demo_training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training with Small sample dataset as demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Unzip sample dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/kang/SAR_OD/anonymous-hub/SAROD/data\n"
     ]
    }
   ],
   "source": [
    "%cd data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "download_for_Demo.sh: line 2: $'\\r': command not found\n",
      "--2020-10-06 08:13:12--  https://docs.google.com/uc?export=download&confirm=WeOp&id=1TIhupsTTtdPHjFzYTVejLyyPo9UzrSk3\n",
      "Resolving docs.google.com (docs.google.com)... 172.217.175.238, 2404:6800:4004:811::200e\n",
      "Connecting to docs.google.com (docs.google.com)|172.217.175.238|:443... connected.\n",
      "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
      "Location: https://doc-04-a0-docs.googleusercontent.com/docs/securesc/to96720jq15e9obcjv3e2ugee26amu92/s40tcvesma7qe6bn0c8jmvg66sv076qf/1601971950000/11079565382010845860/17347132251095186972Z/1TIhupsTTtdPHjFzYTVejLyyPo9UzrSk3?e=download [following]\n",
      "--2020-10-06 08:13:13--  https://doc-04-a0-docs.googleusercontent.com/docs/securesc/to96720jq15e9obcjv3e2ugee26amu92/s40tcvesma7qe6bn0c8jmvg66sv076qf/1601971950000/11079565382010845860/17347132251095186972Z/1TIhupsTTtdPHjFzYTVejLyyPo9UzrSk3?e=download\n",
      "Resolving doc-04-a0-docs.googleusercontent.com (doc-04-a0-docs.googleusercontent.com)... 172.217.175.97, 2404:6800:4004:81f::2001\n",
      "Connecting to doc-04-a0-docs.googleusercontent.com (doc-04-a0-docs.googleusercontent.com)|172.217.175.97|:443... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://docs.google.com/nonceSigner?nonce=h26jt4796ueqo&continue=https://doc-04-a0-docs.googleusercontent.com/docs/securesc/to96720jq15e9obcjv3e2ugee26amu92/s40tcvesma7qe6bn0c8jmvg66sv076qf/1601971950000/11079565382010845860/17347132251095186972Z/1TIhupsTTtdPHjFzYTVejLyyPo9UzrSk3?e%3Ddownload&hash=7pbp3krd6me95o94nk851ogih8tt4qev [following]\n",
      "--2020-10-06 08:13:13--  https://docs.google.com/nonceSigner?nonce=h26jt4796ueqo&continue=https://doc-04-a0-docs.googleusercontent.com/docs/securesc/to96720jq15e9obcjv3e2ugee26amu92/s40tcvesma7qe6bn0c8jmvg66sv076qf/1601971950000/11079565382010845860/17347132251095186972Z/1TIhupsTTtdPHjFzYTVejLyyPo9UzrSk3?e%3Ddownload&hash=7pbp3krd6me95o94nk851ogih8tt4qev\n",
      "Connecting to docs.google.com (docs.google.com)|172.217.175.238|:443... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://doc-04-a0-docs.googleusercontent.com/docs/securesc/to96720jq15e9obcjv3e2ugee26amu92/s40tcvesma7qe6bn0c8jmvg66sv076qf/1601971950000/11079565382010845860/17347132251095186972Z/1TIhupsTTtdPHjFzYTVejLyyPo9UzrSk3?e=download&nonce=h26jt4796ueqo&user=17347132251095186972Z&hash=mhlckadpv8u18muhfssh038dkgkfndqg [following]\n",
      "--2020-10-06 08:13:14--  https://doc-04-a0-docs.googleusercontent.com/docs/securesc/to96720jq15e9obcjv3e2ugee26amu92/s40tcvesma7qe6bn0c8jmvg66sv076qf/1601971950000/11079565382010845860/17347132251095186972Z/1TIhupsTTtdPHjFzYTVejLyyPo9UzrSk3?e=download&nonce=h26jt4796ueqo&user=17347132251095186972Z&hash=mhlckadpv8u18muhfssh038dkgkfndqg\n",
      "Connecting to doc-04-a0-docs.googleusercontent.com (doc-04-a0-docs.googleusercontent.com)|172.217.175.97|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: unspecified [application/x-zip-compressed]\n",
      "Saving to: ‘sample.zip’\n",
      "\n",
      "sample.zip              [    <=>             ]  31.98M  42.1MB/s    in 0.8s    \n",
      "\n",
      "2020-10-06 08:13:15 (42.1 MB/s) - ‘sample.zip’ saved [33529731]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!bash download_for_Demo.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  sample.zip\n",
      "   creating: dt_ver/\n",
      "   creating: dt_ver/test/\n",
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     ]
    },
    {
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     "text": [
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      " extracting: rl_ver/test/labels/P0001_1800_2600_0_800_2.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_1800_2600_0_800_3.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_1800_2600_7800_8600_0.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_1800_2600_7800_8600_1.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_1800_2600_7800_8600_2.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_1800_2600_7800_8600_3.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_2400_3200_4800_5600_0.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_2400_3200_4800_5600_1.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_2400_3200_4800_5600_2.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_2400_3200_4800_5600_3.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_3000_3800_2400_3200_0.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_3000_3800_2400_3200_1.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_3000_3800_2400_3200_2.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_3000_3800_2400_3200_3.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_3000_3800_4200_5000_0.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_3000_3800_4200_5000_1.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_3000_3800_4200_5000_2.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_3000_3800_4200_5000_3.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_3600_4400_2400_3200_0.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_3600_4400_2400_3200_1.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_3600_4400_2400_3200_2.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_3600_4400_2400_3200_3.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_3600_4400_4200_5000_0.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_3600_4400_4200_5000_1.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_3600_4400_4200_5000_2.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_3600_4400_4200_5000_3.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_3600_4400_5400_6200_0.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_3600_4400_5400_6200_1.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_3600_4400_5400_6200_2.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_3600_4400_5400_6200_3.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_3600_4400_6000_6800_0.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_3600_4400_6000_6800_1.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_3600_4400_6000_6800_2.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_3600_4400_6000_6800_3.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_4200_5000_5400_6200_0.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_4200_5000_5400_6200_1.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_4200_5000_5400_6200_2.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_4200_5000_5400_6200_3.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_4800_5600_1800_2600_0.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_4800_5600_1800_2600_1.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_4800_5600_1800_2600_2.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_4800_5600_1800_2600_3.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_4800_5600_2400_3200_0.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_4800_5600_2400_3200_1.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_4800_5600_2400_3200_2.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_4800_5600_2400_3200_3.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_5263_6063_2400_3200_0.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_5263_6063_2400_3200_1.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_5263_6063_2400_3200_2.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_5263_6063_2400_3200_3.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_5263_6063_6000_6800_0.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_5263_6063_6000_6800_1.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_5263_6063_6000_6800_2.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_5263_6063_6000_6800_3.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_5263_6063_6600_7400_0.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_5263_6063_6600_7400_1.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_5263_6063_6600_7400_2.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_5263_6063_6600_7400_3.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_5263_6063_7200_8000_0.txt  \r\n",
      " extracting: rl_ver/test/labels/P0001_5263_6063_7200_8000_1.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_5263_6063_7200_8000_2.txt  \r\n",
      "  inflating: rl_ver/test/labels/P0001_5263_6063_7200_8000_3.txt  \r\n",
      "   creating: rl_ver/train/\r\n",
      "   creating: rl_ver/train/images/\r\n",
      "  inflating: rl_ver/train/images/P0001_0_800_7200_8000_0.jpg  \r\n",
      "  inflating: rl_ver/train/images/P0001_0_800_7200_8000_1.jpg  \r\n",
      "  inflating: rl_ver/train/images/P0001_0_800_7200_8000_2.jpg  \r\n",
      "  inflating: rl_ver/train/images/P0001_0_800_7200_8000_3.jpg  \r\n",
      "  inflating: rl_ver/train/images/P0001_0_800_8400_9200_0.jpg  \r\n",
      "  inflating: rl_ver/train/images/P0001_0_800_8400_9200_1.jpg  \r\n",
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      "  inflating: rl_ver/train/images/P0001_0_800_8400_9200_3.jpg  \r\n",
      "  inflating: rl_ver/train/images/P0001_0_800_9000_9800_0.jpg  \r\n",
      "  inflating: rl_ver/train/images/P0001_0_800_9000_9800_1.jpg  \r\n",
      "  inflating: rl_ver/train/images/P0001_0_800_9000_9800_2.jpg  \r\n",
      "  inflating: rl_ver/train/images/P0001_0_800_9000_9800_3.jpg  \r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  inflating: rl_ver/train/images/P0001_0_800_9600_10400_0.jpg  \n",
      "  inflating: rl_ver/train/images/P0001_0_800_9600_10400_1.jpg  \n",
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      "   creating: rl_ver/train/labels/\n",
      "  inflating: rl_ver/train/labels/P0001_0_800_7200_8000_0.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_0_800_7200_8000_1.txt  \n",
      " extracting: rl_ver/train/labels/P0001_0_800_7200_8000_2.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_0_800_7200_8000_3.txt  \n",
      " extracting: rl_ver/train/labels/P0001_0_800_8400_9200_0.txt  \n",
      " extracting: rl_ver/train/labels/P0001_0_800_8400_9200_1.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_0_800_8400_9200_2.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_0_800_8400_9200_3.txt  \n",
      " extracting: rl_ver/train/labels/P0001_0_800_9000_9800_0.txt  \n",
      " extracting: rl_ver/train/labels/P0001_0_800_9000_9800_1.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_0_800_9000_9800_2.txt  \n",
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      " extracting: rl_ver/train/labels/P0001_0_800_9600_10400_0.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_0_800_9600_10400_1.txt  \n",
      " extracting: rl_ver/train/labels/P0001_0_800_9600_10400_2.txt  \n",
      " extracting: rl_ver/train/labels/P0001_0_800_9600_10400_3.txt  \n",
      " extracting: rl_ver/train/labels/P0001_1200_2000_0_800_0.txt  \n",
      " extracting: rl_ver/train/labels/P0001_1200_2000_0_800_1.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_1200_2000_0_800_2.txt  \n",
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      "  inflating: rl_ver/train/labels/P0001_1200_2000_8400_9200_1.txt  \n",
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      "  inflating: rl_ver/train/labels/P0001_1200_2000_8400_9200_3.txt  \n",
      " extracting: rl_ver/train/labels/P0001_1200_2000_9600_10400_0.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_1200_2000_9600_10400_1.txt  \n",
      " extracting: rl_ver/train/labels/P0001_1200_2000_9600_10400_2.txt  \n",
      " extracting: rl_ver/train/labels/P0001_1200_2000_9600_10400_3.txt  \n",
      " extracting: rl_ver/train/labels/P0001_1800_2600_2400_3200_0.txt  \n",
      " extracting: rl_ver/train/labels/P0001_1800_2600_2400_3200_1.txt  \n",
      " extracting: rl_ver/train/labels/P0001_1800_2600_2400_3200_2.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_1800_2600_2400_3200_3.txt  \n",
      " extracting: rl_ver/train/labels/P0001_1800_2600_3000_3800_0.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_1800_2600_3000_3800_1.txt  \n",
      " extracting: rl_ver/train/labels/P0001_1800_2600_3000_3800_2.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_1800_2600_3000_3800_3.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_1800_2600_3600_4400_0.txt  \n",
      " extracting: rl_ver/train/labels/P0001_1800_2600_3600_4400_1.txt  \n",
      " extracting: rl_ver/train/labels/P0001_1800_2600_3600_4400_2.txt  \n",
      " extracting: rl_ver/train/labels/P0001_1800_2600_3600_4400_3.txt  \n",
      " extracting: rl_ver/train/labels/P0001_1800_2600_4200_5000_0.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_1800_2600_4200_5000_1.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_1800_2600_4200_5000_2.txt  \n",
      " extracting: rl_ver/train/labels/P0001_1800_2600_4200_5000_3.txt  \n",
      " extracting: rl_ver/train/labels/P0001_1800_2600_4800_5600_0.txt  \n",
      " extracting: rl_ver/train/labels/P0001_1800_2600_4800_5600_1.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_1800_2600_4800_5600_2.txt  \n",
      " extracting: rl_ver/train/labels/P0001_1800_2600_4800_5600_3.txt  \n",
      " extracting: rl_ver/train/labels/P0001_2400_3200_1800_2600_0.txt  \n",
      " extracting: rl_ver/train/labels/P0001_2400_3200_1800_2600_1.txt  \n",
      " extracting: rl_ver/train/labels/P0001_2400_3200_1800_2600_2.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_2400_3200_1800_2600_3.txt  \n",
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      "  inflating: rl_ver/train/labels/P0001_2400_3200_4200_5000_1.txt  \n",
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      "  inflating: rl_ver/train/labels/P0001_2400_3200_4200_5000_3.txt  \n",
      " extracting: rl_ver/train/labels/P0001_2400_3200_7200_8000_0.txt  \n",
      " extracting: rl_ver/train/labels/P0001_2400_3200_7200_8000_1.txt  \n",
      " extracting: rl_ver/train/labels/P0001_2400_3200_7200_8000_2.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_2400_3200_7200_8000_3.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3000_3800_1800_2600_0.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_3000_3800_1800_2600_1.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3000_3800_1800_2600_2.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_3000_3800_1800_2600_3.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3000_3800_3000_3800_0.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_3000_3800_3000_3800_1.txt  \n",
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      " extracting: rl_ver/train/labels/P0001_3000_3800_3000_3800_3.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_3000_3800_3600_4400_0.txt  \n",
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      "  inflating: rl_ver/train/labels/P0001_3000_3800_4800_5600_0.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3000_3800_4800_5600_1.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3000_3800_4800_5600_2.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3000_3800_4800_5600_3.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_3000_3800_6000_6800_0.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3000_3800_6000_6800_1.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3000_3800_6000_6800_2.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3000_3800_6000_6800_3.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3000_3800_7200_8000_0.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_3000_3800_7200_8000_1.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3000_3800_7200_8000_2.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3000_3800_7200_8000_3.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3600_4400_1800_2600_0.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_3600_4400_1800_2600_1.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3600_4400_1800_2600_2.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3600_4400_1800_2600_3.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_3600_4400_3000_3800_0.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_3600_4400_3000_3800_1.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3600_4400_3000_3800_2.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_3600_4400_3000_3800_3.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_3600_4400_3600_4400_0.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3600_4400_3600_4400_1.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_3600_4400_3600_4400_2.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_3600_4400_3600_4400_3.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3600_4400_4800_5600_0.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3600_4400_4800_5600_1.txt  \n",
      "  inflating: rl_ver/train/labels/P0001_3600_4400_4800_5600_2.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3600_4400_4800_5600_3.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3600_4400_600_1400_0.txt  \n",
      " extracting: rl_ver/train/labels/P0001_3600_4400_600_1400_1.txt  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  inflating: rl_ver/train/labels/P0001_3600_4400_600_1400_2.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_3600_4400_600_1400_3.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4200_5000_1800_2600_0.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4200_5000_1800_2600_1.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4200_5000_1800_2600_2.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4200_5000_1800_2600_3.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4200_5000_2400_3200_0.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4200_5000_2400_3200_1.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4200_5000_2400_3200_2.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4200_5000_2400_3200_3.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4200_5000_3000_3800_0.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4200_5000_3000_3800_1.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4200_5000_3000_3800_2.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4200_5000_3000_3800_3.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4200_5000_3600_4400_0.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4200_5000_3600_4400_1.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4200_5000_3600_4400_2.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4200_5000_3600_4400_3.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4200_5000_4200_5000_0.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4200_5000_4200_5000_1.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4200_5000_4200_5000_2.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4200_5000_4200_5000_3.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4200_5000_6000_6800_0.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4200_5000_6000_6800_1.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4200_5000_6000_6800_2.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4200_5000_6000_6800_3.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4200_5000_600_1400_0.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4200_5000_600_1400_1.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4200_5000_600_1400_2.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4200_5000_600_1400_3.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4200_5000_6600_7400_0.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4200_5000_6600_7400_1.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4200_5000_6600_7400_2.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4200_5000_6600_7400_3.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4200_5000_7200_8000_0.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4200_5000_7200_8000_1.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4200_5000_7200_8000_2.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4200_5000_7200_8000_3.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4800_5600_0_800_0.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4800_5600_0_800_1.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4800_5600_0_800_2.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4800_5600_0_800_3.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4800_5600_3000_3800_0.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4800_5600_3000_3800_1.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4800_5600_3000_3800_2.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4800_5600_3000_3800_3.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4800_5600_6000_6800_0.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4800_5600_6000_6800_1.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4800_5600_6000_6800_2.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4800_5600_6000_6800_3.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4800_5600_6600_7400_0.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4800_5600_6600_7400_1.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4800_5600_6600_7400_2.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4800_5600_6600_7400_3.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4800_5600_7200_8000_0.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4800_5600_7200_8000_1.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_4800_5600_7200_8000_2.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_4800_5600_7200_8000_3.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_5263_6063_1200_2000_0.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_5263_6063_1200_2000_1.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_5263_6063_1200_2000_2.txt  \r\n",
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      " extracting: rl_ver/train/labels/P0001_5263_6063_5400_6200_0.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_5263_6063_5400_6200_1.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_5263_6063_5400_6200_2.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_5263_6063_5400_6200_3.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_5263_6063_600_1400_0.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_5263_6063_600_1400_1.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_5263_6063_600_1400_2.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_5263_6063_600_1400_3.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_5263_6063_7800_8600_0.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_5263_6063_7800_8600_1.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_5263_6063_7800_8600_2.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_5263_6063_7800_8600_3.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_600_1400_8400_9200_0.txt  \r\n",
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      "  inflating: rl_ver/train/labels/P0001_600_1400_8400_9200_2.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_600_1400_8400_9200_3.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_600_1400_9000_9800_0.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_600_1400_9000_9800_1.txt  \r\n",
      " extracting: rl_ver/train/labels/P0001_600_1400_9000_9800_2.txt  \r\n",
      "  inflating: rl_ver/train/labels/P0001_600_1400_9000_9800_3.txt  \r\n",
      "   creating: rl_ver/val/\r\n",
      "   creating: rl_ver/val/images/\r\n",
      "  inflating: rl_ver/val/images/P0001_1200_2000_4200_5000_0.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_1200_2000_4200_5000_1.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_1200_2000_4200_5000_2.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_1200_2000_4200_5000_3.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_1200_2000_7800_8600_0.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_1200_2000_7800_8600_1.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_1200_2000_7800_8600_2.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_1200_2000_7800_8600_3.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_1800_2600_10190_10990_0.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_1800_2600_10190_10990_1.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_1800_2600_10190_10990_2.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_1800_2600_10190_10990_3.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_1800_2600_8400_9200_0.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_1800_2600_8400_9200_1.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_1800_2600_8400_9200_2.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_1800_2600_8400_9200_3.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_2400_3200_2400_3200_0.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_2400_3200_2400_3200_1.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_2400_3200_2400_3200_2.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_2400_3200_2400_3200_3.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_2400_3200_3600_4400_0.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_2400_3200_3600_4400_1.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_2400_3200_3600_4400_2.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_2400_3200_3600_4400_3.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_3600_4400_6600_7400_0.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_3600_4400_6600_7400_1.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_3600_4400_6600_7400_2.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_3600_4400_6600_7400_3.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_600_1400_7800_8600_0.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_600_1400_7800_8600_1.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_600_1400_7800_8600_2.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_600_1400_7800_8600_3.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_600_1400_9600_10400_0.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_600_1400_9600_10400_1.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_600_1400_9600_10400_2.jpg  \r\n",
      "  inflating: rl_ver/val/images/P0001_600_1400_9600_10400_3.jpg  \r\n",
      "   creating: rl_ver/val/labels/\r\n",
      "  inflating: rl_ver/val/labels/P0001_1200_2000_4200_5000_0.txt  \r\n",
      " extracting: rl_ver/val/labels/P0001_1200_2000_4200_5000_1.txt  \r\n",
      " extracting: rl_ver/val/labels/P0001_1200_2000_4200_5000_2.txt  \r\n",
      "  inflating: rl_ver/val/labels/P0001_1200_2000_4200_5000_3.txt  \r\n",
      " extracting: rl_ver/val/labels/P0001_1200_2000_7800_8600_0.txt  \r\n",
      " extracting: rl_ver/val/labels/P0001_1200_2000_7800_8600_1.txt  \r\n",
      " extracting: rl_ver/val/labels/P0001_1200_2000_7800_8600_2.txt  \r\n",
      "  inflating: rl_ver/val/labels/P0001_1200_2000_7800_8600_3.txt  \r\n",
      "  inflating: rl_ver/val/labels/P0001_1800_2600_10190_10990_0.txt  \r\n",
      " extracting: rl_ver/val/labels/P0001_1800_2600_10190_10990_1.txt  \r\n",
      " extracting: rl_ver/val/labels/P0001_1800_2600_10190_10990_2.txt  \r\n",
      "  inflating: rl_ver/val/labels/P0001_1800_2600_10190_10990_3.txt  \r\n",
      "  inflating: rl_ver/val/labels/P0001_1800_2600_8400_9200_0.txt  \r\n",
      " extracting: rl_ver/val/labels/P0001_1800_2600_8400_9200_1.txt  \r\n",
      " extracting: rl_ver/val/labels/P0001_1800_2600_8400_9200_2.txt  \r\n",
      " extracting: rl_ver/val/labels/P0001_1800_2600_8400_9200_3.txt  \r\n",
      " extracting: rl_ver/val/labels/P0001_2400_3200_2400_3200_0.txt  \r\n",
      "  inflating: rl_ver/val/labels/P0001_2400_3200_2400_3200_1.txt  \r\n",
      "  inflating: rl_ver/val/labels/P0001_2400_3200_2400_3200_2.txt  \r\n",
      " extracting: rl_ver/val/labels/P0001_2400_3200_2400_3200_3.txt  \r\n",
      " extracting: rl_ver/val/labels/P0001_2400_3200_3600_4400_0.txt  \r\n",
      "  inflating: rl_ver/val/labels/P0001_2400_3200_3600_4400_1.txt  \r\n",
      "  inflating: rl_ver/val/labels/P0001_2400_3200_3600_4400_2.txt  \r\n",
      "  inflating: rl_ver/val/labels/P0001_2400_3200_3600_4400_3.txt  \r\n",
      " extracting: rl_ver/val/labels/P0001_3600_4400_6600_7400_0.txt  \r\n",
      " extracting: rl_ver/val/labels/P0001_3600_4400_6600_7400_1.txt  \r\n",
      "  inflating: rl_ver/val/labels/P0001_3600_4400_6600_7400_2.txt  \r\n",
      " extracting: rl_ver/val/labels/P0001_3600_4400_6600_7400_3.txt  \r\n",
      "  inflating: rl_ver/val/labels/P0001_600_1400_7800_8600_0.txt  \r\n",
      "  inflating: rl_ver/val/labels/P0001_600_1400_7800_8600_1.txt  \r\n",
      " extracting: rl_ver/val/labels/P0001_600_1400_7800_8600_2.txt  \r\n",
      " extracting: rl_ver/val/labels/P0001_600_1400_7800_8600_3.txt  \r\n",
      "  inflating: rl_ver/val/labels/P0001_600_1400_9600_10400_0.txt  \r\n",
      " extracting: rl_ver/val/labels/P0001_600_1400_9600_10400_1.txt  \r\n",
      "  inflating: rl_ver/val/labels/P0001_600_1400_9600_10400_2.txt  \r\n",
      " extracting: rl_ver/val/labels/P0001_600_1400_9600_10400_3.txt  \r\n",
      "   creating: test/\r\n",
      "   creating: test/images/\r\n",
      "  inflating: test/images/P0001_0_800_10190_10990.jpg  \r\n",
      "  inflating: test/images/P0001_1200_2000_10190_10990.jpg  \r\n",
      "  inflating: test/images/P0001_1200_2000_3600_4400.jpg  \r\n",
      "  inflating: test/images/P0001_1800_2600_0_800.jpg  \r\n",
      "  inflating: test/images/P0001_1800_2600_7800_8600.jpg  \r\n",
      "  inflating: test/images/P0001_2400_3200_4800_5600.jpg  \r\n",
      "  inflating: test/images/P0001_3000_3800_2400_3200.jpg  \r\n",
      "  inflating: test/images/P0001_3000_3800_4200_5000.jpg  \r\n",
      "  inflating: test/images/P0001_3600_4400_2400_3200.jpg  \r\n",
      "  inflating: test/images/P0001_3600_4400_4200_5000.jpg  \r\n",
      "  inflating: test/images/P0001_3600_4400_5400_6200.jpg  \r\n",
      "  inflating: test/images/P0001_3600_4400_6000_6800.jpg  \r\n",
      "  inflating: test/images/P0001_4200_5000_5400_6200.jpg  \r\n",
      "  inflating: test/images/P0001_4800_5600_1800_2600.jpg  \r\n",
      "  inflating: test/images/P0001_4800_5600_2400_3200.jpg  \r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  inflating: test/images/P0001_5263_6063_2400_3200.jpg  \n",
      "  inflating: test/images/P0001_5263_6063_6000_6800.jpg  \n",
      "  inflating: test/images/P0001_5263_6063_6600_7400.jpg  \n",
      "  inflating: test/images/P0001_5263_6063_7200_8000.jpg  \n",
      "   creating: test/labels/\n",
      "  inflating: test/labels/P0001_0_800_10190_10990.txt  \n",
      "  inflating: test/labels/P0001_1200_2000_10190_10990.txt  \n",
      "  inflating: test/labels/P0001_1200_2000_3600_4400.txt  \n",
      " extracting: test/labels/P0001_1800_2600_0_800.txt  \n",
      "  inflating: test/labels/P0001_1800_2600_7800_8600.txt  \n",
      "  inflating: test/labels/P0001_2400_3200_4800_5600.txt  \n",
      "  inflating: test/labels/P0001_3000_3800_2400_3200.txt  \n",
      "  inflating: test/labels/P0001_3000_3800_4200_5000.txt  \n",
      "  inflating: test/labels/P0001_3600_4400_2400_3200.txt  \n",
      "  inflating: test/labels/P0001_3600_4400_4200_5000.txt  \n",
      " extracting: test/labels/P0001_3600_4400_5400_6200.txt  \n",
      "  inflating: test/labels/P0001_3600_4400_6000_6800.txt  \n",
      "  inflating: test/labels/P0001_4200_5000_5400_6200.txt  \n",
      " extracting: test/labels/P0001_4800_5600_1800_2600.txt  \n",
      "  inflating: test/labels/P0001_4800_5600_2400_3200.txt  \n",
      "  inflating: test/labels/P0001_5263_6063_2400_3200.txt  \n",
      "  inflating: test/labels/P0001_5263_6063_6000_6800.txt  \n",
      "  inflating: test/labels/P0001_5263_6063_6600_7400.txt  \n",
      "  inflating: test/labels/P0001_5263_6063_7200_8000.txt  \n",
      "   creating: train/\n",
      "   creating: train/images/\n",
      "  inflating: train/images/P0001_0_800_7200_8000.jpg  \n",
      "  inflating: train/images/P0001_0_800_8400_9200.jpg  \n",
      "  inflating: train/images/P0001_0_800_9000_9800.jpg  \n",
      "  inflating: train/images/P0001_0_800_9600_10400.jpg  \n",
      "  inflating: train/images/P0001_1200_2000_0_800.jpg  \n",
      "  inflating: train/images/P0001_1200_2000_8400_9200.jpg  \n",
      "  inflating: train/images/P0001_1200_2000_9600_10400.jpg  \n",
      "  inflating: train/images/P0001_1800_2600_2400_3200.jpg  \n",
      "  inflating: train/images/P0001_1800_2600_3000_3800.jpg  \n",
      "  inflating: train/images/P0001_1800_2600_3600_4400.jpg  \n",
      "  inflating: train/images/P0001_1800_2600_4200_5000.jpg  \n",
      "  inflating: train/images/P0001_1800_2600_4800_5600.jpg  \n",
      "  inflating: train/images/P0001_2400_3200_1800_2600.jpg  \n",
      "  inflating: train/images/P0001_2400_3200_4200_5000.jpg  \n",
      "  inflating: train/images/P0001_2400_3200_7200_8000.jpg  \n",
      "  inflating: train/images/P0001_3000_3800_1800_2600.jpg  \n",
      "  inflating: train/images/P0001_3000_3800_3000_3800.jpg  \n",
      "  inflating: train/images/P0001_3000_3800_3600_4400.jpg  \n",
      "  inflating: train/images/P0001_3000_3800_4800_5600.jpg  \n",
      "  inflating: train/images/P0001_3000_3800_6000_6800.jpg  \n",
      "  inflating: train/images/P0001_3000_3800_7200_8000.jpg  \n",
      "  inflating: train/images/P0001_3600_4400_1800_2600.jpg  \n",
      "  inflating: train/images/P0001_3600_4400_3000_3800.jpg  \n",
      "  inflating: train/images/P0001_3600_4400_3600_4400.jpg  \n",
      "  inflating: train/images/P0001_3600_4400_4800_5600.jpg  \n",
      "  inflating: train/images/P0001_3600_4400_600_1400.jpg  \n",
      "  inflating: train/images/P0001_4200_5000_1800_2600.jpg  \n",
      "  inflating: train/images/P0001_4200_5000_2400_3200.jpg  \n",
      "  inflating: train/images/P0001_4200_5000_3000_3800.jpg  \n",
      "  inflating: train/images/P0001_4200_5000_3600_4400.jpg  \n",
      "  inflating: train/images/P0001_4200_5000_4200_5000.jpg  \n",
      "  inflating: train/images/P0001_4200_5000_6000_6800.jpg  \n",
      "  inflating: train/images/P0001_4200_5000_600_1400.jpg  \n",
      "  inflating: train/images/P0001_4200_5000_6600_7400.jpg  \n",
      "  inflating: train/images/P0001_4200_5000_7200_8000.jpg  \n",
      "  inflating: train/images/P0001_4800_5600_0_800.jpg  \n",
      "  inflating: train/images/P0001_4800_5600_3000_3800.jpg  \n",
      "  inflating: train/images/P0001_4800_5600_6000_6800.jpg  \n",
      "  inflating: train/images/P0001_4800_5600_6600_7400.jpg  \n",
      "  inflating: train/images/P0001_4800_5600_7200_8000.jpg  \n",
      "  inflating: train/images/P0001_5263_6063_1200_2000.jpg  \n",
      "  inflating: train/images/P0001_5263_6063_5400_6200.jpg  \n",
      "  inflating: train/images/P0001_5263_6063_600_1400.jpg  \n",
      "  inflating: train/images/P0001_5263_6063_7800_8600.jpg  \n",
      "  inflating: train/images/P0001_600_1400_8400_9200.jpg  \n",
      "  inflating: train/images/P0001_600_1400_9000_9800.jpg  \n",
      "   creating: train/labels/\n",
      "  inflating: train/labels/P0001_0_800_7200_8000.txt  \n",
      "  inflating: train/labels/P0001_0_800_8400_9200.txt  \n",
      "  inflating: train/labels/P0001_0_800_9000_9800.txt  \n",
      " extracting: train/labels/P0001_0_800_9600_10400.txt  \n",
      " extracting: train/labels/P0001_1200_2000_0_800.txt  \n",
      "  inflating: train/labels/P0001_1200_2000_8400_9200.txt  \n",
      " extracting: train/labels/P0001_1200_2000_9600_10400.txt  \n",
      " extracting: train/labels/P0001_1800_2600_2400_3200.txt  \n",
      " extracting: train/labels/P0001_1800_2600_3000_3800.txt  \n",
      " extracting: train/labels/P0001_1800_2600_3600_4400.txt  \n",
      "  inflating: train/labels/P0001_1800_2600_4200_5000.txt  \n",
      " extracting: train/labels/P0001_1800_2600_4800_5600.txt  \n",
      " extracting: train/labels/P0001_2400_3200_1800_2600.txt  \n",
      "  inflating: train/labels/P0001_2400_3200_4200_5000.txt  \n",
      "  inflating: train/labels/P0001_2400_3200_7200_8000.txt  \n",
      "  inflating: train/labels/P0001_3000_3800_1800_2600.txt  \n",
      "  inflating: train/labels/P0001_3000_3800_3000_3800.txt  \n",
      "  inflating: train/labels/P0001_3000_3800_3600_4400.txt  \n",
      " extracting: train/labels/P0001_3000_3800_4800_5600.txt  \n",
      " extracting: train/labels/P0001_3000_3800_6000_6800.txt  \n",
      "  inflating: train/labels/P0001_3000_3800_7200_8000.txt  \n",
      " extracting: train/labels/P0001_3600_4400_1800_2600.txt  \n",
      "  inflating: train/labels/P0001_3600_4400_3000_3800.txt  \n",
      "  inflating: train/labels/P0001_3600_4400_3600_4400.txt  \n",
      " extracting: train/labels/P0001_3600_4400_4800_5600.txt  \n",
      " extracting: train/labels/P0001_3600_4400_600_1400.txt  \n",
      " extracting: train/labels/P0001_4200_5000_1800_2600.txt  \n",
      " extracting: train/labels/P0001_4200_5000_2400_3200.txt  \n",
      "  inflating: train/labels/P0001_4200_5000_3000_3800.txt  \n",
      "  inflating: train/labels/P0001_4200_5000_3600_4400.txt  \n",
      "  inflating: train/labels/P0001_4200_5000_4200_5000.txt  \n",
      "  inflating: train/labels/P0001_4200_5000_6000_6800.txt  \n",
      " extracting: train/labels/P0001_4200_5000_600_1400.txt  \n",
      "  inflating: train/labels/P0001_4200_5000_6600_7400.txt  \n",
      " extracting: train/labels/P0001_4200_5000_7200_8000.txt  \n",
      "  inflating: train/labels/P0001_4800_5600_0_800.txt  \n",
      " extracting: train/labels/P0001_4800_5600_3000_3800.txt  \n",
      " extracting: train/labels/P0001_4800_5600_6000_6800.txt  \n",
      "  inflating: train/labels/P0001_4800_5600_6600_7400.txt  \n",
      "  inflating: train/labels/P0001_4800_5600_7200_8000.txt  \n",
      "  inflating: train/labels/P0001_5263_6063_1200_2000.txt  \n",
      "  inflating: train/labels/P0001_5263_6063_5400_6200.txt  \n",
      " extracting: train/labels/P0001_5263_6063_600_1400.txt  \n",
      " extracting: train/labels/P0001_5263_6063_7800_8600.txt  \n",
      "  inflating: train/labels/P0001_600_1400_8400_9200.txt  \n",
      "  inflating: train/labels/P0001_600_1400_9000_9800.txt  \n",
      "   creating: val/\n",
      "   creating: val/images/\n",
      "  inflating: val/images/P0001_1200_2000_4200_5000.jpg  \n",
      "  inflating: val/images/P0001_1200_2000_7800_8600.jpg  \n",
      "  inflating: val/images/P0001_1800_2600_10190_10990.jpg  \n",
      "  inflating: val/images/P0001_1800_2600_8400_9200.jpg  \n",
      "  inflating: val/images/P0001_2400_3200_2400_3200.jpg  \n",
      "  inflating: val/images/P0001_2400_3200_3600_4400.jpg  \n",
      "  inflating: val/images/P0001_3600_4400_6600_7400.jpg  \n",
      "  inflating: val/images/P0001_600_1400_7800_8600.jpg  \n",
      "  inflating: val/images/P0001_600_1400_9600_10400.jpg  \n",
      "   creating: val/labels/\n",
      "  inflating: val/labels/P0001_1200_2000_4200_5000.txt  \n",
      " extracting: val/labels/P0001_1200_2000_7800_8600.txt  \n",
      "  inflating: val/labels/P0001_1800_2600_10190_10990.txt  \n",
      "  inflating: val/labels/P0001_1800_2600_8400_9200.txt  \n",
      "  inflating: val/labels/P0001_2400_3200_2400_3200.txt  \n",
      "  inflating: val/labels/P0001_2400_3200_3600_4400.txt  \n",
      " extracting: val/labels/P0001_3600_4400_6600_7400.txt  \n",
      "  inflating: val/labels/P0001_600_1400_7800_8600.txt  \n",
      "  inflating: val/labels/P0001_600_1400_9600_10400.txt  \n"
     ]
    }
   ],
   "source": [
    "!unzip sample.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/kang/SAR_OD/anonymous-hub/SAROD\n"
     ]
    }
   ],
   "source": [
    "%cd .."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. training with sample dataset, small batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Apex recommended for faster mixed precision training: https://github.com/NVIDIA/apex\n",
      "GPU device for EfficientOD:  True\n",
      "Your branch is behind 'origin/main' by 2 commits, and can be fast-forwarded.\n",
      "  (use \"git pull\" to update your local branch)\n",
      "\n",
      "Using CUDA device0 _CudaDeviceProperties(name='Graphics Device', total_memory=24190MB)\n",
      "\n",
      "{'cfg': 'yolov5/models/yolov5x_custom.yaml', 'data': 'yolov5/data/HRSID_800_od.yaml', 'hyp': '', 'epochs': 10, 'batch_size': 4, 'img_size': [480, 480], 'rect': False, 'resume': False, 'nosave': False, 'notest': True, 'noautoanchor': True, 'evolve': False, 'bucket': '', 'cache_images': True, 'weights': ' ', 'name': 'yolov5x_800_480_200epoch', 'device': '2', 'multi_scale': False, 'single_cls': True, 'sync_bn': False, 'local_rank': -1, 'total_batch_size': 4, 'world_size': 1}\n",
      "Start Tensorboard with \"tensorboard --logdir=runs\", view at http://localhost:6006/\n",
      "Hyperparameters {'optimizer': 'SGD', 'lr0': 0.01, 'momentum': 0.937, 'weight_decay': 0.0005, 'giou': 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': 0.0, 'scale': 0.5, 'shear': 0.0}\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      8800  yolov5.models.common.Focus              [3, 80, 3]                    \n",
      "  1                -1  1    115520  yolov5.models.common.Conv               [80, 160, 3, 2]               \n",
      "  2                -1  1    315680  yolov5.models.common.BottleneckCSP      [160, 160, 4]                 \n",
      "  3                -1  1    461440  yolov5.models.common.Conv               [160, 320, 3, 2]              \n",
      "  4                -1  1   3311680  yolov5.models.common.BottleneckCSP      [320, 320, 12]                \n",
      "  5                -1  1   1844480  yolov5.models.common.Conv               [320, 640, 3, 2]              \n",
      "  6                -1  1  13228160  yolov5.models.common.BottleneckCSP      [640, 640, 12]                \n",
      "  7                -1  1   7375360  yolov5.models.common.Conv               [640, 1280, 3, 2]             \n",
      "  8                -1  1   4099840  yolov5.models.common.SPP                [1280, 1280, [5, 9, 13]]      \n",
      "  9                -1  1  20087040  yolov5.models.common.BottleneckCSP      [1280, 1280, 4, False]        \n",
      " 10                -1  1    820480  yolov5.models.common.Conv               [1280, 640, 1, 1]             \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  yolov5.models.common.Concat             [1]                           \n",
      " 13                -1  1   5435520  yolov5.models.common.BottleneckCSP      [1280, 640, 4, False]         \n",
      " 14                -1  1    205440  yolov5.models.common.Conv               [640, 320, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  yolov5.models.common.Concat             [1]                           \n",
      " 17                -1  1   1360960  yolov5.models.common.BottleneckCSP      [640, 320, 4, False]          \n",
      " 18                -1  1    922240  yolov5.models.common.Conv               [320, 320, 3, 2]              \n",
      " 19          [-1, 14]  1         0  yolov5.models.common.Concat             [1]                           \n",
      " 20                -1  1   5025920  yolov5.models.common.BottleneckCSP      [640, 640, 4, False]          \n",
      " 21                -1  1   3687680  yolov5.models.common.Conv               [640, 640, 3, 2]              \n",
      " 22          [-1, 10]  1         0  yolov5.models.common.Concat             [1]                           \n",
      " 23                -1  1  20087040  yolov5.models.common.BottleneckCSP      [1280, 1280, 4, False]        \n",
      " 24      [17, 20, 23]  1     40374  yolov5.models.yolo.Detect               [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [320, 640, 1280]]\n",
      "Model Summary: 407 layers, 8.84337e+07 parameters, 8.84337e+07 gradients\n",
      "\n",
      "Optimizer groups: 134 .bias, 142 conv.weight, 131 other\n",
      "Scanning labels data/dt_ver/train/labels.cache (84 found, 0 missing, 0 empty, 0 \n",
      "Caching images (0.1GB): 100%|██████████████████| 84/84 [00:00<00:00, 290.73it/s]\n",
      "Scanning labels data/dt_ver/val/labels.cache (16 found, 0 missing, 0 empty, 0 du\n",
      "Caching images (0.0GB): 100%|██████████████████| 16/16 [00:00<00:00, 290.63it/s]\n",
      "Scanning labels data/dt_ver/val/labels.cache (16 found, 0 missing, 0 empty, 0 du\n",
      "Caching images (0.0GB): 100%|██████████████████| 16/16 [00:00<00:00, 288.95it/s]\n",
      "Your branch is behind 'origin/main' by 2 commits, and can be fast-forwarded.\n",
      "  (use \"git pull\" to update your local branch)\n",
      "\n",
      "Using CUDA device0 _CudaDeviceProperties(name='Graphics Device', total_memory=24190MB)\n",
      "\n",
      "{'cfg': 'yolov5/models/yolov5x_custom.yaml', 'data': 'yolov5/data/HRSID_800_od.yaml', 'hyp': '', 'epochs': 10, 'batch_size': 4, 'img_size': [96, 96], 'rect': False, 'resume': False, 'nosave': False, 'notest': True, 'noautoanchor': True, 'evolve': False, 'bucket': '', 'cache_images': True, 'weights': ' ', 'name': 'yolov5x_800_96_200epoch', 'device': '2', 'multi_scale': False, 'single_cls': True, 'sync_bn': False, 'local_rank': -1, 'total_batch_size': 4, 'world_size': 1}\n",
      "Start Tensorboard with \"tensorboard --logdir=runs\", view at http://localhost:6006/\n",
      "Hyperparameters {'optimizer': 'SGD', 'lr0': 0.01, 'momentum': 0.937, 'weight_decay': 0.0005, 'giou': 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': 0.0, 'scale': 0.5, 'shear': 0.0}\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      8800  yolov5.models.common.Focus              [3, 80, 3]                    \n",
      "  1                -1  1    115520  yolov5.models.common.Conv               [80, 160, 3, 2]               \n",
      "  2                -1  1    315680  yolov5.models.common.BottleneckCSP      [160, 160, 4]                 \n",
      "  3                -1  1    461440  yolov5.models.common.Conv               [160, 320, 3, 2]              \n",
      "  4                -1  1   3311680  yolov5.models.common.BottleneckCSP      [320, 320, 12]                \n",
      "  5                -1  1   1844480  yolov5.models.common.Conv               [320, 640, 3, 2]              \n",
      "  6                -1  1  13228160  yolov5.models.common.BottleneckCSP      [640, 640, 12]                \n",
      "  7                -1  1   7375360  yolov5.models.common.Conv               [640, 1280, 3, 2]             \n",
      "  8                -1  1   4099840  yolov5.models.common.SPP                [1280, 1280, [5, 9, 13]]      \n",
      "  9                -1  1  20087040  yolov5.models.common.BottleneckCSP      [1280, 1280, 4, False]        \n",
      " 10                -1  1    820480  yolov5.models.common.Conv               [1280, 640, 1, 1]             \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  yolov5.models.common.Concat             [1]                           \n",
      " 13                -1  1   5435520  yolov5.models.common.BottleneckCSP      [1280, 640, 4, False]         \n",
      " 14                -1  1    205440  yolov5.models.common.Conv               [640, 320, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  yolov5.models.common.Concat             [1]                           \n",
      " 17                -1  1   1360960  yolov5.models.common.BottleneckCSP      [640, 320, 4, False]          \n",
      " 18                -1  1    922240  yolov5.models.common.Conv               [320, 320, 3, 2]              \n",
      " 19          [-1, 14]  1         0  yolov5.models.common.Concat             [1]                           \n",
      " 20                -1  1   5025920  yolov5.models.common.BottleneckCSP      [640, 640, 4, False]          \n",
      " 21                -1  1   3687680  yolov5.models.common.Conv               [640, 640, 3, 2]              \n",
      " 22          [-1, 10]  1         0  yolov5.models.common.Concat             [1]                           \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 23                -1  1  20087040  yolov5.models.common.BottleneckCSP      [1280, 1280, 4, False]        \n",
      " 24      [17, 20, 23]  1     40374  yolov5.models.yolo.Detect               [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [320, 640, 1280]]\n",
      "Model Summary: 407 layers, 8.84337e+07 parameters, 8.84337e+07 gradients\n",
      "\n",
      "Optimizer groups: 134 .bias, 142 conv.weight, 131 other\n",
      "Scanning labels data/dt_ver/train/labels.cache (84 found, 0 missing, 0 empty, 0 \n",
      "Caching images (0.0GB): 100%|██████████████████| 84/84 [00:00<00:00, 280.03it/s]\n",
      "Scanning labels data/dt_ver/val/labels.cache (16 found, 0 missing, 0 empty, 0 du\n",
      "Caching images (0.0GB): 100%|██████████████████| 16/16 [00:00<00:00, 218.46it/s]\n",
      "Scanning labels data/dt_ver/val/labels.cache (16 found, 0 missing, 0 empty, 0 du\n",
      "Caching images (0.0GB): 100%|██████████████████| 16/16 [00:00<00:00, 217.21it/s]\n",
      "Image sizes 480 train, 480 test\n",
      "Using 4 dataloader workers\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
      "       0/9     5.38G    0.1478   0.02292         0    0.1707        10       480\n",
      "               Class      Images     Targets           P           R      mAP@.5\n",
      "                 all          16           0           0           0           0           0\n",
      "Optimizer stripped from runs/exp22_yolov5x_800_480_200epoch/weights/last_yolov5x_800_480_200epoch.pt, 177.3MB\n",
      "1 epochs completed in 0.004 hours.\n",
      "\n",
      "Image sizes 96 train, 96 test\n",
      "Using 4 dataloader workers\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
      "       0/9     3.25G   0.05669   0.03468         0   0.09136        11        96\n",
      "               Class      Images     Targets           P           R      mAP@.5\n",
      "                 all          16           0           0           0           0           0\n",
      "Optimizer stripped from runs/exp23_yolov5x_800_96_200epoch/weights/last_yolov5x_800_96_200epoch.pt, 177.3MB\n",
      "1 epochs completed in 0.003 hours.\n",
      "\n",
      "\n",
      " detection evaluation start - train task\n",
      "Scanning images: 100%|████████████████████████| 46/46 [00:00<00:00, 2529.57it/s]\n",
      "100%|███████████████████████████████████████████| 46/46 [00:10<00:00,  4.32it/s]\n",
      "480 result - map: 0.0\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - train task\n",
      "Scanning images: 100%|████████████████████████| 46/46 [00:00<00:00, 2275.32it/s]\n",
      "100%|███████████████████████████████████████████| 46/46 [00:07<00:00,  6.40it/s]\n",
      "96 result - map: 8.654797230464887e-06\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|███████████████████████████████████████████| 46/46 [00:01<00:00, 31.92it/s]\n",
      "100%|█████████████████████████████████████████████| 6/6 [00:01<00:00,  3.05it/s]\n",
      "\n",
      "0 Epoch - RL Train mean AP: 0.0 / Efficiency: 0.6875 \n",
      "Train: 0 | Rw: -0.008333 | S: 2.750 | V: 1.215 | #: 5\n",
      "\n",
      " detection evaluation start - val task\n",
      "Scanning images: 100%|██████████████████████████| 9/9 [00:00<00:00, 1942.81it/s]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:02<00:00,  4.24it/s]\n",
      "480 result - map: 0.0\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - val task\n",
      "Scanning images: 100%|██████████████████████████| 9/9 [00:00<00:00, 1952.25it/s]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:01<00:00,  6.08it/s]\n",
      "96 result - map: 0.0\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:00<00:00, 20.60it/s]\n",
      "0 Epoch - RL Eval AP: 0.0 / Efficiency: 0.75 \n",
      "RL Eval - Rw: 0.0444 | S: 3.000 | V: 0.000 | #: 1\n",
      "\n",
      "\n",
      " detection evaluation start - test task\n",
      "Scanning images: 100%|████████████████████████| 19/19 [00:00<00:00, 1902.45it/s]\n",
      "100%|███████████████████████████████████████████| 19/19 [00:04<00:00,  4.37it/s]\n",
      "480 result - map: 0.0\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - test task\n",
      "Scanning images: 100%|████████████████████████| 19/19 [00:00<00:00, 2381.92it/s]\n",
      "100%|███████████████████████████████████████████| 19/19 [00:02<00:00,  6.37it/s]\n",
      "96 result - map: 3.7768425596201576e-06\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|███████████████████████████████████████████| 19/19 [00:00<00:00, 28.59it/s]\n",
      "0 Epoch - RL Test AP: 7.941832527145487e-07 / Efficiency: 0.75 \n",
      "RL Test - Rw: 0.0498 | S: 3.000 | V: 0.000 | #: 1\n",
      "\n",
      "Image sizes 480 train, 480 test\n",
      "Using 4 dataloader workers\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
      "       1/9     6.38G    0.1314   0.02299         0    0.1543         7       480\n",
      "               Class      Images     Targets           P           R      mAP@.5\n",
      "                 all          16           0           0           0           0           0\n",
      "Optimizer stripped from runs/exp22_yolov5x_800_480_200epoch/weights/last_yolov5x_800_480_200epoch.pt, 177.3MB\n",
      "2 epochs completed in 0.002 hours.\n",
      "\n",
      "Image sizes 96 train, 96 test\n",
      "Using 4 dataloader workers\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
      "       1/9     3.67G   0.05601   0.04365         0   0.09966         7        96\n",
      "               Class      Images     Targets           P           R      mAP@.5\n",
      "                 all          16           0           0           0           0           0\n",
      "Optimizer stripped from runs/exp23_yolov5x_800_96_200epoch/weights/last_yolov5x_800_96_200epoch.pt, 177.3MB\n",
      "2 epochs completed in 0.002 hours.\n",
      "\n",
      "\n",
      " detection evaluation start - train task\n",
      "Scanning images: 100%|████████████████████████| 46/46 [00:00<00:00, 2427.99it/s]\n",
      "100%|███████████████████████████████████████████| 46/46 [00:09<00:00,  4.80it/s]\n",
      "480 result - map: 0.0\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - train task\n",
      "Scanning images: 100%|████████████████████████| 46/46 [00:00<00:00, 2349.01it/s]\n",
      "100%|███████████████████████████████████████████| 46/46 [00:07<00:00,  6.25it/s]\n",
      "96 result - map: 1.7403814916229635e-06\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|███████████████████████████████████████████| 46/46 [00:01<00:00, 36.40it/s]\n",
      "100%|███████████████████████████████████████████| 12/12 [00:02<00:00,  4.00it/s]\n",
      "\n",
      "1 Epoch - RL Train mean AP: 3.4482758620689657e-05 / Efficiency: 0.7395833134651184 \n",
      "Train: 1 | Rw: -0.035989 | S: 2.958 | V: 0.908 | #: 9\n",
      "\n",
      " detection evaluation start - val task\n",
      "Scanning images: 100%|██████████████████████████| 9/9 [00:00<00:00, 2319.43it/s]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:02<00:00,  4.35it/s]\n",
      "480 result - map: 0.0\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - val task\n",
      "Scanning images: 100%|██████████████████████████| 9/9 [00:00<00:00, 2114.18it/s]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:01<00:00,  6.42it/s]\n",
      "96 result - map: 0.0\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:00<00:00, 22.81it/s]\n",
      "1 Epoch - RL Eval AP: 0.0 / Efficiency: 0.8333333134651184 \n",
      "RL Eval - Rw: 0.0000 | S: 3.333 | V: 0.500 | #: 2\n",
      "\n",
      "\n",
      " detection evaluation start - test task\n",
      "Scanning images: 100%|████████████████████████| 19/19 [00:00<00:00, 1559.74it/s]\n",
      "100%|███████████████████████████████████████████| 19/19 [00:04<00:00,  4.38it/s]\n",
      "480 result - map: 0.0\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - test task\n",
      "Scanning images: 100%|████████████████████████| 19/19 [00:00<00:00, 2415.85it/s]\n",
      "100%|███████████████████████████████████████████| 19/19 [00:03<00:00,  6.05it/s]\n",
      "96 result - map: 4.234468574193939e-06\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|███████████████████████████████████████████| 19/19 [00:00<00:00, 28.53it/s]\n",
      "1 Epoch - RL Test AP: 0.0 / Efficiency: 0.7894737124443054 \n",
      "RL Test - Rw: 0.0337 | S: 3.158 | V: 0.375 | #: 2\n",
      "\n",
      "Image sizes 480 train, 480 test\n",
      "Using 4 dataloader workers\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
      "       2/9     6.39G    0.1292   0.02432         0    0.1535        15       480\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               Class      Images     Targets           P           R      mAP@.5\n",
      "                 all          16           0           0           0           0           0\n",
      "Optimizer stripped from runs/exp22_yolov5x_800_480_200epoch/weights/last_yolov5x_800_480_200epoch.pt, 177.3MB\n",
      "3 epochs completed in 0.002 hours.\n",
      "\n",
      "Image sizes 96 train, 96 test\n",
      "Using 4 dataloader workers\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
      "       2/9     3.69G    0.0468    0.0427         0    0.0895        13        96\n",
      "               Class      Images     Targets           P           R      mAP@.5\n",
      "                 all          16           0           0           0           0           0\n",
      "Optimizer stripped from runs/exp23_yolov5x_800_96_200epoch/weights/last_yolov5x_800_96_200epoch.pt, 177.3MB\n",
      "3 epochs completed in 0.002 hours.\n",
      "\n",
      "\n",
      " detection evaluation start - train task\n",
      "Scanning images: 100%|████████████████████████| 46/46 [00:00<00:00, 2417.89it/s]\n",
      "100%|███████████████████████████████████████████| 46/46 [00:09<00:00,  4.92it/s]\n",
      "480 result - map: 6.550408783012459e-07\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - train task\n",
      "Scanning images: 100%|████████████████████████| 46/46 [00:00<00:00, 2422.17it/s]\n",
      "100%|███████████████████████████████████████████| 46/46 [00:07<00:00,  6.06it/s]\n",
      "96 result - map: 2.6676151520540636e-06\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|███████████████████████████████████████████| 46/46 [00:01<00:00, 31.81it/s]\n",
      "100%|███████████████████████████████████████████| 18/18 [00:04<00:00,  3.94it/s]\n",
      "\n",
      "2 Epoch - RL Train mean AP: 3.5609551738584e-05 / Efficiency: 0.8194444179534912 \n",
      "Train: 2 | Rw: -0.127710 | S: 3.278 | V: 0.849 | #: 10\n",
      "\n",
      " detection evaluation start - val task\n",
      "Scanning images: 100%|██████████████████████████| 9/9 [00:00<00:00, 2147.13it/s]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:01<00:00,  4.76it/s]\n",
      "480 result - map: 0.0\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - val task\n",
      "Scanning images: 100%|██████████████████████████| 9/9 [00:00<00:00, 1963.01it/s]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:01<00:00,  6.87it/s]\n",
      "96 result - map: 0.0\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:00<00:00, 22.70it/s]\n",
      "2 Epoch - RL Eval AP: 0.0 / Efficiency: 0.9722222089767456 \n",
      "RL Eval - Rw: -0.0889 | S: 3.889 | V: 0.333 | #: 2\n",
      "\n",
      "\n",
      " detection evaluation start - test task\n",
      "Scanning images: 100%|█████████████████████████| 19/19 [00:00<00:00, 754.39it/s]\n",
      "100%|███████████████████████████████████████████| 19/19 [00:03<00:00,  5.10it/s]\n",
      "480 result - map: 1.4332454780157382e-06\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - test task\n",
      "Scanning images: 100%|████████████████████████| 19/19 [00:00<00:00, 2053.22it/s]\n",
      "100%|███████████████████████████████████████████| 19/19 [00:03<00:00,  5.90it/s]\n",
      "96 result - map: 6.43915003219575e-06\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|███████████████████████████████████████████| 19/19 [00:00<00:00, 24.82it/s]\n",
      "2 Epoch - RL Test AP: 1.503476891436133e-06 / Efficiency: 0.9868420958518982 \n",
      "RL Test - Rw: -0.0820 | S: 3.947 | V: 0.229 | #: 2\n",
      "\n",
      "Image sizes 480 train, 480 test\n",
      "Using 4 dataloader workers\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
      "       3/9     6.39G    0.1252   0.02338         0    0.1486         8       480\n",
      "               Class      Images     Targets           P           R      mAP@.5\n",
      "                 all          16          53           0           0    2.89e-05    5.78e-06\n",
      "Optimizer stripped from runs/exp22_yolov5x_800_480_200epoch/weights/last_yolov5x_800_480_200epoch.pt, 177.3MB\n",
      "4 epochs completed in 0.002 hours.\n",
      "\n",
      "Image sizes 96 train, 96 test\n",
      "Using 4 dataloader workers\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
      "       3/9      3.7G   0.04948   0.04697         0   0.09646         6        96\n",
      "               Class      Images     Targets           P           R      mAP@.5\n",
      "                 all          16           0           0           0           0           0\n",
      "Optimizer stripped from runs/exp23_yolov5x_800_96_200epoch/weights/last_yolov5x_800_96_200epoch.pt, 177.3MB\n",
      "4 epochs completed in 0.002 hours.\n",
      "\n",
      "\n",
      " detection evaluation start - train task\n",
      "Scanning images: 100%|████████████████████████| 46/46 [00:00<00:00, 2300.99it/s]\n",
      "100%|███████████████████████████████████████████| 46/46 [00:09<00:00,  4.82it/s]\n",
      "480 result - map: 1.531002937601899e-05\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - train task\n",
      "Scanning images: 100%|████████████████████████| 46/46 [00:00<00:00, 2380.60it/s]\n",
      "100%|███████████████████████████████████████████| 46/46 [00:07<00:00,  5.80it/s]\n",
      "96 result - map: 1.1322891111530476e-06\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|███████████████████████████████████████████| 46/46 [00:00<00:00, 46.19it/s]\n",
      "100%|███████████████████████████████████████████| 24/24 [00:06<00:00,  3.96it/s]\n",
      "\n",
      "3 Epoch - RL Train mean AP: 0.0 / Efficiency: 0.828125 \n",
      "Train: 3 | Rw: -0.126517 | S: 3.312 | V: 0.854 | #: 10\n",
      "\n",
      " detection evaluation start - val task\n",
      "Scanning images: 100%|██████████████████████████| 9/9 [00:00<00:00, 1983.23it/s]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:01<00:00,  4.83it/s]\n",
      "480 result - map: 1.3748854262144822e-05\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - val task\n",
      "Scanning images: 100%|██████████████████████████| 9/9 [00:00<00:00, 1940.31it/s]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:01<00:00,  5.83it/s]\n",
      "96 result - map: 0.0\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:00<00:00, 22.24it/s]\n",
      "3 Epoch - RL Eval AP: 1.3748854262144822e-05 / Efficiency: 1.0 \n",
      "RL Eval - Rw: -0.1081 | S: 4.000 | V: 0.000 | #: 1\n",
      "\n",
      "\n",
      " detection evaluation start - test task\n",
      "Scanning images: 100%|████████████████████████| 19/19 [00:00<00:00, 1541.87it/s]\n",
      "100%|███████████████████████████████████████████| 19/19 [00:04<00:00,  4.65it/s]\n",
      "480 result - map: 1.0190948833065697e-06\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - test task\n",
      "Scanning images: 100%|████████████████████████| 19/19 [00:00<00:00, 2389.42it/s]\n",
      "100%|███████████████████████████████████████████| 19/19 [00:03<00:00,  5.64it/s]\n",
      "96 result - map: 1.489589250944695e-06\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|███████████████████████████████████████████| 19/19 [00:00<00:00, 25.85it/s]\n",
      "3 Epoch - RL Test AP: 1.0110774888864117e-06 / Efficiency: 1.0 \n",
      "RL Test - Rw: -0.0922 | S: 4.000 | V: 0.000 | #: 1\n",
      "\n",
      "Image sizes 480 train, 480 test\n",
      "Using 4 dataloader workers\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
      "       4/9     6.38G    0.1212    0.0214         0    0.1426        16       480\n",
      "               Class      Images     Targets           P           R      mAP@.5\n",
      "                 all          16          53           0           0    0.000155    3.09e-05\n",
      "Optimizer stripped from runs/exp22_yolov5x_800_480_200epoch/weights/last_yolov5x_800_480_200epoch.pt, 177.3MB\n",
      "5 epochs completed in 0.002 hours.\n",
      "\n",
      "Image sizes 96 train, 96 test\n",
      "Using 4 dataloader workers\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
      "       4/9      3.7G   0.04241   0.04113         0   0.08354         3        96\n",
      "               Class      Images     Targets           P           R      mAP@.5\n",
      "                 all          16          53           0           0    6.45e-06    6.45e-07\n",
      "Optimizer stripped from runs/exp23_yolov5x_800_96_200epoch/weights/last_yolov5x_800_96_200epoch.pt, 177.3MB\n",
      "5 epochs completed in 0.002 hours.\n",
      "\n",
      "\n",
      " detection evaluation start - train task\n",
      "Scanning images: 100%|████████████████████████| 46/46 [00:00<00:00, 1289.98it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 46/46 [00:09<00:00,  4.95it/s]\n",
      "480 result - map: 1.1309891013777503e-05\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - train task\n",
      "Scanning images: 100%|████████████████████████| 46/46 [00:00<00:00, 2383.45it/s]\n",
      "100%|███████████████████████████████████████████| 46/46 [00:07<00:00,  5.97it/s]\n",
      "96 result - map: 3.895024292403132e-06\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|███████████████████████████████████████████| 46/46 [00:01<00:00, 30.37it/s]\n",
      "100%|███████████████████████████████████████████| 30/30 [00:07<00:00,  3.89it/s]\n",
      "\n",
      "4 Epoch - RL Train mean AP: 0.0 / Efficiency: 0.8208333253860474 \n",
      "Train: 4 | Rw: -0.127269 | S: 3.283 | V: 0.715 | #: 10\n",
      "\n",
      " detection evaluation start - val task\n",
      "Scanning images: 100%|██████████████████████████| 9/9 [00:00<00:00, 1861.65it/s]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:01<00:00,  4.65it/s]\n",
      "480 result - map: 0.00014018691588785047\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - val task\n",
      "Scanning images: 100%|██████████████████████████| 9/9 [00:00<00:00, 2212.96it/s]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:01<00:00,  5.71it/s]\n",
      "96 result - map: 2.8963120293492952e-06\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:00<00:00, 23.45it/s]\n",
      "4 Epoch - RL Eval AP: 0.00014851485148514854 / Efficiency: 1.0 \n",
      "RL Eval - Rw: -0.0014 | S: 4.000 | V: 0.000 | #: 1\n",
      "\n",
      "\n",
      " detection evaluation start - test task\n",
      "Scanning images: 100%|████████████████████████| 19/19 [00:00<00:00, 2135.19it/s]\n",
      "100%|███████████████████████████████████████████| 19/19 [00:04<00:00,  4.47it/s]\n",
      "480 result - map: 1.2626262626262626e-05\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - test task\n",
      "Scanning images: 100%|████████████████████████| 19/19 [00:00<00:00, 2243.01it/s]\n",
      "100%|███████████████████████████████████████████| 19/19 [00:03<00:00,  5.92it/s]\n",
      "96 result - map: 5.248556646922097e-06\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|███████████████████████████████████████████| 19/19 [00:00<00:00, 22.97it/s]\n",
      "4 Epoch - RL Test AP: 1.2626262626262626e-05 / Efficiency: 1.0 \n",
      "RL Test - Rw: -0.0919 | S: 4.000 | V: 0.000 | #: 1\n",
      "\n",
      "Image sizes 480 train, 480 test\n",
      "Using 4 dataloader workers\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
      "       5/9     6.36G    0.1224    0.0213         0    0.1437        16       480\n",
      "               Class      Images     Targets           P           R      mAP@.5\n",
      "                 all          16          53           0           0    7.47e-05    1.56e-05\n",
      "Optimizer stripped from runs/exp22_yolov5x_800_480_200epoch/weights/last_yolov5x_800_480_200epoch.pt, 177.3MB\n",
      "6 epochs completed in 0.002 hours.\n",
      "\n",
      "Image sizes 96 train, 96 test\n",
      "Using 4 dataloader workers\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
      "       5/9     3.68G   0.04589   0.04131         0    0.0872        14        96\n",
      "               Class      Images     Targets           P           R      mAP@.5\n",
      "                 all          16           0           0           0           0           0\n",
      "Optimizer stripped from runs/exp23_yolov5x_800_96_200epoch/weights/last_yolov5x_800_96_200epoch.pt, 177.3MB\n",
      "6 epochs completed in 0.002 hours.\n",
      "\n",
      "\n",
      " detection evaluation start - train task\n",
      "Scanning images: 100%|████████████████████████| 46/46 [00:00<00:00, 2256.82it/s]\n",
      "100%|███████████████████████████████████████████| 46/46 [00:09<00:00,  4.74it/s]\n",
      "480 result - map: 9.286982455015653e-05\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - train task\n",
      "Scanning images: 100%|████████████████████████| 46/46 [00:00<00:00, 1598.66it/s]\n",
      "100%|███████████████████████████████████████████| 46/46 [00:07<00:00,  6.04it/s]\n",
      "96 result - map: 3.7543447799557315e-06\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|███████████████████████████████████████████| 46/46 [00:01<00:00, 39.75it/s]\n",
      "100%|███████████████████████████████████████████| 36/36 [00:09<00:00,  3.84it/s]\n",
      "\n",
      "5 Epoch - RL Train mean AP: 0.0 / Efficiency: 0.7847222089767456 \n",
      "Train: 5 | Rw: -0.080918 | S: 3.139 | V: 0.861 | #: 13\n",
      "\n",
      " detection evaluation start - val task\n",
      "Scanning images: 100%|██████████████████████████| 9/9 [00:00<00:00, 1900.36it/s]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:01<00:00,  4.86it/s]\n",
      "480 result - map: 3.675664430813173e-05\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - val task\n",
      "Scanning images: 100%|██████████████████████████| 9/9 [00:00<00:00, 1464.04it/s]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:01<00:00,  6.00it/s]\n",
      "96 result - map: 0.0\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:00<00:00, 25.15it/s]\n",
      "5 Epoch - RL Eval AP: 3.541299790356394e-05 / Efficiency: 1.0 \n",
      "RL Eval - Rw: -0.1095 | S: 4.000 | V: 0.000 | #: 1\n",
      "\n",
      "\n",
      " detection evaluation start - test task\n",
      "Scanning images: 100%|████████████████████████| 19/19 [00:00<00:00, 1556.48it/s]\n",
      "100%|███████████████████████████████████████████| 19/19 [00:04<00:00,  4.65it/s]\n",
      "480 result - map: 1.1920403551304838e-05\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - test task\n",
      "Scanning images: 100%|████████████████████████| 19/19 [00:00<00:00, 2338.10it/s]\n",
      "100%|███████████████████████████████████████████| 19/19 [00:03<00:00,  6.02it/s]\n",
      "96 result - map: 9.430149535228346e-06\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|███████████████████████████████████████████| 19/19 [00:00<00:00, 25.63it/s]\n",
      "5 Epoch - RL Test AP: 1.1916365901125387e-05 / Efficiency: 1.0 \n",
      "RL Test - Rw: -0.0913 | S: 4.000 | V: 0.000 | #: 1\n",
      "\n",
      "Image sizes 480 train, 480 test\n",
      "Using 4 dataloader workers\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
      "       6/9     6.38G    0.1214   0.02156         0    0.1429        27       480\n",
      "               Class      Images     Targets           P           R      mAP@.5\n",
      "                 all          16          53           0           0    1.69e-05    2.06e-06\n",
      "Optimizer stripped from runs/exp22_yolov5x_800_480_200epoch/weights/last_yolov5x_800_480_200epoch.pt, 177.3MB\n",
      "7 epochs completed in 0.002 hours.\n",
      "\n",
      "Image sizes 96 train, 96 test\n",
      "Using 4 dataloader workers\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
      "       6/9     3.68G   0.04469   0.04637         0   0.09106         7        96\n",
      "               Class      Images     Targets           P           R      mAP@.5\n",
      "                 all          16           0           0           0           0           0\n",
      "Optimizer stripped from runs/exp23_yolov5x_800_96_200epoch/weights/last_yolov5x_800_96_200epoch.pt, 177.3MB\n",
      "7 epochs completed in 0.002 hours.\n",
      "\n",
      "\n",
      " detection evaluation start - train task\n",
      "Scanning images: 100%|████████████████████████| 46/46 [00:00<00:00, 2239.82it/s]\n",
      "100%|███████████████████████████████████████████| 46/46 [00:09<00:00,  4.94it/s]\n",
      "480 result - map: 0.0003669794375892013\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - train task\n",
      "Scanning images: 100%|████████████████████████| 46/46 [00:00<00:00, 2496.96it/s]\n",
      "100%|███████████████████████████████████████████| 46/46 [00:07<00:00,  5.96it/s]\n",
      "96 result - map: 3.7076566142125973e-06\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|███████████████████████████████████████████| 46/46 [00:01<00:00, 34.86it/s]\n",
      "100%|███████████████████████████████████████████| 42/42 [00:10<00:00,  3.85it/s]\n",
      "\n",
      "6 Epoch - RL Train mean AP: 6.78453947368421e-05 / Efficiency: 0.7797619104385376 \n",
      "Train: 6 | Rw: -0.093759 | S: 3.119 | V: 0.735 | #: 12\n",
      "\n",
      " detection evaluation start - val task\n",
      "Scanning images: 100%|██████████████████████████| 9/9 [00:00<00:00, 1949.83it/s]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:01<00:00,  4.69it/s]\n",
      "480 result - map: 7.411328745367919e-06\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - val task\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scanning images: 100%|██████████████████████████| 9/9 [00:00<00:00, 2252.45it/s]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:01<00:00,  6.35it/s]\n",
      "96 result - map: 0.0\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:00<00:00, 24.00it/s]\n",
      "6 Epoch - RL Eval AP: 7.410544145670124e-06 / Efficiency: 1.0 \n",
      "RL Eval - Rw: -0.1104 | S: 4.000 | V: 0.000 | #: 1\n",
      "\n",
      "\n",
      " detection evaluation start - test task\n",
      "Scanning images: 100%|████████████████████████| 19/19 [00:00<00:00, 1554.05it/s]\n",
      "100%|███████████████████████████████████████████| 19/19 [00:04<00:00,  4.60it/s]\n",
      "480 result - map: 0.009883568335971451\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - test task\n",
      "Scanning images: 100%|████████████████████████| 19/19 [00:00<00:00, 2414.83it/s]\n",
      "100%|███████████████████████████████████████████| 19/19 [00:03<00:00,  5.70it/s]\n",
      "96 result - map: 1.357951258361558e-06\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|███████████████████████████████████████████| 19/19 [00:00<00:00, 32.30it/s]\n",
      "6 Epoch - RL Test AP: 0.009883577827152667 / Efficiency: 1.0 \n",
      "RL Test - Rw: 0.0440 | S: 4.000 | V: 0.000 | #: 1\n",
      "\n",
      "Image sizes 480 train, 480 test\n",
      "Using 4 dataloader workers\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
      "       7/9     6.39G    0.1223   0.02254         0    0.1449        12       480\n",
      "               Class      Images     Targets           P           R      mAP@.5\n",
      "                 all          16          53           0           0    0.000628    0.000122\n",
      "Optimizer stripped from runs/exp22_yolov5x_800_480_200epoch/weights/last_yolov5x_800_480_200epoch.pt, 177.3MB\n",
      "8 epochs completed in 0.002 hours.\n",
      "\n",
      "Image sizes 96 train, 96 test\n",
      "Using 4 dataloader workers\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
      "       7/9      3.7G   0.04222   0.03999         0    0.0822         9        96\n",
      "               Class      Images     Targets           P           R      mAP@.5\n",
      "                 all          16           0           0           0           0           0\n",
      "Optimizer stripped from runs/exp23_yolov5x_800_96_200epoch/weights/last_yolov5x_800_96_200epoch.pt, 177.3MB\n",
      "8 epochs completed in 0.002 hours.\n",
      "\n",
      "\n",
      " detection evaluation start - train task\n",
      "Scanning images: 100%|████████████████████████| 46/46 [00:00<00:00, 2411.85it/s]\n",
      "100%|███████████████████████████████████████████| 46/46 [00:08<00:00,  5.17it/s]\n",
      "480 result - map: 0.011872218217033565\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - train task\n",
      "Scanning images: 100%|████████████████████████| 46/46 [00:00<00:00, 2516.70it/s]\n",
      "100%|███████████████████████████████████████████| 46/46 [00:07<00:00,  6.34it/s]\n",
      "96 result - map: 9.940357852882704e-06\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|███████████████████████████████████████████| 46/46 [00:01<00:00, 35.70it/s]\n",
      "100%|███████████████████████████████████████████| 48/48 [00:12<00:00,  3.93it/s]\n",
      "\n",
      "7 Epoch - RL Train mean AP: 0.0 / Efficiency: 0.828125 \n",
      "Train: 7 | Rw: -0.111698 | S: 3.312 | V: 0.685 | #: 10\n",
      "\n",
      " detection evaluation start - val task\n",
      "Scanning images: 100%|██████████████████████████| 9/9 [00:00<00:00, 1856.80it/s]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:01<00:00,  4.59it/s]\n",
      "480 result - map: 0.00038731327215848506\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - val task\n",
      "Scanning images: 100%|██████████████████████████| 9/9 [00:00<00:00, 2101.59it/s]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:01<00:00,  6.19it/s]\n",
      "96 result - map: 0.0\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:00<00:00, 24.68it/s]\n",
      "7 Epoch - RL Eval AP: 0.00038731327215848506 / Efficiency: 1.0 \n",
      "RL Eval - Rw: -0.0622 | S: 4.000 | V: 0.000 | #: 1\n",
      "\n",
      "\n",
      " detection evaluation start - test task\n",
      "Scanning images: 100%|████████████████████████| 19/19 [00:00<00:00, 1545.94it/s]\n",
      "100%|███████████████████████████████████████████| 19/19 [00:03<00:00,  4.80it/s]\n",
      "480 result - map: 0.04494708890572874\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - test task\n",
      "Scanning images: 100%|████████████████████████| 19/19 [00:00<00:00, 2350.72it/s]\n",
      "100%|███████████████████████████████████████████| 19/19 [00:03<00:00,  5.72it/s]\n",
      "96 result - map: 1.205317740511915e-05\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|███████████████████████████████████████████| 19/19 [00:00<00:00, 31.82it/s]\n",
      "7 Epoch - RL Test AP: 0.044910830841590935 / Efficiency: 1.0 \n",
      "RL Test - Rw: 0.3055 | S: 4.000 | V: 0.000 | #: 1\n",
      "\n",
      "Image sizes 480 train, 480 test\n",
      "Using 4 dataloader workers\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
      "       8/9     6.37G    0.1155   0.02177         0    0.1373        10       480\n",
      "               Class      Images     Targets           P           R      mAP@.5\n",
      "                 all          16          53           0           0     0.00729     0.00184\n",
      "Optimizer stripped from runs/exp22_yolov5x_800_480_200epoch/weights/last_yolov5x_800_480_200epoch.pt, 177.3MB\n",
      "9 epochs completed in 0.002 hours.\n",
      "\n",
      "Image sizes 96 train, 96 test\n",
      "Using 4 dataloader workers\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
      "       8/9     3.69G   0.04809   0.04069         0   0.08877         9        96\n",
      "               Class      Images     Targets           P           R      mAP@.5\n",
      "                 all          16           0           0           0           0           0\n",
      "Optimizer stripped from runs/exp23_yolov5x_800_96_200epoch/weights/last_yolov5x_800_96_200epoch.pt, 177.3MB\n",
      "9 epochs completed in 0.002 hours.\n",
      "\n",
      "\n",
      " detection evaluation start - train task\n",
      "Scanning images: 100%|████████████████████████| 46/46 [00:00<00:00, 2543.51it/s]\n",
      "100%|███████████████████████████████████████████| 46/46 [00:09<00:00,  4.79it/s]\n",
      "480 result - map: 0.018135525965220268\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - train task\n",
      "Scanning images: 100%|████████████████████████| 46/46 [00:00<00:00, 2492.84it/s]\n",
      "100%|███████████████████████████████████████████| 46/46 [00:07<00:00,  6.05it/s]\n",
      "96 result - map: 3.889352516067701e-05\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|███████████████████████████████████████████| 46/46 [00:01<00:00, 42.33it/s]\n",
      "100%|███████████████████████████████████████████| 54/54 [00:13<00:00,  3.97it/s]\n",
      "\n",
      "8 Epoch - RL Train mean AP: 0.00010831889081455805 / Efficiency: 0.8055555820465088 \n",
      "Train: 8 | Rw: -0.084826 | S: 3.222 | V: 0.813 | #: 13\n",
      "\n",
      " detection evaluation start - val task\n",
      "Scanning images: 100%|██████████████████████████| 9/9 [00:00<00:00, 1879.73it/s]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:01<00:00,  4.96it/s]\n",
      "480 result - map: 0.005558398846500737\n",
      "480 result - precison: 0.0\n",
      "480 result - recall: 0.0\n",
      "\n",
      " detection evaluation start - val task\n",
      "Scanning images: 100%|██████████████████████████| 9/9 [00:00<00:00, 2174.34it/s]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:01<00:00,  6.08it/s]\n",
      "96 result - map: 0.0\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:00<00:00, 24.28it/s]\n",
      "8 Epoch - RL Eval AP: 0.005557683692385024 / Efficiency: 1.0 \n",
      "RL Eval - Rw: 0.0201 | S: 4.000 | V: 0.000 | #: 1\n",
      "\n",
      "\n",
      " detection evaluation start - test task\n",
      "Scanning images: 100%|████████████████████████| 19/19 [00:00<00:00, 1968.91it/s]\n",
      "100%|███████████████████████████████████████████| 19/19 [00:03<00:00,  4.85it/s]\n",
      "480 result - map: 0.13170541491462542\n",
      "480 result - precison: 0.2333763616764262\n",
      "480 result - recall: 0.019230769230769232\n",
      "\n",
      " detection evaluation start - test task\n",
      "Scanning images: 100%|████████████████████████| 19/19 [00:00<00:00, 2499.43it/s]\n",
      "100%|███████████████████████████████████████████| 19/19 [00:03<00:00,  6.07it/s]\n",
      "96 result - map: 1.6565529748203947e-05\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 19/19 [00:00<00:00, 27.37it/s]\n",
      "8 Epoch - RL Test AP: 0.1317054108571668 / Efficiency: 1.0 \n",
      "RL Test - Rw: 0.6363 | S: 4.000 | V: 0.000 | #: 1\n",
      "\n",
      "Image sizes 480 train, 480 test\n",
      "Using 4 dataloader workers\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
      "       9/9     6.37G    0.1197   0.01993         0    0.1396         4       480\n",
      "               Class      Images     Targets           P           R      mAP@.5\n",
      "                 all          16          53       0.448      0.0189      0.0283     0.00696\n",
      "Optimizer stripped from runs/exp22_yolov5x_800_480_200epoch/weights/last_yolov5x_800_480_200epoch.pt, 177.3MB\n",
      "10 epochs completed in 0.002 hours.\n",
      "\n",
      "Image sizes 96 train, 96 test\n",
      "Using 4 dataloader workers\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
      "       9/9     3.69G   0.05059   0.04633         0   0.09691        14        96\n",
      "               Class      Images     Targets           P           R      mAP@.5\n",
      "                 all          16           0           0           0           0           0\n",
      "Optimizer stripped from runs/exp23_yolov5x_800_96_200epoch/weights/last_yolov5x_800_96_200epoch.pt, 177.3MB\n",
      "10 epochs completed in 0.002 hours.\n",
      "\n",
      "\n",
      " detection evaluation start - train task\n",
      "Scanning images: 100%|████████████████████████| 46/46 [00:00<00:00, 2107.18it/s]\n",
      "100%|███████████████████████████████████████████| 46/46 [00:09<00:00,  4.95it/s]\n",
      "480 result - map: 0.02847373223195951\n",
      "480 result - precison: 0.07328681652050344\n",
      "480 result - recall: 0.03816793893129771\n",
      "\n",
      " detection evaluation start - train task\n",
      "Scanning images: 100%|████████████████████████| 46/46 [00:00<00:00, 2397.31it/s]\n",
      "100%|███████████████████████████████████████████| 46/46 [00:07<00:00,  6.19it/s]\n",
      "96 result - map: 7.44456286242602e-05\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|███████████████████████████████████████████| 46/46 [00:01<00:00, 31.68it/s]\n",
      "100%|███████████████████████████████████████████| 60/60 [00:15<00:00,  3.99it/s]\n",
      "\n",
      "9 Epoch - RL Train mean AP: 0.0 / Efficiency: 0.8229166865348816 \n",
      "Train: 9 | Rw: -0.070484 | S: 3.292 | V: 0.726 | #: 13\n",
      "\n",
      " detection evaluation start - val task\n",
      "Scanning images: 100%|██████████████████████████| 9/9 [00:00<00:00, 1942.31it/s]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:01<00:00,  4.75it/s]\n",
      "480 result - map: 0.013908494093980048\n",
      "480 result - precison: 0.15939091689856913\n",
      "480 result - recall: 0.018867924528301886\n",
      "\n",
      " detection evaluation start - val task\n",
      "Scanning images: 100%|██████████████████████████| 9/9 [00:00<00:00, 2091.69it/s]\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:01<00:00,  6.23it/s]\n",
      "96 result - map: 0.0\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|█████████████████████████████████████████████| 9/9 [00:00<00:00, 32.59it/s]\n",
      "9 Epoch - RL Eval AP: 0.013908494093980048 / Efficiency: 1.0 \n",
      "RL Eval - Rw: 0.3620 | S: 4.000 | V: 0.000 | #: 1\n",
      "\n",
      "\n",
      " detection evaluation start - test task\n",
      "Scanning images: 100%|████████████████████████| 19/19 [00:00<00:00, 2011.00it/s]\n",
      "100%|███████████████████████████████████████████| 19/19 [00:04<00:00,  4.60it/s]\n",
      "480 result - map: 0.10790939933244514\n",
      "480 result - precison: 0.21107335569375588\n",
      "480 result - recall: 0.067177444894529\n",
      "\n",
      " detection evaluation start - test task\n",
      "Scanning images: 100%|████████████████████████| 19/19 [00:00<00:00, 2097.26it/s]\n",
      "100%|███████████████████████████████████████████| 19/19 [00:03<00:00,  5.94it/s]\n",
      "96 result - map: 8.818813468733299e-05\n",
      "96 result - precison: 0.0\n",
      "96 result - recall: 0.0\n",
      "100%|███████████████████████████████████████████| 19/19 [00:00<00:00, 25.65it/s]\n",
      "9 Epoch - RL Test AP: 0.10790812372451583 / Efficiency: 1.0 \n",
      "RL Test - Rw: 0.5054 | S: 4.000 | V: 0.000 | #: 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!python train.py --epochs 10\\\n",
    "--detector_batch_size 4\\\n",
    "--device 2\\\n",
    "--test_epoch 1\\\n",
    "--eval_epoch 1\\\n",
    "--step_batch_size 2\\\n",
    "--save_path save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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