Face_Mask_Detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Face Mask Detection.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/Adarsh-Saurabh/AI-Champ-/blob/main/Face_Mask_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wBkgaP4IUA3M",
        "outputId": "d08aed27-6ca3-485c-c14a-1f51dd42248d"
      },
      "source": [
        "!curl -L \"https://app.roboflow.com/ds/HZgdG78vaQ?key=NXoeXHuv13\" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100   896  100   896    0     0   1969      0 --:--:-- --:--:-- --:--:--  1964\n",
            "100 2625k  100 2625k    0     0  3713k      0 --:--:-- --:--:-- --:--:-- 3713k\n",
            "Archive:  roboflow.zip\n",
            " extracting: README.roboflow.txt     \n",
            " extracting: data.yaml               \n",
            "   creating: test/\n",
            "   creating: test/images/\n",
            " extracting: test/images/012106_jpg_1140x855_jpg.rf.167606c22f7a3f5f52792f49fb9b3f4a.jpg  \n",
            " extracting: test/images/1140-travelers-at-hong-kong-airport_jpg.rf.f1e73b4e2c34ad19716a4efbaeadf035.jpg  \n",
            " extracting: test/images/1539151332674193npsrq70_jpg.rf.c1b130600de3eaa95a421262adb606c7.jpg  \n",
            " extracting: test/images/1580166133-5b61094cac11561b0cbe09826e32a924_jpg.rf.8a98aaf6d76063f9bb1434a5ab926216.jpg  \n",
            " extracting: test/images/IMG_5690-2_jpg.rf.2e14db5a8a1160fc1eaf2bae6f11d948.jpg  \n",
            " extracting: test/images/PHOTriZgIKIgsnNLdBJG75vj200203_1000x2000_jpg.rf.8357b7b3201196796f09d673ab6a59b2.jpg  \n",
            " extracting: test/images/RZXE24HFK5GSVKVSN57VCVPQ4M_jpg.rf.ceb40d1f1c6c2f90471281801ca220f7.jpg  \n",
            " extracting: test/images/phppaNXxb_jpg.rf.e066052f0479f107d1009a56a66ead8f.jpg  \n",
            " extracting: test/images/r1000019q689461s9r0_jpg.rf.72122222ffb2080d541d957db8693521.jpg  \n",
            " extracting: test/images/sdfsdfsfff_jpg.rf.a1996cfd11dce2aacb38405ace5836af.jpg  \n",
            " extracting: test/images/stsciRq_png_jpg.rf.2d814a77856bbaa656f942555c146dcc.jpg  \n",
            "   creating: test/labels/\n",
            " extracting: test/labels/012106_jpg_1140x855_jpg.rf.167606c22f7a3f5f52792f49fb9b3f4a.txt  \n",
            " extracting: test/labels/1140-travelers-at-hong-kong-airport_jpg.rf.f1e73b4e2c34ad19716a4efbaeadf035.txt  \n",
            " extracting: test/labels/1539151332674193npsrq70_jpg.rf.c1b130600de3eaa95a421262adb606c7.txt  \n",
            " extracting: test/labels/1580166133-5b61094cac11561b0cbe09826e32a924_jpg.rf.8a98aaf6d76063f9bb1434a5ab926216.txt  \n",
            " extracting: test/labels/IMG_5690-2_jpg.rf.2e14db5a8a1160fc1eaf2bae6f11d948.txt  \n",
            " extracting: test/labels/PHOTriZgIKIgsnNLdBJG75vj200203_1000x2000_jpg.rf.8357b7b3201196796f09d673ab6a59b2.txt  \n",
            " extracting: test/labels/RZXE24HFK5GSVKVSN57VCVPQ4M_jpg.rf.ceb40d1f1c6c2f90471281801ca220f7.txt  \n",
            " extracting: test/labels/phppaNXxb_jpg.rf.e066052f0479f107d1009a56a66ead8f.txt  \n",
            " extracting: test/labels/r1000019q689461s9r0_jpg.rf.72122222ffb2080d541d957db8693521.txt  \n",
            " extracting: test/labels/sdfsdfsfff_jpg.rf.a1996cfd11dce2aacb38405ace5836af.txt  \n",
            " extracting: test/labels/stsciRq_png_jpg.rf.2d814a77856bbaa656f942555c146dcc.txt  \n",
            "   creating: train/\n",
            "   creating: train/images/\n",
            " extracting: train/images/-1x-1_jpg.rf.4cfb234e9234721666c74b2c7f0e6537.jpg  \n",
            " extracting: train/images/-I1-MS09uaqsLdGTFkgnS0Rcg1mmPyAj95ySg_eckoM_jpeg_jpg.rf.d22b776ce0a15c2e8d9cb38d5df768fb.jpg  \n",
            " extracting: train/images/0002526673_jpg.rf.8d2d1202c7812659ee956036e96760b4.jpg  \n",
            " extracting: train/images/000_1ov3n5_0_jpeg_jpg.rf.e030198663e4ec8e1b7682155d37d186.jpg  \n",
            " extracting: train/images/000b7b75-1600_jpg.rf.24bc58ac93fb2f48b62e7edd687dbb90.jpg  \n",
            " extracting: train/images/003_1024_jpeg_jpg.rf.1286c8c3ff5b6ef96d30cb422872af87.jpg  \n",
            " extracting: train/images/004_1024_jpeg_jpg.rf.402322f409ccfd7bc4afdf22de63df5b.jpg  \n",
            " extracting: train/images/0209-00176-076b1_jpg.rf.1f14b6cfbaa04814980a3acc6b225bd1.jpg  \n",
            " extracting: train/images/0450908675_50159485_mutation-virus-chine-inquietude_jpg.rf.d9ba6d98e0b5f0952588da2c59899e46.jpg  \n",
            " extracting: train/images/0602623232127-web-tete_jpg.rf.f1a111d326d7b26424880c09c8f5c500.jpg  \n",
            " extracting: train/images/0_10725_jpg.rf.7d1a8c67a4ba036dc0f4e50c4fc2cb2a.jpg  \n",
            " extracting: train/images/1-200102094256105_png_jpg.rf.d10c95acaca7bbe44116411c6efdaff2.jpg  \n",
            " extracting: train/images/1000x-1_jpg.rf.be654d7786f3b16e42416024a248e2c0.jpg  \n",
            " extracting: train/images/1024x576_939430607723_jpg.rf.fb101cdf936aa759682912e1e93e0e90.jpg  \n",
            " extracting: train/images/1024x737_09186876046_jpg.rf.8b074ca9286c1d37660d297759ccb0e8.jpg  \n",
            " extracting: train/images/1029731146_jpg.rf.d29ba97f2c83972ebddd9e0651808182.jpg  \n",
            " extracting: train/images/1042977068_jpg.rf.76d218bf21cd9d8c4db30f39bd5b2db7.jpg  \n",
            " extracting: train/images/1125506397_15801322206131n_jpg.rf.a5e4d500db38c63867f9438aea43fea2.jpg  \n",
            " extracting: train/images/1125506397_15801322207071n_jpg.rf.8442b95cdd71d3567b648f00dab960d1.jpg  \n",
            " extracting: train/images/1125506397_15801322207531n_jpg.rf.a0b60951ef331ba79ac7a3a45ec3c72b.jpg  \n",
            " extracting: train/images/1133x768_20200130000023_jpg.rf.5516daa9c9099b14504e3a7482540cab.jpg  \n",
            " extracting: train/images/11893820-3x2-xlarge_jpg.rf.cdba9c14b0e60f9700a98d7f13f594da.jpg  \n",
            " extracting: train/images/1249493_jpg.rf.cf055c09cce02bbfa47bb5683a545ffb.jpg  \n",
            " extracting: train/images/125895-untitled-design-30_jpg.rf.be82b76c3a5616ea70ac1d744757025a.jpg  \n",
            " extracting: train/images/1288788-une-employee-aide-des-voyageurs-en-provenance-de-chine-le-26-janvier-2020-a-l-aeroport-de-roissy_jpg.rf.e657841bf9d7dabb5ce0fa3cb7d8e6a4.jpg  \n",
            " extracting: train/images/1303078448-China-Coronavirus-Death-Toll-Hits-304_jpg.rf.2eb6286199cf9ed7b842290f748f834f.jpg  \n",
            " extracting: train/images/130624011830-malaysia-smog-students-horizontal-large-gallery_jpg.rf.4b49503ee2d1b993f87d764d1a7be4d6.jpg  \n",
            " extracting: train/images/1422808187-3291816118_jpg.rf.01b3a07b0672bf1263cf4da12f0e586a.jpg  \n",
            " extracting: train/images/1477443223-1445746883_jpg.rf.3c41ae78dbcbd73ef74eec84a47b80cf.jpg  \n",
            " extracting: train/images/1482202839575_jpg.rf.996ceb6eb3f771c75977541b4e2c7773.jpg  \n",
            " extracting: train/images/1553605632_9d5877d8_60_jpg.rf.40a8f1d6fb0072e0031190143685dc60.jpg  \n",
            " extracting: train/images/1579683152-0e3ba5698b090c0fda95caa5d2aa7b31_jpg.rf.f985e7b5c785715b7c88897b89283d8d.jpg  \n",
            " extracting: train/images/1579928067_250120120154270000005e2bca03bf464_jpg.rf.d9ecd2267383cc7691e23781c444bd56.jpg  \n",
            " extracting: train/images/1580048340614_jpg.rf.afaaf40e625d401043d6863c97f89197.jpg  \n",
            " extracting: train/images/1580173904-0001oc33f_jpg.rf.309aad8983a40e9a726914e5a841780b.jpg  \n",
            " extracting: train/images/1_13_jpg.rf.6c03e8bf65f24bf8c917b655c465dfbb.jpg  \n",
            " extracting: train/images/PRI_129079114_1580299603_195_jpg.rf.0e4b07ad034fcdb9ac991f71acdd30b2.jpg  \n",
            " extracting: train/images/PhotoGrid_1580452532909_jpg.rf.b2eb75b267c5b8d4b6953d4202ead7d7.jpg  \n",
            " extracting: train/images/RTX1GIDS_jpg.rf.606235467e05dc6a1ca9b6d77b184795.jpg  \n",
            " extracting: train/images/RTX7CD4D-e1580252893876_jpg.rf.a93fe2c7a4a044c2b4c67da39eca300e.jpg  \n",
            " extracting: train/images/ST_20170401_EASAND_3046471_jpg.rf.d2fd3a2981dfd9bf1393ecca59c2a946.jpg  \n",
            " extracting: train/images/ST_20190917_SPTHAZE_5120715_jpg.rf.a98ce18fdc4fed7741504043bc6d4f54.jpg  \n",
            " extracting: train/images/Smog-Mask-PM2-5-Carbon-Filter-Face-Masks-Fog-Dust-Air-Cleaner-anti-pollution-Face-Protector_jpg.rf.8b5dc4859890d34642d44ee9d1e7a1e8.jpg  \n",
            " extracting: train/images/Surgical-Mask-Effectiveness_jpg.rf.0be27252930f061db9d94bfed0687b5d.jpg  \n",
            " extracting: train/images/TASS38012699_jpg.rf.cae8773dada8155ca988817868565c7c.jpg  \n",
            " extracting: train/images/TB2R28UvUOWBKNjSZKzXXXfWFXa_--1116877752_jpg.rf.a32934a810d6d54b79e6f9174e4bae10.jpg  \n",
            " extracting: train/images/Untitled-design-13_png_jpg.rf.6278a44384901c478753c74485bbe3cd.jpg  \n",
            " extracting: train/images/W37H3GWNO5EV5HTJQNP4KLIRGI_jpg.rf.cc4af91ec16dc42aea583c3143a5c7b9.jpg  \n",
            " extracting: train/images/pic_list_out_2285_jpg.rf.a4534d65d62e233b8a4d652ec8c56958.jpg  \n",
            " extracting: train/images/pollution-mask-CE5HYF_jpg.rf.32804ea4845ebfb89ce4182a5cfd085f.jpg  \n",
            " extracting: train/images/purchase-reauisition-5de4d0f99c77d_jpg.rf.b63291454d3cf26494f373dcc4ebaa4c.jpg  \n",
            " extracting: train/images/r1000019q679o5611r7_jpg.rf.24568cc3c2d761315777521b550fac50.jpg  \n",
            " extracting: train/images/r1p00017o82psp0o0or_jpg.rf.c4fb7c519a18dd50bad492c4b04c7874.jpg  \n",
            " extracting: train/images/r1p00017o8357s6sno6_jpg.rf.738ce8058c926d22f47695a14944e8db.jpg  \n",
            " extracting: train/images/rally-against-an-anti-mask-law-meant-to-deter-anti-government-protesters-in-hong-kong-china-shutterstock-editorial-10435716z_jpg.rf.06ed5b62ccc6b97d08af890ceedada92.jpg  \n",
            " extracting: train/images/rawImage_jpg.rf.f0780dae26e1382002885056e4d366b4.jpg  \n",
            " extracting: train/images/sars-gettyimages-157005245_jpg.rf.2b9812caf7c34e9628ddcf9bf0592022.jpg  \n",
            " extracting: train/images/sdsdsff_jpg.rf.335fc4fe18f99ba36aa730ae49fab615.jpg  \n",
            " extracting: train/images/sgssgg_jpeg_jpg.rf.4d8c9ae0139ccf41ef584f2ca4008092.jpg  \n",
            " extracting: train/images/shutterstock_289132226_jpg.rf.09c3dcb76bffe4d3a426731e196b5f05.jpg  \n",
            " extracting: train/images/shutterstock_584407723_jpg.rf.8e5c871be2056f49095ed060a798dbb2.jpg  \n",
            " extracting: train/images/skynews-coronavirus-london-fashion-week_4920359_jpg.rf.275a89abfa907f101f0128ea61a57dc3.jpg  \n",
            " extracting: train/images/skypost16012020a01s_jpg.rf.595c586c93833ffc157f50386cfda985.jpg  \n",
            " extracting: train/images/smartmi-3pcs-filter-mask-pm25-haze-dustproof-mask-with-vent_jpg.rf.a765fa66023855fd6ecebe636eec791a.jpg  \n",
            " extracting: train/images/so-19-_jpg.rf.dc298bb4b33b5eba23ec9c66a241a09c.jpg  \n",
            " extracting: train/images/so-24---1-_jpg.rf.d1e35810a629fffd1f8970fcb2de2e38.jpg  \n",
            " extracting: train/images/soo-1-_jpg.rf.98ae754f7a7a6e4fd16dec48c4f24406.jpg  \n",
            " extracting: train/images/thai_cov-transmission030220_jpg.rf.dddd8e03453bf3201c0d9cbde1a7896b.jpg  \n",
            " extracting: train/images/thumbnail-ba5c72edb320b49a69e86b05775c49b2-scaled-1_jpeg_jpg.rf.27c8a3a602adaa465b80dc76080ba51c.jpg  \n",
            " extracting: train/images/unnamed_jpg.rf.e45d394fa92215bc277d87efb0ba8208.jpg  \n",
            " extracting: train/images/virus_jpg.rf.8c6c00239a2027f98414c3cac88e1489.jpg  \n",
            " extracting: train/images/w1240-p16x9-5f8aa310737f8e0e6c43d95a68a82b03b6f60106_jpg.rf.3459a185729fd7d0550bd39e4652528d.jpg  \n",
            " extracting: train/images/w1240-p16x9-6f1db5e0460631cfa98d2d6d8032942f81700bb0_jpg.rf.d1bda26eaf2ab4ae6a5934602bf1cf84.jpg  \n",
            "   creating: train/labels/\n",
            " extracting: train/labels/-1x-1_jpg.rf.4cfb234e9234721666c74b2c7f0e6537.txt  \n",
            " extracting: train/labels/-I1-MS09uaqsLdGTFkgnS0Rcg1mmPyAj95ySg_eckoM_jpeg_jpg.rf.d22b776ce0a15c2e8d9cb38d5df768fb.txt  \n",
            " extracting: train/labels/0002526673_jpg.rf.8d2d1202c7812659ee956036e96760b4.txt  \n",
            " extracting: train/labels/000_1ov3n5_0_jpeg_jpg.rf.e030198663e4ec8e1b7682155d37d186.txt  \n",
            " extracting: train/labels/000b7b75-1600_jpg.rf.24bc58ac93fb2f48b62e7edd687dbb90.txt  \n",
            " extracting: train/labels/003_1024_jpeg_jpg.rf.1286c8c3ff5b6ef96d30cb422872af87.txt  \n",
            " extracting: train/labels/004_1024_jpeg_jpg.rf.402322f409ccfd7bc4afdf22de63df5b.txt  \n",
            " extracting: train/labels/0209-00176-076b1_jpg.rf.1f14b6cfbaa04814980a3acc6b225bd1.txt  \n",
            " extracting: train/labels/0450908675_50159485_mutation-virus-chine-inquietude_jpg.rf.d9ba6d98e0b5f0952588da2c59899e46.txt  \n",
            " extracting: train/labels/0602623232127-web-tete_jpg.rf.f1a111d326d7b26424880c09c8f5c500.txt  \n",
            " extracting: train/labels/0_10725_jpg.rf.7d1a8c67a4ba036dc0f4e50c4fc2cb2a.txt  \n",
            " extracting: train/labels/1-200102094256105_png_jpg.rf.d10c95acaca7bbe44116411c6efdaff2.txt  \n",
            " extracting: train/labels/1000x-1_jpg.rf.be654d7786f3b16e42416024a248e2c0.txt  \n",
            " extracting: train/labels/1024x576_939430607723_jpg.rf.fb101cdf936aa759682912e1e93e0e90.txt  \n",
            " extracting: train/labels/1024x737_09186876046_jpg.rf.8b074ca9286c1d37660d297759ccb0e8.txt  \n",
            " extracting: train/labels/1029731146_jpg.rf.d29ba97f2c83972ebddd9e0651808182.txt  \n",
            " extracting: train/labels/1042977068_jpg.rf.76d218bf21cd9d8c4db30f39bd5b2db7.txt  \n",
            " extracting: train/labels/1125506397_15801322206131n_jpg.rf.a5e4d500db38c63867f9438aea43fea2.txt  \n",
            " extracting: train/labels/1125506397_15801322207071n_jpg.rf.8442b95cdd71d3567b648f00dab960d1.txt  \n",
            " extracting: train/labels/1125506397_15801322207531n_jpg.rf.a0b60951ef331ba79ac7a3a45ec3c72b.txt  \n",
            " extracting: train/labels/1133x768_20200130000023_jpg.rf.5516daa9c9099b14504e3a7482540cab.txt  \n",
            " extracting: train/labels/11893820-3x2-xlarge_jpg.rf.cdba9c14b0e60f9700a98d7f13f594da.txt  \n",
            " extracting: train/labels/1249493_jpg.rf.cf055c09cce02bbfa47bb5683a545ffb.txt  \n",
            " extracting: train/labels/125895-untitled-design-30_jpg.rf.be82b76c3a5616ea70ac1d744757025a.txt  \n",
            " extracting: train/labels/1288788-une-employee-aide-des-voyageurs-en-provenance-de-chine-le-26-janvier-2020-a-l-aeroport-de-roissy_jpg.rf.e657841bf9d7dabb5ce0fa3cb7d8e6a4.txt  \n",
            " extracting: train/labels/1303078448-China-Coronavirus-Death-Toll-Hits-304_jpg.rf.2eb6286199cf9ed7b842290f748f834f.txt  \n",
            " extracting: train/labels/130624011830-malaysia-smog-students-horizontal-large-gallery_jpg.rf.4b49503ee2d1b993f87d764d1a7be4d6.txt  \n",
            " extracting: train/labels/1422808187-3291816118_jpg.rf.01b3a07b0672bf1263cf4da12f0e586a.txt  \n",
            " extracting: train/labels/1477443223-1445746883_jpg.rf.3c41ae78dbcbd73ef74eec84a47b80cf.txt  \n",
            " extracting: train/labels/1482202839575_jpg.rf.996ceb6eb3f771c75977541b4e2c7773.txt  \n",
            " extracting: train/labels/1553605632_9d5877d8_60_jpg.rf.40a8f1d6fb0072e0031190143685dc60.txt  \n",
            " extracting: train/labels/1579683152-0e3ba5698b090c0fda95caa5d2aa7b31_jpg.rf.f985e7b5c785715b7c88897b89283d8d.txt  \n",
            " extracting: train/labels/1579928067_250120120154270000005e2bca03bf464_jpg.rf.d9ecd2267383cc7691e23781c444bd56.txt  \n",
            " extracting: train/labels/1580048340614_jpg.rf.afaaf40e625d401043d6863c97f89197.txt  \n",
            " extracting: train/labels/1580173904-0001oc33f_jpg.rf.309aad8983a40e9a726914e5a841780b.txt  \n",
            " extracting: train/labels/1_13_jpg.rf.6c03e8bf65f24bf8c917b655c465dfbb.txt  \n",
            " extracting: train/labels/PRI_129079114_1580299603_195_jpg.rf.0e4b07ad034fcdb9ac991f71acdd30b2.txt  \n",
            " extracting: train/labels/PhotoGrid_1580452532909_jpg.rf.b2eb75b267c5b8d4b6953d4202ead7d7.txt  \n",
            " extracting: train/labels/RTX1GIDS_jpg.rf.606235467e05dc6a1ca9b6d77b184795.txt  \n",
            " extracting: train/labels/RTX7CD4D-e1580252893876_jpg.rf.a93fe2c7a4a044c2b4c67da39eca300e.txt  \n",
            " extracting: train/labels/ST_20170401_EASAND_3046471_jpg.rf.d2fd3a2981dfd9bf1393ecca59c2a946.txt  \n",
            " extracting: train/labels/ST_20190917_SPTHAZE_5120715_jpg.rf.a98ce18fdc4fed7741504043bc6d4f54.txt  \n",
            " extracting: train/labels/Smog-Mask-PM2-5-Carbon-Filter-Face-Masks-Fog-Dust-Air-Cleaner-anti-pollution-Face-Protector_jpg.rf.8b5dc4859890d34642d44ee9d1e7a1e8.txt  \n",
            " extracting: train/labels/Surgical-Mask-Effectiveness_jpg.rf.0be27252930f061db9d94bfed0687b5d.txt  \n",
            " extracting: train/labels/TASS38012699_jpg.rf.cae8773dada8155ca988817868565c7c.txt  \n",
            " extracting: train/labels/TB2R28UvUOWBKNjSZKzXXXfWFXa_--1116877752_jpg.rf.a32934a810d6d54b79e6f9174e4bae10.txt  \n",
            " extracting: train/labels/Untitled-design-13_png_jpg.rf.6278a44384901c478753c74485bbe3cd.txt  \n",
            " extracting: train/labels/W37H3GWNO5EV5HTJQNP4KLIRGI_jpg.rf.cc4af91ec16dc42aea583c3143a5c7b9.txt  \n",
            " extracting: train/labels/pic_list_out_2285_jpg.rf.a4534d65d62e233b8a4d652ec8c56958.txt  \n",
            " extracting: train/labels/pollution-mask-CE5HYF_jpg.rf.32804ea4845ebfb89ce4182a5cfd085f.txt  \n",
            " extracting: train/labels/purchase-reauisition-5de4d0f99c77d_jpg.rf.b63291454d3cf26494f373dcc4ebaa4c.txt  \n",
            " extracting: train/labels/r1000019q679o5611r7_jpg.rf.24568cc3c2d761315777521b550fac50.txt  \n",
            " extracting: train/labels/r1p00017o82psp0o0or_jpg.rf.c4fb7c519a18dd50bad492c4b04c7874.txt  \n",
            " extracting: train/labels/r1p00017o8357s6sno6_jpg.rf.738ce8058c926d22f47695a14944e8db.txt  \n",
            " extracting: train/labels/rally-against-an-anti-mask-law-meant-to-deter-anti-government-protesters-in-hong-kong-china-shutterstock-editorial-10435716z_jpg.rf.06ed5b62ccc6b97d08af890ceedada92.txt  \n",
            " extracting: train/labels/rawImage_jpg.rf.f0780dae26e1382002885056e4d366b4.txt  \n",
            " extracting: train/labels/sars-gettyimages-157005245_jpg.rf.2b9812caf7c34e9628ddcf9bf0592022.txt  \n",
            " extracting: train/labels/sdsdsff_jpg.rf.335fc4fe18f99ba36aa730ae49fab615.txt  \n",
            " extracting: train/labels/sgssgg_jpeg_jpg.rf.4d8c9ae0139ccf41ef584f2ca4008092.txt  \n",
            " extracting: train/labels/shutterstock_289132226_jpg.rf.09c3dcb76bffe4d3a426731e196b5f05.txt  \n",
            " extracting: train/labels/shutterstock_584407723_jpg.rf.8e5c871be2056f49095ed060a798dbb2.txt  \n",
            " extracting: train/labels/skynews-coronavirus-london-fashion-week_4920359_jpg.rf.275a89abfa907f101f0128ea61a57dc3.txt  \n",
            " extracting: train/labels/skypost16012020a01s_jpg.rf.595c586c93833ffc157f50386cfda985.txt  \n",
            " extracting: train/labels/smartmi-3pcs-filter-mask-pm25-haze-dustproof-mask-with-vent_jpg.rf.a765fa66023855fd6ecebe636eec791a.txt  \n",
            " extracting: train/labels/so-19-_jpg.rf.dc298bb4b33b5eba23ec9c66a241a09c.txt  \n",
            " extracting: train/labels/so-24---1-_jpg.rf.d1e35810a629fffd1f8970fcb2de2e38.txt  \n",
            " extracting: train/labels/soo-1-_jpg.rf.98ae754f7a7a6e4fd16dec48c4f24406.txt  \n",
            " extracting: train/labels/thai_cov-transmission030220_jpg.rf.dddd8e03453bf3201c0d9cbde1a7896b.txt  \n",
            " extracting: train/labels/thumbnail-ba5c72edb320b49a69e86b05775c49b2-scaled-1_jpeg_jpg.rf.27c8a3a602adaa465b80dc76080ba51c.txt  \n",
            " extracting: train/labels/unnamed_jpg.rf.e45d394fa92215bc277d87efb0ba8208.txt  \n",
            " extracting: train/labels/virus_jpg.rf.8c6c00239a2027f98414c3cac88e1489.txt  \n",
            " extracting: train/labels/w1240-p16x9-5f8aa310737f8e0e6c43d95a68a82b03b6f60106_jpg.rf.3459a185729fd7d0550bd39e4652528d.txt  \n",
            " extracting: train/labels/w1240-p16x9-6f1db5e0460631cfa98d2d6d8032942f81700bb0_jpg.rf.d1bda26eaf2ab4ae6a5934602bf1cf84.txt  \n",
            "   creating: valid/\n",
            "   creating: valid/images/\n",
            " extracting: valid/images/001_1024_jpeg_jpg.rf.1cf6251db63bfdb4c502bce2349b1c58.jpg  \n",
            " extracting: valid/images/012420_coronoa_masks_web_jpg.rf.c4682aa0da10f1d3c0d66e5485a23ca1.jpg  \n",
            " extracting: valid/images/0200b38c89b16c37c5de8e247bb00c2f_jpg.rf.0bcd1d8b5e8966ba91f7f137af98b0df.jpg  \n",
            " extracting: valid/images/0_8w7mkX-PHcfMM5s6_jpeg_jpg.rf.7e3115bfd1f2b0f39dd1f02517b571db.jpg  \n",
            " extracting: valid/images/101d6660a8974050b613dd2e6bd59390_jpg.rf.d5b181d9163d4f1536c33b8c8ebe91fb.jpg  \n",
            " extracting: valid/images/1030611863_jpg.rf.8826268b25caf4e603624162abd23761.jpg  \n",
            " extracting: valid/images/1152x768_246964803156_jpg.rf.201e2aa2764db2b9d090d0aaba582745.jpg  \n",
            " extracting: valid/images/1155x768_20200129000089_jpg.rf.7fff6466b1b2450647d3038f52a518e3.jpg  \n",
            " extracting: valid/images/135e-huxwryw6451820_jpg.rf.7cc42fb5a5296a34bc4f38cfa1a7ca97.jpg  \n",
            " extracting: valid/images/15-08608-001_jpg.rf.746505a66898ff62dfe4aec193ed360b.jpg  \n",
            " extracting: valid/images/1580053154-5839_jpg.rf.8c051a4d4e50102bd9607513f74bdc66.jpg  \n",
            " extracting: valid/images/1580128422_jpg.rf.8f440a89540a8264804288534e18e230.jpg  \n",
            " extracting: valid/images/1_R_kFK9pNLfKAuopY_lAaPQ_jpeg_jpg.rf.9755d0df7fe63f89c0c63b8a46323459.jpg  \n",
            " extracting: valid/images/UDD3JG3E4JFQ5KSFNGQQ5REINU_jpg.rf.49181438b22c86ee1f164a114eb4f7a5.jpg  \n",
            " extracting: valid/images/US-expands-coronavirus-screenings-United-suspends-some-flights-to-China_jpg.rf.cf515842d1b218a9643c8d95d8c2ef12.jpg  \n",
            " extracting: valid/images/pjecDNMmCUQmbDoJx1fdqolnfHtpsfFK8jTA2Ng0wNg_jpeg_jpg.rf.7be3c1d491be7bb44c3949755deaa404.jpg  \n",
            " extracting: valid/images/r1p00017o80q34s9781_jpg.rf.eec03ae8e54a94d76b91a892774149ef.jpg  \n",
            " extracting: valid/images/remote--1-_jpeg_jpg.rf.0e467a0d682ca18142d8d55f9a57ff53.jpg  \n",
            " extracting: valid/images/soooo_jpg.rf.93f487cb09b518a26b4edeb87f19b6a4.jpg  \n",
            " extracting: valid/images/upload-productImg-1527496119162_320_234_jpeg_jpg.rf.c802d7491bc89c7a128a68dd8816110e.jpg  \n",
            " extracting: valid/images/virus_protection123_jpg.rf.edc4e9b9999c104c5aec8a4cc3fa8d9a.jpg  \n",
            "   creating: valid/labels/\n",
            " extracting: valid/labels/001_1024_jpeg_jpg.rf.1cf6251db63bfdb4c502bce2349b1c58.txt  \n",
            " extracting: valid/labels/012420_coronoa_masks_web_jpg.rf.c4682aa0da10f1d3c0d66e5485a23ca1.txt  \n",
            " extracting: valid/labels/0200b38c89b16c37c5de8e247bb00c2f_jpg.rf.0bcd1d8b5e8966ba91f7f137af98b0df.txt  \n",
            " extracting: valid/labels/0_8w7mkX-PHcfMM5s6_jpeg_jpg.rf.7e3115bfd1f2b0f39dd1f02517b571db.txt  \n",
            " extracting: valid/labels/101d6660a8974050b613dd2e6bd59390_jpg.rf.d5b181d9163d4f1536c33b8c8ebe91fb.txt  \n",
            " extracting: valid/labels/1030611863_jpg.rf.8826268b25caf4e603624162abd23761.txt  \n",
            " extracting: valid/labels/1152x768_246964803156_jpg.rf.201e2aa2764db2b9d090d0aaba582745.txt  \n",
            " extracting: valid/labels/1155x768_20200129000089_jpg.rf.7fff6466b1b2450647d3038f52a518e3.txt  \n",
            " extracting: valid/labels/135e-huxwryw6451820_jpg.rf.7cc42fb5a5296a34bc4f38cfa1a7ca97.txt  \n",
            " extracting: valid/labels/15-08608-001_jpg.rf.746505a66898ff62dfe4aec193ed360b.txt  \n",
            " extracting: valid/labels/1580053154-5839_jpg.rf.8c051a4d4e50102bd9607513f74bdc66.txt  \n",
            " extracting: valid/labels/1580128422_jpg.rf.8f440a89540a8264804288534e18e230.txt  \n",
            " extracting: valid/labels/1_R_kFK9pNLfKAuopY_lAaPQ_jpeg_jpg.rf.9755d0df7fe63f89c0c63b8a46323459.txt  \n",
            " extracting: valid/labels/UDD3JG3E4JFQ5KSFNGQQ5REINU_jpg.rf.49181438b22c86ee1f164a114eb4f7a5.txt  \n",
            " extracting: valid/labels/US-expands-coronavirus-screenings-United-suspends-some-flights-to-China_jpg.rf.cf515842d1b218a9643c8d95d8c2ef12.txt  \n",
            " extracting: valid/labels/pjecDNMmCUQmbDoJx1fdqolnfHtpsfFK8jTA2Ng0wNg_jpeg_jpg.rf.7be3c1d491be7bb44c3949755deaa404.txt  \n",
            " extracting: valid/labels/r1p00017o80q34s9781_jpg.rf.eec03ae8e54a94d76b91a892774149ef.txt  \n",
            " extracting: valid/labels/remote--1-_jpeg_jpg.rf.0e467a0d682ca18142d8d55f9a57ff53.txt  \n",
            " extracting: valid/labels/soooo_jpg.rf.93f487cb09b518a26b4edeb87f19b6a4.txt  \n",
            " extracting: valid/labels/upload-productImg-1527496119162_320_234_jpeg_jpg.rf.c802d7491bc89c7a128a68dd8816110e.txt  \n",
            " extracting: valid/labels/virus_protection123_jpg.rf.edc4e9b9999c104c5aec8a4cc3fa8d9a.txt  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LWoalQanSSLP",
        "outputId": "a8a2b61c-e872-426b-d739-d9fb5942be99"
      },
      "source": [
        "!git clone https://github.com/feat7/yolov5\n",
        "%cd yolov5\n",
        "%pip install -qr requirements.txt\n",
        "\n",
        "import torch\n",
        "from IPython.display import Image, clear_output\n",
        "\n",
        "clear_output()\n",
        "print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Setup complete. Using torch 1.8.1+cu101 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', major=6, minor=0, total_memory=16280MB, multi_processor_count=56)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tmMRYdCBSXzO",
        "outputId": "3d3ac02e-012c-4231-a375-1bec29baa8ac"
      },
      "source": [
        "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images/\n",
        "clear_output()\n",
        "print('Weights Loaded')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Weights Loaded\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gan0UoP-SZ5B",
        "outputId": "29aab9ae-0961-4be0-c6e9-4df75be21448"
      },
      "source": [
        "!pip install gdown wandb"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: gdown in /usr/local/lib/python3.7/dist-packages (3.6.4)\n",
            "Collecting wandb\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/6c/48/b199e2b3b341ac842108c5db4956091dd75d961cfa77aceb033e99cac20f/wandb-0.10.31-py2.py3-none-any.whl (1.8MB)\n",
            "\u001b[K     |████████████████████████████████| 1.8MB 7.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from gdown) (1.15.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from gdown) (4.41.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from gdown) (2.23.0)\n",
            "Collecting sentry-sdk>=0.4.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1c/4a/a54b254f67d8f4052338d54ebe90126f200693440a93ef76d254d581e3ec/sentry_sdk-1.1.0-py2.py3-none-any.whl (131kB)\n",
            "\u001b[K     |████████████████████████████████| 133kB 48.4MB/s \n",
            "\u001b[?25hCollecting shortuuid>=0.5.0\n",
            "  Downloading https://files.pythonhosted.org/packages/25/a6/2ecc1daa6a304e7f1b216f0896b26156b78e7c38e1211e9b798b4716c53d/shortuuid-1.0.1-py3-none-any.whl\n",
            "Collecting pathtools\n",
            "  Downloading https://files.pythonhosted.org/packages/e7/7f/470d6fcdf23f9f3518f6b0b76be9df16dcc8630ad409947f8be2eb0ed13a/pathtools-0.1.2.tar.gz\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.8.1)\n",
            "Collecting configparser>=3.8.1\n",
            "  Downloading https://files.pythonhosted.org/packages/fd/01/ff260a18caaf4457eb028c96eeb405c4a230ca06c8ec9c1379f813caa52e/configparser-5.0.2-py3-none-any.whl\n",
            "Requirement already satisfied: protobuf>=3.12.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.12.4)\n",
            "Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (5.4.8)\n",
            "Requirement already satisfied: promise<3,>=2.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.3)\n",
            "Collecting GitPython>=1.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/27/da/6f6224fdfc47dab57881fe20c0d1bc3122be290198ba0bf26a953a045d92/GitPython-3.1.17-py3-none-any.whl (166kB)\n",
            "\u001b[K     |████████████████████████████████| 174kB 44.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from wandb) (5.4.1)\n",
            "Requirement already satisfied: Click>=7.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (7.1.2)\n",
            "Collecting subprocess32>=3.5.3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/32/c8/564be4d12629b912ea431f1a50eb8b3b9d00f1a0b1ceff17f266be190007/subprocess32-3.5.4.tar.gz (97kB)\n",
            "\u001b[K     |████████████████████████████████| 102kB 12.2MB/s \n",
            "\u001b[?25hCollecting docker-pycreds>=0.4.0\n",
            "  Downloading https://files.pythonhosted.org/packages/f5/e8/f6bd1eee09314e7e6dee49cbe2c5e22314ccdb38db16c9fc72d2fa80d054/docker_pycreds-0.4.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->gdown) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->gdown) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->gdown) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->gdown) (2020.12.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.12.0->wandb) (56.1.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.0; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from GitPython>=1.0.0->wandb) (3.7.4.3)\n",
            "Collecting gitdb<5,>=4.0.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ea/e8/f414d1a4f0bbc668ed441f74f44c116d9816833a48bf81d22b697090dba8/gitdb-4.0.7-py3-none-any.whl (63kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 10.3MB/s \n",
            "\u001b[?25hCollecting smmap<5,>=3.0.1\n",
            "  Downloading https://files.pythonhosted.org/packages/68/ee/d540eb5e5996eb81c26ceffac6ee49041d473bc5125f2aa995cf51ec1cf1/smmap-4.0.0-py2.py3-none-any.whl\n",
            "Building wheels for collected packages: pathtools, subprocess32\n",
            "  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pathtools: filename=pathtools-0.1.2-cp37-none-any.whl size=8786 sha256=5cb57b3165c0836e9cbdcd855fa9d0d2783023c21639d7344d222434545cd3b8\n",
            "  Stored in directory: /root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n",
            "  Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for subprocess32: filename=subprocess32-3.5.4-cp37-none-any.whl size=6489 sha256=0919788f93979391e3d6aef42cc9b9a43db730525c30045ef69b0f3c21e7d38d\n",
            "  Stored in directory: /root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n",
            "Successfully built pathtools subprocess32\n",
            "Installing collected packages: sentry-sdk, shortuuid, pathtools, configparser, smmap, gitdb, GitPython, subprocess32, docker-pycreds, wandb\n",
            "Successfully installed GitPython-3.1.17 configparser-5.0.2 docker-pycreds-0.4.0 gitdb-4.0.7 pathtools-0.1.2 sentry-sdk-1.1.0 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 wandb-0.10.31\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IZhF35qASsSG",
        "outputId": "ffef25fb-11c6-4c2e-e94b-a208eb0983dd"
      },
      "source": [
        "!python train.py --img 416 --batch 32 --epochs 200 --data data.yaml --weights yolov5s.pt --nosave --cache"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/feat7/yolov5 ✅\n",
            "YOLOv5 🚀 v3.1-336-gc03d590 torch 1.8.1+cu101 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)\n",
            "\n",
            "Namespace(adam=False, artifact_alias='latest', batch_size=32, bbox_interval=-1, bucket='', cache_images=True, cfg='', data='data.yaml', device='', entity=None, epochs=200, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[416, 416], label_smoothing=0.0, linear_lr=False, local_rank=-1, multi_scale=False, name='exp', noautoanchor=False, nosave=True, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs/train/exp4', save_period=-1, single_cls=False, sync_bn=False, total_batch_size=32, upload_dataset=False, weights='yolov5s.pt', workers=8, world_size=1)\n",
            "\u001b[34m\u001b[1mtensorboard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
            "2021-05-30 13:13:02.133591: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
            "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mfeat7\u001b[0m (use `wandb login --relogin` to force relogin)\n",
            "2021-05-30 13:13:04.324478: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.10.31\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mexp4\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/feat7/YOLOv5\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/feat7/YOLOv5/runs/112oqsrs\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in /content/yolov5/wandb/run-20210530_131303-112oqsrs\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run `wandb offline` to turn off syncing.\n",
            "\n",
            "Overriding model.yaml nc=80 with nc=2\n",
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1      3520  models.common.Focus                     [3, 32, 3]                    \n",
            "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
            "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
            "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
            "  4                -1  1    156928  models.common.C3                        [128, 128, 3]                 \n",
            "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
            "  6                -1  1    625152  models.common.C3                        [256, 256, 3]                 \n",
            "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
            "  8                -1  1    656896  models.common.SPP                       [512, 512, [5, 9, 13]]        \n",
            "  9                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
            " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
            " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
            " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
            " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
            " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
            " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
            " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
            " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
            " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
            " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
            " 24      [17, 20, 23]  1     18879  models.yolo.Detect                      [2, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
            "Model Summary: 283 layers, 7066239 parameters, 7066239 gradients, 16.4 GFLOPS\n",
            "\n",
            "Transferred 356/362 items from yolov5s.pt\n",
            "Scaled weight_decay = 0.0005\n",
            "Optimizer groups: 62 .bias, 62 conv.weight, 59 other\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../train/labels.cache' images and labels... 73 found, 0 missing, 0 empty, 0 corrupted: 100% 73/73 [00:00<00:00, 499484.82it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.0GB): 100% 73/73 [00:00<00:00, 659.22it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning '../valid/labels.cache' images and labels... 21 found, 0 missing, 0 empty, 0 corrupted: 100% 21/21 [00:00<00:00, 60411.79it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.0GB): 100% 21/21 [00:00<00:00, 87816.93it/s]\n",
            "Plotting labels... \n",
            "\n",
            "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 6.09, Best Possible Recall (BPR) = 1.0000\n",
            "Image sizes 416 train, 416 test\n",
            "Using 2 dataloader workers\n",
            "Logging results to runs/train/exp4\n",
            "Starting training for 200 epochs...\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     0/199    0.824G    0.1209   0.04129   0.03016    0.1924        74       416: 100% 3/3 [00:02<00:00,  1.44it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  1.99it/s]\n",
            "                 all          21         222     0.00149     0.00794    6.68e-05    9.09e-06\n",
            "Images sizes do not match. This will causes images to be display incorrectly in the UI.\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     1/199     2.92G    0.1211   0.05118    0.0299    0.2022       121       416: 100% 3/3 [00:00<00:00,  5.13it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.56it/s]\n",
            "                 all          21         222     0.00205      0.0106    7.33e-05    7.67e-06\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     2/199     2.92G    0.1208   0.04687   0.02937     0.197        79       416: 100% 3/3 [00:00<00:00,  5.33it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.35it/s]\n",
            "                 all          21         222     0.00246     0.00529    7.27e-05    7.27e-06\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     3/199     2.92G     0.118    0.0525   0.02872    0.1992       105       416: 100% 3/3 [00:00<00:00,  4.90it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.54it/s]\n",
            "                 all          21         222     0.00218     0.00529    6.23e-05     7.6e-06\n",
            "Images sizes do not match. This will causes images to be display incorrectly in the UI.\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     4/199     2.92G    0.1176   0.04436   0.02841    0.1904        91       416: 100% 3/3 [00:00<00:00,  5.16it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.28it/s]\n",
            "                 all          21         222     0.00224     0.00529    7.04e-05    8.88e-06\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     5/199     2.92G    0.1162   0.04822   0.02726    0.1917        90       416: 100% 3/3 [00:00<00:00,  5.41it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.25it/s]\n",
            "                 all          21         222     0.00267      0.0185    0.000174    2.55e-05\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     6/199     2.92G    0.1156   0.05728   0.02723    0.2001       130       416: 100% 3/3 [00:00<00:00,  5.48it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.20it/s]\n",
            "                 all          21         222     0.00265     0.00794    0.000113    2.56e-05\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     7/199     2.92G    0.1136   0.05539   0.02525    0.1943        96       416: 100% 3/3 [00:00<00:00,  5.44it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.27it/s]\n",
            "                 all          21         222     0.00509      0.0132    0.000242    4.58e-05\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     8/199     2.92G    0.1118   0.05321   0.02543    0.1904        86       416: 100% 3/3 [00:00<00:00,  5.40it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.12it/s]\n",
            "                 all          21         222     0.00505      0.0132    0.000292       6e-05\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     9/199     2.92G    0.1092   0.05558   0.02542    0.1902        85       416: 100% 3/3 [00:00<00:00,  5.51it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.18it/s]\n",
            "                 all          21         222     0.00522      0.0159     0.00039    7.25e-05\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    10/199     2.92G    0.1064   0.05712   0.02426    0.1878        70       416: 100% 3/3 [00:00<00:00,  5.48it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.17it/s]\n",
            "                 all          21         222     0.00603      0.0185    0.000517    8.93e-05\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    11/199     2.92G    0.1037   0.05578   0.02432    0.1838        62       416: 100% 3/3 [00:00<00:00,  5.37it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.00it/s]\n",
            "                 all          21         222     0.00678      0.0212    0.000629    0.000111\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    12/199     2.92G    0.1059   0.05426   0.02214    0.1823        82       416: 100% 3/3 [00:00<00:00,  5.40it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.20it/s]\n",
            "                 all          21         222     0.00641      0.0238    0.000686    0.000125\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    13/199     2.92G    0.1025   0.05886   0.02293    0.1843        80       416: 100% 3/3 [00:00<00:00,  5.47it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.05it/s]\n",
            "                 all          21         222     0.00589      0.0265    0.000803    0.000143\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    14/199     2.92G    0.1011    0.0566   0.02267    0.1804        88       416: 100% 3/3 [00:00<00:00,  5.27it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.07it/s]\n",
            "                 all          21         222     0.00649      0.0159     0.00103    0.000184\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    15/199     2.92G    0.1018   0.05984   0.02178    0.1834        55       416: 100% 3/3 [00:00<00:00,  5.47it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.93it/s]\n",
            "                 all          21         222     0.00749      0.0635     0.00176    0.000325\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    16/199     2.92G   0.09754   0.05721   0.02162    0.1764        61       416: 100% 3/3 [00:00<00:00,  5.27it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.76it/s]\n",
            "                 all          21         222      0.0101      0.0926     0.00288    0.000505\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    17/199     2.92G   0.09726   0.05785    0.0211    0.1762        84       416: 100% 3/3 [00:00<00:00,  5.40it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.71it/s]\n",
            "                 all          21         222      0.0118      0.0847     0.00366     0.00065\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    18/199     2.92G   0.09659   0.05768   0.01993    0.1742        66       416: 100% 3/3 [00:00<00:00,  5.37it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.79it/s]\n",
            "                 all          21         222      0.0176      0.0873     0.00589     0.00106\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    19/199     2.92G   0.09601   0.06711   0.02052    0.1836        76       416: 100% 3/3 [00:00<00:00,  5.27it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:01<00:00,  1.17s/it]\n",
            "                 all          21         222       0.034      0.0741      0.0107     0.00186\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    20/199     2.92G   0.09811   0.07199   0.02051    0.1906       116       416: 100% 3/3 [00:00<00:00,  3.02it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.39it/s]\n",
            "                 all          21         222      0.0464      0.0979      0.0173     0.00316\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    21/199     2.92G   0.09207   0.05819   0.01932    0.1696        70       416: 100% 3/3 [00:00<00:00,  5.33it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.44it/s]\n",
            "                 all          21         222      0.0648       0.119       0.027     0.00558\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    22/199     2.92G    0.0901   0.05308   0.01911    0.1623        70       416: 100% 3/3 [00:00<00:00,  5.28it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.40it/s]\n",
            "                 all          21         222       0.102       0.161      0.0367     0.00751\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    23/199     2.92G   0.08965   0.06422   0.01799    0.1719        85       416: 100% 3/3 [00:00<00:00,  5.32it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.26it/s]\n",
            "                 all          21         222       0.137       0.192      0.0521     0.00999\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    24/199     2.92G   0.08942   0.06842   0.01865    0.1765        88       416: 100% 3/3 [00:00<00:00,  5.30it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.45it/s]\n",
            "                 all          21         222       0.269       0.171      0.0727      0.0134\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    25/199     2.92G   0.08434   0.05392   0.01936    0.1576        55       416: 100% 3/3 [00:00<00:00,  5.47it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.33it/s]\n",
            "                 all          21         222       0.318       0.186      0.0767      0.0147\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    26/199     2.92G   0.08412   0.06332   0.02145    0.1689        85       416: 100% 3/3 [00:00<00:00,  5.26it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.53it/s]\n",
            "                 all          21         222       0.334       0.195      0.0853      0.0158\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    27/199     2.92G   0.08243   0.05801   0.01859     0.159        74       416: 100% 3/3 [00:00<00:00,  5.51it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.29it/s]\n",
            "                 all          21         222       0.213       0.195      0.0743      0.0178\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    28/199     2.92G   0.08189    0.0559   0.01729    0.1551        72       416: 100% 3/3 [00:00<00:00,  5.48it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.49it/s]\n",
            "                 all          21         222       0.326       0.226      0.0944      0.0216\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    29/199     2.92G   0.08003    0.0522   0.01808    0.1503        57       416: 100% 3/3 [00:00<00:00,  5.49it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.51it/s]\n",
            "                 all          21         222       0.459       0.251       0.134       0.029\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    30/199     2.92G   0.07878   0.05319   0.01684    0.1488        81       416: 100% 3/3 [00:00<00:00,  5.35it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.41it/s]\n",
            "                 all          21         222       0.458       0.265        0.14      0.0309\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    31/199     2.92G   0.07971   0.06605   0.02009    0.1658       146       416: 100% 3/3 [00:00<00:00,  5.39it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.63it/s]\n",
            "                 all          21         222       0.438       0.278       0.145      0.0357\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    32/199     2.92G   0.07557   0.04936   0.01586    0.1408        80       416: 100% 3/3 [00:00<00:00,  5.22it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.75it/s]\n",
            "                 all          21         222       0.427       0.291       0.149      0.0356\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    33/199     2.92G   0.07257    0.0491   0.01648    0.1382        69       416: 100% 3/3 [00:00<00:00,  5.39it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.67it/s]\n",
            "                 all          21         222       0.428       0.289       0.154       0.041\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    34/199     2.92G   0.06982   0.04704   0.01931    0.1362        51       416: 100% 3/3 [00:00<00:00,  5.42it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.82it/s]\n",
            "                 all          21         222       0.669       0.201        0.15      0.0325\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    35/199     2.92G   0.07185   0.04852   0.01882    0.1392        62       416: 100% 3/3 [00:00<00:00,  5.41it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.79it/s]\n",
            "                 all          21         222       0.653        0.18       0.145      0.0321\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    36/199     2.92G   0.07583    0.0529   0.01895    0.1477       127       416: 100% 3/3 [00:00<00:00,  5.27it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.12it/s]\n",
            "                 all          21         222       0.679       0.185        0.16      0.0362\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    37/199     2.92G   0.07398   0.05185   0.01579    0.1416       118       416: 100% 3/3 [00:00<00:00,  5.34it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.90it/s]\n",
            "                 all          21         222       0.693       0.196       0.188      0.0494\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    38/199     2.92G   0.07251   0.05336   0.02059    0.1465       103       416: 100% 3/3 [00:00<00:00,  5.48it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.10it/s]\n",
            "                 all          21         222       0.719       0.196       0.197      0.0639\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    39/199     2.92G   0.06885   0.04682   0.01774    0.1334        80       416: 100% 3/3 [00:00<00:00,  5.40it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:01<00:00,  1.12s/it]\n",
            "                 all          21         222       0.724       0.198       0.154       0.046\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    40/199     2.92G   0.07287   0.04458   0.01583    0.1333        98       416: 100% 3/3 [00:00<00:00,  3.31it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.89it/s]\n",
            "                 all          21         222       0.353       0.323       0.131      0.0275\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    41/199     2.92G    0.0688    0.0433   0.01685     0.129        73       416: 100% 3/3 [00:00<00:00,  5.21it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.32it/s]\n",
            "                 all          21         222       0.715       0.193       0.185       0.046\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    42/199     2.92G   0.06716   0.04887   0.01797     0.134        70       416: 100% 3/3 [00:00<00:00,  5.43it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.43it/s]\n",
            "                 all          21         222       0.248       0.338       0.186      0.0445\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    43/199     2.92G   0.06494   0.04982   0.01555    0.1303        89       416: 100% 3/3 [00:00<00:00,  5.26it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.52it/s]\n",
            "                 all          21         222       0.287       0.344         0.2      0.0572\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    44/199     2.92G   0.06334   0.04682    0.0165    0.1267        83       416: 100% 3/3 [00:00<00:00,  5.47it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.49it/s]\n",
            "                 all          21         222       0.707        0.19       0.187      0.0471\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    45/199     2.92G   0.06976   0.04584    0.0155    0.1311        90       416: 100% 3/3 [00:00<00:00,  5.42it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.60it/s]\n",
            "                 all          21         222       0.679       0.196       0.139      0.0345\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    46/199     2.92G   0.06576    0.0491   0.01691    0.1318        76       416: 100% 3/3 [00:00<00:00,  5.38it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.69it/s]\n",
            "                 all          21         222       0.568       0.334       0.228      0.0718\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    47/199     2.92G   0.06559   0.04928   0.01579    0.1307        95       416: 100% 3/3 [00:00<00:00,  5.39it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.64it/s]\n",
            "                 all          21         222         0.4       0.329       0.216      0.0753\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    48/199     2.92G   0.06426   0.04825   0.02076    0.1333        98       416: 100% 3/3 [00:00<00:00,  5.43it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.63it/s]\n",
            "                 all          21         222       0.396       0.321       0.224      0.0871\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    49/199     2.92G   0.06492   0.03888   0.01588    0.1197        67       416: 100% 3/3 [00:00<00:00,  5.41it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.54it/s]\n",
            "                 all          21         222       0.725       0.201       0.182      0.0681\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    50/199     2.92G   0.06355   0.04635   0.01483    0.1247        67       416: 100% 3/3 [00:00<00:00,  5.39it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.93it/s]\n",
            "                 all          21         222       0.372        0.31       0.239      0.0782\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    51/199     2.92G   0.06501   0.04027   0.01469      0.12        57       416: 100% 3/3 [00:00<00:00,  5.44it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.80it/s]\n",
            "                 all          21         222       0.246       0.357       0.233      0.0947\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    52/199     2.92G   0.06492   0.04195   0.01564    0.1225        65       416: 100% 3/3 [00:00<00:00,  5.47it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.00it/s]\n",
            "                 all          21         222       0.365       0.334       0.217      0.0563\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    53/199     2.92G   0.06195   0.04377   0.01553    0.1212        98       416: 100% 3/3 [00:00<00:00,  5.25it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.56it/s]\n",
            "                 all          21         222       0.325        0.35       0.202      0.0888\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    54/199     2.92G   0.05951   0.04481   0.01531    0.1196        83       416: 100% 3/3 [00:00<00:00,  5.52it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.55it/s]\n",
            "                 all          21         222       0.317       0.339       0.213      0.0669\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    55/199     2.92G   0.05942   0.04266   0.01374    0.1158        92       416: 100% 3/3 [00:00<00:00,  5.32it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.61it/s]\n",
            "                 all          21         222       0.287       0.308       0.124      0.0312\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    56/199     2.92G   0.06532   0.05167   0.01541    0.1324       133       416: 100% 3/3 [00:00<00:00,  5.35it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.73it/s]\n",
            "                 all          21         222       0.296       0.334       0.122      0.0347\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    57/199     2.92G   0.06042   0.03919   0.01577    0.1154        73       416: 100% 3/3 [00:00<00:00,  5.49it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.91it/s]\n",
            "                 all          21         222       0.329       0.339       0.155      0.0415\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    58/199     2.92G   0.06339   0.04583   0.01173     0.121       116       416: 100% 3/3 [00:00<00:00,  5.44it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.58it/s]\n",
            "                 all          21         222        0.29       0.339       0.205      0.0638\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    59/199     2.92G   0.06096   0.04719   0.01715    0.1253       116       416: 100% 3/3 [00:00<00:00,  5.41it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:01<00:00,  1.10s/it]\n",
            "                 all          21         222       0.301       0.359       0.215      0.0746\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    60/199     2.92G   0.05833   0.04744   0.01591    0.1217        97       416: 100% 3/3 [00:00<00:00,  3.14it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.32it/s]\n",
            "                 all          21         222       0.703       0.206       0.193      0.0653\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    61/199     2.92G   0.06455   0.03962   0.01497    0.1191        67       416: 100% 3/3 [00:00<00:00,  5.46it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.65it/s]\n",
            "                 all          21         222       0.728       0.214       0.201      0.0676\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    62/199     2.92G   0.05742   0.03838   0.01914    0.1149        55       416: 100% 3/3 [00:00<00:00,  5.24it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.71it/s]\n",
            "                 all          21         222       0.773       0.198       0.207      0.0844\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    63/199     2.92G   0.05717   0.04108   0.01623    0.1145        82       416: 100% 3/3 [00:00<00:00,  5.50it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.93it/s]\n",
            "                 all          21         222       0.208        0.36       0.189      0.0669\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    64/199     2.92G    0.0611   0.04597   0.01492     0.122       118       416: 100% 3/3 [00:00<00:00,  5.52it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.48it/s]\n",
            "                 all          21         222       0.237       0.365        0.26       0.103\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    65/199     2.92G   0.05811   0.03886   0.01484    0.1118        67       416: 100% 3/3 [00:00<00:00,  5.44it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.72it/s]\n",
            "                 all          21         222       0.179       0.357       0.191      0.0706\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    66/199     2.92G   0.05929   0.04317   0.01635    0.1188        79       416: 100% 3/3 [00:00<00:00,  4.01it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  6.37it/s]\n",
            "                 all          21         222       0.765       0.212        0.22      0.0789\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    67/199     2.92G   0.05769   0.04322   0.01677    0.1177       101       416: 100% 3/3 [00:00<00:00,  5.34it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.78it/s]\n",
            "                 all          21         222       0.274       0.329       0.255       0.116\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    68/199     2.92G   0.05668   0.04185   0.01447     0.113        83       416: 100% 3/3 [00:00<00:00,  5.38it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.90it/s]\n",
            "                 all          21         222       0.815       0.228       0.253      0.0897\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    69/199     2.92G   0.05888   0.04668   0.01746     0.123       100       416: 100% 3/3 [00:00<00:00,  5.24it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.30it/s]\n",
            "                 all          21         222       0.764       0.193       0.208      0.0751\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    70/199     2.92G   0.06241   0.04912   0.01419    0.1257       146       416: 100% 3/3 [00:00<00:00,  5.43it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.78it/s]\n",
            "                 all          21         222       0.775       0.225       0.236      0.0751\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    71/199     2.92G   0.05757   0.04093   0.01507    0.1136        85       416: 100% 3/3 [00:00<00:00,  5.44it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.82it/s]\n",
            "                 all          21         222       0.866       0.225       0.273        0.11\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    72/199     2.92G   0.05323    0.0397    0.0159    0.1088        93       416: 100% 3/3 [00:00<00:00,  5.21it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.21it/s]\n",
            "                 all          21         222        0.23       0.362       0.262       0.114\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    73/199     2.92G   0.05197    0.0425   0.01551      0.11        93       416: 100% 3/3 [00:00<00:00,  5.35it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.90it/s]\n",
            "                 all          21         222       0.173       0.357       0.222      0.0886\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    74/199     2.92G   0.05708     0.042    0.0145    0.1136        90       416: 100% 3/3 [00:00<00:00,  5.26it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.94it/s]\n",
            "                 all          21         222        0.18       0.377       0.226      0.0707\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    75/199     2.92G   0.05665   0.04098   0.01345    0.1111        84       416: 100% 3/3 [00:00<00:00,  5.33it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.82it/s]\n",
            "                 all          21         222       0.776       0.225       0.248      0.0974\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    76/199     2.92G   0.05435   0.03153   0.01365   0.09954        36       416: 100% 3/3 [00:00<00:00,  5.30it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.92it/s]\n",
            "                 all          21         222       0.746       0.204        0.21      0.0706\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    77/199     2.92G   0.05247   0.03658   0.01545    0.1045        53       416: 100% 3/3 [00:00<00:00,  5.39it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.31it/s]\n",
            "                 all          21         222       0.813       0.222       0.248      0.0908\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    78/199     2.92G   0.05555   0.04322   0.01544    0.1142        91       416: 100% 3/3 [00:00<00:00,  5.37it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.41it/s]\n",
            "                 all          21         222       0.896       0.241       0.292       0.138\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    79/199     2.92G   0.04668   0.03899   0.01688    0.1025        60       416: 100% 3/3 [00:00<00:00,  5.28it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:01<00:00,  1.11s/it]\n",
            "                 all          21         222       0.843       0.233       0.268       0.119\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    80/199     2.92G   0.05614   0.03636   0.01286    0.1054        75       416: 100% 3/3 [00:00<00:00,  3.09it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.63it/s]\n",
            "                 all          21         222       0.175       0.444       0.219      0.0676\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    81/199     2.92G    0.0609   0.03735   0.01397    0.1122        99       416: 100% 3/3 [00:00<00:00,  5.51it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.04it/s]\n",
            "                 all          21         222       0.815       0.203       0.217      0.0627\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    82/199     2.92G   0.05822   0.04356   0.01555    0.1173       110       416: 100% 3/3 [00:00<00:00,  5.41it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.84it/s]\n",
            "                 all          21         222       0.843       0.233        0.28        0.13\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    83/199     2.92G   0.05055   0.03737   0.01308     0.101        52       416: 100% 3/3 [00:00<00:00,  5.43it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.11it/s]\n",
            "                 all          21         222        0.85       0.241       0.281       0.116\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    84/199     2.92G   0.05924   0.03702   0.01159    0.1078        73       416: 100% 3/3 [00:00<00:00,  5.34it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.00it/s]\n",
            "                 all          21         222       0.791       0.246       0.253      0.0775\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    85/199     2.92G   0.05871   0.03551   0.01305    0.1073        71       416: 100% 3/3 [00:00<00:00,  5.21it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.84it/s]\n",
            "                 all          21         222       0.799       0.241       0.258      0.0939\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    86/199     2.92G   0.05795   0.05427   0.01808    0.1303       134       416: 100% 3/3 [00:00<00:00,  5.42it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.99it/s]\n",
            "                 all          21         222       0.896       0.235       0.302       0.133\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    87/199     2.92G   0.05488   0.03798   0.01605    0.1089        81       416: 100% 3/3 [00:00<00:00,  5.48it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.62it/s]\n",
            "                 all          21         222       0.842       0.235       0.286      0.0945\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    88/199     2.92G   0.05595    0.0462   0.01546    0.1176       129       416: 100% 3/3 [00:00<00:00,  5.42it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.23it/s]\n",
            "                 all          21         222       0.197       0.378       0.239      0.0959\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    89/199     2.92G   0.05068     0.041   0.01504    0.1067        78       416: 100% 3/3 [00:00<00:00,  5.23it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.47it/s]\n",
            "                 all          21         222       0.269       0.386       0.321       0.167\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    90/199     2.92G   0.04904   0.03763   0.01407    0.1007        70       416: 100% 3/3 [00:00<00:00,  5.39it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.35it/s]\n",
            "                 all          21         222       0.826       0.233       0.282       0.144\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    91/199     2.92G   0.05238   0.04159   0.01636    0.1103        90       416: 100% 3/3 [00:00<00:00,  5.21it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.31it/s]\n",
            "                 all          21         222       0.259       0.356       0.248       0.093\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    92/199     2.92G   0.05244   0.03532   0.01533    0.1031        83       416: 100% 3/3 [00:00<00:00,  5.15it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.13it/s]\n",
            "                 all          21         222       0.218        0.37       0.252       0.113\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    93/199     2.92G   0.04871   0.03979   0.01638    0.1049        62       416: 100% 3/3 [00:00<00:00,  5.37it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.85it/s]\n",
            "                 all          21         222       0.266        0.37       0.288       0.143\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    94/199     2.92G   0.04927   0.03456    0.0149   0.09873        42       416: 100% 3/3 [00:00<00:00,  5.26it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.97it/s]\n",
            "                 all          21         222       0.255       0.367       0.278       0.115\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    95/199     2.92G   0.04985   0.03418   0.01739    0.1014        61       416: 100% 3/3 [00:00<00:00,  5.42it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.62it/s]\n",
            "                 all          21         222       0.252       0.365        0.29       0.131\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    96/199     2.92G   0.05096    0.0459   0.01826    0.1151       108       416: 100% 3/3 [00:00<00:00,  5.24it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.13it/s]\n",
            "                 all          21         222        0.85       0.254       0.304       0.131\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    97/199     2.92G   0.04907   0.03751   0.01644     0.103        97       416: 100% 3/3 [00:00<00:00,  5.41it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.15it/s]\n",
            "                 all          21         222       0.183       0.401       0.233      0.0922\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    98/199     2.92G    0.0514   0.04564   0.01462    0.1117       113       416: 100% 3/3 [00:00<00:00,  5.36it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.30it/s]\n",
            "                 all          21         222       0.263       0.447       0.323       0.148\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "    99/199     2.92G   0.04931   0.03631   0.01525    0.1009        75       416: 100% 3/3 [00:00<00:00,  5.35it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:01<00:00,  1.11s/it]\n",
            "                 all          21         222       0.235       0.489       0.298       0.135\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   100/199     2.92G   0.04826   0.04062   0.01622    0.1051       106       416: 100% 3/3 [00:01<00:00,  2.96it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.82it/s]\n",
            "                 all          21         222       0.237        0.48       0.298       0.124\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   101/199     2.92G   0.05106     0.034   0.01316   0.09822        75       416: 100% 3/3 [00:00<00:00,  5.25it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.01it/s]\n",
            "                 all          21         222       0.238       0.495       0.313       0.118\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   102/199     2.92G   0.04754   0.03947   0.01804     0.105        75       416: 100% 3/3 [00:00<00:00,  5.26it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.13it/s]\n",
            "                 all          21         222       0.269       0.447       0.319       0.144\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   103/199     2.92G    0.0484   0.04195   0.01543    0.1058        89       416: 100% 3/3 [00:00<00:00,  5.34it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.10it/s]\n",
            "                 all          21         222         0.2       0.411       0.216      0.0788\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   104/199     2.92G   0.04983   0.03922   0.01375    0.1028       103       416: 100% 3/3 [00:00<00:00,  5.26it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.64it/s]\n",
            "                 all          21         222       0.275       0.474       0.324       0.144\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   105/199     2.92G   0.04625   0.03549   0.01687   0.09861        62       416: 100% 3/3 [00:00<00:00,  5.43it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.26it/s]\n",
            "                 all          21         222       0.237       0.477       0.304       0.143\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   106/199     2.92G   0.04931   0.04344   0.01527     0.108       125       416: 100% 3/3 [00:00<00:00,  5.39it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.07it/s]\n",
            "                 all          21         222       0.258       0.451        0.33       0.168\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   107/199     2.92G   0.04486   0.03398   0.01487   0.09372        61       416: 100% 3/3 [00:00<00:00,  5.43it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.26it/s]\n",
            "                 all          21         222       0.233        0.48        0.31        0.14\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   108/199     2.92G   0.04593   0.03389   0.01572   0.09554        62       416: 100% 3/3 [00:00<00:00,  5.37it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.61it/s]\n",
            "                 all          21         222       0.229        0.48       0.301       0.146\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   109/199     2.92G   0.04504   0.03784   0.01603   0.09891        74       416: 100% 3/3 [00:00<00:00,  5.18it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.03it/s]\n",
            "                 all          21         222       0.276       0.464        0.33       0.166\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   110/199     2.92G   0.04457   0.03792   0.01534   0.09783        88       416: 100% 3/3 [00:00<00:00,  5.29it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.08it/s]\n",
            "                 all          21         222       0.222       0.461       0.255      0.0983\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   111/199     2.92G   0.04764   0.03306   0.01706   0.09776        61       416: 100% 3/3 [00:00<00:00,  5.31it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.52it/s]\n",
            "                 all          21         222        0.25       0.441       0.286       0.124\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   112/199     2.92G   0.04849   0.03251   0.01397   0.09497        86       416: 100% 3/3 [00:00<00:00,  5.40it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.41it/s]\n",
            "                 all          21         222        0.24        0.48       0.261       0.108\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   113/199     2.92G   0.04754   0.03713   0.01519   0.09986        81       416: 100% 3/3 [00:00<00:00,  5.44it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.17it/s]\n",
            "                 all          21         222       0.271         0.5       0.334       0.168\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   114/199     2.92G   0.04641   0.03923   0.01301   0.09865       104       416: 100% 3/3 [00:00<00:00,  5.33it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.12it/s]\n",
            "                 all          21         222       0.259       0.469        0.33       0.158\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   115/199     2.92G   0.04436   0.03817   0.01478   0.09731        85       416: 100% 3/3 [00:00<00:00,  5.29it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.33it/s]\n",
            "                 all          21         222       0.234       0.538       0.301       0.119\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   116/199     2.92G   0.04812    0.0358   0.01289   0.09681        99       416: 100% 3/3 [00:00<00:00,  5.44it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.46it/s]\n",
            "                 all          21         222       0.278       0.508       0.343       0.153\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   117/199     2.92G   0.04383   0.03551   0.01382   0.09316        76       416: 100% 3/3 [00:00<00:00,  5.21it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.51it/s]\n",
            "                 all          21         222       0.291       0.487       0.347       0.154\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   118/199     2.92G   0.04546   0.02877   0.01138   0.08562        31       416: 100% 3/3 [00:00<00:00,  5.26it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.14it/s]\n",
            "                 all          21         222       0.239        0.49       0.303       0.127\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   119/199     2.92G   0.04661   0.03393   0.01167    0.0922        62       416: 100% 3/3 [00:00<00:00,  5.48it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:01<00:00,  1.09s/it]\n",
            "                 all          21         222       0.264       0.493       0.343       0.168\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   120/199     2.92G   0.04631   0.03757    0.0164    0.1003        76       416: 100% 3/3 [00:00<00:00,  3.06it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.71it/s]\n",
            "                 all          21         222       0.239       0.493        0.33       0.153\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   121/199     2.92G   0.04787   0.03875   0.01285   0.09948       100       416: 100% 3/3 [00:00<00:00,  5.22it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.62it/s]\n",
            "                 all          21         222       0.256       0.523       0.359       0.167\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   122/199     2.92G   0.04442   0.03264   0.01315    0.0902        68       416: 100% 3/3 [00:00<00:00,  5.32it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.61it/s]\n",
            "                 all          21         222       0.271       0.513       0.348       0.167\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   123/199     2.92G   0.04419    0.0414   0.01402   0.09961        99       416: 100% 3/3 [00:00<00:00,  5.41it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.43it/s]\n",
            "                 all          21         222       0.251       0.505       0.341       0.168\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   124/199     2.92G   0.04679   0.03507   0.01302   0.09487        79       416: 100% 3/3 [00:00<00:00,  5.47it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.62it/s]\n",
            "                 all          21         222       0.266       0.483       0.338       0.154\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   125/199     2.92G   0.04243   0.03293   0.01282   0.08818        41       416: 100% 3/3 [00:00<00:00,  5.39it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.28it/s]\n",
            "                 all          21         222       0.253       0.468       0.329       0.166\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   126/199     2.92G   0.04572   0.03559   0.01437   0.09568       111       416: 100% 3/3 [00:00<00:00,  5.40it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.93it/s]\n",
            "                 all          21         222       0.255       0.526       0.342       0.155\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   127/199     2.92G   0.04631   0.03566   0.01312   0.09509        74       416: 100% 3/3 [00:00<00:00,  5.35it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.40it/s]\n",
            "                 all          21         222       0.267       0.462       0.336       0.141\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   128/199     2.92G   0.04416    0.0452   0.01399    0.1034       119       416: 100% 3/3 [00:00<00:00,  5.28it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.15it/s]\n",
            "                 all          21         222       0.275       0.483       0.341        0.17\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   129/199     2.92G   0.04744   0.03534   0.01248   0.09526        76       416: 100% 3/3 [00:00<00:00,  5.35it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.26it/s]\n",
            "                 all          21         222       0.225       0.473        0.32       0.151\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   130/199     2.92G   0.04506    0.0346   0.01213   0.09179        77       416: 100% 3/3 [00:00<00:00,  5.39it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.23it/s]\n",
            "                 all          21         222       0.263        0.48       0.347       0.171\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   131/199     2.92G   0.04283   0.03716   0.01349   0.09348        66       416: 100% 3/3 [00:00<00:00,  5.30it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.28it/s]\n",
            "                 all          21         222       0.249       0.476       0.346       0.163\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   132/199     2.92G   0.04747   0.03276   0.01588    0.0961        71       416: 100% 3/3 [00:00<00:00,  5.42it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.49it/s]\n",
            "                 all          21         222       0.258       0.473       0.337       0.126\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   133/199     2.92G   0.03996   0.03359   0.01642   0.08997        60       416: 100% 3/3 [00:00<00:00,  5.39it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.99it/s]\n",
            "                 all          21         222       0.297       0.526       0.357       0.178\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   134/199     2.92G    0.0411   0.03496   0.01241   0.08846        64       416: 100% 3/3 [00:00<00:00,  5.55it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.24it/s]\n",
            "                 all          21         222       0.263       0.506       0.335       0.166\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   135/199     2.92G   0.04482   0.03407   0.01402   0.09291        83       416: 100% 3/3 [00:00<00:00,  5.42it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.27it/s]\n",
            "                 all          21         222       0.267       0.501        0.33       0.138\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   136/199     2.92G   0.04603   0.02955   0.01347   0.08905        63       416: 100% 3/3 [00:00<00:00,  5.33it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.10it/s]\n",
            "                 all          21         222       0.313       0.445       0.348       0.163\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   137/199     2.92G   0.04088   0.03339   0.01336   0.08763        66       416: 100% 3/3 [00:00<00:00,  5.44it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.99it/s]\n",
            "                 all          21         222       0.321       0.414       0.343       0.166\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   138/199     2.92G   0.04273   0.03601   0.01271   0.09144       117       416: 100% 3/3 [00:00<00:00,  5.32it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.76it/s]\n",
            "                 all          21         222       0.284       0.523       0.352       0.175\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   139/199     2.92G   0.04081   0.04066   0.01226   0.09373       145       416: 100% 3/3 [00:00<00:00,  5.19it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:01<00:00,  1.07s/it]\n",
            "                 all          21         222       0.237        0.57       0.336        0.15\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   140/199     2.92G    0.0427   0.03252   0.01392   0.08913        89       416: 100% 3/3 [00:00<00:00,  3.05it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.06it/s]\n",
            "                 all          21         222       0.268       0.558       0.352       0.165\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   141/199     2.92G   0.04159   0.03015   0.01359   0.08533        55       416: 100% 3/3 [00:00<00:00,  5.06it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.72it/s]\n",
            "                 all          21         222       0.298       0.572       0.357       0.163\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   142/199     2.92G   0.04114   0.03709   0.01299   0.09122        91       416: 100% 3/3 [00:00<00:00,  4.36it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.36it/s]\n",
            "                 all          21         222       0.275       0.542       0.348       0.166\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   143/199     2.92G   0.04306   0.03234   0.01297   0.08836        79       416: 100% 3/3 [00:00<00:00,  5.31it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.47it/s]\n",
            "                 all          21         222       0.306       0.478       0.357        0.17\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   144/199     2.92G   0.04224   0.03492   0.01069   0.08785        83       416: 100% 3/3 [00:00<00:00,  5.43it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.08it/s]\n",
            "                 all          21         222       0.289       0.569       0.375       0.191\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   145/199     2.92G   0.03928   0.03479   0.01263    0.0867        98       416: 100% 3/3 [00:00<00:00,  5.34it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.53it/s]\n",
            "                 all          21         222       0.275       0.592       0.371       0.185\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   146/199     2.92G   0.04224   0.03548   0.01169    0.0894        98       416: 100% 3/3 [00:00<00:00,  5.44it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.96it/s]\n",
            "                 all          21         222       0.276       0.565       0.355       0.163\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   147/199     2.92G   0.03974   0.03483   0.01431   0.08888        81       416: 100% 3/3 [00:00<00:00,  5.11it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.63it/s]\n",
            "                 all          21         222       0.296        0.58       0.369       0.193\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   148/199     2.92G   0.03992   0.03776   0.01108   0.08876        96       416: 100% 3/3 [00:00<00:00,  5.31it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.97it/s]\n",
            "                 all          21         222        0.28       0.593       0.375       0.188\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   149/199     2.92G   0.04022   0.03568   0.01146   0.08736        82       416: 100% 3/3 [00:00<00:00,  5.30it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.64it/s]\n",
            "                 all          21         222       0.264       0.582       0.352       0.165\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   150/199     2.92G   0.04103   0.03138  0.009361   0.08177        61       416: 100% 3/3 [00:00<00:00,  5.15it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.17it/s]\n",
            "                 all          21         222       0.295       0.573       0.364       0.169\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   151/199     2.92G   0.03834   0.03614   0.01162    0.0861        99       416: 100% 3/3 [00:00<00:00,  5.43it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.49it/s]\n",
            "                 all          21         222       0.311       0.585       0.376       0.192\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   152/199     2.92G   0.03801   0.03427   0.01279   0.08508       100       416: 100% 3/3 [00:00<00:00,  5.22it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.47it/s]\n",
            "                 all          21         222       0.303       0.569       0.362       0.181\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   153/199     2.92G   0.04069   0.03326   0.01399   0.08794        82       416: 100% 3/3 [00:00<00:00,  5.37it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.62it/s]\n",
            "                 all          21         222       0.321       0.587        0.38       0.185\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   154/199     2.92G   0.03981   0.03311   0.01127    0.0842        74       416: 100% 3/3 [00:00<00:00,  5.12it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.22it/s]\n",
            "                 all          21         222       0.325       0.577       0.383       0.195\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   155/199     2.92G   0.03955    0.0352   0.01203   0.08678        69       416: 100% 3/3 [00:00<00:00,  5.25it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.91it/s]\n",
            "                 all          21         222        0.33       0.526        0.37       0.186\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   156/199     2.92G   0.03745   0.03249    0.0104   0.08034        68       416: 100% 3/3 [00:00<00:00,  5.32it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.02it/s]\n",
            "                 all          21         222       0.317       0.559       0.368       0.187\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   157/199     2.92G   0.03839   0.03643   0.01125   0.08607       107       416: 100% 3/3 [00:00<00:00,  5.55it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.30it/s]\n",
            "                 all          21         222        0.31        0.56       0.362       0.174\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   158/199     2.92G   0.03993   0.03079   0.01125   0.08197        66       416: 100% 3/3 [00:00<00:00,  5.34it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.24it/s]\n",
            "                 all          21         222       0.335       0.575       0.374       0.188\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   159/199     2.92G     0.039   0.03475  0.009133   0.08288       103       416: 100% 3/3 [00:00<00:00,  5.38it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:01<00:00,  1.10s/it]\n",
            "                 all          21         222       0.283       0.613       0.375       0.185\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   160/199     2.92G   0.03698    0.0311   0.01021   0.07829        62       416: 100% 3/3 [00:01<00:00,  2.93it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.89it/s]\n",
            "                 all          21         222       0.316       0.569       0.381       0.201\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   161/199     2.92G    0.0387   0.03595   0.00983   0.08448        89       416: 100% 3/3 [00:00<00:00,  5.36it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.71it/s]\n",
            "                 all          21         222       0.284       0.575       0.373       0.184\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   162/199     2.92G   0.03859   0.03228   0.00948   0.08035        71       416: 100% 3/3 [00:00<00:00,  5.28it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.12it/s]\n",
            "                 all          21         222       0.294       0.609       0.386       0.198\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   163/199     2.92G   0.03569   0.02902   0.01047   0.07518        63       416: 100% 3/3 [00:00<00:00,  5.45it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.05it/s]\n",
            "                 all          21         222       0.321       0.582       0.387       0.188\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   164/199     2.92G   0.03646   0.03078  0.009686   0.07693        60       416: 100% 3/3 [00:00<00:00,  5.33it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.58it/s]\n",
            "                 all          21         222       0.346       0.626       0.394       0.197\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   165/199     2.92G   0.03699   0.03705  0.009802   0.08385        98       416: 100% 3/3 [00:00<00:00,  5.35it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.79it/s]\n",
            "                 all          21         222       0.314       0.636        0.39       0.191\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   166/199     2.92G    0.0409   0.03353   0.01241   0.08684        40       416: 100% 3/3 [00:00<00:00,  5.16it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.74it/s]\n",
            "                 all          21         222       0.275       0.639       0.384       0.194\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   167/199     2.92G   0.03764   0.02998  0.009586    0.0772        49       416: 100% 3/3 [00:00<00:00,  6.10it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.10it/s]\n",
            "                 all          21         222       0.328       0.364       0.314       0.117\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   168/199     2.92G   0.05013    0.0295  0.008494   0.08812        74       416: 100% 3/3 [00:00<00:00,  5.45it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.23it/s]\n",
            "                 all          21         222       0.304       0.631       0.386       0.172\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   169/199     2.92G   0.04908   0.02923  0.008974   0.08728        47       416: 100% 3/3 [00:00<00:00,  5.93it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.52it/s]\n",
            "                 all          21         222       0.227       0.562       0.275       0.103\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   170/199     2.92G    0.0504   0.03317  0.009603   0.09317        78       416: 100% 3/3 [00:00<00:00,  5.47it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.41it/s]\n",
            "                 all          21         222       0.379       0.567       0.406       0.202\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   171/199     2.92G   0.04504   0.03527   0.01016   0.09047        94       416: 100% 3/3 [00:00<00:00,  5.90it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.36it/s]\n",
            "                 all          21         222        0.23       0.651       0.277       0.111\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   172/199     2.92G   0.04119   0.03155   0.01163   0.08436        69       416: 100% 3/3 [00:00<00:00,  5.51it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.31it/s]\n",
            "                 all          21         222       0.346       0.536       0.381       0.162\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   173/199     2.92G   0.04465   0.03434  0.009592   0.08859        96       416: 100% 3/3 [00:00<00:00,  5.61it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.14it/s]\n",
            "                 all          21         222       0.224       0.597       0.299       0.118\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   174/199     2.92G   0.04727   0.03935  0.009281   0.09591       130       416: 100% 3/3 [00:00<00:00,  5.50it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.71it/s]\n",
            "                 all          21         222       0.373       0.592       0.409       0.203\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   175/199     2.92G   0.04382   0.03505  0.009699   0.08856        91       416: 100% 3/3 [00:00<00:00,  5.82it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.43it/s]\n",
            "                 all          21         222       0.291       0.622       0.364       0.166\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   176/199     2.92G   0.03968   0.02996  0.007858   0.07749        59       416: 100% 3/3 [00:00<00:00,  5.63it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.45it/s]\n",
            "                 all          21         222       0.334       0.567       0.398       0.195\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   177/199     2.92G   0.04169   0.03361  0.007948   0.08325        97       416: 100% 3/3 [00:00<00:00,  5.93it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.99it/s]\n",
            "                 all          21         222       0.238        0.59       0.285       0.112\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   178/199     2.92G   0.04352   0.03282  0.007966    0.0843        94       416: 100% 3/3 [00:00<00:00,  5.51it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.69it/s]\n",
            "                 all          21         222       0.312       0.654       0.407       0.201\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   179/199     2.92G   0.04529   0.02771  0.009567   0.08257        50       416: 100% 3/3 [00:00<00:00,  5.68it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:01<00:00,  1.08s/it]\n",
            "                 all          21         222       0.259       0.575       0.342        0.15\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   180/199     2.92G   0.04477    0.0338   0.01012   0.08868        61       416: 100% 3/3 [00:00<00:00,  3.91it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  2.55it/s]\n",
            "                 all          21         222       0.359       0.639       0.424       0.203\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   181/199     2.92G   0.04326   0.03131  0.009408   0.08397        73       416: 100% 3/3 [00:00<00:00,  5.58it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.01it/s]\n",
            "                 all          21         222       0.257       0.569       0.344       0.149\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   182/199     2.92G   0.04109   0.03656  0.009766   0.08741       119       416: 100% 3/3 [00:00<00:00,  5.48it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.33it/s]\n",
            "                 all          21         222       0.348       0.669       0.426       0.207\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   183/199     2.92G   0.04473   0.03071  0.008292   0.08374        80       416: 100% 3/3 [00:00<00:00,  5.71it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.06it/s]\n",
            "                 all          21         222       0.219       0.606       0.317       0.131\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   184/199     2.92G   0.04675   0.03102  0.008586   0.08635        69       416: 100% 3/3 [00:00<00:00,  5.59it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.23it/s]\n",
            "                 all          21         222       0.363       0.603       0.423       0.204\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   185/199     2.92G   0.04354   0.03117  0.007897    0.0826        90       416: 100% 3/3 [00:00<00:00,  5.75it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.33it/s]\n",
            "                 all          21         222       0.292       0.555       0.331       0.127\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   186/199     2.92G   0.04462   0.03591  0.007888   0.08841        94       416: 100% 3/3 [00:00<00:00,  5.44it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.36it/s]\n",
            "                 all          21         222        0.37         0.6       0.423       0.208\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   187/199     2.92G   0.04175   0.03629  0.007615   0.08566       119       416: 100% 3/3 [00:00<00:00,  5.85it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.09it/s]\n",
            "                 all          21         222        0.28       0.557       0.344       0.149\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   188/199     2.92G    0.0423   0.03449  0.008817   0.08561        97       416: 100% 3/3 [00:00<00:00,  5.65it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.47it/s]\n",
            "                 all          21         222       0.376       0.585       0.428        0.21\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   189/199     2.92G    0.0407   0.03367  0.008581   0.08295        80       416: 100% 3/3 [00:00<00:00,  6.16it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.11it/s]\n",
            "                 all          21         222       0.323       0.567       0.401       0.196\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   190/199     2.92G   0.03828   0.03442  0.008813   0.08151        90       416: 100% 3/3 [00:00<00:00,  5.29it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.23it/s]\n",
            "                 all          21         222       0.353       0.588       0.439       0.213\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   191/199     2.92G   0.03964    0.0323  0.008686   0.08063        75       416: 100% 3/3 [00:00<00:00,  6.01it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.99it/s]\n",
            "                 all          21         222       0.286       0.574       0.369       0.164\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   192/199     2.92G   0.04017   0.03797  0.007156   0.08529       107       416: 100% 3/3 [00:00<00:00,  5.63it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.29it/s]\n",
            "                 all          21         222       0.374        0.59        0.44        0.22\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   193/199     2.92G   0.04142   0.02841   0.00822   0.07805        50       416: 100% 3/3 [00:00<00:00,  5.76it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.15it/s]\n",
            "                 all          21         222       0.252       0.608       0.333       0.139\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   194/199     2.92G   0.04683   0.04604  0.007995    0.1009       164       416: 100% 3/3 [00:00<00:00,  5.36it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.76it/s]\n",
            "                 all          21         222       0.356       0.606       0.438        0.21\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   195/199     2.92G   0.03966   0.02527  0.008254   0.07318        37       416: 100% 3/3 [00:00<00:00,  5.98it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.98it/s]\n",
            "                 all          21         222       0.353       0.501         0.4       0.162\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   196/199     2.92G   0.04494   0.03311   0.00869   0.08674        86       416: 100% 3/3 [00:00<00:00,  5.66it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.40it/s]\n",
            "                 all          21         222       0.409       0.618       0.439       0.222\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   197/199     2.92G   0.04096   0.02921   0.00658   0.07674        67       416: 100% 3/3 [00:00<00:00,  5.78it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.11it/s]\n",
            "                 all          21         222       0.319       0.641       0.372       0.155\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   198/199     2.92G   0.04191   0.03436  0.008094   0.08436        78       416: 100% 3/3 [00:00<00:00,  5.43it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.51it/s]\n",
            "                 all          21         222       0.392       0.547       0.418       0.206\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "   199/199     2.92G   0.04783    0.0293  0.007416   0.08455        56       416: 100% 3/3 [00:00<00:00,  5.73it/s]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 1/1 [00:01<00:00,  1.36s/it]\n",
            "                 all          21         222       0.332       0.608       0.383       0.152\n",
            "                mask          21         189       0.374        0.73       0.561       0.225\n",
            "             no-mask          21          33        0.29       0.485       0.205      0.0793\n",
            "200 epochs completed in 0.050 hours.\n",
            "\n",
            "Optimizer stripped from runs/train/exp4/weights/last.pt, 14.4MB\n",
            "Images sizes do not match. This will causes images to be display incorrectly in the UI.\n",
            "\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Waiting for W&B process to finish, PID 1621\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Program ended successfully.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                                                                                \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Find user logs for this run at: /content/yolov5/wandb/run-20210530_131303-112oqsrs/logs/debug.log\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Find internal logs for this run at: /content/yolov5/wandb/run-20210530_131303-112oqsrs/logs/debug-internal.log\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                 train/box_loss 0.04783\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                 train/obj_loss 0.0293\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                 train/cls_loss 0.00742\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:              metrics/precision 0.33234\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                 metrics/recall 0.6075\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                metrics/mAP_0.5 0.3829\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           metrics/mAP_0.5:0.95 0.15216\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                   val/box_loss 0.0534\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                   val/obj_loss 0.03386\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                   val/cls_loss 0.01418\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                          x/lr0 0.0012\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                          x/lr1 0.0012\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                          x/lr2 0.0413\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                       _runtime 190\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                     _timestamp 1622380573\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                          _step 200\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         train/box_loss ██▇▆▆▅▅▄▄▄▃▃▃▃▃▂▃▃▂▂▂▂▂▂▂▂▁▁▂▁▁▁▁▁▂▁▂▁▁▂\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         train/obj_loss ▅▅▆▇▇▆█▅▄▄▃▄▃▄▃▃▃▃▃▃▂▂▂▂▃▂▂▂▂▂▂▂▁▁▂▂▁▂▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         train/cls_loss █▇▆▅▅▅▅▅▄▄▃▃▃▄▃▃▃▄▄▄▃▃▄▃▃▃▄▃▃▃▃▂▂▂▂▁▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:      metrics/precision ▁▁▁▁▂▄▅▆▇▆▃▃▇▃████▃▂▃▃▃▃▃▃▃▄▃▃▄▃▄▄▃▃▃▃▃▄\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         metrics/recall ▁▁▁▂▂▃▄▃▃▃▅▄▃▅▄▃▃▄▅▆▇▆▆▇▇▆▇▆▆██▇█▅███▇██\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:        metrics/mAP_0.5 ▁▁▁▁▁▂▄▄▄▄▅▃▅▆▆▅▅▆▅▅▇▇▆▇▇▇▇▇▇████▇▆▆▇▇▇█\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:   metrics/mAP_0.5:0.95 ▁▁▁▁▁▂▂▂▃▂▄▂▃▅▅▄▃▄▄▄▅▆▅▇▇▆▇▇▇██▇█▅▅▅▆▆▆▇\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           val/box_loss ██▇▆▆▅▄▄▄▄▃▃▃▂▂▃▃▃▂▂▂▂▂▁▁▂▁▁▁▁▁▁▁▂▂▂▂▂▂▂\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           val/obj_loss ▄▅▇█▇▆▅▅▄▃▃▂▃▃▂▂▁▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           val/cls_loss █▇▅▄▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▂▁▂▂▂▂▂▂▂▁▁▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  x/lr0 ▁▁▂▃▃▄▄▅▅▆▆▇▇▇▇██████████▇▇▇▇▇▆▆▆▆▆▆▅▅▅▆\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  x/lr1 ▁▁▂▃▃▄▄▅▅▆▆▇▇▇▇██████████▇▇▇▇▇▆▆▆▆▆▆▅▅▅▆\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  x/lr2 ████▇▇▇▇▇▇▆▆▆▆▆▅▅▅▅▅▅▄▄▄▄▄▃▃▃▃▃▃▂▂▂▂▂▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:               _runtime ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇██\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:             _timestamp ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇██\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  _step ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 333 media file(s), 1 artifact file(s) and 0 other file(s)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Synced \u001b[33mexp4\u001b[0m: \u001b[34mhttps://wandb.ai/feat7/YOLOv5/runs/112oqsrs\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BmS8GLUcTj_J",
        "outputId": "7e3fd43d-7a1b-46d5-9091-58b9ebbea97c"
      },
      "source": [
        "model = torch.hub.load('feat7/yolov5', 'custom', path_or_model='..write path here..last.pt')\n",
        "model.autoshape()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using cache found in /root/.cache/torch/hub/feat7_yolov5_master\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[31m\u001b[1mrequirements:\u001b[0m PyYAML>=5.3.1 not found and is required by YOLOv5, attempting auto-update...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1      3520  models.common.Focus                     [3, 32, 3]                    \n",
            "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
            "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
            "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
            "  4                -1  1    156928  models.common.C3                        [128, 128, 3]                 \n",
            "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
            "  6                -1  1    625152  models.common.C3                        [256, 256, 3]                 \n",
            "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
            "  8                -1  1    656896  models.common.SPP                       [512, 512, [5, 9, 13]]        \n",
            "  9                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
            " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
            " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
            " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
            " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
            " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
            " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
            " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
            " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
            " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
            " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
            " 24      [17, 20, 23]  1     18879  models.yolo.Detect                      [2, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.7/dist-packages (5.4.1)\n",
            "\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m 1 package updated per /root/.cache/torch/hub/feat7_yolov5_master/requirements.txt\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m ⚠️ \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Model Summary: 283 layers, 7066239 parameters, 7066239 gradients, 16.4 GFLOPS\n",
            "\n",
            "YOLOv5 🚀 2021-5-30 torch 1.8.1+cu101 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Adding autoShape... \n",
            "autoShape already enabled, skipping... \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Vqx5-qpU9O5"
      },
      "source": [
        "from PIL import Image\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "image = cv2.cvtColor(cv2.imread(\"/content/train/images/Smog-Mask-PM2-5-Carbon-Filter-Face-Masks-Fog-Dust-Air-Cleaner-anti-pollution-Face-Protector_jpg.rf.8b5dc4859890d34642d44ee9d1e7a1e8.jpg\"), cv2.COLOR_BGR2RGB)\n",
        "res = model(image)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CoAqdYO8VI7X",
        "outputId": "0a359348-5833-4b9b-ddb7-26028f1e83fe"
      },
      "source": [
        "res.xywh"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[tensor([[345.14999, 109.22031,  92.29999, 151.08438,   0.85742,   0.00000],\n",
              "         [ 67.60000, 288.92499,  76.86250, 115.70000,   0.84082,   0.00000],\n",
              "         [349.04999, 292.50000,  76.70001, 117.00002,   0.82422,   0.00000],\n",
              "         [ 70.27109, 111.67812,  75.09531, 113.99374,   0.81836,   0.00000],\n",
              "         [206.70000, 107.55468,  75.39999, 155.71562,   0.80518,   0.00000],\n",
              "         [204.22186, 296.39999, 105.70624, 161.20001,   0.78369,   0.00000]], device='cuda:0')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "m9s7VuPHXD3p",
        "outputId": "56b5d220-fb6c-4b94-808a-21705d1686db"
      },
      "source": [
        "draw_rect = image.copy()\n",
        "\n",
        "for result in res.xyxy[0].tolist():\n",
        "  x1, y1, x2, y2, confidence, class_name = result\n",
        "\n",
        "  x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)\n",
        "\n",
        "  print(result)\n",
        "  draw_rect = cv2.rectangle(draw_rect, (x1, y1), (x2, y2), (255, 0, 0), 1)\n",
        "\n",
        "plt.imshow(draw_rect)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[299.0, 33.678123474121094, 391.29998779296875, 184.7624969482422, 0.857421875, 0.0]\n",
            "[29.16874885559082, 231.0749969482422, 106.03124237060547, 346.7749938964844, 0.8408203125, 0.0]\n",
            "[310.6999816894531, 233.99998474121094, 387.3999938964844, 351.0, 0.82421875, 0.0]\n",
            "[32.72343444824219, 54.68124771118164, 107.8187484741211, 168.67498779296875, 0.818359375, 0.0]\n",
            "[169.0, 29.696874618530273, 244.39999389648438, 185.41249084472656, 0.80517578125, 0.0]\n",
            "[151.36874389648438, 215.79998779296875, 257.0749816894531, 377.0, 0.78369140625, 0.0]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f2ef1916210>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PXsDKCtjXZuc",
        "outputId": "d3f5089b-6b46-4096-9d1d-2bffc30ad22c"
      },
      "source": [
        "res.xywh[0].tolist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FSdykHrAZzGP"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}