Program_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0eb9de29-e1e9-4435-85e0-eea61be932af",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch\n",
    "import os\n",
    "filepath = 'dataLink/train/'\n",
    "!pip3 install ultralytics\n",
    "import ultralytics\n",
    "ultralytics.checks()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f3fac8e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ultralytics YOLOv8.1.16 🚀 Python-3.9.18 torch-2.2.0+cu121 CPU (AMD EPYC 7V13 64-Core Processor)\n",
      "Setup complete ✅ (256 CPUs, 251.2 GB RAM, 63.3/70.0 GB disk)\n"
     ]
    }
   ],
   "source": [
    "from ultralytics import YOLO\n",
    "model = YOLO('yolov8m.pt') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d8762d21-99a0-4aff-b83d-e3b897ce8bc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8m.pt to 'yolov8m.pt'...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 49.7M/49.7M [00:00<00:00, 368MB/s]\n"
     ]
    }
   ],
   "source": [
    " # load a pretrained model (recommended for training)\n",
    "# model = YOLO('yolov8n.yaml').load('yolov8n.pt')  # build from YAML and transfer weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4d3f8d64-79f6-4959-895a-8ba3604bff9f",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'datasets/train/labels.txt'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m filepath \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdatasets/train/labels.txt\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 2\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m \u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheader\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1024\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m   1011\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m   1012\u001b[0m     dialect,\n\u001b[1;32m   1013\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1020\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m   1021\u001b[0m )\n\u001b[1;32m   1022\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1024\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/io/parsers/readers.py:618\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    615\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    617\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 618\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    620\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    621\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1618\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1615\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1618\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1878\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1876\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m   1877\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1878\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1879\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1880\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1885\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1887\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1888\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1889\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/io/common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    869\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    870\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    871\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m    872\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 873\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    874\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    880\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    881\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m    882\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'datasets/train/labels.txt'"
     ]
    }
   ],
   "source": [
    "filepath = 'datasets/train/labels.txt'\n",
    "df = pd.read_csv(filepath, sep=' ', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b0e40c33-18e0-4d92-bf71-47f80a1f2131",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correcting the adjustment and applying the correct divisions\n",
    "df[2] = df[2] / 960 \n",
    "df[4] = df[4] / 960 \n",
    "df[3] = df[3] / 540 \n",
    "df[5] = df[5] / 540 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "60f9e950-7acd-40db-80e9-cb639c7ffb95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the output directory for demonstration purposes\n",
    "output_dir = \"datasets/train/labels/\"\n",
    "\n",
    "# Determine the maximum length for padding\n",
    "max_length = len(str(df[0].max()))\n",
    "\n",
    "# Process and prepare to save files for a subset of unique identifiers, excluding the first column\n",
    "for value in df[0].unique():  \n",
    "    # Filter rows for the current unique value and exclude the first column\n",
    "    subset_df = df[df[0] == value].iloc[:, 1:]\n",
    "    subset_df.iloc[:, 0] = subset_df.iloc[:, 0] - 1\n",
    "    \n",
    "    file_name = f\"{str(value).zfill(max_length)}.txt\"\n",
    "    \n",
    "    file_path = os.path.join(output_dir, file_name)\n",
    "    subset_df.to_csv(file_path, header=False, index=False, sep=' ')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2a54bd25-f9d2-443a-a6e5-42305da29179",
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath_val = 'datasets/val/labels.txt'\n",
    "df_val = pd.read_csv(filepath_val, sep=' ', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "556da4cf-3700-4a64-af80-e4f03d9770ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correcting the adjustment and applying the correct divisions\n",
    "df_val[2] = df_val[2] / 960 \n",
    "df_val[4] = df_val[4] / 960 \n",
    "df_val[3] = df_val[3] / 540 \n",
    "df_val[5] = df_val[5] / 540 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c167b0f2-fb8b-4c6e-a068-4b012186d185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>0.764583</td>\n",
       "      <td>0.316667</td>\n",
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       "      <td>0.092593</td>\n",
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       "   1         2         3         4         5\n",
       "0  1  0.764583  0.316667  0.095833  0.092593"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the output directory for demonstration purposes\n",
    "output_dir = \"datasets/val/labels/\"\n",
    "max_length = 5\n",
    "for value in df_val[0].unique():  \n",
    "    # Filter rows for the current unique value and exclude the first column\n",
    "    subset_df = df_val[df_val[0] == value].iloc[:, 1:]\n",
    "    subset_df.iloc[:, 0] = subset_df.iloc[:, 0] - 1\n",
    "    \n",
    "    file_name = f\"{str(value).zfill(max_length)}.txt\"\n",
    "    file_path = os.path.join(output_dir, file_name)\n",
    "    \n",
    "    subset_df.to_csv(file_path, header=False, index=False, sep=' ')\n",
    "\n",
    "subset_df_example = df_val[df_val[0] == df_val[0].unique()[0]].iloc[:, 1:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7e541b2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Freezing model layers for faster training\n",
    "def freeze_layer(trainer):\n",
    "    model = trainer.model\n",
    "    num_freeze = 10\n",
    "    print(f\"Freezing {num_freeze} layers\")\n",
    "    freeze = [f'model.{x}.' for x in range(num_freeze)]  # layers to freeze \n",
    "    for k, v in model.named_parameters(): \n",
    "        v.requires_grad = True  # train all layers \n",
    "        if any(x in k for x in freeze): \n",
    "            print(f'freezing {k}') \n",
    "            v.requires_grad = False \n",
    "    print(f\"{num_freeze} layers are freezed.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bcde3670-ab36-4e96-a0f2-a3dbd4a37a0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ultralytics YOLOv8.1.16 🚀 Python-3.8.17 torch-2.0.1 CUDA:0 (NVIDIA GeForce RTX 4090, 24214MiB)\n",
      "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8m.pt, data=data.yaml, epochs=75, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train27, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train27\n",
      "Overriding model.yaml nc=80 with nc=3\n",
      "\n",
      "                   from  n    params  module                                       arguments                     \n",
      "  0                  -1  1      1392  ultralytics.nn.modules.conv.Conv             [3, 48, 3, 2]                 \n",
      "  1                  -1  1     41664  ultralytics.nn.modules.conv.Conv             [48, 96, 3, 2]                \n",
      "  2                  -1  2    111360  ultralytics.nn.modules.block.C2f             [96, 96, 2, True]             \n",
      "  3                  -1  1    166272  ultralytics.nn.modules.conv.Conv             [96, 192, 3, 2]               \n",
      "  4                  -1  4    813312  ultralytics.nn.modules.block.C2f             [192, 192, 4, True]           \n",
      "  5                  -1  1    664320  ultralytics.nn.modules.conv.Conv             [192, 384, 3, 2]              \n",
      "  6                  -1  4   3248640  ultralytics.nn.modules.block.C2f             [384, 384, 4, True]           \n",
      "  7                  -1  1   1991808  ultralytics.nn.modules.conv.Conv             [384, 576, 3, 2]              \n",
      "  8                  -1  2   3985920  ultralytics.nn.modules.block.C2f             [576, 576, 2, True]           \n",
      "  9                  -1  1    831168  ultralytics.nn.modules.block.SPPF            [576, 576, 5]                 \n",
      " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
      " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 12                  -1  2   1993728  ultralytics.nn.modules.block.C2f             [960, 384, 2]                 \n",
      " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
      " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 15                  -1  2    517632  ultralytics.nn.modules.block.C2f             [576, 192, 2]                 \n",
      " 16                  -1  1    332160  ultralytics.nn.modules.conv.Conv             [192, 192, 3, 2]              \n",
      " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 18                  -1  2   1846272  ultralytics.nn.modules.block.C2f             [576, 384, 2]                 \n",
      " 19                  -1  1   1327872  ultralytics.nn.modules.conv.Conv             [384, 384, 3, 2]              \n",
      " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 21                  -1  2   4207104  ultralytics.nn.modules.block.C2f             [960, 576, 2]                 \n",
      " 22        [15, 18, 21]  1   3777433  ultralytics.nn.modules.head.Detect           [3, [192, 384, 576]]          \n",
      "Model summary: 295 layers, 25858057 parameters, 25858041 gradients, 79.1 GFLOPs\n",
      "\n",
      "Transferred 469/475 items from pretrained weights\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train27', view at http://localhost:6006/\n",
      "Freezing layer 'model.22.dfl.conv.weight'\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/labels.cache... 14000 images, 0 backgrounds, 0 corrupt: 100%|██████████| 14000/14000 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/00288.jpeg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/00491.jpeg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/00787.jpeg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/00802.jpeg: 2 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/01505.jpeg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/02366.jpeg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/02431.jpeg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/02549.jpeg: 2 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/02586.jpeg: 1 duplicate labels removed\n",
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      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/03171.jpeg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/03313.jpeg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/03466.jpeg: 14 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/04269.jpeg: 1 duplicate labels removed\n",
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      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/09451.jpeg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/09542.jpeg: 1 duplicate labels removed\n",
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      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/10705.jpeg: 2 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/11764.jpeg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/12460.jpeg: 4 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/12461.jpeg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/12811.jpeg: 3 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/13401.jpeg: 3 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/13536.jpeg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/train/images/13603.jpeg: 2 duplicate labels removed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/val/labels.cache... 2000 images, 0 backgrounds, 0 corrupt: 100%|██████████| 2000/2000 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/val/images/00425.jpeg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/val/images/00954.jpeg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/val/images/01642.jpeg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/val/images/01755.jpeg: 1 duplicate labels removed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting labels to runs/detect/train27/labels.jpg... \n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01, momentum=0.9) with parameter groups 77 weight(decay=0.0), 84 weight(decay=0.0005), 83 bias(decay=0.0)\n",
      "Freezing 10 layers\n",
      "freezing model.0.conv.weight\n",
      "freezing model.0.bn.weight\n",
      "freezing model.0.bn.bias\n",
      "freezing model.1.conv.weight\n",
      "freezing model.1.bn.weight\n",
      "freezing model.1.bn.bias\n",
      "freezing model.2.cv1.conv.weight\n",
      "freezing model.2.cv1.bn.weight\n",
      "freezing model.2.cv1.bn.bias\n",
      "freezing model.2.cv2.conv.weight\n",
      "freezing model.2.cv2.bn.weight\n",
      "freezing model.2.cv2.bn.bias\n",
      "freezing model.2.m.0.cv1.conv.weight\n",
      "freezing model.2.m.0.cv1.bn.weight\n",
      "freezing model.2.m.0.cv1.bn.bias\n",
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      "10 layers are freezed.\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/detect/train27\u001b[0m\n",
      "Starting training for 75 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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      "       1/75      3.74G      1.404     0.9787      1.152         82        640: 100%|██████████| 875/875 [02:08<00:00,  6.81it/s]\n",
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      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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      "       2/75      3.83G      1.368     0.8057      1.131        133        640: 100%|██████████| 875/875 [02:04<00:00,  7.04it/s]\n",
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      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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      "       3/75      3.79G      1.374     0.8098      1.147         97        640: 100%|██████████| 875/875 [02:03<00:00,  7.09it/s]\n",
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      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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      "       4/75       3.8G      1.364     0.7895      1.153        137        640: 100%|██████████| 875/875 [02:02<00:00,  7.14it/s]\n",
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      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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      "       5/75       3.8G      1.342     0.7628      1.152        183        640: 100%|██████████| 875/875 [02:02<00:00,  7.14it/s]\n",
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      "                   all       2000       9061      0.523       0.68      0.604      0.372\n"
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      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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      "       6/75      3.81G       1.33     0.7481      1.149        148        640: 100%|██████████| 875/875 [02:02<00:00,  7.12it/s]\n",
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      "                   all       2000       9061      0.581      0.634      0.624      0.399\n"
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      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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      "       7/75       3.8G      1.312     0.7326      1.139         95        640: 100%|██████████| 875/875 [02:02<00:00,  7.16it/s]\n",
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      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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      "       8/75      3.83G      1.309     0.7248      1.137        106        640: 100%|██████████| 875/875 [02:03<00:00,  7.11it/s]\n",
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      "                   all       2000       9061      0.515       0.64       0.59      0.376\n"
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      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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      "       9/75       3.8G      1.308     0.7258      1.137        118        640: 100%|██████████| 875/875 [02:02<00:00,  7.14it/s]\n",
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      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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      "      10/75      3.84G      1.301     0.7114      1.135        129        640: 100%|██████████| 875/875 [02:02<00:00,  7.15it/s]\n",
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      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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      "      11/75      3.83G      1.294     0.7091      1.127        188        640: 100%|██████████| 875/875 [02:02<00:00,  7.15it/s]\n",
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      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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      "      12/75      3.81G       1.29     0.7042      1.129        165        640: 100%|██████████| 875/875 [02:02<00:00,  7.15it/s]\n",
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      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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      "      13/75       3.8G      1.282     0.6989      1.123        161        640: 100%|██████████| 875/875 [02:02<00:00,  7.14it/s]\n",
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      "      14/75      3.81G      1.284      0.702      1.124        157        640: 100%|██████████| 875/875 [02:02<00:00,  7.14it/s]\n",
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      "      15/75      3.83G      1.286     0.6927      1.119        139        640: 100%|██████████| 875/875 [02:02<00:00,  7.15it/s]\n",
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      "      16/75      3.81G      1.276     0.6884      1.116        129        640: 100%|██████████| 875/875 [02:03<00:00,  7.10it/s]\n",
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      "      17/75       3.8G      1.272     0.6869      1.115        112        640: 100%|██████████| 875/875 [02:02<00:00,  7.13it/s]\n",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      18/75      3.81G      1.271     0.6874      1.117        136        640: 100%|██████████| 875/875 [02:02<00:00,  7.14it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.73it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061       0.58      0.637      0.646      0.419\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      19/75      3.82G      1.266     0.6834      1.114        135        640: 100%|██████████| 875/875 [02:02<00:00,  7.12it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061       0.53      0.713      0.645      0.425\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      20/75      3.81G      1.266     0.6804      1.112        141        640: 100%|██████████| 875/875 [02:02<00:00,  7.11it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.78it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.567       0.68      0.643      0.422\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      21/75       3.8G      1.263     0.6761      1.108        111        640: 100%|██████████| 875/875 [02:02<00:00,  7.14it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.79it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.561       0.69      0.636      0.419\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      22/75       3.8G      1.259      0.679      1.111        110        640: 100%|██████████| 875/875 [02:02<00:00,  7.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.70it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.587       0.65      0.643      0.424\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      23/75      3.81G       1.26     0.6752      1.112        120        640: 100%|██████████| 875/875 [02:03<00:00,  7.10it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.77it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061       0.64      0.593      0.655      0.432\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      24/75      3.84G      1.247      0.671      1.106        198        640: 100%|██████████| 875/875 [02:02<00:00,  7.13it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.71it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.567       0.68      0.652       0.43\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      25/75       3.8G      1.253     0.6711       1.11        156        640: 100%|██████████| 875/875 [02:02<00:00,  7.13it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.73it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.576      0.703      0.668      0.442\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      26/75       3.8G      1.249     0.6688      1.105        102        640: 100%|██████████| 875/875 [02:02<00:00,  7.12it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.67it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.537      0.723      0.651      0.433\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      27/75      3.81G      1.252     0.6744      1.103         95        640: 100%|██████████| 875/875 [02:03<00:00,  7.10it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.597      0.643      0.656      0.443\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
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    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      28/75      3.83G      1.248     0.6661      1.106        152        640: 100%|██████████| 875/875 [02:03<00:00,  7.06it/s]\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061       0.58      0.657      0.649      0.433\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
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    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      29/75       3.8G      1.246     0.6629      1.106        134        640: 100%|██████████| 875/875 [02:05<00:00,  6.97it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.618      0.618      0.647      0.431\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      30/75      3.83G      1.243     0.6653      1.102        161        640: 100%|██████████| 875/875 [02:07<00:00,  6.88it/s]\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.549      0.676      0.644       0.43\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
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    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      31/75      3.84G      1.247     0.6628      1.106        146        640: 100%|██████████| 875/875 [02:07<00:00,  6.88it/s]\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.626      0.598      0.645      0.431\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
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    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      32/75      3.84G      1.242     0.6636      1.107        160        640: 100%|██████████| 875/875 [02:05<00:00,  6.98it/s]\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.557      0.709      0.648      0.433\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
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    {
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      33/75       3.8G      1.234     0.6567      1.099        135        640: 100%|██████████| 875/875 [02:03<00:00,  7.09it/s]\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061       0.59      0.656      0.641      0.427\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
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    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      34/75      3.82G      1.234     0.6604      1.098        141        640: 100%|██████████| 875/875 [02:08<00:00,  6.83it/s]\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.571      0.679      0.641      0.427\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
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    },
    {
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "      35/75      3.81G      1.235     0.6572      1.096        112        640: 100%|██████████| 875/875 [02:05<00:00,  6.99it/s]\n",
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    {
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     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.651      0.567      0.637      0.423\n"
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    {
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     "output_type": "stream",
     "text": [
      "\n"
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    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "      36/75      3.81G      1.232     0.6526      1.098        101        640: 100%|██████████| 875/875 [02:04<00:00,  7.05it/s]\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.602      0.643      0.644       0.43\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
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    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      37/75       3.8G      1.229     0.6518      1.094        107        640: 100%|██████████| 875/875 [02:03<00:00,  7.07it/s]\n",
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.566      0.679      0.646      0.432\n"
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    {
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     "output_type": "stream",
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      "\n"
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    {
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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     "output_type": "stream",
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      "      38/75      3.79G       1.23     0.6537      1.098        194        640: 100%|██████████| 875/875 [02:03<00:00,  7.07it/s]\n",
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     "output_type": "stream",
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      "                   all       2000       9061       0.57      0.664      0.649      0.432\n"
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     "output_type": "stream",
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      "\n"
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    {
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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      "      39/75      3.84G      1.228     0.6495      1.098        124        640: 100%|██████████| 875/875 [02:03<00:00,  7.07it/s]\n",
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      "                   all       2000       9061      0.573      0.684      0.659      0.442\n"
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      "\n"
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      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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      "      40/75      3.81G      1.227     0.6502        1.1        144        640: 100%|██████████| 875/875 [02:03<00:00,  7.08it/s]\n",
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      "\n"
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      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "      41/75       3.8G      1.227     0.6505      1.092        142        640: 100%|██████████| 875/875 [02:04<00:00,  7.05it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.71it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.594      0.656      0.653      0.435\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      42/75      3.81G      1.222     0.6446      1.092        140        640: 100%|██████████| 875/875 [02:04<00:00,  7.01it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.63it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.565       0.67      0.645       0.43\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      43/75      3.81G      1.221      0.645      1.099        113        640: 100%|██████████| 875/875 [02:03<00:00,  7.06it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.68it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.539      0.712      0.654      0.434\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      44/75      3.81G       1.22     0.6426      1.091        159        640: 100%|██████████| 875/875 [02:03<00:00,  7.08it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.73it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.617      0.622      0.646      0.426\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      45/75       3.8G      1.214     0.6428      1.092        167        640: 100%|██████████| 875/875 [02:03<00:00,  7.06it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.71it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.613      0.631      0.649      0.428\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
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    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      46/75       3.8G      1.209     0.6371      1.093         99        640: 100%|██████████| 875/875 [02:03<00:00,  7.07it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.73it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.556      0.698      0.645      0.429\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
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    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      47/75      3.84G      1.212      0.639      1.092        139        640: 100%|██████████| 875/875 [02:03<00:00,  7.09it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.539      0.702      0.641      0.428\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
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    {
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      48/75      3.83G       1.22     0.6419      1.092        129        640: 100%|██████████| 875/875 [02:03<00:00,  7.07it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.71it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.556      0.677      0.643       0.43\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
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    },
    {
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      49/75       3.8G      1.207     0.6352      1.086        103        640: 100%|██████████| 875/875 [02:03<00:00,  7.08it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.585      0.663      0.653      0.437\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
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    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      50/75       3.8G      1.208      0.637      1.085        120        640: 100%|██████████| 875/875 [02:03<00:00,  7.09it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.602      0.643       0.66       0.44\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
     ]
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    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      51/75      3.81G      1.207      0.632      1.083        111        640: 100%|██████████| 875/875 [02:03<00:00,  7.07it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.598      0.666       0.66      0.438\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      52/75      3.83G      1.205      0.633      1.086        123        640: 100%|██████████| 875/875 [02:03<00:00,  7.06it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.599      0.657      0.659      0.439\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      53/75       3.8G      1.207     0.6305      1.089        139        640: 100%|██████████| 875/875 [02:03<00:00,  7.07it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061       0.62      0.658      0.662      0.439\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      54/75      3.81G      1.202      0.629      1.085        166        640: 100%|██████████| 875/875 [02:03<00:00,  7.08it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.614      0.673      0.663       0.44\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
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    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      55/75      3.81G      1.198     0.6257      1.081        129        640: 100%|██████████| 875/875 [02:03<00:00,  7.09it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.591      0.678      0.664      0.441\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      56/75       3.8G      1.197     0.6281      1.081        161        640: 100%|██████████| 875/875 [02:03<00:00,  7.09it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.603      0.669      0.663      0.443\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      57/75       3.8G      1.199     0.6285      1.077        160        640: 100%|██████████| 875/875 [02:03<00:00,  7.07it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.605      0.638      0.661      0.442\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      58/75      3.81G      1.188     0.6223      1.076         98        640: 100%|██████████| 875/875 [02:03<00:00,  7.08it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061       0.61      0.635      0.655      0.438\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      59/75      3.81G      1.188     0.6226      1.076        144        640: 100%|██████████| 875/875 [02:03<00:00,  7.09it/s]\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.589      0.634      0.653      0.438\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      60/75      3.81G       1.19     0.6216      1.079        129        640: 100%|██████████| 875/875 [02:03<00:00,  7.08it/s]\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.585      0.643       0.65      0.437\n"
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    {
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     "output_type": "stream",
     "text": [
      "\n"
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    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      61/75       3.8G      1.187     0.6218      1.069        108        640: 100%|██████████| 875/875 [02:03<00:00,  7.08it/s]\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.584      0.655      0.649      0.436\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
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    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      62/75      3.81G      1.185     0.6186      1.072        188        640: 100%|██████████| 875/875 [02:03<00:00,  7.07it/s]\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.588      0.653       0.65      0.436\n"
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    {
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     "output_type": "stream",
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      "\n"
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    {
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
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      "      63/75      3.81G      1.188     0.6173      1.074        108        640: 100%|██████████| 875/875 [02:03<00:00,  7.08it/s]\n",
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     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.597      0.637      0.648      0.435\n"
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      "\n"
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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      "      64/75      3.81G      1.184     0.6173      1.072        116        640: 100%|██████████| 875/875 [02:03<00:00,  7.08it/s]\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.585      0.649       0.65      0.434\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      65/75       3.8G       1.18     0.6151      1.071         85        640: 100%|██████████| 875/875 [02:03<00:00,  7.08it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.68it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.584      0.658      0.648      0.434\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Closing dataloader mosaic\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      66/75      3.81G      1.201     0.6144      1.122         72        640: 100%|██████████| 875/875 [02:04<00:00,  7.03it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.65it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.555       0.68      0.645      0.432\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      67/75      3.84G      1.195     0.6098      1.115         81        640: 100%|██████████| 875/875 [02:03<00:00,  7.09it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.75it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.551      0.684      0.645      0.432\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      68/75      3.81G       1.19      0.604      1.107         87        640: 100%|██████████| 875/875 [02:03<00:00,  7.08it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.68it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.552       0.68      0.645      0.433\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      69/75       3.8G      1.189     0.6047      1.111         73        640: 100%|██████████| 875/875 [02:03<00:00,  7.09it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.77it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.553      0.676      0.645      0.431\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      70/75      3.81G      1.184     0.6028       1.11        104        640: 100%|██████████| 875/875 [02:03<00:00,  7.10it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.74it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.546      0.674      0.642       0.43\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      71/75      3.82G      1.183     0.5989      1.106         55        640: 100%|██████████| 875/875 [02:03<00:00,  7.09it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.67it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.545      0.682      0.641      0.429\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      72/75      3.81G      1.178     0.5993      1.104         81        640: 100%|██████████| 875/875 [02:03<00:00,  7.10it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.545      0.683      0.639      0.427\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      73/75       3.8G      1.179     0.5946      1.105         83        640: 100%|██████████| 875/875 [02:03<00:00,  7.08it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.74it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.545      0.688      0.637      0.426\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      74/75      3.81G      1.175     0.5933        1.1         95        640: 100%|██████████| 875/875 [02:03<00:00,  7.09it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.66it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.543      0.688      0.636      0.426\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      75/75      3.85G      1.171     0.5935        1.1         58        640: 100%|██████████| 875/875 [02:03<00:00,  7.08it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:05<00:00, 10.72it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.543       0.69      0.637      0.427\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "75 epochs completed in 2.721 hours.\n",
      "Optimizer stripped from runs/detect/train27/weights/last.pt, 52.0MB\n",
      "Optimizer stripped from runs/detect/train27/weights/best.pt, 52.0MB\n",
      "\n",
      "Validating runs/detect/train27/weights/best.pt...\n",
      "Ultralytics YOLOv8.1.16 🚀 Python-3.8.17 torch-2.0.1 CUDA:0 (NVIDIA GeForce RTX 4090, 24214MiB)\n",
      "Model summary (fused): 218 layers, 25841497 parameters, 0 gradients, 78.7 GFLOPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 63/63 [00:06<00:00,  9.79it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.602      0.669      0.663      0.443\n",
      "                   car       2000       8781      0.791      0.884      0.899      0.577\n",
      "          medium truck       2000        163      0.505      0.644      0.551      0.379\n",
      "           large truck       2000        117       0.51      0.479       0.54      0.373\n",
      "Speed: 0.1ms preprocess, 2.0ms inference, 0.0ms loss, 0.2ms postprocess per image\n",
      "Results saved to \u001b[1mruns/detect/train27\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "model = YOLO('yolov8m.pt')\n",
    "model.add_callback(\"on_train_start\", freeze_layer)\n",
    "results = model.train(data='data.yaml', epochs=75, imgsz=640)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "30e49ed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"tuneStatus\">\n",
       "  <div style=\"display: flex;flex-direction: row\">\n",
       "    <div style=\"display: flex;flex-direction: column;\">\n",
       "      <h3>Tune Status</h3>\n",
       "      <table>\n",
       "<tbody>\n",
       "<tr><td>Current time:</td><td>2024-02-20 21:38:01</td></tr>\n",
       "<tr><td>Running for: </td><td>00:00:45.39        </td></tr>\n",
       "<tr><td>Memory:      </td><td>33.5/125.6 GiB     </td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "    </div>\n",
       "    <div class=\"vDivider\"></div>\n",
       "    <div class=\"systemInfo\">\n",
       "      <h3>System Info</h3>\n",
       "      Using AsyncHyperBand: num_stopped=0<br>Bracket: Iter 10.000: None<br>Logical resource usage: 8.0/32 CPUs, 1.0/1 GPUs (0.0/1.0 accelerator_type:G)\n",
       "    </div>\n",
       "    \n",
       "  </div>\n",
       "  <div class=\"hDivider\"></div>\n",
       "  <div class=\"trialStatus\">\n",
       "    <h3>Trial Status</h3>\n",
       "    <table>\n",
       "<thead>\n",
       "<tr><th>Trial name       </th><th>status  </th><th>loc               </th><th style=\"text-align: right;\">      box</th><th style=\"text-align: right;\">     cls</th><th style=\"text-align: right;\">  copy_paste</th><th style=\"text-align: right;\">  degrees</th><th style=\"text-align: right;\">  fliplr</th><th style=\"text-align: right;\">   flipud</th><th style=\"text-align: right;\">    hsv_h</th><th style=\"text-align: right;\">    hsv_s</th><th style=\"text-align: right;\">   hsv_v</th><th style=\"text-align: right;\">       lr0</th><th style=\"text-align: right;\">     lrf</th><th style=\"text-align: right;\">    mixup</th><th style=\"text-align: right;\">  momentum</th><th style=\"text-align: right;\">  mosaic</th><th style=\"text-align: right;\">  perspective</th><th style=\"text-align: right;\">   scale</th><th style=\"text-align: right;\">   shear</th><th style=\"text-align: right;\">  translate</th><th style=\"text-align: right;\">  warmup_epochs</th><th style=\"text-align: right;\">  warmup_momentum</th><th style=\"text-align: right;\">  weight_decay</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>_tune_45af3_00000</td><td>RUNNING </td><td>172.16.3.197:22034</td><td style=\"text-align: right;\">0.0987657</td><td style=\"text-align: right;\">3.58874 </td><td style=\"text-align: right;\">    0.978618</td><td style=\"text-align: right;\">23.8003  </td><td style=\"text-align: right;\">0.83262 </td><td style=\"text-align: right;\">0.0202184</td><td style=\"text-align: right;\">0.0963663</td><td style=\"text-align: right;\">0.345097 </td><td style=\"text-align: right;\">0.712553</td><td style=\"text-align: right;\">0.0548859 </td><td style=\"text-align: right;\">0.718037</td><td style=\"text-align: right;\">0.870012 </td><td style=\"text-align: right;\">  0.82905 </td><td style=\"text-align: right;\">0.778157</td><td style=\"text-align: right;\">  8.71293e-05</td><td style=\"text-align: right;\">0.833037</td><td style=\"text-align: right;\">0.710361</td><td style=\"text-align: right;\">  0.51124  </td><td style=\"text-align: right;\">       2.11827 </td><td style=\"text-align: right;\">        0.613599 </td><td style=\"text-align: right;\">   0.000544883</td></tr>\n",
       "<tr><td>_tune_45af3_00001</td><td>PENDING </td><td>                  </td><td style=\"text-align: right;\">0.113933 </td><td style=\"text-align: right;\">1.77572 </td><td style=\"text-align: right;\">    0.697631</td><td style=\"text-align: right;\">25.5795  </td><td style=\"text-align: right;\">0.68182 </td><td style=\"text-align: right;\">0.943748 </td><td style=\"text-align: right;\">0.0264556</td><td style=\"text-align: right;\">0.69681  </td><td style=\"text-align: right;\">0.410535</td><td style=\"text-align: right;\">0.0461533 </td><td style=\"text-align: right;\">0.782724</td><td style=\"text-align: right;\">0.437032 </td><td style=\"text-align: right;\">  0.644944</td><td style=\"text-align: right;\">0.359508</td><td style=\"text-align: right;\">  0.000616934</td><td style=\"text-align: right;\">0.555872</td><td style=\"text-align: right;\">6.12096 </td><td style=\"text-align: right;\">  0.0169108</td><td style=\"text-align: right;\">       0.716766</td><td style=\"text-align: right;\">        0.897435 </td><td style=\"text-align: right;\">   0.000639921</td></tr>\n",
       "<tr><td>_tune_45af3_00002</td><td>PENDING </td><td>                  </td><td style=\"text-align: right;\">0.122635 </td><td style=\"text-align: right;\">1.86669 </td><td style=\"text-align: right;\">    0.196582</td><td style=\"text-align: right;\"> 7.25893 </td><td style=\"text-align: right;\">0.110375</td><td style=\"text-align: right;\">0.15897  </td><td style=\"text-align: right;\">0.0988374</td><td style=\"text-align: right;\">0.0918403</td><td style=\"text-align: right;\">0.187989</td><td style=\"text-align: right;\">0.06668   </td><td style=\"text-align: right;\">0.673931</td><td style=\"text-align: right;\">0.138183 </td><td style=\"text-align: right;\">  0.679945</td><td style=\"text-align: right;\">0.65633 </td><td style=\"text-align: right;\">  0.000244426</td><td style=\"text-align: right;\">0.227962</td><td style=\"text-align: right;\">4.66311 </td><td style=\"text-align: right;\">  0.587797 </td><td style=\"text-align: right;\">       1.57714 </td><td style=\"text-align: right;\">        0.345525 </td><td style=\"text-align: right;\">   0.000128926</td></tr>\n",
       "<tr><td>_tune_45af3_00003</td><td>PENDING </td><td>                  </td><td style=\"text-align: right;\">0.195817 </td><td style=\"text-align: right;\">2.49841 </td><td style=\"text-align: right;\">    0.523248</td><td style=\"text-align: right;\"> 5.40885 </td><td style=\"text-align: right;\">0.692472</td><td style=\"text-align: right;\">0.0641475</td><td style=\"text-align: right;\">0.0739264</td><td style=\"text-align: right;\">0.035269 </td><td style=\"text-align: right;\">0.254526</td><td style=\"text-align: right;\">0.0821011 </td><td style=\"text-align: right;\">0.10613 </td><td style=\"text-align: right;\">0.265389 </td><td style=\"text-align: right;\">  0.918419</td><td style=\"text-align: right;\">0.566601</td><td style=\"text-align: right;\">  0.000414263</td><td style=\"text-align: right;\">0.106855</td><td style=\"text-align: right;\">3.17983 </td><td style=\"text-align: right;\">  0.266526 </td><td style=\"text-align: right;\">       4.8823  </td><td style=\"text-align: right;\">        0.445219 </td><td style=\"text-align: right;\">   9.60984e-05</td></tr>\n",
       "<tr><td>_tune_45af3_00004</td><td>PENDING </td><td>                  </td><td style=\"text-align: right;\">0.0720931</td><td style=\"text-align: right;\">0.896127</td><td style=\"text-align: right;\">    0.223082</td><td style=\"text-align: right;\"> 0.211296</td><td style=\"text-align: right;\">0.576157</td><td style=\"text-align: right;\">0.248753 </td><td style=\"text-align: right;\">0.0586513</td><td style=\"text-align: right;\">0.0180968</td><td style=\"text-align: right;\">0.746046</td><td style=\"text-align: right;\">0.0575989 </td><td style=\"text-align: right;\">0.930003</td><td style=\"text-align: right;\">0.572252 </td><td style=\"text-align: right;\">  0.721056</td><td style=\"text-align: right;\">0.592042</td><td style=\"text-align: right;\">  0.000962189</td><td style=\"text-align: right;\">0.243007</td><td style=\"text-align: right;\">7.35194 </td><td style=\"text-align: right;\">  0.610035 </td><td style=\"text-align: right;\">       0.658989</td><td style=\"text-align: right;\">        0.680511 </td><td style=\"text-align: right;\">   0.00066741 </td></tr>\n",
       "<tr><td>_tune_45af3_00005</td><td>PENDING </td><td>                  </td><td style=\"text-align: right;\">0.178599 </td><td style=\"text-align: right;\">2.40884 </td><td style=\"text-align: right;\">    0.618015</td><td style=\"text-align: right;\">22.5596  </td><td style=\"text-align: right;\">0.301575</td><td style=\"text-align: right;\">0.0191932</td><td style=\"text-align: right;\">0.0881735</td><td style=\"text-align: right;\">0.623278 </td><td style=\"text-align: right;\">0.652729</td><td style=\"text-align: right;\">0.0447181 </td><td style=\"text-align: right;\">0.847945</td><td style=\"text-align: right;\">0.290078 </td><td style=\"text-align: right;\">  0.865802</td><td style=\"text-align: right;\">0.660174</td><td style=\"text-align: right;\">  0.000606393</td><td style=\"text-align: right;\">0.579591</td><td style=\"text-align: right;\">4.23855 </td><td style=\"text-align: right;\">  0.860475 </td><td style=\"text-align: right;\">       4.06899 </td><td style=\"text-align: right;\">        0.37668  </td><td style=\"text-align: right;\">   0.000297437</td></tr>\n",
       "<tr><td>_tune_45af3_00006</td><td>PENDING </td><td>                  </td><td style=\"text-align: right;\">0.137379 </td><td style=\"text-align: right;\">1.83939 </td><td style=\"text-align: right;\">    0.162493</td><td style=\"text-align: right;\">40.1366  </td><td style=\"text-align: right;\">0.998847</td><td style=\"text-align: right;\">0.714241 </td><td style=\"text-align: right;\">0.0896547</td><td style=\"text-align: right;\">0.330806 </td><td style=\"text-align: right;\">0.392278</td><td style=\"text-align: right;\">0.0135561 </td><td style=\"text-align: right;\">0.3053  </td><td style=\"text-align: right;\">0.868126 </td><td style=\"text-align: right;\">  0.816587</td><td style=\"text-align: right;\">0.149448</td><td style=\"text-align: right;\">  0.000919483</td><td style=\"text-align: right;\">0.6335  </td><td style=\"text-align: right;\">1.00227 </td><td style=\"text-align: right;\">  0.725575 </td><td style=\"text-align: right;\">       2.87163 </td><td style=\"text-align: right;\">        0.620541 </td><td style=\"text-align: right;\">   0.000590873</td></tr>\n",
       "<tr><td>_tune_45af3_00007</td><td>PENDING </td><td>                  </td><td style=\"text-align: right;\">0.145537 </td><td style=\"text-align: right;\">1.92346 </td><td style=\"text-align: right;\">    0.793698</td><td style=\"text-align: right;\">38.5112  </td><td style=\"text-align: right;\">0.054338</td><td style=\"text-align: right;\">0.521037 </td><td style=\"text-align: right;\">0.0722056</td><td style=\"text-align: right;\">0.779744 </td><td style=\"text-align: right;\">0.877969</td><td style=\"text-align: right;\">0.0123908 </td><td style=\"text-align: right;\">0.849528</td><td style=\"text-align: right;\">0.0185218</td><td style=\"text-align: right;\">  0.906781</td><td style=\"text-align: right;\">0.199997</td><td style=\"text-align: right;\">  0.00017163 </td><td style=\"text-align: right;\">0.32398 </td><td style=\"text-align: right;\">7.29991 </td><td style=\"text-align: right;\">  0.0105427</td><td style=\"text-align: right;\">       2.03592 </td><td style=\"text-align: right;\">        0.0657086</td><td style=\"text-align: right;\">   0.000569101</td></tr>\n",
       "<tr><td>_tune_45af3_00008</td><td>PENDING </td><td>                  </td><td style=\"text-align: right;\">0.123901 </td><td style=\"text-align: right;\">1.10399 </td><td style=\"text-align: right;\">    0.227415</td><td style=\"text-align: right;\">26.5459  </td><td style=\"text-align: right;\">0.944372</td><td style=\"text-align: right;\">0.186193 </td><td style=\"text-align: right;\">0.0934214</td><td style=\"text-align: right;\">0.552569 </td><td style=\"text-align: right;\">0.48207 </td><td style=\"text-align: right;\">0.0345417 </td><td style=\"text-align: right;\">0.9288  </td><td style=\"text-align: right;\">0.490459 </td><td style=\"text-align: right;\">  0.867677</td><td style=\"text-align: right;\">0.739551</td><td style=\"text-align: right;\">  0.000209844</td><td style=\"text-align: right;\">0.28075 </td><td style=\"text-align: right;\">3.98221 </td><td style=\"text-align: right;\">  0.65711  </td><td style=\"text-align: right;\">       0.823471</td><td style=\"text-align: right;\">        0.590404 </td><td style=\"text-align: right;\">   3.18389e-05</td></tr>\n",
       "<tr><td>_tune_45af3_00009</td><td>PENDING </td><td>                  </td><td style=\"text-align: right;\">0.0244422</td><td style=\"text-align: right;\">0.455549</td><td style=\"text-align: right;\">    0.588317</td><td style=\"text-align: right;\">40.3502  </td><td style=\"text-align: right;\">0.758379</td><td style=\"text-align: right;\">0.020651 </td><td style=\"text-align: right;\">0.0679393</td><td style=\"text-align: right;\">0.408327 </td><td style=\"text-align: right;\">0.482921</td><td style=\"text-align: right;\">0.00581234</td><td style=\"text-align: right;\">0.440072</td><td style=\"text-align: right;\">0.383464 </td><td style=\"text-align: right;\">  0.718482</td><td style=\"text-align: right;\">0.320017</td><td style=\"text-align: right;\">  0.000263322</td><td style=\"text-align: right;\">0.195207</td><td style=\"text-align: right;\">6.63078 </td><td style=\"text-align: right;\">  0.891305 </td><td style=\"text-align: right;\">       1.88876 </td><td style=\"text-align: right;\">        0.170623 </td><td style=\"text-align: right;\">   0.000696343</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "  </div>\n",
       "</div>\n",
       "<style>\n",
       ".tuneStatus {\n",
       "  color: var(--jp-ui-font-color1);\n",
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       ".tuneStatus .systemInfo {\n",
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       ".tuneStatus td {\n",
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       ".tuneStatus .trialStatus {\n",
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       ".tuneStatus h3 {\n",
       "  font-weight: bold;\n",
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       ".tuneStatus .vDivider {\n",
       "  border-left-width: var(--jp-border-width);\n",
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_yaml_path = os.path.abspath('data.yaml')\n",
    "model = YOLO('runs/detect/train27/weights/best.pt')\n",
    "result_grid = model.tune(data=data_yaml_path, epochs = 10, imgsz = 640, use_ray=True, gpu_per_trial = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d91c9293-d128-4864-bb3e-ab5f6817c815",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ultralytics YOLOv8.1.16 🚀 Python-3.8.17 torch-2.0.1 CUDA:0 (NVIDIA GeForce RTX 4090, 24214MiB)\n",
      "Model summary (fused): 218 layers, 25841497 parameters, 0 gradients, 78.7 GFLOPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/val/labels.cache... 2000 images, 0 backgrounds, 0 corrupt: 100%|██████████| 2000/2000 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/val/images/00425.jpeg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/val/images/00954.jpeg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/val/images/01642.jpeg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/val/images/01755.jpeg: 1 duplicate labels removed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 125/125 [00:07<00:00, 17.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all       2000       9061      0.604       0.67      0.663      0.443\n",
      "                   car       2000       8781      0.791      0.883      0.899      0.576\n",
      "          medium truck       2000        163      0.505      0.644      0.551      0.378\n",
      "           large truck       2000        117      0.517      0.484       0.54      0.374\n",
      "Speed: 0.1ms preprocess, 2.1ms inference, 0.0ms loss, 0.2ms postprocess per image\n",
      "Results saved to \u001b[1mruns/detect/val2\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([    0.57649,     0.37754,     0.37384])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load a model\n",
    "# model = YOLO('yolov8n.pt')  # load an official model\n",
    "model = YOLO('runs/detect/train27/weights/best.pt')  # load a custom model\n",
    "\n",
    "# Validate the model\n",
    "metrics = model.val()  # no arguments needed, dataset and settings remembered\n",
    "metrics.box.map    # map50-95\n",
    "metrics.box.map50  # map50\n",
    "metrics.box.map75  # map75\n",
    "metrics.box.maps   # a list contains map50-95 of each category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ea9b290c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "image 1/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00001.jpeg: 384x640 23 cars, 6.9ms\n",
      "image 2/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00002.jpeg: 384x640 3 cars, 6.5ms\n",
      "image 3/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00003.jpeg: 384x640 16 cars, 6.8ms\n",
      "image 4/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00004.jpeg: 384x640 8 cars, 6.7ms\n",
      "image 5/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00005.jpeg: 384x640 8 cars, 6.6ms\n",
      "image 6/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00006.jpeg: 384x640 14 cars, 6.4ms\n",
      "image 7/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00007.jpeg: 384x640 2 cars, 6.3ms\n",
      "image 8/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00008.jpeg: 384x640 3 cars, 1 large truck, 6.6ms\n",
      "image 9/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00009.jpeg: 384x640 6 cars, 6.3ms\n",
      "image 10/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00010.jpeg: 384x640 1 car, 6.3ms\n",
      "image 11/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00011.jpeg: 384x640 6 cars, 2 medium trucks, 6.5ms\n",
      "image 12/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00012.jpeg: 384x640 3 cars, 6.6ms\n",
      "image 13/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00013.jpeg: 384x640 4 cars, 6.2ms\n",
      "image 14/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00014.jpeg: 384x640 2 cars, 6.5ms\n",
      "image 15/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00015.jpeg: 384x640 12 cars, 6.3ms\n",
      "image 16/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00016.jpeg: 384x640 6 cars, 6.3ms\n",
      "image 17/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00017.jpeg: 384x640 1 car, 1 medium truck, 6.2ms\n",
      "image 18/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00018.jpeg: 384x640 4 cars, 6.5ms\n",
      "image 19/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00019.jpeg: 384x640 5 cars, 6.1ms\n",
      "image 20/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00020.jpeg: 384x640 19 cars, 1 large truck, 6.6ms\n",
      "image 21/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00021.jpeg: 384x640 5 cars, 6.6ms\n",
      "image 22/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00022.jpeg: 384x640 8 cars, 1 medium truck, 6.2ms\n",
      "image 23/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00023.jpeg: 384x640 5 cars, 6.2ms\n",
      "image 24/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00024.jpeg: 384x640 6 cars, 1 medium truck, 6.5ms\n",
      "image 25/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00025.jpeg: 384x640 14 cars, 6.2ms\n",
      "image 26/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00026.jpeg: 384x640 3 cars, 6.2ms\n",
      "image 27/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00027.jpeg: 384x640 7 cars, 6.5ms\n",
      "image 28/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00028.jpeg: 384x640 14 cars, 1 large truck, 6.0ms\n",
      "image 29/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00029.jpeg: 384x640 6 cars, 6.1ms\n",
      "image 30/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00030.jpeg: 384x640 5 cars, 6.5ms\n",
      "image 31/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00031.jpeg: 384x640 11 cars, 5.8ms\n",
      "image 32/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00032.jpeg: 384x640 11 cars, 6.1ms\n",
      "image 33/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00033.jpeg: 384x640 5 cars, 6.5ms\n",
      "image 34/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00034.jpeg: 384x640 4 cars, 6.2ms\n",
      "image 35/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00035.jpeg: 384x640 16 cars, 6.5ms\n",
      "image 36/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00036.jpeg: 384x640 1 car, 6.6ms\n",
      "image 37/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00037.jpeg: 384x640 11 cars, 6.2ms\n",
      "image 38/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00038.jpeg: 384x640 12 cars, 6.1ms\n",
      "image 39/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00039.jpeg: 384x640 2 cars, 6.5ms\n",
      "image 40/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00040.jpeg: 384x640 15 cars, 6.2ms\n",
      "image 41/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00041.jpeg: 384x640 3 cars, 6.2ms\n",
      "image 42/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00042.jpeg: 384x640 2 cars, 5.9ms\n",
      "image 43/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00043.jpeg: 384x640 31 cars, 6.0ms\n",
      "image 44/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00044.jpeg: 384x640 16 cars, 6.1ms\n",
      "image 45/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00045.jpeg: 384x640 13 cars, 1 large truck, 6.1ms\n",
      "image 46/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00046.jpeg: 384x640 2 cars, 6.4ms\n",
      "image 47/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00047.jpeg: 384x640 4 cars, 6.2ms\n",
      "image 48/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00048.jpeg: 384x640 7 cars, 6.4ms\n",
      "image 49/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00049.jpeg: 384x640 8 cars, 1 large truck, 5.9ms\n",
      "image 50/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00050.jpeg: 384x640 2 cars, 6.2ms\n",
      "image 51/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00051.jpeg: 384x640 2 cars, 5.9ms\n",
      "image 52/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00052.jpeg: 384x640 6 cars, 6.0ms\n",
      "image 53/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00053.jpeg: 384x640 5 cars, 5.9ms\n",
      "image 54/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00054.jpeg: 384x640 4 cars, 6.2ms\n",
      "image 55/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00055.jpeg: 384x640 7 cars, 5.9ms\n",
      "image 56/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00056.jpeg: 384x640 12 cars, 6.2ms\n",
      "image 57/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00057.jpeg: 384x640 3 cars, 6.0ms\n",
      "image 58/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00058.jpeg: 384x640 6 cars, 6.2ms\n",
      "image 59/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/00059.jpeg: 384x640 5 cars, 6.0ms\n",
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      "image 1982/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/01982.jpeg: 384x640 6 cars, 6.6ms\n",
      "image 1983/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/01983.jpeg: 384x640 6 cars, 6.6ms\n",
      "image 1984/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/01984.jpeg: 384x640 1 car, 1 medium truck, 6.1ms\n",
      "image 1985/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/01985.jpeg: 384x640 6 cars, 1 medium truck, 5.7ms\n",
      "image 1986/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/01986.jpeg: 384x640 6 cars, 6.3ms\n",
      "image 1987/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/01987.jpeg: 384x640 23 cars, 1 medium truck, 1 large truck, 6.0ms\n",
      "image 1988/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/01988.jpeg: 384x640 1 car, 5.9ms\n",
      "image 1989/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/01989.jpeg: 384x640 4 cars, 6.4ms\n",
      "image 1990/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/01990.jpeg: 384x640 3 cars, 6.5ms\n",
      "image 1991/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/01991.jpeg: 384x640 3 cars, 6.1ms\n",
      "image 1992/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/01992.jpeg: 384x640 1 car, 6.4ms\n",
      "image 1993/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/01993.jpeg: 384x640 4 cars, 5.9ms\n",
      "image 1994/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/01994.jpeg: 384x640 10 cars, 6.4ms\n",
      "image 1995/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/01995.jpeg: 384x640 36 cars, 2 large trucks, 6.6ms\n",
      "image 1996/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/01996.jpeg: 384x640 12 cars, 1 large truck, 5.7ms\n",
      "image 1997/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/01997.jpeg: 384x640 12 cars, 1 large truck, 6.3ms\n",
      "image 1998/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/01998.jpeg: 384x640 10 cars, 6.0ms\n",
      "image 1999/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/01999.jpeg: 384x640 3 cars, 6.3ms\n",
      "image 2000/2000 /media/imaginarium/12T_2/A_Work/A_DL/Program_2/datasets/test/images/02000.jpeg: 384x640 3 cars, 6.3ms\n",
      "Speed: 0.7ms preprocess, 6.2ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)\n",
      "Results saved to \u001b[1mruns/detect/predict2\u001b[0m\n",
      "2000 labels saved to runs/detect/predict2/labels\n"
     ]
    }
   ],
   "source": [
    "from ultralytics import YOLO\n",
    "\n",
    "# Load a pretrained YOLOv8n model\n",
    "model = YOLO('runs/detect/train27/weights/best.pt')\n",
    "\n",
    "# Define path to the image file\n",
    "source = 'datasets/test/images/'\n",
    "\n",
    "# Run inference on the source\n",
    "results = model.predict(source, save_txt = True, save_conf = True, conf = 0.05) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "57b85ff4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8ad58a2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Processing file: 01998.txt\n",
      "Processing file: 01999.txt\n",
      "Processing file: 02000.txt\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# Define the input directory where your files are located\n",
    "input_dir = \"runs/detect/predict2/labels\"\n",
    "output_file_path = \"datasets/test/labels.txt\"\n",
    "\n",
    "# Automatically find all .txt files in the directory\n",
    "files = os.listdir(input_dir)\n",
    "files = [f for f in files if f.endswith('.txt')]\n",
    "\n",
    "# Initialize a counter\n",
    "counter = 1\n",
    "\n",
    "# Initialize an empty list to store dataframes\n",
    "dfs = []\n",
    "\n",
    "# Process each file\n",
    "for file in sorted(files):\n",
    "    file_path = os.path.join(input_dir, file)\n",
    "    print(f\"Processing file: {file}\")  # Print the file name being processed\n",
    "    \n",
    "    # Read the file into a DataFrame, assuming space as the delimiter\n",
    "    df = pd.read_csv(file_path, header=None, sep=' ')\n",
    "    \n",
    "    # Increment the first column by 1 to adjust class numbers from 0-2 to 1-3\n",
    "    df.iloc[:, 0] = df.iloc[:, 0] + 1\n",
    "    \n",
    "    # Insert the counter as the first column of the DataFrame\n",
    "    df.insert(0, 'Counter', counter)\n",
    "    \n",
    "    # Append the DataFrame to the list\n",
    "    dfs.append(df)\n",
    "    \n",
    "    # Increment the counter for the next file\n",
    "    counter += 1\n",
    "\n",
    "# Combine all DataFrames into a single one\n",
    "combined_df = pd.concat(dfs, ignore_index=True)\n",
    "combined_df.to_csv(output_file_path, index=False, header=False, sep=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e3a303d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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