# CustomClassification **Repository Path**: MrDreamQ/custom-classification ## Basic Information - **Project Name**: CustomClassification - **Description**: 参数分离的自定义数据集 - **Primary Language**: Python - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2022-03-26 - **Last Updated**: 2022-03-29 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # custom Classifiction training [![image](https://img.shields.io/badge/Ubuntu-20.04-red)](https://ubuntu.com//)[![image](https://img.shields.io/badge/Python-3.6-yellow)](https://www.python.org/)[![image](https://img.shields.io/badge/Pytorch-1.8.0+-orange)](https://pytorch.org/get-started/previous-versions/)[![image](https://img.shields.io/badge/CUDA-11.1-green)](https://developer.nvidia.com/cuda-11.1.1-download-archive?target_os=Linux)[![image](https://img.shields.io/badge/CUDNN-8.0.5-blue)](https://developer.nvidia.com/rdp/cudnn-download) 本人的自制训练集的分类网络训练库——轻量型网络训练、部署的详细过程。 网上的不少分类网络的训练库,但许多都没有配置教程,超参数、路径等参数的读取、数据集准备等。因此本人基于[MobilenetV2V3](https://gitee.com/isyangwei/Mobilenet?_from=gitee_search)的训练库,加入自定义参数及数据集的设置和读取,通过修改配置文件尽可能**减少对源文件的修改**。 本人自制数据集及损失、准确率示意图如下。 本人先使用`MobilenetV3_small`进行训练,损失值一直在0.1左右,准确率也只有86左右,换至`MobilenetV3_large`后,损失值下降值0.03,准确率有98左右。 ![数据集示意图.png](https://s2.loli.net/2022/03/29/9oh74cSYNPJ8RF1.png) 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AQEXQZajMniE4nPq8E/FpHAQAAAAAgDtFlREBymgjLTAAAAAAAiAi6jAhITBahywAAAAAAICLoMiKAx38CAAAAABAxdBkRkNhDRMTVrHUOAAAAAADiEF1GBITWmLhbtc4BAAAAAEAcosuIgFPzMlhjAgAAAACA+ugyIuDU8zJYYwIAAAAAgProMiLAGvoeE9aYAAAAAACgPrqMCGCNCQAAAAAAEUOXEQFJrDEBAAAAACBS6DIigDUmAAAAAABEjFGtGzU0NKxYsUJRFJ/PV1RUNGjQoI5TwWBwzZo15eXler1+wIABRUVFoeOHDx9+9NFHvV7vihUr+vbtq1YS7bHGBAAAAACAiFGty1i7dm1hYWFOTk5DQ0NxcfGmTZs6Tr366qsZGRmhCmPdunU7duyYPHlyVVXVb3/727Vr1/bp00etDNEiMTQvgzUmAAAAAACoT7U1JnV1dTk5OSKSlpZms9mam7/+Tb65uXnGjBmh7by8vIqKChF56qmnlixZEodFhvxrXoabNSYAAAAAAKhPnS4jEAgYjV9P8bDb7Q6Ho2P3lltuSU9PD22Xlpbm5eWJSHl5+f79+++666677777iy++UCVGtNDpJCFJgkHqDAAAAAAAVKdOl+H1eg0GQ8euyWTy+XxnXlZWVtbS0pKbmysihw8f7tu37+OPP7506dJly5Z5vV5VkkSLxNBXmfDIDAAAAAAAVKbO8zLMZnMgEOjY9fv9CQkJp11TX1+/YcOG9evXh3ZHjBhx2WWXiUhmZuall1566NChMWPGdL7+2LFjHdtut9vn81VWVqqStgvYjRazSM2xIz5X4Nuv7k5OnDiRkJDgcrm0DoJwnThxwuv1JiUlaR0E4aqpqdHr9RaLResgCFdtbW2PHj30er6hLLYpilJbWxtDn3nwTbxeb01NjdVq1ToIwtXW1tbQ0NB5EjpiVGNjo8fjCQaDWgdBuOrr6+12uzp/Jk+biFFdXd2rV6/OF3i93iVLlixdurSj4zCbzZ1ffua8jB49enRsK4piMBhsNpsqabuAPjlFaiVJp1hiJ3PXaGlpsVqtMTSU+CZJSUnJycnJyclaB0G4EhMTbTYbXUYcCA0lXUasUxQlNJRaB0G4zGZzU1MTQxkHdDqdz+djKONAIBCIrV8q8U08Ho+o+D0mdrv98OHDw4YNczqdra2tKSkpHaeCweDSpUvnzZvXu3fvjoPZ2dmlpaUTJkzw+Xy7du26+eabT7thRkZGx7bP5zMYDKmpqWqljbgeaSKSrA9KDGXuEq2trVarNZaGEt+goaEhJSWFvwzigM1mS0lJOXMyHWJOcnJyamoqXUasUxQlNJRaB0G4PB5PU1MTQxkHDAaD3+9nKONAe3u7yWRiKONAaCaEal3G4sWLly9fHgwGPR5PcXGxiOzcufPgwYMFBQXbtm3bu3dvx8SN1NTUZcuW3X333Q8//PALL7zQ3Nx85513xtsEvNDzMtw8LwMAAAAAAJWp1mWkpaWVlJR0PpKfn5+fny8is2bNmjVr1mnXW63W5cuXq/XuUYdnfwIAAAAAEBnMRI0Mq01EpI0uAwAAAAAAldFlRAZrTAAAAAAAiAy6jMhIYo0JAAAAAAARQZcRGVa6DAAAAAAAIoIuIzJ49icAAAAAAJFBlxEZdBkAAAAAAEQGXUZk0GUAAAAAABAZdBmRYU4QnU78Xgn4tY4CAAAAAEBcocuImOQ0EaZmAAAAAACgMrqMiElMFhFxNWudAwAAAACAuEKXETGnHpnRqnUOAAAAAADiCl1GxCT2EGFeBgAAAAAAKqPLiJhTa0x4XgYAAAAAAGqiy4iY0LwMN2tMAAAAAABQE11GxJx6XgZrTAAAAAAAUBNdRsRYk0VE2lhjAgAAAACAmugyIoY1JgAAAAAARIBRrRs1NDSsWLFCURSfz1dUVDRo0KCOU8FgcM2aNeXl5Xq9fsCAAUVFRZ1fOGfOnGnTpk2fPl2tJNEiiTUmAAAAAACoT7V5GWvXri0sLHz00UdXrly5atWqzqdeffXVjIyMJ598cuPGjQaDYceOHR2nXn/9db0+TueGWPkeEwAAAAAA1Kdaj1BXV5eTkyMiaWlpNputufnr+QjNzc0zZswIbefl5VVUVIS2W1tb//rXv8bhjIyQ0BoTF2tMAAAAAABQkzpdRiAQMBq/Xq5it9sdDkfH7i233JKenh7aLi0tzcvLC20/9thj8+fPVyVANOJ7TAAAAAAAiAB1ugyv12swGDp2TSaTz+c787KysrKWlpbc3FwROXDggMViGTJkiCoBolGoy+DZnwAAAAAAqEqdZ3+azeZAINCx6/f7ExISTrumvr5+w4YN69evFxFFUZ544onQ9jc5cuRIx7bX6/V6vceOHVMlbZcZaLbqfe6Kss8US6LWWaJFTU2NxWLpvAQJMaqqqqqtrS0pKUnrIAhXdXW1oigWi0XrIAiXw+FITEyM26dQdRuKojgcjuTkZK2DIFxer/fEiRMmk0nrIAhXW1tbY2Oj1imggsbGRq/X6/f7tQ6CcNXX19vtdnW6jNMmYlRXV/fq1avzBV6vd8mSJUuXLg11HHv27Glra1u6dKmIVFZWBoPBxsbGW2+9tfNLevbs2bHtdDqNRmNmZqYqabtOok187swEk5IRa8kjxufzJSQkdKw5Quxqa2vLyMjg03YcaGpqyszMpMuIA7W1tZmZmXQZsU5RlLq6utj7zIMzeL1ej8fDUMaB0O8vDGUc0Ov1/KmMD8FgUFT8Tla73X748OFhw4Y5nc7W1taUlJTO77R06dJ58+b17t07dGTChAnPPfdcaPsvf/lLe3v7mU8A7XwHl8tlMBhsNptaabtI9gj5Z23S0X0yaKjWUaJFUlKS1WqNvaHEGZKSkpKTkxnKOJCYmJicnHzmZDrEnMTERJvNRpcR6xRFCQ2l1kEQLpPJxFDGDY/Hw1DGAZ/PF5O/VOIMLpdLVOwyFi9evHz58mAw6PF4iouLRWTnzp0HDx4sKCjYtm3b3r17OyZupKamLlu2TK33jWrjpso/t0vpWzLlBq2jAAAAAAAQJ1TrMtLS0kpKSjofyc/Pz8/PF5FZs2bNmjXrm1547bXXqpUh6uSOl+RUqTku1V9Kn8FapwEAAAAAIB4wEzWS9Hq5aIqIyEfvaB0FAAAAAIA4QZcRYeOmiojseUcUResoAAAAAADEA7qMCOs/VDKypLVJPv9Y6ygAAAAAAMQDuozIm3iNiMgelpkAAAAAAKACuozIG3+l6HSy/33xurWOAgAAAABAzKPLiLyUTLlglAR88sn7WkcBAAAAACDm0WV0iXFXirDMBAAAAAAAFdBldIkLJ4vRLF/sk6Y6raMAAAAAABDb6DK6hMUqIy+WYFD2vKt1FAAAAAAAYhtdRlcJLTP56K9a5wAAAAAAILbRZXSV4RPEmiw1x+WrI1pHAQAAAAAghtFldBW9XsZNFeEJoAAAAAAAhIUuowuFlpnsfVcUResoAAAAAADEKrqMLjRgmGRkSWuTlH2kdRQAAAAAAGIVXUbXmniNiMje97TOAQAAAABArKLL6FrjrxSdTj75u3jdWkcBAAAAACAm0WV0rZRMuWCUBHyyf5fWUQAAAAAAiElGtW7U0NCwYsUKRVF8Pl9RUdGgQYM6TgWDwTVr1pSXl+v1+gEDBhQVFYnIwYMHS0pKEhMT3W73woULhw8frlaSaDfuSvnyE9nzrkz4kdZRAAAAAACIPap1GWvXri0sLMzJyWloaCguLt60aVPHqVdffTUjIyNUYaxbt27Hjh35+fkrV67ctGmTzWZzOp2LFi165pln1EoS7S6cLC8/Jkf2SVOdpGRqnQYAAAAAgBij2hqTurq6nJwcEUlLS7PZbM3NzR2nmpubZ8yYEdrOy8urqKhoamqaOnWqzWYTkfT0dJPJpFaMGGCxyqhLJBjkCaAAAAAAAHwP6nQZgUDAaPx6iofdbnc4HB27t9xyS3p6emi7tLQ0Ly8vNTV1zpw5oSNNTU2pqamqxIgZ464UESl9W+scAAAAAADEHnW6DK/XazAYOnZNJpPP5zvzsrKyspaWltzc3M4HV69eXVhYqEqMmDF8gliTpea4VH2pdRQAAAAAAGKMOs/LMJvNgUCgY9fv9yckJJx2TX19/YYNG9avX9/54ObNmydNmpSdnX3mPcvKyjrf0Ov1HjlyRJW00aDnkPEpn2xvfPe/6y6ZpXWWLlVTU5OQkNDQ0KB1EISrqqqqubk5KSlJ6yAIV2VlZSAQMJvNWgdBuKqqqiwWi17PN5TFNkVRqqqqrFar1kEQLp/P53A4+CMZB9ra2hobG9vb27UOgnA1NTV5PB6Px6N1EISrvr7ebrer02WcNhGjurq6V69enS/wer1LlixZunRp547j7bff9ng8P/3pT896z/79+3ds19XVGY3GrKwsVdJGA/2ka+ST7SlffGSaOU+6019ywWAwISGhY80RYpfP58vIyEhOTtY6CMLV1tZmt9vPLKARc5xOZ1ZWFr84xTpFURobG+PpM0+35fF4/H4/QxkHWltbTSYTQxkHLBaLx+NhKONAaFGIat9jYrfbDx8+PGzYMKfT2drampKS0nEqGAwuXbp03rx5vXv37jj46aef7tix4+GHH/6mG3b+997m5maDwRBX/wI87ELJyNLVO5KqD8uwcVqn6TpWq9VqtcbVUHZXVqs1MTGRoYwDoT+SdBlxIDSUdBmxTlEU/qKMDwaDgaGMD4qiuFwuhjIOeDwenU7HUMaBxMREUbHLWLx48fLly4PBoMfjKS4uFpGdO3cePHiwoKBg27Zte/fu7Zi4kZqaumzZsnnz5o0aNWrhwoWhg7Nnz544caJaYWLDhKvlzadlz7vdqssAAAAAACBMqnUZaWlpJSUlnY/k5+fn5+eLyKxZs2bNOv2pELt27VLrrWNV3o/kzadl3y65YZGYWKkOAAAAAMB3wkxU7aRkygUjJeCT/Tu1jgIAAAAAQMygy9DUuCtFRPa8q3UOAAAAAABiBl2Gpi6aIgajfP6xtDZqHQUAAAAAgNhAl6Epi1VGXizBoHz0jtZRAAAAAACIDXQZWmOZCQAAAAAA54MuQ2vDJ4g1Waq/lJrjWkcBAAAAACAG0GVozWCUiy4XESl9S+soAAAAAADEALqMKBBaZlL6VwkGtY4CAAAAAEC0o8uIAoN+IBlZ0tooX+zTOgoAAAAAANGOLiM6jA89AZRvMwEAAAAA4FvQZUSH0DKTT94Xv0/rKAAAAAAARDW6jOiQkSWDRojXLZ/8XesoAAAAAABENbqMqDFuqojI3ne1zgEAAAAAQFSjy4gaF10uBqMc3iOtjVpHAQAAAAAgetFlRA1rsvwgTxRF9r6ndRQAAAAAAKIXXUY0CT0BdA/LTAAAAAAA+EZ0GdFkxESxJstXX0jNca2jAAAAAAAQpYwRvfu2bdu2b99uMBiGDBkyf/78zqcOHjxYUlKSmJjodrsXLlw4fPjwiCaJDQajXDhZdr8me96Rn/xS6zQAAAAAAESjCM7LqKqq2r1798aNGx977LH29vbdu3d3nAoGgytXrly/fv1jjz22atWq1atXRy5GjBk7VUTko3ckGNQ6CgAAAAAA0SiCXcb27dunT58e2r7uuuvee+/rR1o2NTVNnTrVZrOJSHp6uslkilyMGHPBSEnJlKY6+fITraMAAAAAABCNIthl1NTU2O320Hbv3r2rq6s7TqWmps6ZMye03dTUlJqaGrkYsSfvahGRPe9onQMAAAAAgGgUwS7D6/UaDIbQttFo9Pl8Z71s9erVhYWFkYsReyb8SERk307xn/1/MQAAAAAAurMIPvvTbDYHAoHQdiAQSEhIOPOazZs3T5o0KTs7+8xTn332Wcd2IBDweDxlZWURihptBvYaZK0tr/rryy2DL9I6i8pqa2stFsvJkye1DoJwVVdXNzQ0JCYmah0E4Tp+/LjH4zGbzVoHQbiOHz9uMBj0er6hLLYpinL8+HGW38YBn8934sQJRVG0DoJwuVyuxsZGr9erdRCEq6mpyev1tvvPoEYAACAASURBVLa2ah0E4XI6nXa7PYJdRlZWlsPhGDZsmIhUVVV1rDfp8Pbbb3s8np/+9KdnffngwYM7tk+ePGk2m89aecQlw8U/lld/l1V1IHPqdVpnUZnZbLZarenp6VoHQbh0Ol1GRkboqTeIaYFAoH///metmxFbXC5XdnY2XUasUxTF4/F0n888cczj8ej1eoYyDrS0tCQlJQ0aNEjrIAiX0+l0u919+/bVOgjClZSUJBGdlzF58uRHHnlk8uTJIvLyyy9PnTq189lPP/10x44dDz/88De9vPMHa6PRqNfrLRZLxMJGmbwfyV+e0h/eY1H8Yk3WOo2azGaz2WzuRkMZv0LjyFDGAYYybphMJovFQpcR6xRFCQ2l1kEQrmAwyGee+ODz+RjK+GA2m9vb2xnKOBCaUBzBLqNv3775+fkFBQVms3nw4MGTJk0SkTVr1kyZMmXs2LHz5s0bNWrUwoULQxfPnj174sSJkQsTY6zJMjxPPvtA9rwrl03XOg0AAAAAAFEkgl2GiMycOXPmzJmdj9x3332hjV27dkX0rWPeuKny2Qey5x26DAAAAAAAOmMmarQadYlYrHL8sNQ7tI4CAAAAAEAUocuIVgajXHi5iEjpW1pHAQAAAAAgitBlRLFxU0VEPnxb6xwAAAAAAEQRuowodsEoScmUpjo5+qnWUQAAAAAAiBZ0GVFMp5PxV4mI7HlH6ygAAAAAAEQLuozoNvHHIiIfb5f2gNZRAAAAAACICnQZ0S0jS/oPFa9bDnygdRQAAAAAAKICXUbUG3elCMtMAAAAAAA4hS4j6l10uej1cqhU3K1aRwEAAAAAQHt0GVEvOVWGjZf2gHz8N62jAAAAAACgPbqMWDB+qojInne1zgEAAAAAgPboMmLByEvEYpXyg1Lv0DoKAAAAAAAao8uIBSazjP6hiMhHPAEUAAAAANDd0WXEiPF8mwkAAAAAACJ0GTFj8GhJyZR6hxz7TOsoAAAAAABoiS4jRuh0Mm6qiMhengAKAAAAAOjW6DJix/irREQ+3i7tAa2jAAAAAACgGaNaN2poaFixYoWiKD6fr6ioaNCgQZ3PVlRUvPTSS4cOHdq6dWvoSGVl5apVq8xmc2tr6/z580ePHq1WkrhlHyB9c6TqiBz8UEZdonUaAAAAAAC0oVqXsXbt2sLCwpycnIaGhuLi4k2bNnWcqqioeP7552+44YaysrKOg6tXr37wwQf79OnT2tp6++23v/jii2oliWfjr5SqI7LnHboMAAAAAEC3pdoak7q6upycHBFJS0uz2WzNzc0dpwYOHPjAAw+EznZwuVx9+vQRkeTk5PT0dI/Ho1aSeDb2CtHr5bN/iLtV6ygAAAAAAGhDnS4jEAgYjV9P8bDb7Q6H49wv6d+//759+0Skuro6GAwmJCSokiTOJafK0LHSHpB9O7WOAgAAAACANtRZY+L1eg0GQ8euyWTy+XznfsmCBQuuv/76rKys8vLyp59+WpUY3cK4qVL2kex5Ry7+idZRAAAAAADQgDpdhtlsDgS+/nINv9//rfMsli1btnXr1oEDBzqdzgceeKCkpMRqtXa+YP/+/R3biqK43e4DBw6okjam6Qzpw00W/dEDh0v/15+YonWc81ZbW2uxWFJSYi85TuNwOE6ePJmYmKh1EISrsrKytbXVbDZrHQThOnr0aDAY1Ov5hrLYpijKsWPHdDqd1kEQLp/PV1NT863/vIfo53K5mpqa2tratA6CcDU1NXm93sbGRq2DIFxOp9Nut6vTZZw2EaO6urpXr17nfu+kpKSBAweKSHp6+kUXXbR///6JEyd2vmbEiBEd2zU1NQkJCcOHD1clbazTjb5M9rw7xPllcOxNWmc5bzabzWq1ZmRkaB0E4QqNo81m0zoIwmU0Gvv3789CvzjQ3t4+fPhwuoxYpyhKMBjkM08c8Hg8iYmJQ4YM0ToIwtXS0lJfX3/atzQiFtXX17vd7n79+mkdBOGqqakRFb/HxG63Hz58eNiwYU6ns7W19dz/8J6amlpZWenz+cxmczAY3Ldv389+9rPTrun8AA69Xq/T6TovY+nWxl8le97V73lHrr5F6yjnzfAvWgdBuPR6PUMZHxjKuBEaSrqMWKfT6UJDqXUQhCv055GhjAMMZdzgN5G4ERpE1bqMxYsXL1++PBgMejye4uJiEdm5c+fBgwcLCgrOvFiv1y9atGjBggVWq9Xlck2bNq13795qJYl/Qy6U5FSpd8jxMhmQq3UaAAAAAAC6lGpdRlpaWklJSecj+fn5+fn5nY9s2bKlY3vs2LFjx45V6927F51OJlwlf3tJ/vaS3LpU6zQAAAAAAHQpZqLGprwfi4js3yVffaF1FAAAAAAAuhRdRmzq1V9+OENE5KUSCQa1TgMAAAAAQNehy4hZ18yRpBSp/Fw+eF3rKAAAAAAAdB26jJhlSZQZhSIir/1B3K1apwEAAAAAoIvQZcSysVfIoBHibpVXN2kdBQAAAACALkKXEeN+vlgMRvnwLan8XOsoAAAAAAB0BbqMGNerv+TPFBF5cY0oitZpAAAAAACIOLqM2Hf1L8SWLo5j8vdXtY4CAAAAAEDE0WXEPpNFrpsvIvLG09LWpHUaAAAAAAAiiy4jLoz+oeSMEa9L/udJraMAAAAAABBZdBnx4sZ7xWCUve/Jsc+0jgIAAAAAQATRZcSLjCy54kYRkT+u5SGgAAAAAIA4RpcRR668SVJ7Sm2l7HhZ6ygAAAAAAEQKXUYcMZrkhoUiIm89Ky1OrdMAAAAAABARdBnxZfgEGTlJ/F7ZtlHrKAAAAAAARARdRty57i4xmmT/LjmyT+soAAAAAACojy4j7qT2lKtuFhH54zppD2idBgAAAAAAlanWZTQ0NCxatOiee+4pLCwsLy8/7WxFRcWaNWvmzp3b+eDhw4cLCgrmzp1bVVWlVgyIiEyZLRlZUu+Q9/6kdRQAAAAAAFRmVOtGa9euLSwszMnJaWhoKC4u3rRpU8epioqK559//oYbbigrK+s4WFVV9dvf/nbt2rV9+vRRKwNOMRjlxnvlicXyzn/JhKsktafWgQAAAAAAUI1q8zLq6upycnJEJC0tzWazNTc3d5waOHDgAw88EDrb4amnnlqyZAlFRqTkjJEx+RLwy8sbtI4CAAAAAICa1OkyAoGA0fj1FA+73e5wOM79kvLy8v37999111133333F198oUoM/JuZ88Rkkc8+kEOlWkcBAAAAAEA16nQZXq/XYDB07JpMJp/Pd+6XHD58uG/fvo8//vjSpUuXLVvm9XpVSYKv2dLlx7eKiLxUIgG/xmEAAAAAAFCJOs/LMJvNgcDXX5nh9/sTEhLO/ZIRI0ZcdtllIpKZmXnppZceOnRozJgxnS/Yu3dv5123271//35V0nYfurQhw1J6WRpra/5r3YkLf6x1HBGRkydPWiyWHj16aB0E4XI4HCdOnEhMTNQ6CMJVWVnZ1NRkNpu1DoJwHT16NBAI6PV8Q1lsUxSlvLxcURStgyBcPp+vpqbG7XZrHQThcrlcTU1NnVfQI0Y1Nzd7vd76+nqtgyBcDQ0NdrtdnS7jtIkY1dXVvXr1OvdLOn9uNplMZ87LGDt2bMe2w+E4cuTI6NGj1QjbzaQ8KBsW2A/ssF/7C8nI0jqNfPXVV1arNSMjQ+sgCFePHj0yMzNtNpvWQRAuq9U6YMCAby2gEf30ev2IESPoMmKdoihGo3HUqFFaB0G4PB5PSkrK0KFDtQ6CcLW0tNTV1WVnZ2sdBOGqr693u939+vXTOgjCVVNTIyo++9Nutx8+fFhEnE5na2trSkrKua/Pzs4uLS0VEZ/Pt2vXrv/4j/9QKwn+TfYIGXeltAfkj+u0jgIAAAAAgApU+07WxYsXL1++PBgMejye4uJiEdm5c+fBgwcLCgrOev3dd9/98MMPv/DCC83NzXfeeafValUrCU43/Vfy6f/KkX2yf5eM/qHWaQAAAAAACItqXUZaWlpJSUnnI/n5+fn5+Z2PbNmypWPbarUuX75crXfHuSSlyE/myisbZdtG+UGemCxaBwIAAAAA4PtjVW33cOk0ycqWFqe89azWUQAAAAAACAtdRveg08nP7xMR2fmK1FZqnQYAAAAAgO+PLqPb6D9ULr5G2gPy4lqtowAAAAAA8P3RZXQn194h1mQp/0z2vqd1FAAAAAAAvie6jO7Emiw/u0NE5H+eFK9L6zQAAAAAAHwfdBndzMRrpP9QaWuSN57WOgoAAAAAAN8HXUb38/P7RKeTv78qjmNaRwEAAAAA4LzRZXQ/Wdly6TRRFHlxjdZRAAAAAAA4b3QZ3dJP5kpSilR+Lh++pXUUAAAAAADOD11Gt2RJlOkFIiJ/2SzuVq3TAAAAAABwHugyuqtxU2XQCGlrktf+oHUUAAAAAADOA11GN/bzxaLXywevS+XnWkcBAAAAAOC7osvoxnr1l/zrJBiUF9dIMKh1GgAAAAAAvhO6jO7tx7eKLV0cx2T3a1pHAQAAAADgO6HL6N5MFpk5T0Tk9S3S1qR1GgAAAAAAvh1dRrc3Jl9yxoi7Vf6/32sdBQAAAACAb0eXAZEb7xWDUUrf5iGgAAAAAIDop1qX0dDQsGjRonvuuaewsLC8vPy0sxUVFWvWrJk7d+6ZL5wzZ86f//xntWLg+8jIkimzRUReXCOKonUaAAAAAADORbUuY+3atYWFhY8++ujKlStXrVrV+VRFRcXzzz8/Y8YMnU532qtef/11vZ65IVHgqpsltac4jsn7/6N1FAAAAAAAzkW1HqGuri4nJ0dE0tLSbDZbc3Nzx6mBAwc+8MADobOdtba2/vWvf50+fbpaGfD9GU0y624RkTeflRan1mkAAAAAAPhG6nQZgUDAaDR27NrtdofD8a2veuyxx+bPn69KAKhgxMUyfIJ4XfLnTVpHAQAAAADgGxm//ZLvwOv1GgyGjl2TyeTz+c79kgMHDlgsliFDhpSVlZ31gkAg0LGtKEowGGxvb1clLb7RdXcbVs2Rj/+mTLo2OOgHkXiH9n+JxM3RlRRFYSjjA0MZN0JDGQwGtQ6CsCiKEhpKrYMgXO3t7QxlfGAo4wa/icSN0CCq02WYzebO1YPf709ISDjH9YqiPPHEE+vXrz/HNZ999lnn6z0ez6FDh8KPinPrOfpK+943/P/vw1/MeiAS96+trbVYLLW1tZG4ObqSw+Gor69PTEzUOgjCVVlZ6XK5zGaz1kEQrvLycp1Ox1OoYp2iKOXl5Z3/iQgxyufz1dTUdP6EjBjlcrmamprcbrfWQRCupqYmr9fb0tKidRCEy+l09unTR50u47SJGNXV1b169TrH9Xv27Glra1u6dKmIVFZWBoPBxsbGW2+9tfM1o0eP7th2OByff/75yJEjVUmLcxk5Uir2WeqqR775uNx4r/Qfqu7tv/rqK6vVmpGRoe5t0fWSkpIyMzNtNpvWQRAus9k8YMCAcxfQiAnBYHDEiBF0GbFOURSdTsdnnjjg8XiSk5OHDlX5oxS6XktLS11dXXZ2ttZBEK76+nq3292vXz+tgyBcNTU1ota8DBGx2+2HDx8eNmyY0+lsbW1NSUk5x8UTJkx47rnnQtt/+ctf2tvbeQJoFJnzG3l2hVR/KesL5fLr5Wd3ah0IAAAAAICvqdZlLF68ePny5cFg0OPxFBcXi8jOnTsPHjxYUFCg1lugi/S5QIo2y3t/knf+S7b/t3zyd/k/RXLBKK1jAQAAAAAgomKXkZaWVlJS0vlIfn5+fn5+5yNbtmw584XXXnutWhmgGoNRrrpJLpwsz6+SikPy+ELJ+7FM/5UkJGmdDAAAAADQ3bGqFt+sZ19ZsEFmLZCERPnwTXl4jnzyd60zAQAAAAC6O7oMnJNOJ5dcK8XPyA/ypNkpT/9GNj8orY1axwIAAAAAdF90GfgOeqTL7SvlF0vEliYHP5T/vFX+8YbWmQAAAAAA3RRdBr6zMfly/1aZ8CNxt8qf1svjC6XeoXUmAAAAAEC3Q5eB85Fok5/fJ/PWSppdjn4qq2+Tv70kQUXrWAAAAACAboQuA+cvZ4wUb5XJ10u7X/7ye1lXII5jWmcCAAAAAHQXdBn4XkwWmXanLPqdZGVL1Zey9lfy+hYJ+LWOBQAAAACIf3QZCEPfwbL4KfnJL0VvkHdflFVzpeKQ1pkAAAAAAHGOLgPh0etl6s/l/i1ywSipd8hjd8t/Pypel9axAAAAAABxiy4DasjIkrtK5IaFYkmU3a/Jf94qBz/UOhMAAAAAID7RZUA9F/9Eip+WH+RJs1M2PyjPrpC2Jq0zAQAAAADiDV0GVNUjXW5fKbcuE1u67Nsp/3mrlL6tdSYAAAAAQFyhy0AEjL5MHnhaJl4jrhZ5cY08sVgaarTOBAAAAACIE3QZiIyEJJm9SO4qkYwsObJPVs2VndskqGgdCwAAAAAQ8+gyEEkXjJL7t8qU2RIIyJ9/J+sKTE6H1pkAAAAAALGNLgMRZjTJtbfL4k3Sb4hUfWl/uti6+1WtMwEAAAAAYhhdBrpEVrbc+zu59nYRSfzbC/LX/9I6EAAAAAAgVhnVulFDQ8OKFSsURfH5fEVFRYMGDep8tqKi4qWXXjp06NDWrVtDRw4ePFhSUpKYmOh2uxcuXDh8+HC1kiB6TZl9IjPb/sf/1L35jFiskn+d1oEAAAAAALFHtS5j7dq1hYWFOTk5DQ0NxcXFmzZt6jhVUVHx/PPP33DDDWVlZaEjwWBw5cqVmzZtstlsTqdz0aJFzzzzjFpJEM0C6X2a/u+y1Od+I3/+nRjNcsm1WicCAAAAAMQY1daY1NXV5eTkiEhaWprNZmtubu44NXDgwAceeCB0NqSpqWnq1Kk2m01E0tPTTSaTWjEQ/dp7DZB5a8VilZcfkw/f0joOAAAAACDGqNNlBAIBo/HrKR52u93hONfXVaSmps6ZMye03dTUlJqaqkoMxIx+Q6TgETFb5E/rqDMAAAAAAOdFnTUmXq/XYDB07JpMJp/P9x1fu3r16sLCwjOPezyeju1AIKAoitfrDTMnNOfz+QwGg9frld4X6Of+P6Y/PCh/WucPinLh5VpHw/nx+Xxer9dsNmsdBOEKDaVOp9M6CMLl9/u9Xq9ez1O9Y5uiKKGh1DoIwuX1ekP/gdU6CMLFUMYNn8/HUMaHUNugTpdhNpsDgUDHrt/vT0hI+C4v3Lx586RJk7Kzs8889eWXX3ZsBwIBn8937Nix8KNCW7W1tRaLpampSURErIlX39n/jSdNL62rcja0XHChxuFwPqqrq9va2hITE7UOgnBVV1crikItFQccDkdCQgJdRqxTFMXhcPBf1zjg8/lOnDjReeYyYpTL5WpsbAwGg1oHQbiamppCzZTWQRAup9PZv39/df7zetpEjOrq6l69en3rq95++22Px/PTn/70rGdHjBjRsR36fJabmxt+VGgrOTnZarVmZGSc2s/Nlb59ZPNDfd97WgYMlJGTNE2H82CxWDIzM0NPvUFM0+v1AwYM+I4FNKKZ3+/Pzc2ly4h1iqK0t7fzmScOeDyehISEoUOHah0E4WppaamrqzvrP74ittTX17vd7n79+mkdBOGqqakRFZ/9abfbDx8+LCJOp7O1tTUlJeXc13/66ac7duy466671AqAWJU7XuYsExF5+rdS9pHWaQAAAAAA0U61aW+LFy9evnx5MBj0eDzFxcUisnPnzoMHDxYUFJz1+nnz5o0aNWrhwoWh3dmzZ0+cOFGtMIgxIyfJLQ/JsyvkD0vkzodlCItNAAAAAADfSLUuIy0traSkpPOR/Pz8/Pz8zke2bNnSsb1r1y613hrxYPQP5ab75flV8vsHpeARuWCk1oEAAAAAAFGKVbWIGmOvkNn3Srtfnvq1HD2gdRoAAAAAQJSiy0A0ybtaZt8rPq889WupOKR1GgAAAABANKLLQJTJu1pmLRCfV35XJF99oXUaAAAAAEDUoctA9LnkWpleIF63PLFYHMe0TgMAAAAAiC50GYhK+dfJj/6veNpk4yKpPa51GgAAAABAFKHLQLS6+heSf524WuTxhVJXpXUaAAAAAEC0oMtAFJteIJdcK61NsmGhOE9onQYAAAAAEBXoMhDdZi2QvKulxSmPU2cAAAAAAEToMhADZt8rY6+QxpPy+EJpPKl1GgAAAACAxugyEPV0Ornp1zL6h6fqjBan1oEAAAAAAFqiy0As0Onllodk5CRxnpANC6W1UetAAAAAAADN0GUgRuj1MmeZ5I6Xuip5fKG4WrQOBAAAAADQBl0GYofeILetkCEXSm2lbFwknjatAwEAAAAANECXgZhiMMrtK+WCkeI4Jk8sFq9b60AAAAAAgK5Gl4FYYzLLnavlgpHy1RfyuyLqDAAAAADobugyEIPMFrlztQwcLhWH5HdF4vNqHQgAAAAA0HXoMhCbzBYpeET6DZGKQ/LUr8Xv0zoQAAAAAKCLRLbL2LZt2/z58xcsWLBx48bTTgUCgTfffHPu3LnvvfdeRDMgblmsMm+tZGXL0QOy+UFpD2gdCAAAAADQFSLYZVRVVe3evXvjxo2PPfZYe3v77t27O5/dsmWL1+udPHlye3t75DIgziUkyfz10qu/fPFP+cMSUfj/EgAAAADEvwh2Gdu3b58+fXpo+7rrrjtt/sWdd945ffp0i8USuQDoFhJtcleJ9OwnZR/JM8u1TgMAAAAAiDhj5G5dU1MzYcKE0Hbv3r2rq6sj917o1pJTZf562bBAPv1fWTj1304l9ZAe6dIjU3qkSUqm2NKlR7r0yJAe6ZKSISaqNAAAAACIPRHsMrxer8FgOPU2RqPPd35PZ2xra+vY9vl87e3tnY8gRrnd7mAwmJCQoPJ9DRbdnOWmf7yua3bqWhp0LU5dS4PO65K2ZmlrFkf5WV8UtFiDtvSgLS1oSw/2SA8mp369a0sLJiSpHDK+uN1ul8ul1/P84Jjndrvb2tpY7hcHQkPJn8pYpyhKaCi1DoJweTwehjI+uFwuhjI+uFwuj8fDUMYBl8slEe0yzGZzIHDqcYyBQOB8f32trKzs2Pb7/YFAwOFwqJkPWqitrU1ISPB6I/MtqqOvPu2AqbnO0NZkdDUb2hqN7hZDa4PR1WxoazK6mw2uZp3XrfNWSV3VWW8WNJgCiT3ak1IDST3ak1J9Kb1cg/7D3yMjIslj0MmTJ/1+f1ISjU/Mq6urM5lMZrNZ6yAIl9PpdDgcdBmxTlGU0FBqHQTh8vl8dXV1/EUZB9ra2hobG/mLMg40NTV5PB6dTqd1EISrvr4+Ozs7gl1GVlaWw+EYNmyYiFRVVdnt9vN6eW5ubse2w+GwWCw5OTkqR0SXS0hIsFqtGRld1gic8/8zTfXSXC/NTmlxSrPz1HZoo6FW1+43tdSbWuq/vv79P0nPvjI8T4ZPkNxxkY4e5QwGQ2Zmps1m0zoIwqUoyoABA9SfLYUu53a7c3Jy6DJinaIoXq+XzzxxwOPxGI1GhjIOtLS01NXVZWdnax0E4aqvr3e73f369dM6CMIV+h0kgl3G5MmTH3nkkcmTJ4vIyy+/PHXq1G97BdC1UjIk5ZtbFVeLNNdLc4M010lTvXz5iRzZLyer5OQrsusVMZplyBgZPkF+MFEyendhaAAAAADo7iLYZfTt2zc/P7+goMBsNg8ePHjSpEkismbNmilTpowdOzZy7wuoI9EmiTbpPejU7hU3SsAvR/bLoVI5VConvzq18cpG6dlXhk+Q4RMkZ4wYTVpmBgAAAIBuIIJdhojMnDlz5syZnY/cd999nXdnz54d0QCAmowmyR0nueNkRqE01Mhn/5BDpXJkn5yskpP/I7v+R0wWyRnNZA0AAAAAiKjIdhlA3Eqzy6XT5NJp0h6QI/vk0EdyqFRqK5msAQAAAACRRpcBhMdglGHjZNg4mV4gDTVy8EM5VCpfMFkDAAAAACKFLgNQT5pdLvmZXPIzaQ98/WSNzpM1MvvK8PEyfILkjBaTReu4AAAAABCT6DKACDAYZdhYGTb2X5M1SuVQqXzxT6mrkver5P0/fz1ZY/h4yeyrdVwAAAAAiCV0GUCEpdnlkmvlkmtFRD7/WA59JGUfyYnyU5M1RCQrW/7jsrO8UCdiNIvJLCbLqQ2jSUwWMZnFaBaTRUymf22YxZzQpT8UAAAAAGiHLgPoQkMvkqEXybQ7/22yhuOYOI6pc39L4r8VHEazpPWUa+ZKr/7q3B8AAAAAogBdBqCFzpM1jn0mnjYJ+MTnk4BP/F7x+yTgk4BffN5/O+L3/WsjdMR/asPTduq2Xpd4//2NjpfJ/vflihvlR7eIydzFPyUAAAAARAJdBqC17BHq3CfUa3zdengl4JfDe+Xt5+S9P8rHf5Npv5LRP1TnvQAAAABAO3QZQLwwWc7y3SjZI2TcVPnjWvnyE3lmueSOk9n3SmpPLfIB+P/bu/Owps68feB3AmQl7JAAIiAgKi641IWqUGs3u1mdTvf113aqtXbejrW1He37ykwXtYsdO7Uzfdu+XacdndrdWi1qFZeq4IIKbiiFsCeQff/9kQiIWhECh+D9ubi8nnNOzjnfeAiBO895HiIiIiLyD7HQBRBRN4tJwJxXcfcCqCJxeBdeuB/rPobLKXRZREREREREncQsg+jSMPpKPPt/uPxGOO34/j289CCOFAldExERERERUWcwyyC6ZMgU+N0TePLvSBiA+ir8/Sl88BcYGoUui4iIiIiI6OIwyyC6xPTLwLyVuGU2pAoUbcRf78em1XC7hS6LiIiIiIioo5hlEF16RGJMnoHn3kd2LmxmrHkLSx/GqcNCl0VERERERNQhzDKILlWqKNy3ELOXIkqD6pN4/XH8axlMzUKXRUREREREdAHMMogubRkjsfAjXHMPPB7sWIsX7kPhN0LXRGfZtR7rPha6CCIiIiKi3iJY6AKIqBe49j6MuQqfvYKje/Hv17Hz0eCmsAAAIABJREFUB/z+v5AwQOiyCGjQ4t+vo3Q3ABwpwr3PQRUpdE1ERERERALzW5ah0+ny8/Pdbrfdbp8/f35KSkrbratXry4oKAgKCsrIyJgzZw6AioqKl156SSKRGI3GOXPmjBgxwl+VEFFnxCTgsVewewPWvIWTh7D0EUyajmkPQqYQurJL2PpP8e3/ti4eLcbLD+GupzF4rHA1EREREREJz29ZxrJly2bPnp2enq7T6RYsWLBy5cqWTZWVlYWFhStWrACwfPnywsLCnJycl19++bnnnktISDAajQ8//PCnn37qr0qIqPNGX4msCfj6H9j2LX5eg+LNuPlRjJ4idFmXnpOH8OlS1JwCgIQBuOMpRMbhwxdRugv/fA6TZ+DGhxHEjnVEREREdIny23gZ9fX16enpACIjI1UqVXNz6wiCBQUF06dP97Znzpy5YcMGAGazOSEhAUBoaGhUVJTVavVXJUTUJTIFbv0j/vg3aJJhaMRHL2DFk6itELqsS4bVhM9fw/K5qDkFqRzTZ2HeSvTLgDIcj76Emx6BOAibVuPVx1BfJXStRERERETC8E+W4XQ6g4NbPyFUq9VarbZlsaamRq1We9sajaaqqgpAUlJScXExgKqqKo/HI5PJ/FIJEflH/0F46p+YPgtSOY7tw5KH8e3/wmEXuqy+rqgAL9yPbd/C48GgMVjwPnJnQtTmB/UVv8cTbyBKg6pjWPowijcJVysRERERkWD800XZZrMFBQW1LIaEhNjt9nNuDQ4O9m564oknbr311vj4+PLy8vfee+/sYzY1NbW0rVary+UyGAx+qZYEZDKZXC6XRCIRuhDqmFFXi9LHyL5/J7hkG9Z/6vnlR+v1DzsHjQVgMpn8GEGKDDqRqUlkbhaZmsXmZjgdzsFj3VHx/jp+7yduqpOu+XvwsWIAHlWkddpDzqwcADj7515EvGj2a7I1K4IPbMX/5Tv2bbXd8AdPSOdfU2az2Wg0OhyOzldPvYPZbDYYDGIxZygLbG6323sphS6Euspms/FS9g1Go9FkMvFS9gFGo9FqtfJS9gEmkwn+yjIkEonT6WxZdDgcbf/IabvV6XR6Nz3//PPvvvtucnJyY2Pjs88++9prr8nl8rbHrKura2nbbDan01lfX++XaklAOp1OKpW63W6hC6GLceWD8rTLYn76MLipXv7Ji+aUYQ1T7tE3mQF05O6w4Ob6IKtJbG4OshiDLAax1RRkbg6yGoMsBrHZEGQ1im3ms/eS/vC+LS7ZNPAy46DxLkW4/59UbxKx6/uIHV+LXA4AzdlTGyfc4gmR4Ld/4k25XxWbGlPwUUjRT6Lj+2uue9Qe179zZ9fr9TKZTCqVdm536j2amprq6+uZZQQ6t9vtvZRCF0JdZbPZ9Ho9L2UfYDKZ9Hq9UqkUuhDqKr1eb7PZ+KlqH9DY2DhgwAD/ZBntOmJUVVXFxcW1LMbHx2u12szMTACVlZVqtbqxsVGpVCYnJwOIiooaNWrU3r17x48f3/aY3tE3vLRarVQqTU1N9Uu1JKCQkBC5XB4dHS10IXSRUlMx8Vqs+wjrP1GU71e8Oz90/PSQtJtDg0UwNsGoh7EJpiYY9af/bfatdNg6dHxVFELDoVC1rjm2T1p7Ulp7MmrLKqRnY9QUjJh0xgP6hpOH8Nkr0JYDQNJA3PansMS0sA7um5qK8VPwfn6w9kTiv/IxfRZyZ3aiBIfD0b9/f97o1wcYjcbU1FRmGYHO2y+Dv/P0AVarVSwW81L2AQaDQalU8lL2AQ0NDRaLpV+/fkIXQl2lUCjgx3lM1Gp1aWlpZmZmY2Oj0WgMD2/9HDUvL2/JkiV5eXkAVq1aNXXq1IiIiIqKCrvdLpFIPB5PcXHxTTfd5K9KiKhbhEhw/YO4bCo+fx3H9kVuX4Ptay68l0SG0AioIqFQITQcygiEhkMZjtBwhEb4GvLQc+xot+HAVuwpQMk2HC3G0WJ8/iqGjMOoKzEsB5LA/8PbasI372Dr1wAgVWDaA5h8y0UfJK4/5v8T/16Owq+x5i2U7sHdz/TBxIeIiIiI6Ex+yzLmzZu3ePFij8djtVoXLFgAYNOmTQcPHpw1a1ZiYmJubu6sWbMkEklaWlpOTg6AJ5988oknnpDL5Waz+eabb9ZoNP6qhIi6UVx/zHkVv6xzrVkpBkTeSMIXT0ScTivCfJmFKrLz84ZKpBg1BaOmwGbBvp+xpwBle3BwBw7ugESGoTkYdQUGXRao85IWbcQXf4ehEQCGTcTMxxHehc5Ktz6BQWPwyRIc2oGX/x/uW4QBQ/1VKRERERFRLyTyeDxC13BhWq128+bNt912m9CFUFf9+uuvvMekbzhx4kRMTIxK1YNdAMwG7N2Moo04WgzvDy6FCiMmYdSVSBt2xmQfvZmuBp+9htJdABAejdufwqAx/jmyvg7vPo+KMohEuOpOXHMfOnavQVlZGe8x6Rv279+flZXFe0wCndvtLikpGTZsmNCFUFdZrdZTp04NHDhQ6EKoqwwGQ319Pe8x6QN4j0mf4Z0pNTA/0iSiS5BChQnXY8L1MDVhTwGKClB+ENu+w7bvEB6NkVdg1BQk9eJfGd1ubPw3fvgAdhvEYkyegesegMR/I25GxOKPf8N37+Gnz7DuY5Tuwf2LEBHrt+MTEREREfUazDKIKNAowzFpOiZNR1MD9vyEogJUlGHjKmxchdhEjJqC0Vcitpcl7hVl+HQptCcAoF8G7ngKCQP8fxZxEG54CIPG4P18nDyElx/CPc9iyDj/n4iIiIiISFDMMogoYIVH44pbccWt0NVi93rsKYD2BH74ED98iMR0X6jRlXEo/MJmxtfvoPBreDyQKTDtQUy8GSJRN54xPRtPv4MP/oqjxfjnc7j8RkyfjeCQbjwjEREREVHPYpZBRIEvMg5T78TUO1FXid3rUbQRlUdReRTf/BOpQzF6CrLzhJndo3gTvngTzY0AMGISZj4OVVRPnFcVidlL8dNn+O49bP0ax/bjgecRl9QTpyYiIiIi6n7MMoioD4lNxLX34dr7UHkMRQUo2ojj+3F8P1avQOYYjLoCwy6HVN4TlbQd4zMiFrf9yW9jfHaQSIQrb8fAUXj3eVSX45VHcctjGD+tR2sgIiIiIuoezDKIqC9KTENiGm54CCcPoWgjijfh0A4c2uHbGhmH0Agow8+YTda3JgKh4ZCHdunsP32O79+H0w4AU27DtfchRNLVZ9Q5SQPx9Dv4dCn2bcFnr+LwLtw+DzKFMMUQEREREfkJswwi6tOSByN5MG5+FMf3o2gj9m6GUQ9dLXS1v7VXUDBCw6E8nXH4Io8z1yjCzjHsRbsxPu96Bprk7npqHSRT4oH/RuHX+OIt7N2MijLcv6hXT/hCRERERHQhzDKI6BIgEiFtONKG43dzYTbAoINRD4PudKMRBj2MTWhugFEHuw0uJ5oa0NRwgcN6Aw5f2BEBhw2/rAMAmRI3PITLb+yBZ9ZROTcidRje/x/UVuDV2bjhIVx5u9A1ERERERF1ErMMIrrEKFRQqKDu/1uPadC2STq8YUeb+MNi9D3M1ARTE2pOnbHvyCtwy6weGuPzosSnYN7bWPMWCr/GN+/gSBHuegaqSKHLIiIiIiK6aMwyiIjOEh2P6PjfeoC+DgYdzM1oamiNOUzNyJ3Z02N8XpQQCW59Aukj8PmrKN2NJQ/hrgUQhwldFhERERHRxWGWQUR08SJiERErdBGdNTIPyYPw/mJUlOEfC2Kzp6LfHKFrIiIiIiK6CGKhCyAioh4XpcGTf0ferfB4Iot+lPxzAfZtEbomIiIiIqKOYpZBRHSpuvkPePQlpzJCXH4Q7/03XnwAuzcIXRMRERER0YUxyyAiuoRljjlxd77jlscRk4DaCnz0Iv56L7Z/B5dT6MqIiIiIiM6LWQYR0SXNIw5yjb0Gz76Pe55FfCrqq/DZq/jLPfh5DRx2oasjIiIiIjoHjv1JRESASIxRUzBqCg4UYv2nOHkI/1mBdR/hilsx8WZIZELXR0RERETUilkGERG1MTQHQ3NQVoT1n+BIEb7+Jzb8C5NvweQZkIcKXRwREREREeDHLEOn0+Xn57vdbrvdPn/+/JSUlLZbV69eXVBQEBQUlJGRMWeOb/K/0tLS119/3Waz5efnJyYm+qsSIiLqqoEjMXAkKsrww4c4uB1rP0DBKky8CXm/Q2iE0MURERER0aXOb1nGsmXLZs+enZ6ertPpFixYsHLlypZNlZWVhYWFK1asALB8+fLCwsKcnJzKysr/+Z//WbZsWUJCgr9qICIif0oaiIfyUX0SP36M4o3Y8C9s/gITpuHKOxAWJXRxRERERHTp8tvYn/X19enp6QAiIyNVKlVzc3PLpoKCgunTp3vbM2fO3LBhA4C333574cKFDDKIiHo7TTLueRbPfoDx0+B2YfMXWHwnPn8VDdVCV0ZERERElyj/ZBlOpzM4uLWLh1qt1mq1LYs1NTVqtdrb1mg0VVVVAMrLy/fu3fv444/PnTv3yJEjfimDiIi6S7QGtz2JRZ8gdyaCgrDtO7xwLz5+CbUVQldGRERERJcc/9xjYrPZgoKCWhZDQkLsdvs5twYHB3s3lZaWPvzww3/729/q6+vnzp373nvvSaXStsdsaGhoaZvNZpfLpdfr/VItCai5udnhcLT9bqEAZTAYQkJCXC6X0IVQVxkMhqamJqvV2rGHi5F3h2jcjdJtX0t3fi/atR67NzgGjbXm3eaK69+9hdKFGI1GvV4vFnO29cDmdru9l1LoQqirbDabwWDgpewDjEYjL2Xf0NzcbLFYeCn7gObmZrVa7Z8sQyKROJ3OlkWHwyGTyc651el0ejdlZWVNmjQJQExMzMSJEw8dOpSdnd2uvpa2zWZzuVwGg8Ev1ZKATCaTy+Vq24uHApTJZJJIJB6PR+hCqKvMZrPBYGgbQHfIZTeIRlypKv4pdPcPIYd2hBzaYU0d3jT+Jnt8WveUSRfmvZTMMgKd2+32XkqhC6GustlsvJR9g8lkMplMvJR9gNFotFqtvJR9gMlkgr/6ZbTriFFVVRUXF9eyGB8fr9VqMzMzAVRWVnrvN5FIJG13t9ls7Y6Zmpra0tZqtRKJJCkpyS/VkoBEIpFcLo+Ojha6EOoqp9MZExOjUqmELoS6ymKx9OvXr20AfRHSBuKmB7H9O/z0mezEPtmJfUgfgavuxsCR/i6TLkyv1yclJTHLCHRut7u5uZm/8/QBVqvV7XbzUvYBBoNBKpXyUvYBCoXC+2uP0IVQV3nDBL99PK5Wq0tLSzMzMxsbG41GY3h4eMumvLy8JUuW5OXlAVi1atXUqVMBpKam7ty5c+zYsXa7ffPmzXfffbe/KiEiop4TIsGk6Zg0Hb+sw7qPcXQvju5F/0xcfTeyJnT0IHZrmy8bHFbYrHDYfIt2C+w22K1w2GCzwGHzLUrlCItGWBTCohEeDVWkr0F+ZzXBoEMsf/kjIiKi3sJvWca8efMWL17s8XisVuuCBQsAbNq06eDBg7NmzUpMTMzNzZ01a5ZEIklLS8vJyQEwd+7cF1988ZNPPmlubv7DH/4gl8v9VQkREQngsqtx2dUo2oj1n+BUKd5ZiIQBSM+GSOSLIbxRhcMOm+V02wabFc6LvL3lglSRCItCWAzCohAe1Zp3hEUhUu3nc3WQqQk2i+9LFYVojTBlXCybBfu2YM9POPwLAITHYOAoDByFjJHMjIiIiEhYfssyIiMjX3vttbZrcnNzc3Nzve0ZM2bMmDGj7Va5XL548WJ/nZ2IiHqFkXkYmYeSbfjxE5w8hKrjHdpLIoVEhhCZr+H7kiLkdEMqB0Tn3tflQHMjmht8/xqbYNDBoEPlsXM/XqE63Y8j6ozeHGFRCI9BiPTce9nMsFpgt8Bqht0Kqxl2i2+NzXLmVssZjzSf66bchDQMnYChOUga2KH/nx5mt+HAVuwpQMm2M9Y31eOXdfhlHQDEJSEjGwNHI30EFLzXjIiIiHoah2AkIiJ/y5qArAk4UoQTByGVIUQKiRQS+enMQnpmWnGe+KDT9HWt0UazDs31aG6EQYemejQ1wGyA2YDq8nPvK1UgLAqqiNZuFN5eJP5VdQxVx7DuI0TEYtjlyBqPzDF+PkXn7NuCogIUb2pdM2AYRk9Bdh4UKlQdx9FilO3B0b2orUBtBbZ+DQCJ6cjIRsZIpA2HlL0siYiIqCcwyyAiou6RMRIZQgwCGhGLiNjzbjXo0NwIQyOaGnz/tjR0NbCZUWdG3a/t95LIIFVAKodUDpkCEjmkp9fIlZDIfJukcsjaLirO0WfBYUPpbuzfigOF0Nfh5zX4eQ0kMgzNwfCJGHSZAHHAgUIUbcSBrbCfHoc7ZQiyc5Gdd8a9JAkDkDAAk2cAwMlDKNuDI8U4UoTKo6g8io2rACB5MDJHIWMk0rPPOg0RERGR3zDLICKiS4kqEqpI4Dxzx5oNaG6A2XA6j1D4Mgs/CpFiaA6G5gDA8QMoKUTxZjRWY89P2PMTAGSORtYEjJiMsCh/nvdsB3egeBP2bYHN7FuTNBDZeRh1xW+FQV7Jg5E8GFfdBQBlRThShCNFOHnI97XuYwDIGImBI5GejZQh3fk0iIiI6FLELIOIiOg0hapHR38YMBQDhuLGR1BbgQOF2F+IkwdRuhulu/GfFUga6Es9Egb47YxuN44Wo2gj9v3cOpZHQhpG5mH0lYiM+82dz2PgSN8svDYzju71RRvV5b6AA4BMgbThvn468akQnWfoEyIiIqIOY5ZBREQktLgkTLkNU26DqcnXXeLgDlSUoaIM37+PKI1vrNCu3LNzbB92rT8jwlD3x8g8jJrit8lWpQrfUCkAjHoc3YvS3ThajPoqlGxHyXYAUKgwcBQyspE+AnH9/XNe6iW05YhPEboIIiK6VDDLICIi6jWU4b7Zbb3DauzdjIM70FiNzV9g8xeQKTB4HEbmIXNMR8dMPVGC4o3YuxlNDb41sYnIzkN2rj+7e5wtNALZucjOBYDGat/gGmW7YWxC8Sbf8KLhMcgYiSHjMDKvGyuh7uawo+gnbPoCVccgkWHwZRg2EYPHcoIbIiLqVswyiIiIep/2w2psw8EdqC5HUQGKCgBg8FgMn4ihOQiNOMfup0pRvBHFm6Cr9a2J0iA7FyPz0C+jp57DaVEajJ+G8dMAnJ4MpQhHi9FUj10/YteP+OAv59hLpoRU1jovr3fuG98kOOeZu/eMlaenzqHu01CNLWuw/XtYTb41div2/oy9PwNAahaGjEfWeMSnClgjERH1VcwyiIiIejffsBoPo64S+7fgQCFOlODQThzaic9eRfJgDJ2ArBwA+PUo9v+MPQVorPbtGxGLkXkYMRnJgwV8Bq3aTYZypBjH98PUBLsVdhvsFthtvklwrabWv5A7LUSKAcOQkY2Bo5A0sKtHoxaHf8GWL333DQHol4FJ0zH2GujrsH8rSrajdBdOlOBECb79X0RpMDQHQ8b2lrmHiYioT2CWQUREFCBiE33DapgNOLAV+wtxeJdv6pBv380KDhE7Hb5HqqIw6goMn4QBQwWt+Dd5J0PBHefY5LD5Qg2HFTZr66L3y2GDzQqHtf1Khw2202mIwwazAQ4bSnehdBcAyJS+UCM9G5rkHn6ufYTFiB1r8fOa1rBszFRMvLk1KYuIxaTpmDQddhtKd+FAIUq2obEam/+Dzf+BRIZBYzA0B1kTeAfKJWrrVzA24Zp7hK6DiPoCZhlERESBRqHC2Gsx9loAKNnu7awhNjVDofKNUtGVUUJ7gxApQqRQhvnhULWnUFaEsj04UoT9W7F/KwCoIjFwFDJHY+BohEf74Sx9XtUxbF6DHd/7FsNjcPmNmHD9uW9xAiCRYtjlGHY5AJwowcHtKNkGbTn2bcG+LQCQMgRZ4zF4HBLPM0Ey9THbv8e6D313vR3+Bfc8iyiN0DURUWBjlkFERBTIssYjazyAI5vWpk26WiwWC11QLxPXH3H9MfFmACg/iLI9KNuDY/uwewN2bwCAuCRkZGPgaKSPYGeBcygqwOY1KC/xLaZnY+LNGDHpIo6QmoXULFz//9BYjf1bcWgnSnej/CDKD+LbdxGpxtAJGDIeg3gHSh+1az1++BD1lQCgSYFRj/KDWPoIfvdHjJ4idHFEFMCYZRAREfUF1qhEoUvo9VKGIGUIrr4bdhuOFqNsD0p3o7octRXY+jVEIiQMQMYoDByJtOGQyIQuV1CGRmz9GoXfwtAIABIZxlyF3BmIS+r8MaM0yJ2J3JmwWXBoJ0q249AO6Grw8xr8vAZSOQaOxtDxyJoAZbi/ngcJxuPB3s1Y+3+oOQUAiemY9gCGjIPFiI9fRsk2fPQC9m3GnU9DKhe6ViIKSMwyiIiI6BIjkWLIOAwZBwBGPQ7/gtI9KNuNymOoPIaN/0ZQMPpnYuAoZIxEyhAEXUq/Lx3bhy1fYt8WuF0AEJuIiTdj7LWQKfx2CqncdzOUx40TJSjZhgPbUFuB/VuwfwtEIvQf5OtwlMA7UAJTyTZ89z6qjgGAJhnX3o/hEyESAYA8FA/lY9u3WPN37NuCiiO4fxH6ZwpbLxEFokvpvZmIiIiondAIjLkKY64CAO0JX2eNY3t903D88CFCpBgw1Jdr+HEyFIsRNgtsFtjMpxsW2MwiqzmuqhK2asQkIibhvANS+J3Dhp3r8PMXvk/RAQzNweU3YtBl3XhSkRgDhmHAMNz4CBqqsW8zSrbj2D7fiLbfvYfwaAybiMFjfcET9X4l2/HDB6goA4AoDa69F5ddfY6HTbge6dn44C/49QheewxX34XrHujhSoko0DHLICIiIgIAxKciPhW5MwHg2F6U7cHh3Th1GKW7UbobOD0ZSno2Bo5CtKZNGGE9HUmcbjtssJhaowq7DVYTrGbYLbBa4LSfrwQRoAaw+zvfskQGdX9EaRCbiOh4X8AREevPZ11fiZ+/xI61sJl9z3H8NEy6uaeHZozW4Irf44rfw2pCyXYcKMShX9DUgC1fYsuXiElEeDSCghAWg6g4hMcgKh7h0YiIhTy0R+uk8zm2F9+9h+MHACAiFtfcg/HTfuvxsYn401v45h1s+BfWfYzSPbj3OQ4ISkQdxyyDiIiI6CxpI5A2Atc9AKsJR4pQugdHilBb0ToZShfJFJAqIJVDKm83NofJZFIqlbBbUVsBmwUVZb5PudtS90d0POKSEKVBXD9ExSP24gdMKdmGLV/i8C7fYkIaJk3H6CsRIuncc/IPmRKjr8ToKwHgSJEv16iv9A0eeTapApGxiIhDRCwi1YiIQZQa4bGd+Q+hzvF2oinbAwCqKFx1JyZN7+i+NzyEQZfhoxdx8hCWPIKZc87dj4OI6CzMMoiIiIjOT6bEsIkYNhEAdLU4vAtlu3GkCHYbpLLWPEIq71hbDqkcIdLznc3tdh8vKRk2bJhv2dSM+krUa9FQhfoqX9vQiJpTqDmFgzta9xQHITIOMYmIOd19IyYB0QnnCCbMBmz/Hlu/QmM1AAQFY/gkTJqO1Cy//sf5Q8ZIZIzE9FmwmtFU3/qlb9Mw6lB9EtUnz7F7aDgi1YiIRUQsIuIQ6c074hAeDRFn/PEH7Ql8847v+1AZjitvw8Sbf+Pb+9zSR+Dpd3wDgn6yBAcKccd8f47PQkR9lN+yDJ1Ol5+f73a77Xb7/PnzU1JS2m5dvXp1QUFBUFBQRkbGnDlz2m564IEHbr755unTOxzfEhEREQkiMg4TpmHCb/ac9y9lGJRhSB58xkqHDXW/orEGtRVo0KJBi7pKNFb72qVnHiE8GjGJiI5HbCLCY3G0GDt/8G0Ki0LOjci5Hqqonnk2nSdTQNYf6v7n3qqrRXMD9HVoqkdzI3S1rUnHOXu1AL5QIyIW6mSkDfetDAqGQgVlWM8NUxK4qk9i7fvY+zMAyJS44vfIndH5GUm8A4Lu/AH/WYF9W1BRhnue643hGgUupwM71ipPHJQ57OifgZF5vKGpD/BblrFs2bLZs2enp6frdLoFCxasXLmyZVNlZWVhYeGKFSsALF++vLCwMCcnx7vp22+/FYuZixMRERF1WIgUCWnnmOOjrhINWtT9isZqX8xRcwpNDWhqwLF9ZzxywDBMmo7s3B4ruXtFxiEyrn3i42U2oKkeTQ3Q16G5Afpa6Bt8SceJkt86pkwJZZgv2lCEQRkGZTgUKijCEHq6oQy7FLsP1FVi7f9hz08AECLF5Fsw5TYoVH448thrMGAYPnwBpw7jjSc4ICj5jb4Ob81HbYXvdr59m/Dde3j4L907tjF1P79lGfX19enp6QAiIyNVKlVzc3NYWJh3U0FBQUu3i5kzZ7733nveLMNoNK5bt2769Okul8tfZRARERFdomITEZuIQWPOWKmvQ0MV6qrQWI26XyFTYvItiE8VqMQep1BBoTrv822oRnMDDDoY9TA1waiHsQmmJhj0MOl9/VwuKDSiNenw9qNRhEERFiSRy41WqKMQHuPf5ySYxmr88GFrv57cmZh6h5/7sMQk4L9W4Lv38OPHvgFB73kO0fz8vLMqyhDXD9JLL3FrZ8OnqK04Y43bhe/fZ5YR6PyTZTidzuDg1kOp1WqtVtuSZdTU1IwdO9bb1mg0VVVV3vby5cvnzJlz+PBhv9RARERERO15R4tIGyF0Hb1StOYCfydbzWfEHL6wQw9jE8wGNDfCpIdRD6P+7F1DgCQAa4AQKaLjEZPQOkprdDyi47vpOXWLpnr8+Am2fuVbvPxGXH0Pwrrt1qRpD2DQZfjwrzh5CEsewu/mckDQi3OgEHs3Y9/PsNsAIDwa6mRoUqBOgjoZ8an+6UfTy5kNsJhgM8FqxoFtres9gAgAcKoUFiMnQgpo/skybDZbUFBQy2JISIjdbj/n1uDgYO+mAwcOSKXSjIyM82UZtbW1LW2TyeR0Ouvr6/1SLQlIp9NZLBaPxyN0IdRVOp1OJBLZbDahC6H/ZXieAAAUL0lEQVSu0uv1SqVSKr3Iodqo92lqaqqvr+edm4HO7XZ7L6XQhVALCZSxUMZCfd5HiJvqxeZmkcUoNunFZoPIYhQb9TA1uXR1CrNOZDGiuhzV5e32ckVqXFEad6TaFRHnik5wRcS5Ynrd3Ctic7P859XyXb6+GNbhuebJv3OHx8LuRrd+l4ZpRA+9HPrdP6UHt+GTJbaizcZpD3tkym484/kZjUadTqdS9fa//yVlu6QHt0nKfhE5zpz12XujmXeiGQCAR6FyxvRzxSS6ohOcsUmu6AR3WHRPl9thIoddZDOLbCaRzSKyW8VWk8hmEdnMIptFZDV5G2K7RWQ1i2ynvxznnffaF2QAABqrfnX3mW5Tl5jGxka1Wu2fLEMikTidzpZFh8Mhk8nOudXpdMpkMrfb/eabb7766qu/ccy2fyM5nU63282/mvoAu90uFot5KfsAh8Nht9t5KfsAXsc+w3spmWUEOu8w6nxVBhiZCrL2f+jabLaqqqrU1FSRzRKirwnS1wY31QXpaoL1tcFNtUHNDUG66iBddbu9XKooZ6TGGR7rjFS7ItTOiDhnpMYjxCy5YqtJteNr5Z71IpcDgHnQeMPlM5yRau9z65ESgqzTHlUkD41Y/4H00PbgX8t01z9q65fZI6c+g91udzgcvfZVKTuyW35kl/zIbpHDV6EjJtGSOd4yaJz3egU11wc3akPqK4MbqkIaq4IbqsRmQ8ipQyGnDrUcxCOROaITndEJjugEZ1SCMzrBGRHX3ZUHGfViS7PYbBCbm4MsRrHVKDY1ic3NYqtJbDWL7RaRzSy2mjp3cI9U7pbI3RK5R6oIafhVZLOc/RhrsNTTWy8r/TZv9wj/ZBntOmJUVVXFxbV+98fHx2u12szMTACVlZVqtXrXrl0mk2nRokUAKioqPB6PXq+///772x4zKSmppa3VaiUSSWJir8uq6WJ5PB65XB4d3XujX+ogu90eExPT+z+joAsymUwJCQltA2gKUI2NjYmJicwyAp3b7dbr9fydpw+wWq1Op/P0pUxvv9nlRGPN6al2q9CgRUMVGrRBhsYgQ2P7nnKqSN8kuzGJiE5AbAJiErvxNgGrGQWfY9N/YDMDwLCJmPaAQpMszKALibfhsjy8vzjoVGnMZy/iyjtw3X0QB114R/8xGAwhISG961XpcuLQThRvwoFtvssEQJOC7FyMvjIkJiEECGt5cGIicOZdZkY9ak6i+hRqTqLmFGpOipoaJNpjEu2x1seESBHXD+pkaJKhToamP2ISO/o/73S0H4bGqIep+Yy7tCzGjj7Z4BDIlJApIVdCpoRMcbqtgEwJWShkivabpAoREAT4yv3hA6z9oP1hsyYkJF8yIwf1Od4BLvw29qdarS4tLc3MzGxsbDQajeHh4S2b8vLylixZkpeXB2DVqlVTp04dO3bsBx/4vp++/vprl8vFOVmJiIiI6FIRFOwbq7Uthx31VWjUoq4S9VWtSYdBB4Ou/cQrLVPJ+l3Vcd/fmalZuOkPSBnSXSfqoEg1/utNrPsY37+H9Z+gbDfuXdiTA4KK9bXS2gr0S4QQHWTaO7QTRRtxoLA1C4hJxIhJyM5Fv4yOHiQ0AqERZwyj4538qPokak6h9hSqT8JsQOUxVB47Y8e4pNZ0QyrzjZXbmlacbjs61tkhNNxXiTLc1/Y2VJGQKiAPhVwJZfiFj3NB19wLbTn2bm5dkzQQ02f54cgkKL9lGfPmzVu8eLHH47FarQsWLACwadOmgwcPzpo1KzExMTc3d9asWRKJJC0trWVCViIiIiIi8gmRID4F8Snt13tnVKmv9EUbjdWo/bX9PLv+FZ+KGx7CkHHdeIqLdfVdGHwZ3l+MU6VY+ghuexIj87rlRHYrKo+h6ji0x6EtR+VRpc2iBPAvIDoe8anQpECTAk0yEs+aF7n7HNyBoo04sBXW070wYvthxGSMmHQREcZviNIgSnPGvB5GvbfLBmpO+b70daitQG0F9m+58AHDY9rEExEIDT/diEBoGJQRUIZd+CB+dP8i/HrEePSAw2aLTB/SjVEg9SBRQIzCqNVqN2/efNtttwldCHXVr7/+yntM+oYTJ07wHpO+oaysrH///rzHpA/Yv39/VlYW7zEJdG63u6SkZNiwYUIXQl1ltVpPnTo1cODAbj+Tww6PBx6371+3+3S77co27fYPPr3Y0hYHITWr28vuHJsFq5Zj13oAuOwqzHzcDxOO1p5CzSlUHIH2BKqOo7H9ICa/Rd0fmhTEpyAhDeokxPXvajHtHChsfyNJXBKyJ2PYJPQ768al7maznM41TqLmFNyuNiFFOJQRp/tZhPfaWWAbGhosFku/fv2ELoS6qqamxm9jfxIRERERkQB6w40PPUYqx13PIDsXH72IX37Esf24fxGSLiYwMhtQeRRVx1F1HNoTqD7Z/oaIECk0yYhPRcIAxA9Av3SDC/X19anJ/VH7q29KGu0JaMvRUOX7277l5oVgCdRJrX034lMQqYZIdFYRv8npwKGd2LsZJdtae2FoUjBiMrInQ5NycUfzI6kc/TPRX4DhV4nOiVkGEREREREFjqwJeOZdvJ+P8hIsn4vr7seU284dGbicqDnpSy6qTqDqOAyNZzxAJEJMwunkIhUJAxCTANGZHdwMBgAQB0GTDE0ykOtb73Sg5hS0J04HHOXQ1bQfY0IqhzoZ8SmIT4UmGZpUhJ+ne7LDjoM7sHczDm5Hy6Qb8anIzsXIK9oPrUJEzDKIiIiIiCjAhMdg7mv46XN8/z6+eQeHd+He56CKhL7O1+HCm1/UVsDtOmNHZRjiU88IL0LazxvTUcEhSEw7Y9QMuw3VJ3y5hvffpnqcOoxTh1sfo1D5Qo34FGhSEJuIEyXYuxkHd8Bu9T0mIQ3ZkzHyCsQkdLI2oksAswwiIiIiIgo0IjGuvB0DR+GDv+JoMf56H4DWcSVaJKb7UoN+6dCkIDymG0uSSNF/EPoPal1jNUN7AtrjqKlA1TFUHYfZgOMHcPzAOXZPTEf2ZGTnMcIg6ghmGUREREREFJiSBuKpt/HlShR+AwCRcYhPRWK6736QhB6cauScZAqkZp0xlqpRj+pyVB5DzSlf943YRIyYjOy8npxolqgPYJZBREREREQBSyLDrX/EpOmIVEMqF7qaCwmNQHo20rOFroMo4DHLICIiIiKiACfgBB9EJATOQk9EREREREREgYRZBhEREREREREFEmYZRERERERERBRImGUQERERERERUSBhlkFEREREREREgYRZBhEREREREREFEmYZRERERERERBRImGUQERERERERUSBhlkFEREREREREgYRZBhEREREREREFEmYZRERERERERBRImGUQERERERERUSBhlkFEREREREREgYRZBhEREREREREFEpHH4xG6hnPTarUtbYPBcOLEiQEDBghYD/mF0WgMDg6WyWRCF0Jd1dzcLJfLQ0JChC6Eukqv16tUqqCgIKELoa6qr6+Pjo4WiURCF0Jd4vF4GhoaYmJihC6EusrlchkMhoiICKELoa5yOBwWiyUsLEzoQqirrFar0+kMDQ0VuhDqKrPZPGLEiGChy+io1NRUftv1AU6nUyKRKBQKoQuhrrLZbAqFQiqVCl0IdZXFYlEoFIyl+gCj0ahUKsVi9rgMbG6322Qy8XeePsDhcNjtdl7KPsBms7ndbl7KPkAsFvNV2Td4O2T03iwjPj7+gmso4NjtdqVSyY+b+gCj0RgVFcXPKPoAnU4XGxsrl8uFLoS6qqamRqPRsItNoHO5XHV1dfydpw+wWCxGo5GXsg9obm52OBy8lH1AfX29yWTipewz+OkNEREREREREQUSZhlEREREREREFEiYZRARERERERFRIOm985gQEREREREREZ2N/TKIiIiIiIiIKJAwyyAiIiIiIiKiQMIsg4iIiIiIiIgCCbMMIiIiIiIiIgokwUIXQJeQm266KTY21tv+/e9/f8011whbD12UpqamNWvWfP75559++mlYWJh35erVqwsKCoKCgjIyMubMmSNshdRB57yUTz/9dF1dnUgkAjBixIi5c+cKWiN1yFdfffXVV1+FhoZ6PJ78/PywsDCXy7V06dLq6mqbzXbrrbdOmTJF6Brpwjwez9KlS8vLy8Vicf/+/efPn+9dzzfNQPT6668fOXIkODg4Pj7+mWeeAaDT6fLz891ut91unz9/fkpKitA10oUZDIb8/HybzWaz2aZNm3bTTTcB0Gq1d9xxR1pamvcxixYtSk5OFrRMuggrV64sLy9/6aWXABw4cODNN9+USqVyufzPf/6zUqkUujrqJGYZ1HOSkpLefPNNoaugTlq2bNl1112XkZHRsqaysrKwsHDFihUAli9fXlhYmJOTI1yB1FFnX0oARqPx3XffFaok6oSKiooffvjhH//4h1gs3rp161tvvfX0009/9dVXKSkpzzzzjMvlmj179pgxY1riKuq1vvzyy+joaG+E8corr2zcuDEvLw980wxAa9eulcvl3qv2zjvv/Pjjj1ddddWyZctmz56dnp6u0+kWLFiwcuVKocukC3vllVfuuuuuESNGAJg9e/bll18eHR1dV1d3++23P/roo0JXRxetsrKypKTEm1l4PJ5XX311xYoVCoVi+/btb7/99pNPPil0gdRJvMeEeojJZAoNDRW6Cuq8/Pz8nJwc7+f2XgUFBdOnT/e2Z86cuWHDBoFKo4tz9qUE0G6Rer+qqqq7775bLBYDGDduXHl5OYCCgoJbbrkFQFBQ0FVXXbV9+3Zhi6SOaG5u9l41AOPGjTt58iT4phmYoqKiZs6c6W0PHz786NGjAOrr69PT0wFERkaqVKrm5mYhS6SOGTt2rDfIADBo0CDvq7Kurk6tVgtaF3XSa6+99vjjj3vbR48eHTx4sEKhADB+/PjS0lJBS6MuYb8M6iF1dXUnT56cO3euyWS6/vrrZ8yYIXRF1FU1NTVjx471tjUaTVVVlbD1UFfU1NTMnz9fp9NlZmb+8Y9/DA7mu0NvN27cuJb2jh07xo8fD8BqtUqlUu9KjUZz7NgxYYqji3Hvvfe2tHfu3Dlt2jTwTTMwtbwnulyu1atX33PPPU6ns+2PU7VardVq2Vuq9/O+DAHodLri4uKHH34YQF1d3datW9etW+fxeGbPnj18+HBBa6SOWrt27ZgxYyIjI72LtbW1bTMpl8slUF3kB/xtlXpIRETEI488MmXKFJfLNX/+/H79+rW85VOAstlsQUFB3nZwcLDdbhe2HuqKe++997rrrgsODv7000/feuutlo8vqPczmUwfffTRG2+8gTN/J+OrMuAcPnzYYDAMGjQIfNMMZAsXLty0adO11147dOhQk8nU8kYJICQkhK/KQFFfX//nP/95165dL7zwglwuBzBy5Mhhw4ZlZWXpdLrZs2e//fbbjKV6P5PJ9M033/ztb3+rr6/3rmn76ysAj8cjUGnkB7zHhHpIRESEdxS6oKCgWbNm8X6EPkAikTidTm/b6XTKZDJh66GuuPHGG70fHt5xxx3FxcVCl0Md5Xa7Fy1aNH/+/JCQEJx5rxBflYGloaHhjTfeePrpp72LfNMMXPn5+Zs2bYqJifn3v//d9o0SgMPh4KsyUMTExKxcuXLLli3ffvut93ahzMzMrKwsAJGRkTfccMPu3buFrpEu7I033njsscfavjm2e1W2zTUo4DDLoB6yZ8+empoab7vdDxEKUPHx8Vqt1tuurKzkTaSBq6amZs+ePS2LfF8PIC+//PKMGTNSU1O9iwqFwmKxeNtVVVVxcXHClUYXwWazLVy4cNGiRS1/6PJNMxBt2LDBZrMBEIlEt99++9atW9t1xOCrMlB8++233oZMJrv++ut37doFYO3atS0P4KsyIBiNxqKionffffepp55avHjxzp07Fy5cqNFoqqurWx7De0wCGrMM6iEOh+Nf//qXt71+/foxY8YIWw91XV5e3hdffOFtr1q1aurUqcLWQ50WGhq6cuVKbzfLAwcOJCYmCl0RdchHH32UnJx8+eWXt6yZMmXKqlWrALhcrrVr13JqoYDg8XgWLVr02GOPaTSalpV80wxEx44d++6777ztPXv2DBkyBIBarfYOLtjY2Gg0GsPDw4UskTrmm2++8Q6oDKCoqGjw4MEASktLWwZU3rx587Bhw4QqjzooNDT0888/X7p06dKlSxctWjR27Nj8/Py0tLRDhw6ZTCYAW7du9fa1oQDF8TKoh4wbN66oqGju3LkikUij0TzwwANCV0RdlZiYmJubO2vWLIlEkpaWxr+aApdSqbzzzjtnz54tk8lEItHChQuFrogu7OTJk6+99trkyZN/+eUX75rnnnvuxhtvXLJkyRNPPGGz2e68806VSiVskdQRq1ev3r17d8un9xEREc8//zzfNAPRgw8++Ne//nX9+vVSqVSlUv35z38GMG/evMWLF3s8HqvVumDBAqFrpA55/vnnX3jhBW979OjR3jlNHnnkkcWLF3/22WcWi+W6665rGz5SABGJRPPmzfvTn/4kk8mkUumiRYuErog6T8TxToiIiIiIiIgogPAeEyIiIiIiIiIKJMwyiIiIiIiIiCiQMMsgIiIiIiIiokBy0WN/ut1ui8VitVrtdrvT6XS73QDEYnFwcLBEIpHJZHK5XCxmREJERERERERE3eIixv602+0Gg8FoNF7wkaGhoSqVSiKRdK02IiIiIiIiIqL2Oppl6HS65ubmizp0WFhYZGRkp6oiIiIiIiIiIjq3C2cZdru9oaGhZdrziyKRSKKjo9lBg4iIiIiIiIj85QJZhtVqraur8w6K0TlisTg2NlYmk3X6CERERERERERELX5rkE673d7FIAOA2+2uq6vrXLcOIiIiIiIiIqJ2/j+N0M7SHW1BcgAAAABJRU5ErkJggg==) 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## 库结构 > ./Classification/ > ├── config.yaml 超参数、路径参数 > ├── data 数据集、生成文件、预训练学习模型存储处 > │ ├── dataset 数据集存放 > │ │ ├── runs tensorboard缓存、训练模型保存处 > │ │ ├── train > │ │ └── val > │ ├── mobilenet_v3_large.pth mobilenetV3_large 预训练模型 > │ ├── mobilenetv3-small.pth mobilenetV3_small 预训练模型 > │ └── model 目标模型存储处 > │ ├── model.onnx > │ └── network-epoch74_loss0.0347_acc0.986.pth > ├── MyDataLoader 自定义数据集加载器 > │ ├── MyDataLoader.py > ├── network 各类网络模型(增加中...) > │ ├── model_v3.py > ├── predict.py 预测文件 > ├── README.md > ├── requirements.txt > ├── train.py 训练文件 > └── utils 实用工具类、方法存储 > ├── classDictory.py > ├── ConfigReader.py > ├── __init__.py > ├── pth2onnx.py > └── shuffle.py ## 环境准备 在相应conda环境下 ```bash pip install -r requirements.txt ``` ## 数据集准备 将各类别的数据集图像放入各个**命名**的文件夹中,将各类别文件夹放入`data/dataset`目录下 由于在自定义的数据集加载器`MyDataLoader`中使用`ImageFolder`类,因此每张图像读取后都会附带对应的、以文件夹名为顺序的`label` > 如dataset目录下有 > > class0 > > class1 > > class2 > > ... > > 则dataset加载后class0的图像label为0, class1的图像label为1, class2的图像label为2... > > 此方法也免去了一些训练文件中使用文本文件读取图像并手动加标签的麻烦 放置好数据集后,终端或在IDE中运行`utils/shuffle.py`文件以自动在`dataset`目录下生成训练集目录`train`与测试集目录`test` ```bash # 项目根目录下 python3 utils/shuffle.py ``` 运行后目录中有打乱分离后的各类别数据集的文件夹*(train:val=7:3)* data目录下可存放多个datasets,想要训练哪个数据集,记得在`config.yaml`修改`dataset_path`、`save_path`即可 ![2022-03-29_12-33.png](https://s2.loli.net/2022/03/29/mY56CeHg4s9kzAB.png) ## 训练 参数的读取,数据集的载入、训练、验证等已写好,在`config.yaml`中配置好参数和路径,项目根目录下运行`train.py`即可。 ![2022-03-29_12-33_1.png](https://s2.loli.net/2022/03/29/oYkZ5ShqetQHdGD.png) 注意,文件中的网络模型是**mobilenetV3**,若有其他网络模型,可以放入`network`目录中,然后在`train.py`下做相应修改(包括模型的设置、数据的变换等),本人之后可能持续更新一些模型,之后将模型选择写入`config.yaml`中。 ## 预测 训练好的模型将保存至`dataset/runs/`路径下,选择相应模型,移至`data/model`中存储 然后只需指定相应的pth模型文件路径及图像路径,运行`predict.py`即可(图像能为数据集的类别文件夹,也可为单张图像) 喜欢的话点个star~