b828410d6caf.jpg  \n",
            " extracting: train/red_shirt/00000072_jpg.rf.1546c153128668d3d65cc8fc5583f3d3.jpg  \n",
            " extracting: train/red_shirt/00000073_jpg.rf.30b31474aa777bf7cfa62e1f73c0d28a.jpg  \n",
            " extracting: train/red_shirt/00000075_jpg.rf.8684e29d0061e6dfe4fb8a67a640b4b1.jpg  \n",
            " extracting: train/red_shirt/00000076_png.rf.903430452aba27bb0e2149cfaa39c970.jpg  \n",
            " extracting: train/red_shirt/00000077_jpg.rf.5270a791c4799c99f1538f43dca008df.jpg  \n",
            " extracting: train/red_shirt/00000078_jpg.rf.84a850695906eabd47e5af686bc395cf.jpg  \n",
            " extracting: train/red_shirt/00000079_jpg.rf.6d5eaf85a019ea8a3e6f253f448c25eb.jpg  \n",
            " extracting: train/red_shirt/00000080_jpg.rf.b2bec42ea5d2da2ffcf917e6c6a21082.jpg  \n",
            " extracting: train/red_shirt/00000081_png.rf.f05c0eb153a37ce3a7323171d82987b5.jpg  \n",
            " extracting: train/red_shirt/00000084_jpg.rf.a23bdfddfb0612ddff46e1e17d71193f.jpg  \n",
            " extracting: train/red_shirt/00000085_jpg.rf.24ea680a21cb8794abff0a3d03d29bb9.jpg  \n",
            " extracting: train/red_shirt/00000086_jpeg.rf.fc3b16261ff8d98fabf8e4a729e1f1e3.jpg  \n",
            " extracting: train/red_shirt/00000088_jpeg.rf.ec2799102ed8766a93cf64bddd5a5c49.jpg  \n",
            " extracting: train/red_shirt/00000089_jpg.rf.ca36e7e1f3e43d31973d39e065fcaada.jpg  \n",
            " extracting: train/red_shirt/00000092_png.rf.e3ac5da987da893da8c2b0070fe84a4c.jpg  \n",
            " extracting: train/red_shirt/00000093_jpg.rf.6315c552225728586b5f82ba948ef12b.jpg  \n",
            " extracting: train/red_shirt/00000094_jpg.rf.7cab883837be9dcd5cc623ac2d0b4e85.jpg  \n",
            " extracting: train/red_shirt/00000095_jpg.rf.5cdd8921e46ce194daba7e2bb4499e86.jpg  \n",
            " extracting: train/red_shirt/00000097_jpg.rf.d98f184a9342cd74c9c379a77faecf16.jpg  \n",
            " extracting: train/red_shirt/00000098_jpg.rf.1f5a91afee27a6d9d5896a84fc9933e4.jpg  \n",
            " extracting: train/red_shirt/00000102_jpg.rf.3ea30d02427f3c537883f50eb9a5214d.jpg  \n",
            " extracting: train/red_shirt/00000104_jpeg.rf.a8631b2d8f9a3d07bd83ebdaa72d5c29.jpg  \n",
            " extracting: train/red_shirt/00000106_jpg.rf.66df28a90135f60e98f0c84bd8824a75.jpg  \n",
            " extracting: train/red_shirt/00000107_jpg.rf.cf172ba1f160e91b06cc84bafe08c8ef.jpg  \n",
            " extracting: train/red_shirt/00000108_jpg.rf.d92e2eda443da4303cfa1360414ed4f2.jpg  \n",
            " extracting: train/red_shirt/00000109_jpeg.rf.f1316f925b762f09f6751014e956e42f.jpg  \n",
            " extracting: train/red_shirt/00000110_jpg.rf.0e46e92be1d60e5d9fd04261f4a8e207.jpg  \n",
            " extracting: train/red_shirt/00000114_jpg.rf.01d3bcb8684559d8eb268428c24b3741.jpg  \n",
            " extracting: train/red_shirt/00000116_jpg.rf.dd2c4649f6e7146752aaf2c5087d9eb4.jpg  \n",
            " extracting: train/red_shirt/00000122_png.rf.2d83f84fcd4d858afcb960e4f50965fa.jpg  \n",
            " extracting: train/red_shirt/00000123_jpg.rf.d75e87b7782ab4e86d2d448166b0b12c.jpg  \n",
            " extracting: train/red_shirt/00000125_jpg.rf.1434a882f9080e2aef6cb49a4986d45b.jpg  \n",
            " extracting: train/red_shirt/00000126_jpg.rf.090c5dc0925bbba14af98165680e9957.jpg  \n",
            " extracting: train/red_shirt/00000127_jpg.rf.2c42ab1dca551146f4871da1b53bb290.jpg  \n",
            " extracting: train/red_shirt/00000129_jpg.rf.2ee4f8bb5c14045536f2fd35845632af.jpg  \n",
            " extracting: train/red_shirt/00000131_jpg.rf.cbb9b10c4e58f625687e4b258fe21fdd.jpg  \n",
            " extracting: train/red_shirt/00000132_jpg.rf.2bea88badf4447e1ea52d5d368f581fb.jpg  \n",
            " extracting: train/red_shirt/00000133_jpg.rf.7f6abc0f8813e07cac875ca8f642d82a.jpg  \n",
            " extracting: train/red_shirt/00000134_jpg.rf.bef81ccfd65de769f892225ca56d7241.jpg  \n",
            " extracting: train/red_shirt/00000135_jpg.rf.9efc34e926761a83e61a03b5faf03946.jpg  \n",
            " extracting: train/red_shirt/00000136_jpg.rf.48091d71430d719ef7da332ccff0d1e2.jpg  \n",
            " extracting: train/red_shirt/00000137_jpg.rf.f12f31540e51aa26b7a433255aca3a4e.jpg  \n",
            " extracting: train/red_shirt/00000138_jpg.rf.46d5845ea35d3e3f9fcfd81869a6aae3.jpg  \n",
            " extracting: train/red_shirt/00000140_jpg.rf.b90ffeb4d3255fd75185ea4d4cf16129.jpg  \n",
            " extracting: train/red_shirt/00000142_jpg.rf.f44e60b7ef46fd3571a0b5d5ed6c6981.jpg  \n",
            " extracting: train/red_shirt/00000143_jpg.rf.d139c93c6ce7f9e7803a4bbbdaba12f4.jpg  \n",
            " extracting: train/red_shirt/00000145_jpg.rf.304348b8643a943a4f99d0de8827d376.jpg  \n",
            " extracting: train/red_shirt/00000147_jpg.rf.7cb70abfae05993c5a26b5d8fa88a503.jpg  \n",
            " extracting: train/red_shirt/00000149_jpg.rf.c8b9163c5db51f452897aed03a703ab9.jpg  \n",
            " extracting: train/red_shirt/00000150_jpg.rf.cf25f992e1dc9f8ee8b1f03ab3e9d945.jpg  \n",
            " extracting: train/red_shirt/00000151_jpg.rf.d07ff93dc6c39bf603725861894a4bb9.jpg  \n",
            " extracting: train/red_shirt/00000152_png.rf.f35eb1e326b709c7efc99ffee52bd6f8.jpg  \n",
            " extracting: train/red_shirt/00000154_jpg.rf.9cff79ea87db0b74006ef8e4c9ec6d27.jpg  \n",
            " extracting: train/red_shirt/00000155_jpg.rf.284c38027dd9b5d8ddfed06a01535fdf.jpg  \n",
            " extracting: train/red_shirt/00000157_jpg.rf.45243f60ec98747f7bddb8b1ba585f19.jpg  \n",
            " extracting: train/red_shirt/00000159_jpg.rf.823ed16c6a6b29c8cfd8493766be3b6f.jpg  \n",
            " extracting: train/red_shirt/00000160_jpg.rf.8e667970c66bbc3782d268238688e8f0.jpg  \n",
            " extracting: train/red_shirt/00000163_jpg.rf.4108283aaeba4a1b75f78433e282fd28.jpg  \n",
            " extracting: train/red_shirt/00000164_jpg.rf.be2a1ce6581d58eaa867292acfa97578.jpg  \n",
            " extracting: train/red_shirt/00000165_jpg.rf.8cf08d7f5afd806953a6bcef2d8d435d.jpg  \n",
            " extracting: train/red_shirt/00000167_jpg.rf.e903d4948844330090c5915c069d12e9.jpg  \n",
            " extracting: train/red_shirt/00000171_jpg.rf.1ed32ff92b1d9c3c5d881fc1b3b5ebd1.jpg  \n",
            " extracting: train/red_shirt/00000172_jpg.rf.042fa7870cb5f2d302a71f31c3eb423a.jpg  \n",
            " extracting: train/red_shirt/00000173_jpeg.rf.e907fba6c01d67a72fb4fe5b5b1b49ef.jpg  \n",
            " extracting: train/red_shirt/00000174_jpg.rf.08446c3ebdbe257d54db93cd4c9f42e7.jpg  \n",
            " extracting: train/red_shirt/00000175_jpg.rf.92eb6af2ae8b5a925f0f6c3425ebcc66.jpg  \n",
            " extracting: train/red_shirt/00000176_jpg.rf.ceb9ee7985dde33c0ac366a0f4d274a2.jpg  \n",
            " extracting: train/red_shirt/00000177_jpg.rf.a4ed4c6cb625b137bc4211468b2b7d12.jpg  \n",
            " extracting: train/red_shirt/00000178_jpg.rf.f3acd09bf50f2c8aafe0b5807b76b30c.jpg  \n",
            " extracting: train/red_shirt/00000179_jpeg.rf.7bbab5e2a28d6f42caa2b66f0f6c53fd.jpg  \n",
            " extracting: train/red_shirt/00000180_jpg.rf.f1afef67d4e05a486c44db12da6266d8.jpg  \n",
            " extracting: train/red_shirt/00000181_jpg.rf.7ef197077d78f15317501e40fc9a660f.jpg  \n",
            " extracting: train/red_shirt/00000182_jpg.rf.d704e468ee7960e753084607086172e0.jpg  \n",
            " extracting: train/red_shirt/00000183_jpg.rf.59f3712a3366655cc71f78cc4ece802f.jpg  \n",
            " extracting: train/red_shirt/00000184_jpg.rf.35b1783c73c0d86973e87ba95e5bdc2a.jpg  \n",
            " extracting: train/red_shirt/00000186_jpeg.rf.acb05b2e9cee81d0771911fe7efb1984.jpg  \n",
            " extracting: train/red_shirt/00000187_jpg.rf.c5e9ac297fb90459c9415d02b2e91b8a.jpg  \n",
            " extracting: train/red_shirt/00000189_jpeg.rf.94a2540c7988ae50029d3779ee78d59a.jpg  \n",
            " extracting: train/red_shirt/00000190_jpg.rf.c81000b28fa53d0e1116391c8abd0ae5.jpg  \n",
            " extracting: train/red_shirt/00000193_jpeg.rf.1eba4f9cc0376d20f37b5520ac68391a.jpg  \n",
            " extracting: train/red_shirt/00000195_jpg.rf.69651f2c30edb8b788fc4f806db66588.jpg  \n",
            " extracting: train/red_shirt/00000199_jpg.rf.879a721325e6069728cf94ac984b1cc5.jpg  \n",
            " extracting: train/red_shirt/00000200_jpg.rf.a0f25b999865008a1711abf628a7979b.jpg  \n",
            " extracting: train/red_shirt/00000202_jpg.rf.5c3bf0c0f048cacc1417441d6793a103.jpg  \n",
            " extracting: train/red_shirt/00000203_jpg.rf.d27303a78ca40af559a69c3d70cf6033.jpg  \n",
            " extracting: train/red_shirt/00000206_jpg.rf.bb048d561dd525cc5d12c5bb714a0c85.jpg  \n",
            " extracting: train/red_shirt/00000207_jpg.rf.d755cc11e3ec18573d75dfb6c38f2fcd.jpg  \n",
            " extracting: train/red_shirt/00000208_jpg.rf.b068fecdf9187b4884286a89c3633916.jpg  \n",
            " extracting: train/red_shirt/00000209_jpg.rf.b963ce6e745cb44a141cebd64f424781.jpg  \n",
            " extracting: train/red_shirt/00000210_jpg.rf.2880dda0261abfc3a1c5510a2e9e32b9.jpg  \n",
            " extracting: train/red_shirt/00000211_jpg.rf.4182a115663ff9903329d72d2d63c445.jpg  \n",
            " extracting: train/red_shirt/00000212_jpg.rf.8f123202c5e47b06dcac35b5b6c10607.jpg  \n",
            " extracting: train/red_shirt/00000213_jpg.rf.e49b3cb7e7a67a9efaa6ae6685e7ce06.jpg  \n",
            " extracting: train/red_shirt/00000214_jpeg.rf.af10ba2bfd025756f0e73f7b9e1094ba.jpg  \n",
            " extracting: train/red_shirt/00000217_jpeg.rf.d7ef965c363478e5633672c9ec0c67ac.jpg  \n",
            " extracting: train/red_shirt/00000218_jpg.rf.06308fc507d61e9c18bdb422c62e386b.jpg  \n",
            " extracting: train/red_shirt/00000221_jpg.rf.0bc2e7c4e24b9dc1986da7a69156ae80.jpg  \n",
            " extracting: train/red_shirt/00000223_jpeg.rf.cbefd43687798b27bd5f7541cfe4fa8f.jpg  \n",
            " extracting: train/red_shirt/00000224_jpg.rf.eef9b5276e6c0e8fc6b94cfbab86279a.jpg  \n",
            " extracting: train/red_shirt/00000227_jpg.rf.97bad84804a56bbb199b2295a5b1d552.jpg  \n",
            " extracting: train/red_shirt/00000228_jpg.rf.5e2357f5a2e6dc0fc70bb29bca11bc29.jpg  \n",
            " extracting: train/red_shirt/00000229_jpg.rf.d60a7669e29b443434eb2f22c7d9496e.jpg  \n",
            " extracting: train/red_shirt/00000230_jpg.rf.b50f8e2ad73158fd830668da69755131.jpg  \n",
            " extracting: train/red_shirt/00000232_jpg.rf.ab25e203503d61142253a6f86318f9fa.jpg  \n",
            " extracting: train/red_shirt/00000233_jpg.rf.f0ad2b0160aa6229f6535ab2f6525a56.jpg  \n",
            " extracting: train/red_shirt/00000239_jpg.rf.5db450e75513323d27e7d81ee283e3c5.jpg  \n",
            " extracting: train/red_shirt/00000240_jpg.rf.6fa9f98e8b1c9226712dda951a3517ed.jpg  \n",
            " extracting: train/red_shirt/00000242_jpg.rf.0ce319d120c9682b125a0a9b47be0e5f.jpg  \n",
            " extracting: train/red_shirt/00000243_jpg.rf.bf701cc6d0a4144fcafcebdcffe95f48.jpg  \n",
            " extracting: train/red_shirt/00000244_jpeg.rf.df2a49c49857b687b1cea86c88d24218.jpg  \n",
            " extracting: train/red_shirt/00000245_jpeg.rf.b1eee26a5249c9237e79c600b2c3b6a5.jpg  \n",
            " extracting: train/red_shirt/00000247_jpg.rf.de7c7eef45279d795771cdd9d81ba397.jpg  \n",
            " extracting: train/red_shirt/00000249_jpg.rf.e2cce068c71028ba535a4d177134bdef.jpg  \n",
            " extracting: train/red_shirt/00000251_jpg.rf.43d6ca193ec11058358f36b4d798612c.jpg  \n",
            " extracting: train/red_shirt/00000254_png.rf.172879834fd3a9dd7f4b67b6fa7b9dad.jpg  \n",
            " extracting: train/red_shirt/00000255_jpg.rf.d85c52dbc4fccb9a862b1683174cfa1a.jpg  \n",
            " extracting: train/red_shirt/00000259_jpg.rf.a4135a26a8a1118956ce1fa2aa306e64.jpg  \n",
            " extracting: train/red_shirt/00000265_png.rf.956f4ef2a17eca06b35dc27f566908a1.jpg  \n",
            " extracting: train/red_shirt/00000266_jpg.rf.5427da8ace9d59ca676b20a1ab108da1.jpg  \n",
            " extracting: train/red_shirt/00000267_jpg.rf.b02cde1883591aa07cdff8623e2e9531.jpg  \n",
            " extracting: train/red_shirt/00000268_jpg.rf.06ccb3d4dd5acd073333877a40d96a8e.jpg  \n",
            " extracting: train/red_shirt/00000270_jpg.rf.3aa64d8b55f93151e60c51357f502948.jpg  \n",
            " extracting: train/red_shirt/00000272_jpg.rf.632dd3abcc90b6f4614956ce01834754.jpg  \n",
            " extracting: train/red_shirt/00000273_jpg.rf.17b421247eb50d90f83b7bdf41e318d9.jpg  \n",
            " extracting: train/red_shirt/00000274_jpg.rf.5ed297a61a5349f517031693874d16d6.jpg  \n",
            " extracting: train/red_shirt/00000275_jpg.rf.9e50ba1f00780dfa2249e44e56d965aa.jpg  \n",
            " extracting: train/red_shirt/00000278_jpg.rf.128b53e270cd009b42f4898aca9884c1.jpg  \n",
            " extracting: train/red_shirt/00000279_jpg.rf.4b6e1c325d642f4dc297f75c99a0d9f6.jpg  \n",
            " extracting: train/red_shirt/00000280_jpg.rf.baed3da2497e2cd2e4734ac9befc1ffc.jpg  \n",
            " extracting: train/red_shirt/00000281_jpg.rf.1dc4b568f0210f75e9767fe2cd80b5a7.jpg  \n",
            " extracting: train/red_shirt/00000285_jpg.rf.56986210169c848285e471208e34bef8.jpg  \n",
            " extracting: train/red_shirt/00000288_jpg.rf.209c12f884d848a4669b1d7aa63a8f3d.jpg  \n",
            " extracting: train/red_shirt/00000289_jpg.rf.076e807ae0d903c5360ef0dd8fc997b6.jpg  \n",
            " extracting: train/red_shirt/00000290_jpg.rf.ab8b52cee8bb7a21d73a5d1a126b973a.jpg  \n",
            " extracting: train/red_shirt/00000294_jpg.rf.8a1bad77d1b729b5c39ea62d55533555.jpg  \n",
            " extracting: train/red_shirt/00000296_jpeg.rf.7dba05886dd81f3e09cf6b294cb4476c.jpg  \n",
            " extracting: train/red_shirt/00000298_jpg.rf.1947991d6e8cfa26a7e16a0c6c1427c2.jpg  \n",
            " extracting: train/red_shirt/00000301_jpg.rf.21164f5088e210807474f6525355ea80.jpg  \n",
            " extracting: train/red_shirt/00000302_jpeg.rf.6447336d6348e108b66727bfa698f14d.jpg  \n",
            " extracting: train/red_shirt/00000304_jpg.rf.cd2b1ce0df8d267b9a65c8ccbbb16ab1.jpg  \n",
            " extracting: train/red_shirt/00000305_jpg.rf.0aa4c13eb3d494ca7fce4c94a67fe89c.jpg  \n",
            " extracting: train/red_shirt/00000306_jpeg.rf.014e3cf8214a98b2635ee0b32e6db644.jpg  \n",
            " extracting: train/red_shirt/00000307_jpg.rf.7c5bc658e1f0a18500f34f2a282a95ea.jpg  \n",
            " extracting: train/red_shirt/00000310_jpg.rf.279364ab8b1d8f8088b8a5bfbf20cd75.jpg  \n",
            " extracting: train/red_shirt/00000311_jpg.rf.bd89865eab5279c0c88798e7ac7a1c8b.jpg  \n",
            " extracting: train/red_shirt/00000312_jpg.rf.1fa23d66791b6387777826cce83c5cf5.jpg  \n",
            " extracting: train/red_shirt/00000313_jpg.rf.cfb935d1e998c1d42b012bba07b71942.jpg  \n",
            " extracting: train/red_shirt/00000314_jpg.rf.48477ff7547d22b8949264b63eba0278.jpg  \n",
            " extracting: train/red_shirt/00000315_jpg.rf.6c430023d71abe7cdc620f6ea14b9255.jpg  \n",
            " extracting: train/red_shirt/00000317_jpg.rf.286b08ed7f47a5989843788bef177813.jpg  \n",
            " extracting: train/red_shirt/00000319_jpg.rf.dd38bfcdf1ba855dc7054327f49238cd.jpg  \n",
            " extracting: train/red_shirt/00000320_jpg.rf.be418d8730c6704762631bf6e8539c98.jpg  \n",
            " extracting: train/red_shirt/00000323_jpg.rf.1675b26ffdc019774926f0d4047023a6.jpg  \n",
            " extracting: train/red_shirt/00000325_jpg.rf.e4282e8aa883835b3acdfc933eec8756.jpg  \n",
            " extracting: train/red_shirt/00000330_jpg.rf.5b1b486f71d7572d480398ac6fc1e3d2.jpg  \n",
            " extracting: train/red_shirt/00000333_jpg.rf.cc0670e4e43946dfcd3b9a292a97bfb8.jpg  \n",
            " extracting: train/red_shirt/00000336_jpg.rf.36aaafb47837d0fbc54bd359fc834271.jpg  \n",
            " extracting: train/red_shirt/00000337_jpg.rf.bed79f2e05414c5cd07f2d0a3e707f7f.jpg  \n",
            " extracting: train/red_shirt/00000344_jpg.rf.50cdc3cc6c00433ae6859b1763d0d5b6.jpg  \n",
            " extracting: train/red_shirt/00000345_jpg.rf.1b5a19911897e665eb7b58f1683f0724.jpg  \n",
            " extracting: train/red_shirt/00000346_jpeg.rf.5028fcf2fa40f54c81cf53b9e57b0823.jpg  \n",
            " extracting: train/red_shirt/00000351_jpg.rf.2dd2ff7e678bfebddce853d6e1e63ce2.jpg  \n",
            " extracting: train/red_shirt/00000354_jpg.rf.a02adab66af183787e982ac87bdf9670.jpg  \n",
            " extracting: train/red_shirt/00000356_jpg.rf.d9f95bd4e7b5d01a3e33944630b7f458.jpg  \n",
            " extracting: train/red_shirt/00000357_jpg.rf.96845cb648ff0ceb373d20fc9b855ea7.jpg  \n",
            " extracting: train/red_shirt/00000358_jpg.rf.15208a80695cf5483e81f56de91cc04c.jpg  \n",
            " extracting: train/red_shirt/00000363_jpg.rf.c3ab13909b1592297b752639c2aa8f79.jpg  \n",
            " extracting: train/red_shirt/00000367_jpg.rf.2ed686d12917926c1a35366c51af1da8.jpg  \n",
            " extracting: train/red_shirt/00000368_jpg.rf.0ccae5aa6dcaaed61260f304bd968d53.jpg  \n",
            " extracting: train/red_shirt/00000369_jpg.rf.1279bb71f84d94f0379a94815b3ee658.jpg  \n",
            " extracting: train/red_shirt/00000375_jpg.rf.40e017b39f066ac9f4c2149846deefbe.jpg  \n",
            " extracting: train/red_shirt/00000376_jpg.rf.e8b16c05bc9f697e7ee6011bbd191adf.jpg  \n",
            " extracting: train/red_shirt/00000382_jpg.rf.d86e080c9ae1670fac9d122bdc3277f4.jpg  \n",
            " extracting: train/red_shirt/00000383_jpg.rf.14ade608fe8f03d715277a1d591d5eca.jpg  \n",
            " extracting: train/red_shirt/00000386_jpeg.rf.070bb21ae990b484bca8f52e07f2432d.jpg  \n",
            " extracting: train/red_shirt/00000388_jpg.rf.e8ff033c239ba745ebed15380b79fba2.jpg  \n",
            "   creating: valid/\n",
            "   creating: valid/black_jeans/\n",
            " extracting: valid/black_jeans/00000004_jpg.rf.9f51309d2c1ef37f0733ccc22fdef577.jpg  \n",
            " extracting: valid/black_jeans/00000007_jpg.rf.c40da934355b3935a68adcb34fb43728.jpg  \n",
            " extracting: valid/black_jeans/00000008_jpeg.rf.2c3d67f3a205fe6c2260bc8d1bb63871.jpg  \n",
            " extracting: valid/black_jeans/00000026_jpg.rf.a4632441b5459912341523f205b13bec.jpg  \n",
            " extracting: valid/black_jeans/00000031_jpeg.rf.71fa912232ecfcb9a2721faedf118a5b.jpg  \n",
            " extracting: valid/black_jeans/00000033_jpeg.rf.a1df6fb6ead2f07703c519089a7b8541.jpg  \n",
            " extracting: valid/black_jeans/00000036_jpg.rf.790116bd27c6de30e08fcc530be09dcd.jpg  \n",
            " extracting: valid/black_jeans/00000039_jpg.rf.df7a529bbfa3aee69c2c0cd7438d0f80.jpg  \n",
            " extracting: valid/black_jeans/00000054_jpg.rf.576583abd74f009ecb84d441038e36db.jpg  \n",
            " extracting: valid/black_jeans/00000056_jpg.rf.d623503ab121bc85793f1f5394316de5.jpg  \n",
            " extracting: valid/black_jeans/00000072_jpg.rf.73f46af5658a13b868dddc37abed6f70.jpg  \n",
            " extracting: valid/black_jeans/00000074_jpg.rf.a42315d1c36ebbd8728a5c6aeea7cc38.jpg  \n",
            " extracting: valid/black_jeans/00000075_jpg.rf.94c3f024e79010fa82e515e2793e7cc8.jpg  \n",
            " extracting: valid/black_jeans/00000079_png.rf.829c6454a6d0393c6d93167c5fc6d17b.jpg  \n",
            " extracting: valid/black_jeans/00000084_jpg.rf.74e741992d65f2df99a2cb9772bf3ba7.jpg  \n",
            " extracting: valid/black_jeans/00000095_jpg.rf.5e2f90ac528eb75d58577029f249f753.jpg  \n",
            " extracting: valid/black_jeans/00000103_jpg.rf.7d7b9bfa5042a6068d8e692a549292d5.jpg  \n",
            " extracting: valid/black_jeans/00000105_jpg.rf.5b33496734535d541b8960ea95c9d731.jpg  \n",
            " extracting: valid/black_jeans/00000106_jpeg.rf.4f9cdbd01b2178110239c88d1b25565a.jpg  \n",
            " extracting: valid/black_jeans/00000117_jpg.rf.bd05f164b42fba5b99fd6146451f498e.jpg  \n",
            " extracting: valid/black_jeans/00000123_jpg.rf.e9f0fdf5d0dee1bfac98ded6cbdafdaa.jpg  \n",
            " extracting: valid/black_jeans/00000131_jpg.rf.807921db1375a5e0ffd664e165e5f142.jpg  \n",
            " extracting: valid/black_jeans/00000132_jpg.rf.782d58d3fee1c09bd198be100b842d0b.jpg  \n",
            " extracting: valid/black_jeans/00000144_jpg.rf.65282525b66e599f1bcf31c99f9adbb0.jpg  \n",
            " extracting: valid/black_jeans/00000145_jpg.rf.3ca90f69d26139f8f0821905f2005f9a.jpg  \n",
            " extracting: valid/black_jeans/00000146_jpg.rf.97e2eb9a3c162c305b9364e3d55cbf6a.jpg  \n",
            " extracting: valid/black_jeans/00000156_jpg.rf.0082b2afd14515d1217928440f56692d.jpg  \n",
            " extracting: valid/black_jeans/00000157_jpg.rf.2a53916bf4cd9af6917c289c73950a8b.jpg  \n",
            " extracting: valid/black_jeans/00000163_jpg.rf.516c27290c896bc0f27030f475d36561.jpg  \n",
            " extracting: valid/black_jeans/00000165_jpeg.rf.d6e7480f1e1a42db5e89165c91a627ec.jpg  \n",
            " extracting: valid/black_jeans/00000168_jpg.rf.a61e7b80e098789954ede6ed65f6f318.jpg  \n",
            " extracting: valid/black_jeans/00000169_png.rf.47d3674723306ff13749d4c984dfd775.jpg  \n",
            " extracting: valid/black_jeans/00000172_jpg.rf.79f07b735b7c47fa3d56843cce97e590.jpg  \n",
            " extracting: valid/black_jeans/00000173_jpg.rf.642877740e63595d14dc70d8cccb8bd0.jpg  \n",
            " extracting: valid/black_jeans/00000176_jpg.rf.b581c3be60b201ce9bd8b91049af950d.jpg  \n",
            " extracting: valid/black_jeans/00000193_jpg.rf.9afcbc7fc63ac1f8b9a44a04261501f0.jpg  \n",
            " extracting: valid/black_jeans/00000196_jpg.rf.3138f994a195555362669b5be8a7eb38.jpg  \n",
            " extracting: valid/black_jeans/00000205_jpg.rf.3a21147bd39983b9ddfa26dcef58c442.jpg  \n",
            " extracting: valid/black_jeans/00000206_jpg.rf.63e165e36bc1673c32cf45e4bfc777d9.jpg  \n",
            " extracting: valid/black_jeans/00000234_jpeg.rf.2509ada02a463604b67a2fa74ca51833.jpg  \n",
            " extracting: valid/black_jeans/00000246_jpeg.rf.aa987a78b86eb4a88dd0403a47b499d0.jpg  \n",
            " extracting: valid/black_jeans/00000250_jpg.rf.1822b3c39afa5f9bd7b36ce1bc48aa03.jpg  \n",
            " extracting: valid/black_jeans/00000252_jpg.rf.62b7b7f6050fc757ccd60b87664307ae.jpg  \n",
            " extracting: valid/black_jeans/00000260_jpg.rf.f3f04336957ceec03cf189c39dde287c.jpg  \n",
            " extracting: valid/black_jeans/00000261_jpeg.rf.387490f750ffc0190ac34d83c0ee1776.jpg  \n",
            " extracting: valid/black_jeans/00000276_jpg.rf.0adf57772b2cb02610de3c92efcbcf0a.jpg  \n",
            " extracting: valid/black_jeans/00000279_jpeg.rf.b6eedb6dd8aaea65aba67806a449230e.jpg  \n",
            " extracting: valid/black_jeans/00000292_jpg.rf.c9c90f92018ff06e40a6d9f0d6ce9017.jpg  \n",
            " extracting: valid/black_jeans/00000302_jpeg.rf.5a93364869cf3e11c54fb089e8964912.jpg  \n",
            " extracting: valid/black_jeans/00000305_jpg.rf.5eb9441f1c1f8437f197bda79b8d2f48.jpg  \n",
            " extracting: valid/black_jeans/00000309_jpg.rf.8698e61c84593caf32291de917474e21.jpg  \n",
            " extracting: valid/black_jeans/00000315_jpeg.rf.bfc203fd01dc752ba7720b7f2ce917f6.jpg  \n",
            " extracting: valid/black_jeans/00000317_jpeg.rf.d63b6e9aec03135665d07c961935a032.jpg  \n",
            " extracting: valid/black_jeans/00000319_jpg.rf.37e561841bce715761f274fe6a94f581.jpg  \n",
            " extracting: valid/black_jeans/00000326_jpg.rf.7c89652ff6e31d0d8bea58406bd2486e.jpg  \n",
            " extracting: valid/black_jeans/00000328_jpg.rf.ef348fe60361569369c6ab9ac8cb185e.jpg  \n",
            " extracting: valid/black_jeans/00000330_jpg.rf.8f2cd2ed654e829b3aad82147b228bbf.jpg  \n",
            " extracting: valid/black_jeans/00000334_jpg.rf.0807688fc00aae38313bbe8d21f95668.jpg  \n",
            " extracting: valid/black_jeans/00000341_jpeg.rf.3990a2598467d36029ea7e9fe6c0aeb7.jpg  \n",
            " extracting: valid/black_jeans/00000343_JPG.rf.ea369849299ca2f9f1169c0d9f7788e7.jpg  \n",
            " extracting: valid/black_jeans/00000350_jpg.rf.4c5bd0bda8be03fc769a1f724ded3c8d.jpg  \n",
            "   creating: valid/blue_dress/\n",
            " extracting: valid/blue_dress/00000001_jpg.rf.2a05c3ca168d2d6b252950737218f09b.jpg  \n",
            " extracting: valid/blue_dress/00000006_jpeg.rf.2bead7228470abc364b68d9465aa72cf.jpg  \n",
            " extracting: valid/blue_dress/00000009_jpg.rf.d9f5c4ca527329882820f743681a92a1.jpg  \n",
            " extracting: valid/blue_dress/00000017_jpg.rf.234305b42a8d7c0a17693abaed8475c5.jpg  \n",
            " extracting: valid/blue_dress/00000027_jpg.rf.7d6a9e9f0055e623a34bc0e54eb1f60f.jpg  \n",
            " extracting: valid/blue_dress/00000036_jpg.rf.2547ca6af4d903a74800bf6262408d95.jpg  \n",
            " extracting: valid/blue_dress/00000037_jpg.rf.9ce995062be7649431a0c462db23914e.jpg  \n",
            " extracting: valid/blue_dress/00000069_jpg.rf.a60a6474f32334dc0bcd6571ad41aa26.jpg  \n",
            " extracting: valid/blue_dress/00000078_jpeg.rf.30fd3cb3e67711a5b35d58e08dd09ad9.jpg  \n",
            " extracting: valid/blue_dress/00000081_jpg.rf.0ea5225ea8ff815cd5d392311548f65c.jpg  \n",
            " extracting: valid/blue_dress/00000083_jpg.rf.83b2e9f3f1f4ed89bd22a40d93dccfd6.jpg  \n",
            " extracting: valid/blue_dress/00000084_jpg.rf.ad1bba9781e15f89151576c78056b4e3.jpg  \n",
            " extracting: valid/blue_dress/00000089_jpg.rf.dbcc1f0884892a6cd28e8517ebdf45c3.jpg  \n",
            " extracting: valid/blue_dress/00000093_jpg.rf.15df70c91e2b27aba71b98249de769b5.jpg  \n",
            " extracting: valid/blue_dress/00000103_jpg.rf.09a73b39e79802ab035aa8e19d9a926c.jpg  \n",
            " extracting: valid/blue_dress/00000106_jpg.rf.794816d4df5354a49569dbcbd94f194a.jpg  \n",
            " extracting: valid/blue_dress/00000107_jpg.rf.c2d486336a36006285e39112278b950f.jpg  \n",
            " extracting: valid/blue_dress/00000108_jpg.rf.148ed565d705128d1d87c51cfbd85ac3.jpg  \n",
            " extracting: valid/blue_dress/00000112_jpg.rf.d2ac2aceb4437b9a1afcbe74ae5acbcc.jpg  \n",
            " extracting: valid/blue_dress/00000113_jpg.rf.75032141cba8995781b166f79a2c5a85.jpg  \n",
            " extracting: valid/blue_dress/00000122_jpg.rf.bf8b76b72d1135e57525d6a10decb2af.jpg  \n",
            " extracting: valid/blue_dress/00000128_jpeg.rf.4257ab414bfb58dcccdde5a830f36bc2.jpg  \n",
            " extracting: valid/blue_dress/00000131_jpeg.rf.9e2447e8b1c4f7728434e0a2c15886b0.jpg  \n",
            " extracting: valid/blue_dress/00000133_jpg.rf.49a194d79fbc1c8886e9507a226aef05.jpg  \n",
            " extracting: valid/blue_dress/00000136_jpg.rf.f999c65d65b03662148dae08934ddeb2.jpg  \n",
            " extracting: valid/blue_dress/00000139_jpg.rf.95a7d26a406080ea0dfe368541dadc07.jpg  \n",
            " extracting: valid/blue_dress/00000142_jpg.rf.3c1a9ea5cef99435f947df03fe6c1154.jpg  \n",
            " extracting: valid/blue_dress/00000157_jpg.rf.77d7459199d65446aed068d42333865f.jpg  \n",
            " extracting: valid/blue_dress/00000160_jpg.rf.5a86ab201a197a23265fa63d9033000a.jpg  \n",
            " extracting: valid/blue_dress/00000175_jpeg.rf.405fd3056f2cd20d297418c6cf569ec3.jpg  \n",
            " extracting: valid/blue_dress/00000181_jpg.rf.b2ea438c9a73c2184261196db2ef8c64.jpg  \n",
            " extracting: valid/blue_dress/00000188_jpg.rf.687c2fdf95bf6c3be0d22e01f4774ebf.jpg  \n",
            " extracting: valid/blue_dress/00000189_jpg.rf.17a6b15bfe40bd714b6c5f07faddc21d.jpg  \n",
            " extracting: valid/blue_dress/00000192_jpg.rf.53c62ae9c1d4102b3dfbc0f26d08f7f2.jpg  \n",
            " extracting: valid/blue_dress/00000204_jpeg.rf.e7b9cb8e79c852cb0fbd6b8eade99e29.jpg  \n",
            " extracting: valid/blue_dress/00000206_jpg.rf.90f26d2b44a17b5130d1b6e33fac6d4d.jpg  \n",
            " extracting: valid/blue_dress/00000207_jpeg.rf.a2b8d565d7233ad1ff7f6725368f1292.jpg  \n",
            " extracting: valid/blue_dress/00000209_jpg.rf.0bcc02625be505eaa51cdeb7e5249d1b.jpg  \n",
            " extracting: valid/blue_dress/00000211_jpg.rf.513380de5df49ead7e484695e2f4c263.jpg  \n",
            " extracting: valid/blue_dress/00000215_jpg.rf.81604316fa58930f197a2571366666d3.jpg  \n",
            " extracting: valid/blue_dress/00000223_jpg.rf.757de069b533885384eaa86e36ecd88e.jpg  \n",
            " extracting: valid/blue_dress/00000225_jpg.rf.e60af4c78516728d753859e8b493d407.jpg  \n",
            " extracting: valid/blue_dress/00000226_jpg.rf.b67103e72b36631f8afb4dbac43fc2bb.jpg  \n",
            " extracting: valid/blue_dress/00000232_jpeg.rf.47c193753179b29690a13dca75ed56b1.jpg  \n",
            " extracting: valid/blue_dress/00000234_jpg.rf.c54ce02df56a630faf88607808d4d94d.jpg  \n",
            " extracting: valid/blue_dress/00000235_jpeg.rf.bb57cec50a4532c82f36f343e6682cc6.jpg  \n",
            " extracting: valid/blue_dress/00000241_jpg.rf.75939d9f48458c5471a3a7e3c5a29e36.jpg  \n",
            " extracting: valid/blue_dress/00000261_jpeg.rf.a8bffedb8014789003734421eacc4599.jpg  \n",
            " extracting: valid/blue_dress/00000271_jpg.rf.ccf38d733679f186c4ae9b6e237b744e.jpg  \n",
            " extracting: valid/blue_dress/00000276_jpeg.rf.03e865b7b06d6848cba186f9c5c8ef04.jpg  \n",
            " extracting: valid/blue_dress/00000277_JPG.rf.b25d1a6448ea0f92a5151e96c8375fb4.jpg  \n",
            " extracting: valid/blue_dress/00000283_jpg.rf.0edd6b4825fb56e051a35a0b2ef02cde.jpg  \n",
            " extracting: valid/blue_dress/00000284_jpg.rf.dab0ae29a8c38d0a122c7dba56ea3003.jpg  \n",
            " extracting: valid/blue_dress/00000285_jpg.rf.6d70b9a8135621300c13b13d3fe89db0.jpg  \n",
            " extracting: valid/blue_dress/00000287_jpg.rf.00d828ae6eee45902e845e828e2c5fb4.jpg  \n",
            " extracting: valid/blue_dress/00000292_jpg.rf.903a76c81abe11e99663cbe7108ba204.jpg  \n",
            " extracting: valid/blue_dress/00000296_jpg.rf.604ec15660000eef19775b5c5695e3b6.jpg  \n",
            " extracting: valid/blue_dress/00000301_jpg.rf.7fee2e6b2a21602b0a067d20e7662645.jpg  \n",
            " extracting: valid/blue_dress/00000305_jpg.rf.31fd5eeb1170149849a666f8f170ab92.jpg  \n",
            " extracting: valid/blue_dress/00000306_jpeg.rf.fdc4296a9c0bac30eaebe2e8fcf99656.jpg  \n",
            " extracting: valid/blue_dress/00000320_jpg.rf.2f9498c92e4d9e42b5282639566eea75.jpg  \n",
            " extracting: valid/blue_dress/00000335_JPG.rf.7ef10b97c4db7320f0ae27671464c723.jpg  \n",
            " extracting: valid/blue_dress/00000344_JPG.rf.c0f149a749fcb9661f429a4d49d396b3.jpg  \n",
            " extracting: valid/blue_dress/00000355_jpg.rf.605025f8f136228c1045c42a5c398e34.jpg  \n",
            " extracting: valid/blue_dress/00000358_jpeg.rf.9ec4f491eba1c9e277dfcac9407f0da5.jpg  \n",
            " extracting: valid/blue_dress/00000361_jpg.rf.3303c56b5fcdf31ed8fd386f7679ee22.jpg  \n",
            " extracting: valid/blue_dress/00000362_jpg.rf.f1109c7ad070fb1c110f5992b26b5d28.jpg  \n",
            " extracting: valid/blue_dress/00000373_jpg.rf.74cc89e5b26fe5e7298053f86809c5f3.jpg  \n",
            " extracting: valid/blue_dress/00000377_jpg.rf.496cd1d9e5a83d8dd39b216b3c1dea2f.jpg  \n",
            " extracting: valid/blue_dress/00000381_jpeg.rf.35107c3b0e1ede50038ef4d15b9584c7.jpg  \n",
            " extracting: valid/blue_dress/00000387_jpg.rf.985df7cba098ae379a418c271b3f2e9e.jpg  \n",
            " extracting: valid/blue_dress/00000394_jpg.rf.c3a4fd76347825788cb06a024e4bedc1.jpg  \n",
            "   creating: valid/blue_jeans/\n",
            " extracting: valid/blue_jeans/00000003_jpg.rf.a7f47b4de616e6cbd9a699d958df3977.jpg  \n",
            " extracting: valid/blue_jeans/00000005_jpg.rf.bc45260a05fa8d4e72ed9dc368f41ea0.jpg  \n",
            " extracting: valid/blue_jeans/00000017_jpg.rf.db4e1e525b10bf87f1d735cd04c3b7f0.jpg  \n",
            " extracting: valid/blue_jeans/00000019_jpg.rf.0eec3c9052bd6bd9da25275349bc3123.jpg  \n",
            " extracting: valid/blue_jeans/00000026_jpeg.rf.7974b95f403802f6fbeb011fca0f57c9.jpg  \n",
            " extracting: valid/blue_jeans/00000027_jpg.rf.3bfaa9be38296ac8fb3be5d12090461c.jpg  \n",
            " extracting: valid/blue_jeans/00000029_jpg.rf.120b506f61a0d6aff64732f8968f6dfc.jpg  \n",
            " extracting: valid/blue_jeans/00000032_jpg.rf.3b60cbd8f6d8a9f31fad53cb521eaaf5.jpg  \n",
            " extracting: valid/blue_jeans/00000035_jpg.rf.5efecc9b57f91c058b0cf69a86b4ea25.jpg  \n",
            " extracting: valid/blue_jeans/00000040_jpg.rf.c5580f75a8a4c9dd75593dfb525e7a9c.jpg  \n",
            " extracting: valid/blue_jeans/00000048_jpeg.rf.e95f9ce488339ebee881b2e52f211cee.jpg  \n",
            " extracting: valid/blue_jeans/00000049_jpg.rf.4816c79c8f0f4662a2ba37bd61413201.jpg  \n",
            " extracting: valid/blue_jeans/00000057_jpg.rf.67ebb6788f0f0d51d308a398feefdf51.jpg  \n",
            " extracting: valid/blue_jeans/00000060_jpeg.rf.0bc9e71cac47c66af5dfca85055e26da.jpg  \n",
            " extracting: valid/blue_jeans/00000061_jpg.rf.45b7914f67ec7abdd1380dca3759f2ef.jpg  \n",
            " extracting: valid/blue_jeans/00000068_jpeg.rf.0bf7a6364e115399f7af1836b1187d54.jpg  \n",
            " extracting: valid/blue_jeans/00000072_jpg.rf.0fd370366a12d56201bc6ec838535ba3.jpg  \n",
            " extracting: valid/blue_jeans/00000077_jpg.rf.2c13a324d827aa0cf0a9ea70f77b8eae.jpg  \n",
            " extracting: valid/blue_jeans/00000078_jpeg.rf.af62a8a430482f60d0b6228074c2cb82.jpg  \n",
            " extracting: valid/blue_jeans/00000083_jpg.rf.f0bad221aa27a1d9b1c16fa54f496ae8.jpg  \n",
            " extracting: valid/blue_jeans/00000087_jpg.rf.0de4b94c848aa013f5aaac3703e0fbfb.jpg  \n",
            " extracting: valid/blue_jeans/00000088_jpg.rf.bbaeb1983bda438e844abfeca8203aaa.jpg  \n",
            " extracting: valid/blue_jeans/00000097_jpg.rf.7e5b2e3299f695a6d893585358ee2eb5.jpg  \n",
            " extracting: valid/blue_jeans/00000098_jpg.rf.35aec6492c3946391e2f7e080d811305.jpg  \n",
            " extracting: valid/blue_jeans/00000099_jpg.rf.b73ae8a884e6df0d3b2c9b3f3102772e.jpg  \n",
            " extracting: valid/blue_jeans/00000100_jpg.rf.e62e187a45f4d4d01c895c75007ddf27.jpg  \n",
            " extracting: valid/blue_jeans/00000106_jpg.rf.640d1e12a0fdf5cf0e81b486f0028f94.jpg  \n",
            " extracting: valid/blue_jeans/00000131_jpg.rf.d9ad4663bd4a4646bbbb836fcc582b67.jpg  \n",
            " extracting: valid/blue_jeans/00000135_jpg.rf.ad260d8aa8e2edc606ee2e822b90961c.jpg  \n",
            " extracting: valid/blue_jeans/00000137_jpeg.rf.93d832f0d222d584ef4e503a5469ebb6.jpg  \n",
            " extracting: valid/blue_jeans/00000145_jpeg.rf.c2cacb57d872cdddd9db88de8ee5ca69.jpg  \n",
            " extracting: valid/blue_jeans/00000146_jpg.rf.c33a796de6b1909270535b86b59097e2.jpg  \n",
            " extracting: valid/blue_jeans/00000150_jpg.rf.72347e10c60a41ad2e8238b6c267dccc.jpg  \n",
            " extracting: valid/blue_jeans/00000154_jpg.rf.c5aa557f501d78ee600dae3dba5fb4d0.jpg  \n",
            " extracting: valid/blue_jeans/00000159_jpg.rf.2eb3f8af829d31118f74b8bbcc1b42b5.jpg  \n",
            " extracting: valid/blue_jeans/00000160_jpg.rf.a432def61f907f1d81146a19ea47e2a0.jpg  \n",
            " extracting: valid/blue_jeans/00000165_png.rf.607d90f625e85793623e2dfd2167959b.jpg  \n",
            " extracting: valid/blue_jeans/00000169_jpg.rf.f47eebb3dcb63de1db50ac997ced99c9.jpg  \n",
            " extracting: valid/blue_jeans/00000171_png.rf.3bd9e212bca6290dff9f2948df19abb2.jpg  \n",
            " extracting: valid/blue_jeans/00000179_jpeg.rf.82fca5fa6cf42614c3edfd37a1ca86f6.jpg  \n",
            " extracting: valid/blue_jeans/00000185_jpeg.rf.550580fe1514cf9b6d806a79c05641e4.jpg  \n",
            " extracting: valid/blue_jeans/00000194_jpg.rf.da31498140bfe2004984c9a822427a40.jpg  \n",
            " extracting: valid/blue_jeans/00000198_jpg.rf.701cf220c40ed50cf406341bfe7a9354.jpg  \n",
            " extracting: valid/blue_jeans/00000205_jpg.rf.9272926830e545a004380c0ce697cbc0.jpg  \n",
            " extracting: valid/blue_jeans/00000214_jpg.rf.ec88fe1d54d023391b1f3fb645a9e0d6.jpg  \n",
            " extracting: valid/blue_jeans/00000221_jpg.rf.d15586d7bd678b47dad014894fe3b02b.jpg  \n",
            " extracting: valid/blue_jeans/00000229_png.rf.3ed395564de911af3222aa1ab5ceecab.jpg  \n",
            " extracting: valid/blue_jeans/00000240_jpg.rf.f80fa734c04d9d094c5c557bfa2040af.jpg  \n",
            " extracting: valid/blue_jeans/00000242_jpg.rf.708a4f449e1cd831e56243b070eb0b97.jpg  \n",
            " extracting: valid/blue_jeans/00000248_jpeg.rf.95fcb87c1ca2618f86faa4d7bd848cf7.jpg  \n",
            " extracting: valid/blue_jeans/00000262_jpg.rf.7c82576a62c7f493a79842f9c3026fbc.jpg  \n",
            " extracting: valid/blue_jeans/00000268_jpeg.rf.ca15eabd9414e4efa0434ac9d13d10d2.jpg  \n",
            " extracting: valid/blue_jeans/00000274_jpeg.rf.cf18b38dcf2a1e43745bbdca7d187cd7.jpg  \n",
            " extracting: valid/blue_jeans/00000290_jpeg.rf.a4c03a44837ba9a89fe20f2f888d24e2.jpg  \n",
            " extracting: valid/blue_jeans/00000294_jpeg.rf.077c6e8f9c49415fcea5077d666f8a5a.jpg  \n",
            " extracting: valid/blue_jeans/00000296_jpeg.rf.0641784157e6ab603aba577b8bdc3958.jpg  \n",
            " extracting: valid/blue_jeans/00000302_jpg.rf.69ad020729a73cecf6171b0bd3dd6597.jpg  \n",
            " extracting: valid/blue_jeans/00000305_jpg.rf.3c227bb081a89759d6384ba222b1cc7f.jpg  \n",
            " extracting: valid/blue_jeans/00000314_jpeg.rf.133a64527c7b936e015901abc62905f9.jpg  \n",
            " extracting: valid/blue_jeans/00000330_jpg.rf.1f8259f978891447377b7a1882eb7e40.jpg  \n",
            " extracting: valid/blue_jeans/00000331_jpeg.rf.5632833b8fd0e14cd967029a7c53b131.jpg  \n",
            " extracting: valid/blue_jeans/00000332_jpg.rf.95bc7e22201a25d96d36eb8337c52f73.jpg  \n",
            " extracting: valid/blue_jeans/00000333_jpg.rf.d2c17d825da57232b53ccf07dd2ecbe2.jpg  \n",
            " extracting: valid/blue_jeans/00000343_jpeg.rf.e8fd754a2faf8d9ba1fa334d15fe9b4b.jpg  \n",
            " extracting: valid/blue_jeans/00000347_jpg.rf.3862c645530849042e040f295d363009.jpg  \n",
            " extracting: valid/blue_jeans/00000355_jpeg.rf.54f439e77ff7de8196b5a3b1dfbccbba.jpg  \n",
            " extracting: valid/blue_jeans/00000359_jpg.rf.003f3ce7515083ada8c459bc36be45ee.jpg  \n",
            " extracting: valid/blue_jeans/00000365_jpeg.rf.36e80d603548e0da9549b856a25b2226.jpg  \n",
            " extracting: valid/blue_jeans/00000368_jpg.rf.227b60632eb30dd55029d9176f374d98.jpg  \n",
            "   creating: valid/blue_shirt/\n",
            " extracting: valid/blue_shirt/00000005_jpg.rf.85673affbdf21efe54c1b5a3ba8be681.jpg  \n",
            " extracting: valid/blue_shirt/00000006_jpg.rf.abc4324e014d4b8a9ec605cfd563ab72.jpg  \n",
            " extracting: valid/blue_shirt/00000010_jpg.rf.976fb5f87c4bdec0e6b444ab62f0592e.jpg  \n",
            " extracting: valid/blue_shirt/00000013_jpg.rf.0756eed737379b924287b7c752c8c7af.jpg  \n",
            " extracting: valid/blue_shirt/00000025_jpg.rf.b4eabcd00ade5d0bc205d751549f8543.jpg  \n",
            " extracting: valid/blue_shirt/00000034_jpeg.rf.f7cac97382fef0439336d5dde37827ca.jpg  \n",
            " extracting: valid/blue_shirt/00000036_jpg.rf.82657350d27652a7b05bdc59ea3b627f.jpg  \n",
            " extracting: valid/blue_shirt/00000043_jpg.rf.94f177013d0caadbcb1961955d54b164.jpg  \n",
            " extracting: valid/blue_shirt/00000047_jpeg.rf.a2bc9914c3bc2258a75c399f6d85679e.jpg  \n",
            " extracting: valid/blue_shirt/00000049_jpg.rf.20526ac327d58c39e718a9f7a68d2c50.jpg  \n",
            " extracting: valid/blue_shirt/00000053_jpg.rf.a6aeac65a735222b949620aa72e05121.jpg  \n",
            " extracting: valid/blue_shirt/00000064_jpg.rf.df8076b760c86b2e68856467aca722a9.jpg  \n",
            " extracting: valid/blue_shirt/00000065_jpg.rf.11edd8405766678112ce324237e9f020.jpg  \n",
            " extracting: valid/blue_shirt/00000066_jpeg.rf.d8b0d4c88d91a63cc9bcc50c1bc66d16.jpg  \n",
            " extracting: valid/blue_shirt/00000075_jpg.rf.605774c4c2275ba07a9826c127b296d6.jpg  \n",
            " extracting: valid/blue_shirt/00000080_jpg.rf.929a36f1692393c80cce6c7208bc4246.jpg  \n",
            " extracting: valid/blue_shirt/00000098_jpg.rf.f0c5d442786552e48337798c502c9c43.jpg  \n",
            " extracting: valid/blue_shirt/00000110_jpg.rf.870b2931d2d9f92e32c86b3804ee6946.jpg  \n",
            " extracting: valid/blue_shirt/00000119_jpg.rf.3cc86f3c5581800cc3dfa7980c8cccef.jpg  \n",
            " extracting: valid/blue_shirt/00000122_jpg.rf.ead592ee5e00bf740bc6865d70d31287.jpg  \n",
            " extracting: valid/blue_shirt/00000124_jpg.rf.eb9c690d418b914902641757f0b3c9cb.jpg  \n",
            " extracting: valid/blue_shirt/00000129_jpg.rf.5c56d8f48a911124ef9950bb3da5bb17.jpg  \n",
            " extracting: valid/blue_shirt/00000142_jpg.rf.2ecac23f6edc9a42c7bfd256948c8cb1.jpg  \n",
            " extracting: valid/blue_shirt/00000149_jpg.rf.93e591658737eeb334aa7ef4b6f16d0f.jpg  \n",
            " extracting: valid/blue_shirt/00000155_jpg.rf.b4efae4f5cd605061e59e024229bada4.jpg  \n",
            " extracting: valid/blue_shirt/00000158_jpg.rf.2c2b8b33f3210b91c7e4853d87fb0669.jpg  \n",
            " extracting: valid/blue_shirt/00000159_jpg.rf.8aa9b2d6bc7559e51fa13b1842ea6c13.jpg  \n",
            " extracting: valid/blue_shirt/00000166_jpg.rf.a21af821e763c0c78e7cc9adbe434371.jpg  \n",
            " extracting: valid/blue_shirt/00000173_jpg.rf.ec618ca96f051196ba29e20dfc5199fa.jpg  \n",
            " extracting: valid/blue_shirt/00000174_jpg.rf.4f32eeaf3fe81fa787b1b6f0e10ab044.jpg  \n",
            " extracting: valid/blue_shirt/00000178_jpg.rf.c4f4768bb6090f521ebd158dd7da3d1d.jpg  \n",
            " extracting: valid/blue_shirt/00000179_jpg.rf.6d3f8dae1749a92f4c96d6024ba3fd6e.jpg  \n",
            " extracting: valid/blue_shirt/00000188_jpeg.rf.dbe08cc0dedb55c0ec86fc4e3e3cd988.jpg  \n",
            " extracting: valid/blue_shirt/00000200_jpg.rf.7b3f6d7aa102931fe363e174265c4e0e.jpg  \n",
            " extracting: valid/blue_shirt/00000202_jpg.rf.47d88959e754b915adcdd68113d0fc92.jpg  \n",
            " extracting: valid/blue_shirt/00000206_jpg.rf.bbb73b432456e4904000fe1fa030c404.jpg  \n",
            " extracting: valid/blue_shirt/00000215_jpg.rf.00441d0fdcbd0434596e849a3de9dceb.jpg  \n",
            " extracting: valid/blue_shirt/00000216_jpg.rf.c03d01e78183e88230ebb7dded2ee0d7.jpg  \n",
            " extracting: valid/blue_shirt/00000219_jpg.rf.b7804bc5422a947a469fe093bc230bfd.jpg  \n",
            " extracting: valid/blue_shirt/00000220_jpg.rf.d1bc93c671b43b813270177d855f062a.jpg  \n",
            " extracting: valid/blue_shirt/00000228_jpeg.rf.6b602fe2f83f59eeb48932b8f0e6f8b5.jpg  \n",
            " extracting: valid/blue_shirt/00000235_jpg.rf.b0d01a735f22e2392fba26fc2d028ef6.jpg  \n",
            " extracting: valid/blue_shirt/00000240_png.rf.f5f7c33de0cdeb1bbaa52fea86542da3.jpg  \n",
            " extracting: valid/blue_shirt/00000247_jpeg.rf.60e3b5f47f0c580e27a9752a7a4a5835.jpg  \n",
            " extracting: valid/blue_shirt/00000257_jpg.rf.8466c38e47db397c702097b1d1d17f2f.jpg  \n",
            " extracting: valid/blue_shirt/00000262_jpeg.rf.69a577ab5e7f23fe528acf3b1a260adc.jpg  \n",
            " extracting: valid/blue_shirt/00000271_jpg.rf.12d056319d47145fde92226a4b69cff1.jpg  \n",
            " extracting: valid/blue_shirt/00000276_jpg.rf.15c93174a68b50e24b9b1deda6de1f2e.jpg  \n",
            " extracting: valid/blue_shirt/00000280_jpg.rf.ad2c387ca8500f896bdbcdf5ba148793.jpg  \n",
            " extracting: valid/blue_shirt/00000281_jpg.rf.30625226ecacdaad99c2190f0ce2342e.jpg  \n",
            " extracting: valid/blue_shirt/00000285_jpg.rf.b6c2166fc5b2a695b02ab586dbf04e89.jpg  \n",
            " extracting: valid/blue_shirt/00000294_jpg.rf.d290e97095e14e9cc10fcdd4dceeb597.jpg  \n",
            " extracting: valid/blue_shirt/00000295_jpg.rf.70c534a54fd0fb01c7913cee6f9946cd.jpg  \n",
            " extracting: valid/blue_shirt/00000297_jpg.rf.29f15b48001233628f99176309a29d3b.jpg  \n",
            " extracting: valid/blue_shirt/00000323_jpg.rf.83ea269650d4b5de4a4eae953b29254a.jpg  \n",
            " extracting: valid/blue_shirt/00000325_JPG.rf.cdf420d2024c7e3dc3437142446183e4.jpg  \n",
            " extracting: valid/blue_shirt/00000326_jpg.rf.fda290ff8336dad144d451c0c8e90be9.jpg  \n",
            " extracting: valid/blue_shirt/00000327_jpeg.rf.90dded1944199cb278fc3f01d14fc69b.jpg  \n",
            " extracting: valid/blue_shirt/00000332_jpg.rf.f317b96921d1632745984388b0df491f.jpg  \n",
            " extracting: valid/blue_shirt/00000333_jpg.rf.dd503096b666a7fb061080f1c8b85f44.jpg  \n",
            " extracting: valid/blue_shirt/00000338_jpg.rf.9d0ec3cc66a033796a5499c536aa2a93.jpg  \n",
            " extracting: valid/blue_shirt/00000340_jpg.rf.d84feb133b601fb3fbb39610782f935d.jpg  \n",
            " extracting: valid/blue_shirt/00000356_jpeg.rf.c007725f642e28364b14b2817be7edc7.jpg  \n",
            " extracting: valid/blue_shirt/00000357_jpeg.rf.06a41bdbc1f62c8ceb2c972383f06b96.jpg  \n",
            " extracting: valid/blue_shirt/00000361_jpg.rf.cf7fe5540d3b4fb390d3ba23ee71418f.jpg  \n",
            " extracting: valid/blue_shirt/00000371_jpg.rf.61ecaaafe7bbf14422ba288fc144d270.jpg  \n",
            " extracting: valid/blue_shirt/00000373_jpg.rf.29a2538f5291c8eae033351aefdc4c28.jpg  \n",
            " extracting: valid/blue_shirt/00000378_jpg.rf.56d6d849936ee50810b6a0fa4964192c.jpg  \n",
            " extracting: valid/blue_shirt/00000379_jpg.rf.8d039616a765f0cdc4ddaca9366795c5.jpg  \n",
            " extracting: valid/blue_shirt/00000380_jpg.rf.a5dcc0a3ac02cabe6540562166fafc1a.jpg  \n",
            " extracting: valid/blue_shirt/00000381_jpg.rf.f27349efcc37bd029fa3387a23908ac7.jpg  \n",
            " extracting: valid/blue_shirt/00000385_jpeg.rf.5c4c12b3b85690211c2fd6b8d5ebe230.jpg  \n",
            "   creating: valid/red_dress/\n",
            " extracting: valid/red_dress/00000004_jpg.rf.cc6e4d1e84338e8e8392f2d4d77b2696.jpg  \n",
            " extracting: valid/red_dress/00000013_jpg.rf.b5df03cf0f8b7fa63a2a865db7a89a27.jpg  \n",
            " extracting: valid/red_dress/00000014_jpg.rf.846599982761c1499112119c564a261b.jpg  \n",
            " extracting: valid/red_dress/00000016_jpg.rf.8fa4709e476fc2ef17d6e2511650c8f2.jpg  \n",
            " extracting: valid/red_dress/00000028_jpg.rf.9d3bd6d4d9eff84e72556a938da3cc41.jpg  \n",
            " extracting: valid/red_dress/00000034_jpg.rf.643a8055a157248246a7bbfbf3f539fe.jpg  \n",
            " extracting: valid/red_dress/00000036_jpg.rf.055be55751a8080ebbf534e5516b0afa.jpg  \n",
            " extracting: valid/red_dress/00000037_jpg.rf.73e58510652acaeb2031a55b8f66e166.jpg  \n",
            " extracting: valid/red_dress/00000040_jpg.rf.5bf851e7cabdff68fc3a7a35c9f0ea42.jpg  \n",
            " extracting: valid/red_dress/00000041_jpg.rf.fe7356e662e939a65f82815b1d3e1b5e.jpg  \n",
            " extracting: valid/red_dress/00000047_png.rf.c4d3f2b7f4921e1c817a017a3bb02e77.jpg  \n",
            " extracting: valid/red_dress/00000048_jpg.rf.182d7e2d23e02448120032d07fb40290.jpg  \n",
            " extracting: valid/red_dress/00000051_jpg.rf.a026c64e64e5e54f344edac5927d09cc.jpg  \n",
            " extracting: valid/red_dress/00000063_jpg.rf.9be5a321ca2fe85ee54d9fad70af24ce.jpg  \n",
            " extracting: valid/red_dress/00000069_jpg.rf.491a95da33b90766874f0759cafa8c61.jpg  \n",
            " extracting: valid/red_dress/00000072_jpg.rf.bde419a1ba96a938712761b57e0624f3.jpg  \n",
            " extracting: valid/red_dress/00000082_jpg.rf.0b5c0c48d211d044e647163d6fea2a1d.jpg  \n",
            " extracting: valid/red_dress/00000096_jpg.rf.04a0c051168147f1c83280b57124d09d.jpg  \n",
            " extracting: valid/red_dress/00000097_jpg.rf.34ff924a54d6bdb9ca491e8885809550.jpg  \n",
            " extracting: valid/red_dress/00000109_jpg.rf.71a6e9ff24e86a115f869d33c28c4554.jpg  \n",
            " extracting: valid/red_dress/00000123_jpg.rf.5fdacface0aac70bfcdb288b27291cdf.jpg  \n",
            " extracting: valid/red_dress/00000126_jpg.rf.48b5cbf5dacc4b3b34abb012c7140d43.jpg  \n",
            " extracting: valid/red_dress/00000138_jpg.rf.39f1fddadf925681c021e717d9ff6212.jpg  \n",
            " extracting: valid/red_dress/00000139_jpg.rf.758859e842f75b47d35c7a7904689b5d.jpg  \n",
            " extracting: valid/red_dress/00000143_jpeg.rf.e49c6d2e079672c5708b6357ac5e4427.jpg  \n",
            " extracting: valid/red_dress/00000147_jpg.rf.dad0152735b50e5f819c2bfaabb1d05a.jpg  \n",
            " extracting: valid/red_dress/00000151_jpg.rf.756256723aac0bb2aeee11b54c58730c.jpg  \n",
            " extracting: valid/red_dress/00000156_jpg.rf.edacf4ab0ed8ddb2c78666515fca6e6a.jpg  \n",
            " extracting: valid/red_dress/00000158_jpg.rf.f5393205e798f3da8bcb94e5ddfd46be.jpg  \n",
            " extracting: valid/red_dress/00000178_jpg.rf.4d167a6aab91fefcebad23f7bc44b3df.jpg  \n",
            " extracting: valid/red_dress/00000181_jpg.rf.d6aeeecd683fbf5454cd325d08cb3c7d.jpg  \n",
            " extracting: valid/red_dress/00000187_jpg.rf.ffa8469bb0b98f9c0d8279b55aa97fdb.jpg  \n",
            " extracting: valid/red_dress/00000188_jpg.rf.02e195054ca6c724f5b2c85af9507c93.jpg  \n",
            " extracting: valid/red_dress/00000200_jpeg.rf.f49e3e1cc391ce00dde87b4e5159f3de.jpg  \n",
            " extracting: valid/red_dress/00000204_jpeg.rf.55d726e8542ef07ea130aef01468b5b5.jpg  \n",
            " extracting: valid/red_dress/00000205_jpg.rf.30c03c355d6d12338fc342bc918b32b5.jpg  \n",
            " extracting: valid/red_dress/00000207_jpg.rf.ce6688e0ae3ed2eb258288c1713f40b2.jpg  \n",
            " extracting: valid/red_dress/00000211_jpeg.rf.e323a9be177a541f16731df57fd8bc29.jpg  \n",
            " extracting: valid/red_dress/00000214_jpg.rf.5db3afac56221d2c1c16e3c1ba9f53dc.jpg  \n",
            " extracting: valid/red_dress/00000222_jpg.rf.df31a1490e25bef9c30e690b413e8157.jpg  \n",
            " extracting: valid/red_dress/00000223_jpg.rf.4deab3c76d468fdb0165c5e132210bf2.jpg  \n",
            " extracting: valid/red_dress/00000228_jpg.rf.35f1486bd42e9e04b29267e8a09fd260.jpg  \n",
            " extracting: valid/red_dress/00000232_jpg.rf.d304495ca66d2b9ba65c10ac207c0661.jpg  \n",
            " extracting: valid/red_dress/00000233_jpg.rf.958b6b925a41e4805bfe66e037dfacbc.jpg  \n",
            " extracting: valid/red_dress/00000247_jpg.rf.a1ca4148c7113fa76e5d727853fbc67c.jpg  \n",
            " extracting: valid/red_dress/00000251_jpg.rf.95b649736ba025c7d924dbd71cc77528.jpg  \n",
            " extracting: valid/red_dress/00000252_jpg.rf.e7463b7209572505955e328d613318a2.jpg  \n",
            " extracting: valid/red_dress/00000253_jpg.rf.b08ba842cba0c0e6fbba6a4017aababf.jpg  \n",
            " extracting: valid/red_dress/00000259_jpg.rf.200a4ab95a653a2cdf2514bcd1eb4b4a.jpg  \n",
            " extracting: valid/red_dress/00000274_jpg.rf.1780bab1d9d955d2f8ff116f8d95bb1a.jpg  \n",
            " extracting: valid/red_dress/00000281_jpg.rf.0d0e3bd86772c8d9c8cd09e5415ea271.jpg  \n",
            " extracting: valid/red_dress/00000288_jpg.rf.9fbb5ae58665c8ef6de405b2034c01c3.jpg  \n",
            " extracting: valid/red_dress/00000292_jpg.rf.91bc0fa2a32483585d7b269d9880a329.jpg  \n",
            " extracting: valid/red_dress/00000296_jpg.rf.e1dcc550d7ec2883958b752961432ba3.jpg  \n",
            " extracting: valid/red_dress/00000297_jpg.rf.06c68b8ab62b72138e4fa5f6231dd278.jpg  \n",
            " extracting: valid/red_dress/00000308_jpg.rf.81934b3da8c311c6118087f8d0c4be97.jpg  \n",
            " extracting: valid/red_dress/00000311_jpg.rf.d4f12e8abbf8adb872ab888477c90dd3.jpg  \n",
            " extracting: valid/red_dress/00000315_jpg.rf.32462cbb189da14345257459a9bc11be.jpg  \n",
            " extracting: valid/red_dress/00000317_jpg.rf.4a5763ab836407da3bd14251eb39369e.jpg  \n",
            " extracting: valid/red_dress/00000319_jpg.rf.6180982db98a5e4f2d93a97718025dbf.jpg  \n",
            " extracting: valid/red_dress/00000328_jpg.rf.63f0f810332d0693dfda4b9807130a65.jpg  \n",
            " extracting: valid/red_dress/00000330_jpg.rf.2042b4c5df342ec8b080b91897834837.jpg  \n",
            " extracting: valid/red_dress/00000334_jpg.rf.c6f522e79295d2e3972ba3ace3ab1af7.jpg  \n",
            " extracting: valid/red_dress/00000338_jpg.rf.0c58b2727b92b96f569d9a025c926052.jpg  \n",
            " extracting: valid/red_dress/00000347_jpg.rf.dd7aedaa072e01eb57c5ef6f49d47a6b.jpg  \n",
            " extracting: valid/red_dress/00000355_jpg.rf.735475aabfe3008638cb6ead0628dc01.jpg  \n",
            " extracting: valid/red_dress/00000361_jpg.rf.86732854ca53a1c0b750402724f711e9.jpg  \n",
            " extracting: valid/red_dress/00000363_jpg.rf.b46c297df24c93059d77a88b36189a15.jpg  \n",
            " extracting: valid/red_dress/00000366_jpg.rf.a3a853536ebcff8ba66a81bfbe417cbd.jpg  \n",
            " extracting: valid/red_dress/00000370_jpg.rf.511e5b09d47742a6e4d05c6dc06fdae2.jpg  \n",
            " extracting: valid/red_dress/00000376_jpeg.rf.cd6c7d5de03a56f31958bd3d7459f366.jpg  \n",
            " extracting: valid/red_dress/00000383_jpg.rf.9936f1105c0f796ee6ef0eed1d1ee7f8.jpg  \n",
            " extracting: valid/red_dress/00000384_jpeg.rf.c952e0a340b6ac7085a5250bafab63c9.jpg  \n",
            " extracting: valid/red_dress/00000387_jpg.rf.e97fe017a4d3f9271fada645b42752eb.jpg  \n",
            " extracting: valid/red_dress/00000390_jpg.rf.c66fb950206e0d70c632cd02416aa061.jpg  \n",
            "   creating: valid/red_shirt/\n",
            " extracting: valid/red_shirt/00000000_jpg.rf.2c0fba0ba271c827d5e454f4df4dc23f.jpg  \n",
            " extracting: valid/red_shirt/00000004_jpg.rf.67f1358a3b4aba18a3bfb37d670aefb2.jpg  \n",
            " extracting: valid/red_shirt/00000018_jpg.rf.1fb83b3e934a020e9b8fd90f12da5e01.jpg  \n",
            " extracting: valid/red_shirt/00000022_jpg.rf.cdbcedcab25e3b3f730033ba3a4143df.jpg  \n",
            " extracting: valid/red_shirt/00000026_png.rf.82d7518b53704396f8e3cd694bfc523a.jpg  \n",
            " extracting: valid/red_shirt/00000030_png.rf.9b90a8728f7574132fe01e4e66060816.jpg  \n",
            " extracting: valid/red_shirt/00000032_jpg.rf.768986751e054d3efa7f68e3b4ce70e1.jpg  \n",
            " extracting: valid/red_shirt/00000039_JPG.rf.a50dce7e8aa351f68e548aeafe7e5096.jpg  \n",
            " extracting: valid/red_shirt/00000040_png.rf.d449bb6837d10c6cf3547540cc1a3930.jpg  \n",
            " extracting: valid/red_shirt/00000045_jpg.rf.5e0bf7f84655907c1ede66c2ee221fe6.jpg  \n",
            " extracting: valid/red_shirt/00000048_jpg.rf.f6c310e600f79948487de1714ac04d26.jpg  \n",
            " extracting: valid/red_shirt/00000050_jpg.rf.1400c37f892d28b085170e7d68c6e696.jpg  \n",
            " extracting: valid/red_shirt/00000052_jpg.rf.75507d2e5e65c015f32925ef0cbe460a.jpg  \n",
            " extracting: valid/red_shirt/00000067_jpg.rf.1943f2c9eed4f632e46f9aad0aacb015.jpg  \n",
            " extracting: valid/red_shirt/00000069_jpg.rf.b89fd9aa8f61d836b5485f5c35d42570.jpg  \n",
            " extracting: valid/red_shirt/00000070_jpg.rf.64c7e9ef57962a699935edf0e47c13f8.jpg  \n",
            " extracting: valid/red_shirt/00000074_jpg.rf.62702bcd801cbda2f8461c88448c1a58.jpg  \n",
            " extracting: valid/red_shirt/00000091_jpg.rf.e51812b21ced87bf93e17a09e0aff1ad.jpg  \n",
            " extracting: valid/red_shirt/00000096_jpg.rf.178289f64bc9d1977f4eb63b2b5433f1.jpg  \n",
            " extracting: valid/red_shirt/00000103_jpg.rf.cfc19da4ee720b99ccea4a8631db38ea.jpg  \n",
            " extracting: valid/red_shirt/00000105_jpg.rf.7336bda6105fe100d3b162c4a80a46e9.jpg  \n",
            " extracting: valid/red_shirt/00000111_jpg.rf.fb982c32a38d1526903f214841d28a27.jpg  \n",
            " extracting: valid/red_shirt/00000112_jpg.rf.aab5d7414c835fe05894000ac16f51e4.jpg  \n",
            " extracting: valid/red_shirt/00000115_jpg.rf.c7c0df05f2c6af9a4e57eecc0fcdf833.jpg  \n",
            " extracting: valid/red_shirt/00000117_jpg.rf.a99592eeb5efd2adc433414dc450a992.jpg  \n",
            " extracting: valid/red_shirt/00000121_jpg.rf.8b3fc0cf51366a0d77a50892a3693383.jpg  \n",
            " extracting: valid/red_shirt/00000130_jpg.rf.353e4ac20f98e8a5f7a79761dd26ff2c.jpg  \n",
            " extracting: valid/red_shirt/00000141_jpg.rf.6c26397f175f533814df686363dd9e52.jpg  \n",
            " extracting: valid/red_shirt/00000144_jpg.rf.03ebe61c561eed98fb8ae73b13192723.jpg  \n",
            " extracting: valid/red_shirt/00000146_jpg.rf.b1a92cf76d339b8ab3adb8280006f214.jpg  \n",
            " extracting: valid/red_shirt/00000153_jpg.rf.3fff495001c65c6ecc48843e9b38de8d.jpg  \n",
            " extracting: valid/red_shirt/00000156_jpg.rf.bc0ffec348297c23f6701118e593e6d0.jpg  \n",
            " extracting: valid/red_shirt/00000158_jpg.rf.b6d814db35d29e8c954e7b4c4b21310d.jpg  \n",
            " extracting: valid/red_shirt/00000161_jpg.rf.d4c79782a3642c5a4067abfdd30a2ced.jpg  \n",
            " extracting: valid/red_shirt/00000162_jpg.rf.dafa4f592981f59be1f6b24a4e653ddb.jpg  \n",
            " extracting: valid/red_shirt/00000170_jpg.rf.937bbc12afc96c1189ddfcf61ac3f274.jpg  \n",
            " extracting: valid/red_shirt/00000201_jpg.rf.8d70ba365919bb58206fdb6ecb1afadf.jpg  \n",
            " extracting: valid/red_shirt/00000205_jpg.rf.893e8f892c15917cbc043f5cebe96807.jpg  \n",
            " extracting: valid/red_shirt/00000222_jpg.rf.0d154a35c46d71cb2fe47407c61bb1ea.jpg  \n",
            " extracting: valid/red_shirt/00000225_jpg.rf.a6df27950627765162031c431f4d15fe.jpg  \n",
            " extracting: valid/red_shirt/00000226_jpg.rf.ae6b3f492012f7791126a195924cba8f.jpg  \n",
            " extracting: valid/red_shirt/00000234_jpg.rf.a404b0affeac72570947800963b5754a.jpg  \n",
            " extracting: valid/red_shirt/00000235_jpg.rf.46a64c857d780d37991c3b314332dd86.jpg  \n",
            " extracting: valid/red_shirt/00000241_jpeg.rf.43c1be55ac3f49755efc20bdd390b8ab.jpg  \n",
            " extracting: valid/red_shirt/00000246_jpg.rf.c5b4575a7b97eed1bb747698b6bf5b0e.jpg  \n",
            " extracting: valid/red_shirt/00000253_jpg.rf.3974862096b73cbed819e2268c25e91f.jpg  \n",
            " extracting: valid/red_shirt/00000257_jpeg.rf.7d52a23641cfb5c6776324048159cd49.jpg  \n",
            " extracting: valid/red_shirt/00000258_jpg.rf.e3bc3e068e0afe8a6d7d6497c583aae1.jpg  \n",
            " extracting: valid/red_shirt/00000260_jpg.rf.028b8482df759bbc9cf4fc9cfeea4d84.jpg  \n",
            " extracting: valid/red_shirt/00000261_jpg.rf.cbd3d1af500b855fc25bd4a22018ba87.jpg  \n",
            " extracting: valid/red_shirt/00000271_jpg.rf.099616bc54294ef46c745d2292c75faa.jpg  \n",
            " extracting: valid/red_shirt/00000276_jpg.rf.c5baf7cb591a33caedf22de55ec099fe.jpg  \n",
            " extracting: valid/red_shirt/00000283_jpg.rf.f324a388b549c648da4868104fcc99ba.jpg  \n",
            " extracting: valid/red_shirt/00000287_jpeg.rf.f914342e9277c61a282f686c6e767202.jpg  \n",
            " extracting: valid/red_shirt/00000295_jpg.rf.6a87ffabfe73ebc16fd140ffe3d07445.jpg  \n",
            " extracting: valid/red_shirt/00000297_jpg.rf.817b83571a34dde653a6ec4662175392.jpg  \n",
            " extracting: valid/red_shirt/00000299_jpg.rf.4714906ea13b0818b07adfac40b9aa5e.jpg  \n",
            " extracting: valid/red_shirt/00000308_jpg.rf.59774f0824d4ec6fce65f45e57167ea4.jpg  \n",
            " extracting: valid/red_shirt/00000328_jpg.rf.bf34c19137bd1ea48dd46ec8285e6b85.jpg  \n",
            " extracting: valid/red_shirt/00000329_jpg.rf.b3f20ce2d693aaecfae8d4c5973c2f6a.jpg  \n",
            " extracting: valid/red_shirt/00000334_jpg.rf.33d282c79b3abb3ace572df0f47a0e0b.jpg  \n",
            " extracting: valid/red_shirt/00000348_jpg.rf.630672bf9e98b608256837710c2e8223.jpg  \n",
            " extracting: valid/red_shirt/00000349_jpg.rf.052012e83019ec8b08b2e05cd6997d9a.jpg  \n",
            " extracting: valid/red_shirt/00000360_jpeg.rf.3d5b346b0cfba02826100e18ccce4890.jpg  \n",
            " extracting: valid/red_shirt/00000361_jpg.rf.e1e77601c7dabd7e1320ab2aaa8a3d2a.jpg  \n",
            " extracting: valid/red_shirt/00000371_jpg.rf.d56eb8c16f268c8c6c13443cf6035128.jpg  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P3s26C4KlKe4",
        "outputId": "22a65c9f-1b22-4e80-8ca4-1ef79b6dc959"
      },
      "source": [
        "cd .."
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TD25Pe2_nljS"
      },
      "source": [
        "# Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KEwi_2bHwqT7"
      },
      "source": [
        "tf.compat.v1.disable_eager_execution()"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PISyOosHnpNj"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.preprocessing.image import img_to_array,ImageDataGenerator\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MultiLabelBinarizer\n",
        "import matplotlib.pyplot as plt\n",
        "from imutils import paths,resize\n",
        "import numpy as np\n",
        "import random\n",
        "import pickle\n",
        "import os\n",
        "import cv2\n",
        "\n",
        "from keras.models import Sequential\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from keras.layers import BatchNormalization,Conv2D,MaxPooling2D,GlobalAveragePooling2D,Activation,Flatten,Dropout,Dense\n",
        "from keras import backend as K"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Krxq-zGJvB_"
      },
      "source": [
        "# Dataset pre-processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CgKtFvOzpe5y"
      },
      "source": [
        "IMG_DIMS= (96,96,3) # The dimensions on which model has to be built"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Pt1emzcJyXz",
        "outputId": "a5acbb0e-66c2-47ef-fd6e-9da16a06d3d5"
      },
      "source": [
        "DATASET_PATH= '/content/dataset'\n",
        "\n",
        "imagePaths = sorted(list(paths.list_images(DATASET_PATH)))\n",
        "print(\"Image path list:> \",imagePaths)\n",
        "random.seed(42)\n",
        "random.shuffle(imagePaths)\n",
        "\n",
        "#initialize data and labels list \n",
        "data = []\n",
        "labels = []\n",
        "\n",
        "for imagepath in imagePaths:\n",
        "    image= cv2.imread(imagepath)\n",
        "    image= cv2.resize(image,(IMG_DIMS[1],IMG_DIMS[0])) # width,height\n",
        "    image = img_to_array(image)\n",
        "    data.append(image)\n",
        "\n",
        "    #extract the labels from the \"folder names\"\n",
        "    l = imagepath.split(\"/\")[-2].split(\"_\")\n",
        "    labels.append(l)\n",
        "\n",
        "# convert the data and the labels to an array\n",
        "data = np.array(data, dtype=\"float\") / 255.0\n",
        "labels = np.array(labels)\n",
        "\n",
        "\n",
        "print(\"[INFO] data matrix: {} images ({:.2f}MB)\".format(\n",
        "\tlen(imagePaths), data.nbytes / (1024 * 1000.0)))\n",
        "print(labels)\n",
        "\n"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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'/content/dataset/valid/red_shirt/00000360_jpeg.rf.3d5b346b0cfba02826100e18ccce4890.jpg', '/content/dataset/valid/red_shirt/00000361_jpg.rf.e1e77601c7dabd7e1320ab2aaa8a3d2a.jpg', '/content/dataset/valid/red_shirt/00000371_jpg.rf.d56eb8c16f268c8c6c13443cf6035128.jpg']\n",
            "[INFO] data matrix: 2076 images (448.42MB)\n",
            "[['blue' 'dress']\n",
            " ['red' 'shirt']\n",
            " ['blue' 'jeans']\n",
            " ...\n",
            " ['blue' 'shirt']\n",
            " ['blue' 'jeans']\n",
            " ['blue' 'dress']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pYt13MURJyP3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c9fa315-f247-4267-8197-3e64bd026fdb"
      },
      "source": [
        "print(\"[INFO] class labels:\")\n",
        "mlb = MultiLabelBinarizer() #binarize the labels ---> encoding\n",
        "labels_encoded = mlb.fit_transform(labels)\n",
        "\n",
        "for (i, label) in enumerate(mlb.classes_):\n",
        "\tprint(\"{}. {}\".format(i + 1, label))\n",
        " \n",
        "# first 9 encoded labels\n",
        "for i in range(0,9):\n",
        "    print(labels_encoded[i])"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] class labels:\n",
            "1. black\n",
            "2. blue\n",
            "3. dress\n",
            "4. jeans\n",
            "5. red\n",
            "6. shirt\n",
            "[0 1 1 0 0 0]\n",
            "[0 0 0 0 1 1]\n",
            "[0 1 0 1 0 0]\n",
            "[1 0 0 1 0 0]\n",
            "[0 0 1 0 1 0]\n",
            "[0 1 0 1 0 0]\n",
            "[0 1 1 0 0 0]\n",
            "[0 0 1 0 1 0]\n",
            "[1 0 0 1 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9rUJVlflAUG1"
      },
      "source": [
        "# Visualize the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kManhGIHD1Wd"
      },
      "source": [
        "#<----------------------------------------------FOR CODE TESTING------------------------------------>\n",
        "# classes= {0: 'black', 1: 'blue', 2: 'dress', 3: 'jeans', 4: 'red', 5: 'shirt'}\n",
        "# tests= [1,2]\n",
        "\n",
        "# for test_i in tests:\n",
        "#     if test_i in classes:\n",
        "#         print(classes[test_i])\n",
        "\n",
        "# qwert= [classes[test_i] for test_i in tests if test_i in classes]\n",
        "# print(qwert)\n",
        "\n",
        "\n",
        "# plt.imshow(data[0])\n",
        "# b,g,r = cv2.split(data[0])\n",
        "# rgb_img = cv2.merge([r,g,b])  \n",
        "# plt.imshow(rgb_img) \n",
        "\n",
        "# data1 = data[0][:]\n",
        "# plt.imshow(data1)\n",
        "# data1[:,:,0], data1[:,:,2] =  data1[:,:,2], data1[:,:,0]\n",
        "# plt.imshow(data1)\n",
        "\n",
        "# B G R --> R G B \n",
        "# plt.imshow(data[0])\n",
        "# print(data[0].shape)\n",
        "# data[0][:,:,]"
      ],
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        },
        "id": "7pNGvzLdAXVB",
        "outputId": "2dcb523e-fcd0-440b-e427-49258e04ea3d"
      },
      "source": [
        "# CLASSES= {}\n",
        "CLASSES= {}\n",
        "for i,labels in enumerate(mlb.classes_):\n",
        "    CLASSES[i] = labels\n",
        "print(\"Classes available:> \",CLASSES)\n",
        "plt.figure(figsize=(15, 15))\n",
        "for i in range(0,9):\n",
        "    plt.subplot(5, 3, i+1)\n",
        "    b,g,r = cv2.split(data[i]) #splitting the image to (B G R) as the image was read in openCV\n",
        "    data_rgb= cv2.merge([r,g,b]) # merge back the image array to R G B channels\n",
        "    plt.imshow(data_rgb) #'image_list' is the list of images\n",
        "    label_list= np.argsort(labels_encoded[i])[::-1][:2] #top 2 labels with 1\n",
        "    label_twin= [CLASSES[labels] for labels in label_list if labels in CLASSES] #convert number encoding to labels\n",
        "    plt.xlabel(label_twin[1]+\"-\"+label_twin[0]) # 'labels' is the list of labels\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classes available:>  {0: 'black', 1: 'blue', 2: 'dress', 3: 'jeans', 4: 'red', 5: 'shirt'}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 9 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R1CMK3u-JyHt"
      },
      "source": [
        "(trainX, testX, trainY, testY) = train_test_split(data,labels, test_size=0.2, random_state=42)\n",
        "\n",
        "#Augmentations to be applied\n",
        "aug = ImageDataGenerator(rotation_range=25, width_shift_range=0.1,\n",
        "\theight_shift_range=0.1, shear_range=0.2, zoom_range=0.2,\n",
        "\thorizontal_flip=True, fill_mode=\"nearest\")"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dxYhLCIfIaCI"
      },
      "source": [
        "# Model building"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RB8Qk36HHs8h"
      },
      "source": [
        "# import the necessary packages\n",
        "\n",
        "class SmallerVGGNet:\n",
        "    @staticmethod\n",
        "    def build(width, height, depth, classes, finalAct=\"softmax\"):\n",
        "        # initialize the model along with the input shape to be\n",
        "        # \"channels last\" and the channels dimension itself\n",
        "        model = Sequential()\n",
        "        inputShape = (height, width, depth)\n",
        "        chanDim = -1\n",
        "\n",
        "\t\t# if we are using \"channels first\", update the input shape\n",
        "\t\t# and channels dimension\n",
        "        if K.image_data_format() == \"channels_first\":\n",
        "            inputShape = (depth, height, width)\n",
        "            chanDim = 1\n",
        "\n",
        "\t\t# CONV => RELU => POOL\n",
        "        model.add(Conv2D(32, (3, 3), padding=\"same\",\n",
        "\t\t\tinput_shape=inputShape))\n",
        "        model.add(Activation(\"relu\"))\n",
        "        model.add(BatchNormalization(axis=chanDim))\n",
        "        model.add(MaxPooling2D(pool_size=(3, 3)))\n",
        "        model.add(Dropout(0.25))\n",
        "\n",
        "\t    # (CONV => RELU) * 2 => POOL\n",
        "        model.add(Conv2D(64, (3, 3), padding=\"same\"))\n",
        "        model.add(Activation(\"relu\"))\n",
        "        model.add(BatchNormalization(axis=chanDim))\n",
        "        model.add(Conv2D(64, (3, 3), padding=\"same\"))\n",
        "        model.add(Activation(\"relu\"))\n",
        "        model.add(BatchNormalization(axis=chanDim))\n",
        "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "        model.add(Dropout(0.25))\n",
        "\n",
        "\t\t# (CONV => RELU) * 2 => POOL\n",
        "        model.add(Conv2D(128, (3, 3), padding=\"same\"))\n",
        "        model.add(Activation(\"relu\"))\n",
        "        model.add(BatchNormalization(axis=chanDim))\n",
        "        model.add(Conv2D(128, (3, 3), padding=\"same\"))\n",
        "        model.add(Activation(\"relu\"))\n",
        "        model.add(BatchNormalization(axis=chanDim))\n",
        "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "        model.add(Dropout(0.25))\n",
        "\n",
        "        # first (and only) set of FC => RELU layers\n",
        "        model.add(Flatten())\n",
        "        model.add(Dense(1024))\n",
        "        model.add(Activation(\"relu\"))\n",
        "        model.add(BatchNormalization())\n",
        "        model.add(Dropout(0.5))\n",
        "       \n",
        "        # softmax classifier\n",
        "        model.add(Dense(classes))\n",
        "        model.add(Activation(finalAct))\n",
        "        \n",
        "        # plot the model architecture in shell\n",
        "        model.summary()\n",
        "\t\t# return the constructed network architecture\n",
        "        return model\n",
        "\n",
        "class SimpleNet:\n",
        "    @staticmethod\n",
        "    def build(width, height, depth, classes, finalAct=\"softmax\"):\n",
        "        # initialize the model along with the input shape to be\n",
        "        # \"channels last\" and the channels dimension itself\n",
        "        model = Sequential()\n",
        "        inputShape = (height, width, depth)\n",
        "        chanDim = -1\n",
        "\n",
        "\t\t# if we are using \"channels first\", update the input shape\n",
        "\t\t# and channels dimension\n",
        "        if K.image_data_format() == \"channels_first\":\n",
        "            inputShape = (depth, height, width)\n",
        "            chanDim = 1\n",
        "\n",
        "\t\t# CONV => RELU => POOL\n",
        "        model.add(Conv2D(32, (3, 3), padding=\"same\",\n",
        "\t\t\tinput_shape=inputShape))\n",
        "        model.add(Activation(\"relu\"))\n",
        "        model.add(BatchNormalization(axis=chanDim))\n",
        "        model.add(MaxPooling2D(pool_size=(3, 3)))\n",
        "        model.add(Dropout(0.25))\n",
        "\n",
        "\t    # (CONV => RELU) * 2 => POOL\n",
        "        model.add(Conv2D(64, (3, 3), padding=\"same\"))\n",
        "        model.add(Activation(\"relu\"))\n",
        "        model.add(BatchNormalization(axis=chanDim))\n",
        "        model.add(Conv2D(64, (3, 3), padding=\"same\"))\n",
        "        model.add(Activation(\"relu\"))\n",
        "        model.add(BatchNormalization(axis=chanDim))\n",
        "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "        model.add(Dropout(0.25))\n",
        "\n",
        "\t\t# (CONV => RELU) * 2 => POOL\n",
        "        model.add(Conv2D(128, (3, 3), padding=\"same\"))\n",
        "        model.add(Activation(\"relu\"))\n",
        "        model.add(BatchNormalization(axis=chanDim))\n",
        "        model.add(Conv2D(128, (3, 3), padding=\"same\"))\n",
        "        model.add(Activation(\"relu\"))\n",
        "        model.add(BatchNormalization(axis=chanDim))\n",
        "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "        model.add(Dropout(0.25))\n",
        "\n",
        "\t\t# (CONV => RELU) * 2 => POOL\n",
        "        model.add(Conv2D(256, (3, 3), padding=\"same\"))\n",
        "        model.add(Activation(\"relu\"))\n",
        "        model.add(BatchNormalization(axis=chanDim))\n",
        "        model.add(Conv2D(256, (3, 3), padding=\"same\"))\n",
        "        model.add(Activation(\"relu\"))\n",
        "        model.add(BatchNormalization(axis=chanDim))\n",
        "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "        model.add(Dropout(0.25))\n",
        "        \n",
        "        # use global average pooling instead of fc layer\n",
        "        model.add(GlobalAveragePooling2D())\n",
        "        model.add(Activation(\"relu\"))\n",
        "        model.add(BatchNormalization())\n",
        "        model.add(Dropout(0.5))\n",
        "       \n",
        "        # softmax classifier\n",
        "        model.add(Dense(classes))\n",
        "        model.add(Activation(finalAct))\n",
        "        \n",
        "        # plot the model architecture in shell\n",
        "        model.summary()\n",
        "\t\t# return the constructed network architecture\n",
        "        return model\n"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S4NOPZBiIrcx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f8ca5c8-a8de-4afd-b853-29a58e4f2277"
      },
      "source": [
        "# model = SmallerVGGNet.build(\n",
        "# \twidth=IMG_DIMS[1], height=IMG_DIMS[0],\n",
        "# \tdepth=IMG_DIMS[2], classes=len(mlb.classes_),\n",
        "# \tfinalAct=\"sigmoid\") #sigmoid will be used for Multi-label classification\n",
        "\n",
        "model = SimpleNet.build(\n",
        "\twidth=IMG_DIMS[1], height=IMG_DIMS[0],\n",
        "\tdepth=IMG_DIMS[2], classes=len(mlb.classes_),\n",
        "\tfinalAct=\"sigmoid\") #sigmoid will be used for Multi-label classification"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/layers/normalization/batch_normalization.py:520: _colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_19 (Conv2D)           (None, 96, 96, 32)        896       \n",
            "_________________________________________________________________\n",
            "activation_23 (Activation)   (None, 96, 96, 32)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_21 (Batc (None, 96, 96, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_11 (MaxPooling (None, 32, 32, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_13 (Dropout)         (None, 32, 32, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_20 (Conv2D)           (None, 32, 32, 64)        18496     \n",
            "_________________________________________________________________\n",
            "activation_24 (Activation)   (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_22 (Batc (None, 32, 32, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_21 (Conv2D)           (None, 32, 32, 64)        36928     \n",
            "_________________________________________________________________\n",
            "activation_25 (Activation)   (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_23 (Batc (None, 32, 32, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_12 (MaxPooling (None, 16, 16, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_14 (Dropout)         (None, 16, 16, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_22 (Conv2D)           (None, 16, 16, 128)       73856     \n",
            "_________________________________________________________________\n",
            "activation_26 (Activation)   (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_24 (Batc (None, 16, 16, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv2d_23 (Conv2D)           (None, 16, 16, 128)       147584    \n",
            "_________________________________________________________________\n",
            "activation_27 (Activation)   (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_25 (Batc (None, 16, 16, 128)       512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_13 (MaxPooling (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_15 (Dropout)         (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_24 (Conv2D)           (None, 8, 8, 256)         295168    \n",
            "_________________________________________________________________\n",
            "activation_28 (Activation)   (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_26 (Batc (None, 8, 8, 256)         1024      \n",
            "_________________________________________________________________\n",
            "conv2d_25 (Conv2D)           (None, 8, 8, 256)         590080    \n",
            "_________________________________________________________________\n",
            "activation_29 (Activation)   (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_27 (Batc (None, 8, 8, 256)         1024      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_14 (MaxPooling (None, 4, 4, 256)         0         \n",
            "_________________________________________________________________\n",
            "dropout_16 (Dropout)         (None, 4, 4, 256)         0         \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_1 ( (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "activation_30 (Activation)   (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_28 (Batc (None, 256)               1024      \n",
            "_________________________________________________________________\n",
            "dropout_17 (Dropout)         (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 6)                 1542      \n",
            "_________________________________________________________________\n",
            "activation_31 (Activation)   (None, 6)                 0         \n",
            "=================================================================\n",
            "Total params: 1,169,286\n",
            "Trainable params: 1,166,918\n",
            "Non-trainable params: 2,368\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rDKrNBbhqB7g",
        "outputId": "a3ff30c3-0bd8-412d-e6fc-5b988bf2b2de"
      },
      "source": [
        "INIT_LR = 1e-3\n",
        "EPOCHS = 75\n",
        "opt = Adam(learning_rate=INIT_LR, decay=INIT_LR / EPOCHS)\n",
        "BS = 32\n",
        "\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
        "\tmetrics=[\"accuracy\"])\n",
        "\n",
        "History = model.fit(\n",
        "\taug.flow(trainX, trainY, batch_size=BS),\n",
        "\tvalidation_data=(testX, testY),\n",
        "\tsteps_per_epoch=len(trainX) // BS,\n",
        "\tepochs=EPOCHS, verbose=1)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/75\n",
            "51/51 [==============================] - ETA: 0s - batch: 25.0000 - size: 31.9216 - loss: 0.3864 - accuracy: 0.8455"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:2470: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
            "  warnings.warn('`Model.state_updates` will be removed in a future version. '\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "51/51 [==============================] - 9s 106ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.3864 - accuracy: 0.8455 - val_loss: 1.0040 - val_accuracy: 0.6218\n",
            "Epoch 2/75\n",
            "51/51 [==============================] - 6s 115ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.2435 - accuracy: 0.9089 - val_loss: 0.9717 - val_accuracy: 0.6194\n",
            "Epoch 3/75\n",
            "51/51 [==============================] - 6s 112ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.1989 - accuracy: 0.9287 - val_loss: 1.3302 - val_accuracy: 0.6214\n",
            "Epoch 4/75\n",
            "51/51 [==============================] - 6s 112ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.1618 - accuracy: 0.9391 - val_loss: 1.2516 - val_accuracy: 0.6350\n",
            "Epoch 5/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.1570 - accuracy: 0.9470 - val_loss: 0.8065 - val_accuracy: 0.7548\n",
            "Epoch 6/75\n",
            "51/51 [==============================] - 6s 114ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.1403 - accuracy: 0.9506 - val_loss: 0.4323 - val_accuracy: 0.8506\n",
            "Epoch 7/75\n",
            "51/51 [==============================] - 6s 112ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.1296 - accuracy: 0.9557 - val_loss: 0.4174 - val_accuracy: 0.8526\n",
            "Epoch 8/75\n",
            "51/51 [==============================] - 6s 112ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.1265 - accuracy: 0.9594 - val_loss: 0.3851 - val_accuracy: 0.8622\n",
            "Epoch 9/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.1262 - accuracy: 0.9564 - val_loss: 0.1062 - val_accuracy: 0.9579\n",
            "Epoch 10/75\n",
            "51/51 [==============================] - 6s 114ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.1032 - accuracy: 0.9661 - val_loss: 0.0875 - val_accuracy: 0.9708\n",
            "Epoch 11/75\n",
            "51/51 [==============================] - 6s 112ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0891 - accuracy: 0.9709 - val_loss: 0.1011 - val_accuracy: 0.9639\n",
            "Epoch 12/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0826 - accuracy: 0.9716 - val_loss: 0.0831 - val_accuracy: 0.9756\n",
            "Epoch 13/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0904 - accuracy: 0.9685 - val_loss: 0.0740 - val_accuracy: 0.9720\n",
            "Epoch 14/75\n",
            "51/51 [==============================] - 6s 112ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0794 - accuracy: 0.9729 - val_loss: 0.0675 - val_accuracy: 0.9748\n",
            "Epoch 15/75\n",
            "51/51 [==============================] - 6s 112ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0802 - accuracy: 0.9736 - val_loss: 0.0947 - val_accuracy: 0.9688\n",
            "Epoch 16/75\n",
            "51/51 [==============================] - 6s 112ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0721 - accuracy: 0.9759 - val_loss: 0.1102 - val_accuracy: 0.9788\n",
            "Epoch 17/75\n",
            "51/51 [==============================] - 6s 111ms/step - batch: 25.0000 - size: 32.0000 - loss: 0.0831 - accuracy: 0.9705 - val_loss: 0.1028 - val_accuracy: 0.9692\n",
            "Epoch 18/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 31.8431 - loss: 0.0819 - accuracy: 0.9705 - val_loss: 0.0544 - val_accuracy: 0.9820\n",
            "Epoch 19/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0741 - accuracy: 0.9743 - val_loss: 0.0797 - val_accuracy: 0.9688\n",
            "Epoch 20/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 32.0000 - loss: 0.0725 - accuracy: 0.9745 - val_loss: 0.0339 - val_accuracy: 0.9884\n",
            "Epoch 21/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 31.8431 - loss: 0.0536 - accuracy: 0.9797 - val_loss: 0.0356 - val_accuracy: 0.9868\n",
            "Epoch 22/75\n",
            "51/51 [==============================] - 6s 112ms/step - batch: 25.0000 - size: 32.0000 - loss: 0.0524 - accuracy: 0.9848 - val_loss: 0.0684 - val_accuracy: 0.9776\n",
            "Epoch 23/75\n",
            "51/51 [==============================] - 6s 112ms/step - batch: 25.0000 - size: 31.8431 - loss: 0.0664 - accuracy: 0.9787 - val_loss: 0.1301 - val_accuracy: 0.9559\n",
            "Epoch 24/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 32.0000 - loss: 0.0638 - accuracy: 0.9777 - val_loss: 0.0484 - val_accuracy: 0.9844\n",
            "Epoch 25/75\n",
            "51/51 [==============================] - 6s 112ms/step - batch: 25.0000 - size: 31.8431 - loss: 0.0526 - accuracy: 0.9834 - val_loss: 0.0807 - val_accuracy: 0.9712\n",
            "Epoch 26/75\n",
            "51/51 [==============================] - 6s 111ms/step - batch: 25.0000 - size: 32.0000 - loss: 0.0588 - accuracy: 0.9801 - val_loss: 0.0550 - val_accuracy: 0.9764\n",
            "Epoch 27/75\n",
            "51/51 [==============================] - 6s 114ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0509 - accuracy: 0.9842 - val_loss: 0.0497 - val_accuracy: 0.9816\n",
            "Epoch 28/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 31.8431 - loss: 0.0491 - accuracy: 0.9828 - val_loss: 0.0806 - val_accuracy: 0.9736\n",
            "Epoch 29/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0541 - accuracy: 0.9826 - val_loss: 0.0561 - val_accuracy: 0.9828\n",
            "Epoch 30/75\n",
            "51/51 [==============================] - 6s 114ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0596 - accuracy: 0.9785 - val_loss: 0.0556 - val_accuracy: 0.9820\n",
            "Epoch 31/75\n",
            "51/51 [==============================] - 6s 116ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0549 - accuracy: 0.9811 - val_loss: 0.0350 - val_accuracy: 0.9884\n",
            "Epoch 32/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 32.0000 - loss: 0.0508 - accuracy: 0.9827 - val_loss: 0.0672 - val_accuracy: 0.9796\n",
            "Epoch 33/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0571 - accuracy: 0.9801 - val_loss: 0.0818 - val_accuracy: 0.9671\n",
            "Epoch 34/75\n",
            "51/51 [==============================] - 6s 116ms/step - batch: 25.0000 - size: 31.8431 - loss: 0.0438 - accuracy: 0.9850 - val_loss: 0.0453 - val_accuracy: 0.9832\n",
            "Epoch 35/75\n",
            "51/51 [==============================] - 6s 115ms/step - batch: 25.0000 - size: 32.0000 - loss: 0.0550 - accuracy: 0.9825 - val_loss: 0.0425 - val_accuracy: 0.9868\n",
            "Epoch 36/75\n",
            "51/51 [==============================] - 6s 116ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0495 - accuracy: 0.9829 - val_loss: 0.0423 - val_accuracy: 0.9856\n",
            "Epoch 37/75\n",
            "51/51 [==============================] - 6s 115ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0414 - accuracy: 0.9865 - val_loss: 0.0461 - val_accuracy: 0.9860\n",
            "Epoch 38/75\n",
            "51/51 [==============================] - 6s 115ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0571 - accuracy: 0.9809 - val_loss: 0.0403 - val_accuracy: 0.9872\n",
            "Epoch 39/75\n",
            "51/51 [==============================] - 6s 115ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0508 - accuracy: 0.9822 - val_loss: 0.1936 - val_accuracy: 0.9339\n",
            "Epoch 40/75\n",
            "51/51 [==============================] - 6s 114ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0409 - accuracy: 0.9864 - val_loss: 0.0454 - val_accuracy: 0.9832\n",
            "Epoch 41/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0513 - accuracy: 0.9827 - val_loss: 0.2297 - val_accuracy: 0.9167\n",
            "Epoch 42/75\n",
            "51/51 [==============================] - 6s 114ms/step - batch: 25.0000 - size: 31.8431 - loss: 0.0405 - accuracy: 0.9852 - val_loss: 0.0442 - val_accuracy: 0.9828\n",
            "Epoch 43/75\n",
            "51/51 [==============================] - 6s 114ms/step - batch: 25.0000 - size: 32.0000 - loss: 0.0437 - accuracy: 0.9853 - val_loss: 0.1201 - val_accuracy: 0.9491\n",
            "Epoch 44/75\n",
            "51/51 [==============================] - 6s 115ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0577 - accuracy: 0.9813 - val_loss: 0.0935 - val_accuracy: 0.9704\n",
            "Epoch 45/75\n",
            "51/51 [==============================] - 6s 114ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0622 - accuracy: 0.9810 - val_loss: 0.0487 - val_accuracy: 0.9860\n",
            "Epoch 46/75\n",
            "51/51 [==============================] - 6s 114ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0393 - accuracy: 0.9862 - val_loss: 0.0361 - val_accuracy: 0.9864\n",
            "Epoch 47/75\n",
            "51/51 [==============================] - 6s 114ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0429 - accuracy: 0.9857 - val_loss: 0.0878 - val_accuracy: 0.9651\n",
            "Epoch 48/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0413 - accuracy: 0.9855 - val_loss: 0.1570 - val_accuracy: 0.9611\n",
            "Epoch 49/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0384 - accuracy: 0.9873 - val_loss: 0.0295 - val_accuracy: 0.9908\n",
            "Epoch 50/75\n",
            "51/51 [==============================] - 6s 111ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0370 - accuracy: 0.9881 - val_loss: 0.0418 - val_accuracy: 0.9876\n",
            "Epoch 51/75\n",
            "51/51 [==============================] - 6s 115ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0332 - accuracy: 0.9902 - val_loss: 0.0246 - val_accuracy: 0.9908\n",
            "Epoch 52/75\n",
            "51/51 [==============================] - 6s 112ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0287 - accuracy: 0.9907 - val_loss: 0.0473 - val_accuracy: 0.9884\n",
            "Epoch 53/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0278 - accuracy: 0.9908 - val_loss: 0.0665 - val_accuracy: 0.9820\n",
            "Epoch 54/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 32.0000 - loss: 0.0257 - accuracy: 0.9912 - val_loss: 0.0365 - val_accuracy: 0.9880\n",
            "Epoch 55/75\n",
            "51/51 [==============================] - 6s 112ms/step - batch: 25.0000 - size: 31.8431 - loss: 0.0381 - accuracy: 0.9873 - val_loss: 0.0220 - val_accuracy: 0.9924\n",
            "Epoch 56/75\n",
            "51/51 [==============================] - 6s 112ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0320 - accuracy: 0.9900 - val_loss: 0.1799 - val_accuracy: 0.9419\n",
            "Epoch 57/75\n",
            "51/51 [==============================] - 6s 112ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0312 - accuracy: 0.9899 - val_loss: 0.0377 - val_accuracy: 0.9888\n",
            "Epoch 58/75\n",
            "51/51 [==============================] - 6s 112ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0316 - accuracy: 0.9885 - val_loss: 0.0324 - val_accuracy: 0.9892\n",
            "Epoch 59/75\n",
            "51/51 [==============================] - 6s 114ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0291 - accuracy: 0.9887 - val_loss: 0.0358 - val_accuracy: 0.9888\n",
            "Epoch 60/75\n",
            "51/51 [==============================] - 6s 114ms/step - batch: 25.0000 - size: 32.0000 - loss: 0.0289 - accuracy: 0.9902 - val_loss: 0.0283 - val_accuracy: 0.9904\n",
            "Epoch 61/75\n",
            "51/51 [==============================] - 6s 111ms/step - batch: 25.0000 - size: 31.8431 - loss: 0.0401 - accuracy: 0.9873 - val_loss: 0.1559 - val_accuracy: 0.9579\n",
            "Epoch 62/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 32.0000 - loss: 0.0378 - accuracy: 0.9868 - val_loss: 0.0406 - val_accuracy: 0.9900\n",
            "Epoch 63/75\n",
            "51/51 [==============================] - 6s 110ms/step - batch: 25.0000 - size: 31.8431 - loss: 0.0391 - accuracy: 0.9867 - val_loss: 0.0456 - val_accuracy: 0.9856\n",
            "Epoch 64/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0342 - accuracy: 0.9883 - val_loss: 0.0990 - val_accuracy: 0.9728\n",
            "Epoch 65/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0471 - accuracy: 0.9841 - val_loss: 0.0295 - val_accuracy: 0.9900\n",
            "Epoch 66/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 32.0000 - loss: 0.0285 - accuracy: 0.9904 - val_loss: 0.0736 - val_accuracy: 0.9760\n",
            "Epoch 67/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0412 - accuracy: 0.9862 - val_loss: 0.0312 - val_accuracy: 0.9912\n",
            "Epoch 68/75\n",
            "51/51 [==============================] - 6s 112ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0295 - accuracy: 0.9898 - val_loss: 0.0357 - val_accuracy: 0.9896\n",
            "Epoch 69/75\n",
            "51/51 [==============================] - 6s 111ms/step - batch: 25.0000 - size: 31.8431 - loss: 0.0231 - accuracy: 0.9913 - val_loss: 0.0338 - val_accuracy: 0.9884\n",
            "Epoch 70/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0280 - accuracy: 0.9903 - val_loss: 0.0263 - val_accuracy: 0.9916\n",
            "Epoch 71/75\n",
            "51/51 [==============================] - 6s 113ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0281 - accuracy: 0.9915 - val_loss: 0.0229 - val_accuracy: 0.9928\n",
            "Epoch 72/75\n",
            "51/51 [==============================] - 6s 111ms/step - batch: 25.0000 - size: 32.0000 - loss: 0.0208 - accuracy: 0.9937 - val_loss: 0.0511 - val_accuracy: 0.9864\n",
            "Epoch 73/75\n",
            "51/51 [==============================] - 6s 112ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0299 - accuracy: 0.9908 - val_loss: 0.3330 - val_accuracy: 0.8710\n",
            "Epoch 74/75\n",
            "51/51 [==============================] - 6s 111ms/step - batch: 25.0000 - size: 31.9216 - loss: 0.0194 - accuracy: 0.9943 - val_loss: 0.0290 - val_accuracy: 0.9908\n",
            "Epoch 75/75\n",
            "51/51 [==============================] - 6s 114ms/step - batch: 25.0000 - size: 31.8431 - loss: 0.0265 - accuracy: 0.9915 - val_loss: 0.0403 - val_accuracy: 0.9852\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jhXvz_p7sW0E"
      },
      "source": [
        "# Plotting metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "N0WYScj-rfE7",
        "outputId": "e91e3672-c6ba-4097-8f66-124de00974e1"
      },
      "source": [
        "plt.style.use(\"ggplot\")\n",
        "plt.figure()\n",
        "N = EPOCHS\n",
        "plt.plot(np.arange(0, N), History.history[\"loss\"], label=\"train_loss\")\n",
        "plt.plot(np.arange(0, N), History.history[\"val_loss\"], label=\"val_loss\")\n",
        "plt.plot(np.arange(0, N), History.history[\"accuracy\"], label=\"train_acc\")\n",
        "plt.plot(np.arange(0, N), History.history[\"val_accuracy\"], label=\"val_acc\")\n",
        "plt.title(\"Training Loss and Accuracy\")\n",
        "plt.xlabel(\"Epoch #\")\n",
        "plt.ylabel(\"Loss/Accuracy\")\n",
        "plt.legend(loc=\"upper left\")"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff7697a50d0>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X6bXcEwZz2-Y"
      },
      "source": [
        "# Inference"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a2O5IjeLy1fO"
      },
      "source": [
        "! curl https://raw.githubusercontent.com/ItchyHiker/Multi_Label_Classification_Keras/master/examples/example_01.jpg > example_01.jpg\n",
        "! curl https://raw.githubusercontent.com/ItchyHiker/Multi_Label_Classification_Keras/master/examples/example_02.jpg > example_02.jpg\n",
        "! curl https://raw.githubusercontent.com/ItchyHiker/Multi_Label_Classification_Keras/master/examples/example_03.jpg > example_03.jpg\n",
        "! curl https://raw.githubusercontent.com/ItchyHiker/Multi_Label_Classification_Keras/master/examples/example_04.jpg > example_04.jpg\n",
        "! curl https://raw.githubusercontent.com/ItchyHiker/Multi_Label_Classification_Keras/master/examples/example_05.jpg > example_05.jpg\n",
        "! curl https://raw.githubusercontent.com/ItchyHiker/Multi_Label_Classification_Keras/master/examples/example_06.jpg > example_06.jpg\n",
        "! curl https://raw.githubusercontent.com/ItchyHiker/Multi_Label_Classification_Keras/master/examples/example_07.jpg > example_07.jpg\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5-QQmV2N0kIX",
        "outputId": "0ff1a096-35b6-4e2a-e492-5fddf23030d0"
      },
      "source": [
        "image= cv2.imread(\"example_01.jpg\")\n",
        "output = resize(image, width=400)\n",
        "image = cv2.resize(image, (96, 96))\n",
        "image = image.astype(\"float\") / 255.0 #normalize\n",
        "image = img_to_array(image) #\n",
        "image = np.expand_dims(image, axis=0)\n",
        "\n",
        "proba = model.predict(image)[0]\n",
        "idxs = np.argsort(proba)[::-1][:2]\n",
        "print(proba, idxs)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[7.6473865e-05 2.3821267e-06 9.9951601e-01 4.0902691e-05 1.0000000e+00\n",
            " 1.2813995e-03] [4 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bdl34_j80wQ2",
        "outputId": "0fdb997f-c458-40ad-80f2-49dbec338903"
      },
      "source": [
        "for (i, j) in enumerate(idxs):\n",
        "\t# build the label and draw the label on the image\n",
        "\tlabel = \"{}: {:.2f}%\".format(mlb.classes_[j], proba[j] * 100)\n",
        "\tcv2.putText(output, label, (10, (i * 30) + 25), \n",
        "\t\tcv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)\n",
        " \n",
        " # show the probabilities for each of the individual labels\n",
        "for (label, p) in zip(mlb.classes_, proba):\n",
        "\tprint(\"{}: {:.2f}%\".format(label, p * 100))"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "black: 0.01%\n",
            "blue: 0.00%\n",
            "dress: 99.95%\n",
            "jeans: 0.00%\n",
            "red: 100.00%\n",
            "shirt: 0.13%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 630
        },
        "id": "Ic-v6IHo5rIN",
        "outputId": "41e9ad2e-1c12-4ab7-a8fd-77272d3648f3"
      },
      "source": [
        "from google.colab.patches import cv2_imshow\n",
        "cv2_imshow(output)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=400x613 at 0x7FF7E024BFD0>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kbkM9D8y7yVO"
      },
      "source": [
        "# Personal testing space-->"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iieDDe2A7tZE",
        "outputId": "0eeb6273-a4c1-4770-c5b5-bcc7f844545b"
      },
      "source": [
        "image= cv2.imread(\"example_01.jpg\")\n",
        "output = resize(image, width=400)\n",
        "image = cv2.resize(image, (96, 96))\n",
        "image = image.astype(\"float\") / 255.0 #normalize\n",
        "image = img_to_array(image) #\n",
        "print(\"shape of the image array:> \",image.shape) #(96,96,3)\n",
        "image = np.expand_dims(image, axis=0)\n",
        "print(\"shape of the image array after expanding:> \",image.shape) #(1,96,96,3)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape of the image array:>  (96, 96, 3)\n",
            "shape of the image array after expanding:>  (1, 96, 96, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wj9Tm7q36Ho0",
        "outputId": "20146de5-c0be-49a8-f905-96e7d4ab622a"
      },
      "source": [
        "proba = model.predict(image)\n",
        "print(\"probabilities scores :> \",proba,proba.shape) #6 probabilities for each classes is printed\n",
        "proba= proba[0]\n",
        "print(\"probabilities scores :> \",proba)\n",
        "idxs= np.argsort(proba)\n",
        "print(\"probabilities scores indexes(ascending) :> \",idxs)\n",
        "idxs= np.argsort(proba)[::-1]\n",
        "print(\"probabilities scores indexes(descending) :> \",idxs)\n",
        "idxs= np.argsort(proba)[::-1][:2] # AS 2 LABELS(PREDICTION) ARE THERE FOR EACH IMAGE\n",
        "print(\"top 2 probabilities scores indexes(descending) :> \",idxs)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "probabilities scores :>  [[7.6473865e-05 2.3821267e-06 9.9951601e-01 4.0902691e-05 1.0000000e+00\n",
            "  1.2813995e-03]] (1, 6)\n",
            "probabilities scores :>  [7.6473865e-05 2.3821267e-06 9.9951601e-01 4.0902691e-05 1.0000000e+00\n",
            " 1.2813995e-03]\n",
            "probabilities scores indexes(ascending) :>  [1 3 0 5 2 4]\n",
            "probabilities scores indexes(descending) :>  [4 2 5 0 3 1]\n",
            "top 2 probabilities scores indexes(descending) :>  [4 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bQnf4Gn3-wps"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}